The problem of multivariate alarm analysis and rationalization is complex and important in the area of smart alarm management due to the interrelationships between variables. The technique of capturing and visualizing the correlation information, especially from historical alarm data directly, is beneficial for further analysis. In this paper, the Gaussian kernel method is applied to generate pseudo continuous time series from the original binary alarm data. This can reduce the influence of missed, false, and chattering alarms. By taking into account time lags between alarm variables, a correlation color map of the transformed or pseudo data is used to show clusters of correlated variables with the alarm tags reordered to better group the correlated alarms. Thereafter correlation and redundancy information can be easily found and used to improve the alarm settings; and statistical methods such as singular value decomposition techniques can be applied within each cluster to help design multivariate alarm strategies. Industrial case studies are given to illustrate the practicality and efficacy of the proposed method. This improved method is shown to be better than the alarm similarity color map when applied in the analysis of industrial alarm data.
Detection of Outliers in Large Dataset using Distributed ApproachEditor IJMTER
In this paper, a distributed method is introduced for detecting distance-based outliers in very large
data sets. The approach is based on the concept of outlier detection solving set, which is a small subset of the data
set that can be also employed for predicting novel outliers. The method exploits parallel computation in order to
obtain vast time savings. Indeed, beyond preserving the correctness of the result, the proposed schema exhibits
excellent performances. From the theoretical point of view, for common settings, the temporal cost of our
algorithm is expected to be at least three orders of magnitude faster than the classical nested-loop like approach to
detect outliers. Experimental results show that the algorithm is efficient and that it’s running time scales quite well
for an increasing number of nodes. We discuss also a variant of the basic strategy which reduces the amount of
data to be transferred in order to improve both the communication cost and the overall runtime. Importantly, the
solving set computed in a distributed environment has the same quality as that produced by the corresponding
centralized method.
Fuzzy logic applications for data acquisition systems of practical measurement IJECEIAES
In laboratory works, the error in measurement, reading the measurring devices, similarity of experimental data and lack of understanding of practicum materials are often found. These will lead to the inacurracy and invalid in data obtanined. As an alternative solution, application of fuzzy logic to the data acquisition system using a web server. This research focuses on the design of data acquisition systems with the target of reducing the error rate in measuring experimental data on the laboratory. Data measurement on laboratory practice module is done by taking the analog data resulted from the measurement. Furthermore, the data are converted into digital data via arduino and stored on the server. To get valid data, the server will process the data by using fuzzy logic method. The valid data are integrated into a web server so that it can be accessed as needed. The results showed that the data acquisition system based on fuzzy logic is able to provide recommendation of measurement result on the lab works based on the degree value of membership and truth value. Fuzzy logic will select the measured data with a maximum error percentage of 5% and select the measurement result which has minimum error rate.
Comparative study of various supervisedclassification methodsforanalysing def...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Detection of Outliers in Large Dataset using Distributed ApproachEditor IJMTER
In this paper, a distributed method is introduced for detecting distance-based outliers in very large
data sets. The approach is based on the concept of outlier detection solving set, which is a small subset of the data
set that can be also employed for predicting novel outliers. The method exploits parallel computation in order to
obtain vast time savings. Indeed, beyond preserving the correctness of the result, the proposed schema exhibits
excellent performances. From the theoretical point of view, for common settings, the temporal cost of our
algorithm is expected to be at least three orders of magnitude faster than the classical nested-loop like approach to
detect outliers. Experimental results show that the algorithm is efficient and that it’s running time scales quite well
for an increasing number of nodes. We discuss also a variant of the basic strategy which reduces the amount of
data to be transferred in order to improve both the communication cost and the overall runtime. Importantly, the
solving set computed in a distributed environment has the same quality as that produced by the corresponding
centralized method.
Fuzzy logic applications for data acquisition systems of practical measurement IJECEIAES
In laboratory works, the error in measurement, reading the measurring devices, similarity of experimental data and lack of understanding of practicum materials are often found. These will lead to the inacurracy and invalid in data obtanined. As an alternative solution, application of fuzzy logic to the data acquisition system using a web server. This research focuses on the design of data acquisition systems with the target of reducing the error rate in measuring experimental data on the laboratory. Data measurement on laboratory practice module is done by taking the analog data resulted from the measurement. Furthermore, the data are converted into digital data via arduino and stored on the server. To get valid data, the server will process the data by using fuzzy logic method. The valid data are integrated into a web server so that it can be accessed as needed. The results showed that the data acquisition system based on fuzzy logic is able to provide recommendation of measurement result on the lab works based on the degree value of membership and truth value. Fuzzy logic will select the measured data with a maximum error percentage of 5% and select the measurement result which has minimum error rate.
Comparative study of various supervisedclassification methodsforanalysing def...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
Data imputing uses to posit missing data values, as missing data have a negative effect on the computation validity of models. This study develops a genetic algorithm (GA) to optimize imputing for missing cost data of fans used in road tunnels by the Swedish Transport Administration (Trafikverket). GA uses to impute the missing cost data using an optimized valid data period. The results show highly correlated data (R- squared 0.99) after imputing the missing data. Therefore, GA provides a wide search space to optimize imputing and create complete data. The complete data can be used for forecasting and life cycle cost analysis. Ritesh Kumar Pandey | Dr Asha Ambhaikar"Data Imputation by Soft Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14112.pdf http://www.ijtsrd.com/computer-science/real-time-computing/14112/data-imputation-by-soft-computing/ritesh-kumar-pandey
Overview of soft intelligent computing technique for supercritical fluid extr...IJAAS Team
Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
Data mining techniques application for prediction in OLAP cubeIJECEIAES
Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
GI-ANFIS APPROACH FOR ENVISAGE HEART ATTACK DISEASE USING DATA MINING TECHNIQUESAM Publications
The process of selecting a subset of relevant features from the feature space for use in model construction and used to carry out the feature selection process is called as pre processing step. The filter approach computationally fast and given accuracy results. The Professional Medical Conduct Board Actions data consist of all public actions taken against physicians, physician assistants, specialist assistants, and medical professional. The Classification and Regression Trees (CART), which described the generation of binary decision trees CART were invented independently of one another at around the same time, yet follow a similar approach for learning decision trees from training tuples. The research used GI-ANFIS is used to data mining technique on heart data sets to provide the diagnosis results.
Influence over the Dimensionality Reduction and Clustering for Air Quality Me...IJAEMSJORNAL
The current trend in the industry is to analyze large data sets and apply data mining, machine learning techniques to identify a pattern. But the challenges with huge data sets are the high dimensions associated with it. Sometimes in data analytics applications, large amounts of data produce worse performance. Also, most of the data mining algorithms are implemented column wise and too many columns restrict the performance and make it slower. Therefore, dimensionality reduction is an important step in data analysis. Dimensionality reduction is a technique that converts high dimensional data into much lower dimension, such that maximum variance is explained within the first few dimensions. This paper focuses on multivariate statistical and artificial neural networks techniques for data reduction. Each method has a different rationale to preserve the relationship between input parameters during analysis. Principal Component Analysis which is a multivariate technique and Self Organising Map a neural network technique is presented in this paper. Also, a hierarchical clustering approach has been applied to the reduced data set. A case study of Air quality measurement has been considered to evaluate the performance of the proposed techniques.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
Data imputing uses to posit missing data values, as missing data have a negative effect on the computation validity of models. This study develops a genetic algorithm (GA) to optimize imputing for missing cost data of fans used in road tunnels by the Swedish Transport Administration (Trafikverket). GA uses to impute the missing cost data using an optimized valid data period. The results show highly correlated data (R- squared 0.99) after imputing the missing data. Therefore, GA provides a wide search space to optimize imputing and create complete data. The complete data can be used for forecasting and life cycle cost analysis. Ritesh Kumar Pandey | Dr Asha Ambhaikar"Data Imputation by Soft Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14112.pdf http://www.ijtsrd.com/computer-science/real-time-computing/14112/data-imputation-by-soft-computing/ritesh-kumar-pandey
Overview of soft intelligent computing technique for supercritical fluid extr...IJAAS Team
Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
Data mining techniques application for prediction in OLAP cubeIJECEIAES
Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
GI-ANFIS APPROACH FOR ENVISAGE HEART ATTACK DISEASE USING DATA MINING TECHNIQUESAM Publications
The process of selecting a subset of relevant features from the feature space for use in model construction and used to carry out the feature selection process is called as pre processing step. The filter approach computationally fast and given accuracy results. The Professional Medical Conduct Board Actions data consist of all public actions taken against physicians, physician assistants, specialist assistants, and medical professional. The Classification and Regression Trees (CART), which described the generation of binary decision trees CART were invented independently of one another at around the same time, yet follow a similar approach for learning decision trees from training tuples. The research used GI-ANFIS is used to data mining technique on heart data sets to provide the diagnosis results.
Influence over the Dimensionality Reduction and Clustering for Air Quality Me...IJAEMSJORNAL
The current trend in the industry is to analyze large data sets and apply data mining, machine learning techniques to identify a pattern. But the challenges with huge data sets are the high dimensions associated with it. Sometimes in data analytics applications, large amounts of data produce worse performance. Also, most of the data mining algorithms are implemented column wise and too many columns restrict the performance and make it slower. Therefore, dimensionality reduction is an important step in data analysis. Dimensionality reduction is a technique that converts high dimensional data into much lower dimension, such that maximum variance is explained within the first few dimensions. This paper focuses on multivariate statistical and artificial neural networks techniques for data reduction. Each method has a different rationale to preserve the relationship between input parameters during analysis. Principal Component Analysis which is a multivariate technique and Self Organising Map a neural network technique is presented in this paper. Also, a hierarchical clustering approach has been applied to the reduced data set. A case study of Air quality measurement has been considered to evaluate the performance of the proposed techniques.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
Multimode system condition monitoring using sparsity reconstruction for quali...IJECEIAES
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
Data repository for sensor network a data mining approachijdms
The development of sensor data repositories will aid the researchers to create benchmark dataset. These
benchmark dataset will provide a platform for all the researchers to access the data, test and compare the
accuracy of their algorithms. However, the storage and management of sensor data itself is a challenging
task due to various reasons such as noisy, redundant, missing, and faulty data. Therefore it is very
important to create a data repository which contains the precise and accurate data and also storage and
management of data is effective. Hence, in this paper we are proposing to use the combination of
quantitative association rules and decision tree for classification of faulty data and normal data. Usage of
multiple linear regression models for the estimation of missing data. A symbolic table approach for storage
and management of sensor data. And development of a graphical user interface for visualization of sensor
data.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
UNIT - 5 : 20ACS04 – PROBLEM SOLVING AND PROGRAMMING USING PYTHONNandakumar P
UNIT-V INTRODUCTION TO NUMPY, PANDAS, MATPLOTLIB
Exploratory Data Analysis (EDA), Data Science life cycle, Descriptive Statistics, Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA. Data Visualization: Scatter plot, bar chart, histogram, boxplot, heat maps, etc.
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
Noise-robust classification with hypergraph neural networknooriasukmaningtyas
This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
Traffic Outlier Detection by Density-Based Bounded Local Outlier FactorsITIIIndustries
Outlier detection (OD) is widely used in many fields, such as finance, information and medicine, in cleaning up datasets and keeping the useful information. In a traffic system, it alerts the transport department and drivers with abnormal traffic situations such as congestion and traffic accident. This paper presents a density-based bounded LOF (BLOF) method for large-scale traffic video data in Hong Kong. A dimension reduction by principal component analysis (PCA) was accomplished on the spatial-temporal traffic signals. Previously, a density-based local outlier factor (LOF) method on a two-dimensional (2D) PCAproceeded spatial plane was performed. In this paper, a threedimensional (3D) PCA-proceeded spatial space for the classical density-based OD is firstly compared with the results from the 2D counterpart. In our experiments, the classical density-based LOF OD has been applied to the 3D PCA-proceeded data domain, which is new in literature, and compared to the previous 2D domain. The average DSRs has increased about 2% in the PM sessions: 91% (2D) and 93% (3D). Also, comparing the classical density-based LOF and the new BLOF OD methods, the average DSRs in the supervised approach has increased from 94% (LOF) to 96% (BLOF) for the AM sessions and from 93% (LOF) to 95% (BLOF) for the PM sessions.
Application Of Extreme Value Theory To Bursts PredictionCSCJournals
Bursts and extreme events in quantities such as connection durations, file sizes, throughput, etc. may produce undesirable consequences in computer networks. Deterioration in the quality of service is a major consequence. Predicting these extreme events and burst is important. It helps in reserving the right resources for a better quality of service. We applied Extreme value theory (EVT) to predict bursts in network traffic. We took a deeper look into the application of EVT by using EVT based Exploratory Data Analysis. We found that traffic is naturally divided into two categories, Internal and external traffic. The internal traffic follows generalized extreme value (GEV) model with a negative shape parameter, which is also the same as Weibull distribution. The external traffic follows a GEV with positive shape parameter, which is Frechet distribution. These findings are of great value to the quality of service in data networks, especially when included in service level agreement as traffic descriptor parameters.
An optimal general type-2 fuzzy controller for Urban Traffic NetworkISA Interchange
Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the traffic information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the modified backtracking search algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
State of charge estimation of lithium-ion batteries using fractional order sl...ISA Interchange
This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov’s stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estima- tion method is effective, and the designed observers have good performance.
Fractional order PID for tracking control of a parallel robotic manipulator t...ISA Interchange
This paper presents the tracking control for a robotic manipulator type delta employing fractional order PID controllers with computed torque control strategy. It is contrasted with an integer order PID controller with computed torque control strategy. The mechanical structure, kinematics and dynamic models of the delta robot are descripted. A SOLIDWORKS/MSC-ADAMS/MATLAB co-simulation model of the delta robot is built and employed for the stages of identification, design, and validation of control strategies. Identification of the dynamic model of the robot is performed using the least squares algorithm. A linearized model of the robotic system is obtained employing the computed torque control strategy resulting in a decoupled double integrating system. From the linearized model of the delta robot, fractional order PID and integer order PID controllers are designed, analyzing the dynamical behavior for many evaluation trajectories. Controllers robustness is evaluated against external disturbances employing performance indexes for the joint and spatial error, applied torque in the joints and trajectory tracking. Results show that fractional order PID with the computed torque control strategy has a robust performance and active disturbance rejection when it is applied to parallel robotic manipulators on tracking tasks.
Fuzzy logic for plant-wide control of biological wastewater treatment process...ISA Interchange
The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.
Design and implementation of a control structure for quality products in a cr...ISA Interchange
In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen HYSYS dynamic environment under real operating conditions. The simulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns.
Model based PI power system stabilizer design for damping low frequency oscil...ISA Interchange
This paper explores a two-level control strategy by blending a local controller with a centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favorably compared with some controllers prevalent in the literature.
A comparison of a novel robust decentralized control strategy and MPC for ind...ISA Interchange
Abstract: In this work we have developed a novel, robust practical control structure to regulate an industrial methanol distillation column. This proposed control scheme is based on a override control framework and can manage a non-key trace ethanol product impurity specification while maintaining high product recovery. For comparison purposes, an MPC with a discrete process model (based on step tests) was also developed and tested. The results from process disturbance testing shows that, both the MPC and the proposed controller were capable of maintaining both the trace level ethanol specification in the distillate (XD) and high product recovery (β). Closer analysis revealed that the MPC controller has a tighter XD control, while the proposed controller was tighter in β control. The tight XD control allowed the MPC to operate at a higher XD set point (closer to the 10 ppm AA grade methanol standard), allowing for savings in energy usage. Despite the energy savings of the MPC, the proposed control scheme has lower installation and running costs. An economic analysis revealed a multitude of other external economic and plant design factors, that should be considered when making a decision between the two controllers. In general, we found relatively high energy costs favor MPC.
Fault detection of feed water treatment process using PCA-WD with parameter o...ISA Interchange
Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA- WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000 MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an auto- matic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results.
Model-based adaptive sliding mode control of the subcritical boiler-turbine s...ISA Interchange
As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.
A Proportional Integral Estimator-Based Clock Synchronization Protocol for Wi...ISA Interchange
Clock synchronization is an issue of vital importance in applications of wireless sensor networks (WSNs). This paper proposes a proportional integral estimator-based protocol (EBP) to achieve clock synchronization for wireless sensor networks. As each local clock skew gradually drifts, synchronization accuracy will decline over time. Compared with existing consensus-based approaches, the proposed synchronization protocol improves synchronization accuracy under time-varying clock skews. Moreover, by restricting synchronization error of clock skew into a relative small quantity, it could reduce periodic re-synchronization frequencies. At last, a pseudo-synchronous implementation for skew compensation is introduced as synchronous protocol is unrealistic in practice. Numerical simulations are shown to illustrate the performance of the proposed protocol.
An artificial intelligence based improved classification of two-phase flow patte...ISA Interchange
Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are re- corded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows.
New Method for Tuning PID Controllers Using a Symmetric Send-On-Delta Samplin...ISA Interchange
In this paper we present a new method for tuning PI controllers with symmetric send-on-delta (SSOD) sampling strategy. First we analyze the conditions that produce oscillations in event based systems considering SSOD sampling strategy. The Describing Function is the tool used to address the problem. Once the conditions for oscillations are established, a new robustness to oscillation performance measure is introduced which entails with the concept of phase margin, one of the most traditional measures of relative stability in closed-loop control systems. Therefore, the application of the proposed robustness measure is easy and intuitive. The method is tested by both simulations and experiments. Additionally, a Java application has been developed to aid in the design according to the results presented in the paper.
Load estimator-based hybrid controller design for two-interleaved boost conve...ISA Interchange
This paper is devoted to the development of a hybrid controller for a two-interleaved boost converter dedicated to renewable energy and automotive applications. The control requirements, resumed in fast transient and low input current ripple, are formulated as a problem of fast stabilization of a predefined optimal limit cycle, and solved using hybrid automaton formalism. In addition, a real time estimation of the load is developed using an algebraic approach for online adjustment of the hybrid controller. Mathematical proofs are provided with simulations to illustrate the effectiveness and the robustness of the proposed controller despite different disturbances. Furthermore, a fuel cell system supplying a resistive load through a two-interleaved boost converter is also highlighted.
Effects of Wireless Packet Loss in Industrial Process Control SystemsISA Interchange
Timely and reliable sensing and actuation control are essential in networked control. This depends on not only the precision/quality of the sensors and actuators used but also on how well the communications links between the field instruments and the controller have been designed. Wireless networking offers simple deployment, reconfigurability, scalability, and reduced operational expenditure, and is easier to upgrade than wired solutions. However, the adoption of wireless networking has been slow in industrial process control due to the stochastic and less than 100% reliable nature of wireless communications and lack of a model to evaluate the effects of such communications imperfections on the overall control performance. In this paper, we study how control performance is affected by wireless link quality, which in turn is adversely affected by severe propagation loss in harsh industrial environments, co-channel interference, and unintended interference from other devices. We select the Tennessee Eastman Challenge Model (TE) for our study. A decentralized process control system, first proposed by N. Ricker, is adopted that employs 41 sensors and 12 actuators to manage the production process in the TE plant. We consider the scenario where wireless links are used to periodically transmit essential sensor measurement data, such as pressure, temperature and chemical composition to the controller as well as control commands to manipulate the actuators according to predetermined setpoints. We consider two models for packet loss in the wireless links, namely, an independent and identically distributed (IID) packet loss model and the two-state Gilbert-Elliot (GE) channel model. While the former is a random loss model, the latter can model bursty losses. With each channel model, the performance of the simulated decentralized controller using wireless links is compared with the one using wired links providing instant and 100% reliable communications. The sensitivity of the controller to the burstiness of packet loss is also characterized in different process stages. The performance results indicate that wireless links with redundant bandwidth reservation can meet the requirements of the TE process model under normal operational conditions. When disturbances are introduced in the TE plant model, wireless packet loss during transitions between process stages need further protection in severely impaired links. Techniques such as re-transmission scheduling, multi-path routing and enhanced physical layer design are discussed and the latest industrial wireless protocols are compared.
Fault Detection in the Distillation Column ProcessISA Interchange
Chemical plants are complex large-scale systems which need designing robust fault detection schemes to ensure high product quality, reliability and safety under different operating conditions. The present paper is concerned with a feasibility study of the application of the black-box modeling method and Kullback Leibler divergence (KLD) to the fault detection in a distillation column process. A Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) polynomial model is firstly developed to estimate the nonlinear behavior of the plant. Furthermore, the KLD is applied to detect abnormal modes. The proposed FD method is implemented and validated experimentally using realistic faults of a distillation plant of laboratory scale. The experimental results clearly demonstrate the fact that proposed method is effective and gives early alarm to operators.
Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank SystemISA Interchange
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H1 framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
A KPI-based process monitoring and fault detection framework for large-scale ...ISA Interchange
Large-scale processes, consisting of multiple interconnected sub-processes, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel re- presentation of each sub-process, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods.
An adaptive PID like controller using mix locally recurrent neural network fo...ISA Interchange
Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional integral derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initi- alized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on- line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.
A method to remove chattering alarms using median filtersISA Interchange
Chattering alarms are the most found nuisance alarms that will probably reduce the usability and result in a confidence crisis of alarm systems for industrial plants. This paper addresses the chattering alarm reduction using median filters. Two rules are formulated to design the window size of median filters. If the alarm probability is estimated using process data, one rule is based on the probability of alarms to satisfy some requirements on the false alarm rate, or missed alarm rate. If there are only historical alarm data available, the other rule is based on percentage reduction of chattering alarms using alarm duration distribution. Experimental results for industrial cases testify that the proposed method is effective.
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Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
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• Four (4) workplace discipline methods you should consider
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Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
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Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
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Memorandum Of Association Constitution of Company.pptseri bangash
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A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
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Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
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Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
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Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
Putting the SPARK into Virtual Training.pptxCynthia Clay
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RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
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2. 500 F. Yang et al. / ISA Transactions 51 (2012) 499–506
There are two ways to capture correlation from alarm data:
one is to employ Pearson’s correlation coefficients as done for
continuous data by assigning a value of 1 for the periods when the
alarm is active and a value of 0 when it is inactive [9]; the other
is to introduce similarity measures based on binary data [10,11].
By computing the correlation coefficient or similarity measure for
each pair of variables, a matrix is constructed. However, this matrix
which is composed of specific numbers is not generally convenient
for inspection or cursory analysis. Therefore the correlation values
are converted into a color map and this correlation color map
offers better visualization capability; it uses different colors to
show different degrees of correlation [12,13]. The colors are
usually discretized into several levels according to the color code.
Through the reordering and clustering of variables, the color-coded
correlation map can show the clusters of alarm tags intuitively. In
this way, the problem of a large number of variables is separated
into smaller sub-problems with much fewer variables.
The classical correlation color map only uses the correlation
coefficients. However, it may be ineffective when there is a time lag
between two variables. Hence the correlation coefficients should
be lag-adjusted to take into account the delays between each
pair of variables. The alarm similarity color map (ASCM), which
is specially designed for alarm data analysis [10], has proved to
be an effective method for visualization. It captures time-delayed
similarity information from binary data. However it has some
disadvantages. Firstly, in order to weaken the sensitivity caused
by the time shift, each unique alarm is padded with extra 1’s
to enrich the data; or in a similar form, alarms are checked
within constantly divided time windows (equivalent to down
sampling) [11]. This step is short of physical evidence and as a
result of this individual false alarms and chattering alarms may be
magnified unreasonably. In this paper we suggest another method
in which each unique alarm is replaced by a Gaussian distribution
along with its neighbors in the time axis [14]. This set of generated
continuous data with numerical values is labeled as pseudo data.
Based on this data set, we use Pearson’s correlation coefficients to
measure the correlation. This method has some similar problems
as the padding method; however, it provides a better way of
transforming binary alarm data into continuous data that can
be analyzed by statistical approaches. Another disadvantage of
the ASCM method is that it takes into account all the alarm
tags separately regardless their physical relationship. If typical
analogue alarms (HI/LO/HH/LL) associated with the same process
variable are grouped together, the result should be expected to be
more reasonable. Thirdly, the similarity measure only indicates the
distance but loses the direction (positive or negative correlation),
while the correlation coefficient indicates both the similarity and
the direction.
In this paper, a new approach to analyzing multiple alarm series
is proposed to find redundancy. By converting binary data into
continuous (pseudo) data via a novel Gaussian kernel method,
statistical methods are applied, including an improved correlation
and singular value analysis. The numerical results can be visualized
by the ASCM tool via appropriate ordering and clustering of alarm
tags.
2. Basic method for correlation analysis
2.1. Gaussian kernel method for data preprocessing
Alarm data is binary data with ‘1’s’ and ‘0’s’ to denote abnormal
and normal states respectively. Since alarm data is obtained and
discretized from continuous process data according to alarm limits,
some information is lost in this process. However, alarm data
does have advantages as well: (1) binary data is easy for storage
and convenient for statistical analysis; (2) it has much higher
sampling frequencies than process data and thus includes detailed
information; and (3) it includes more types of data such as digital
alarms showing the status of some elements (e.g. if a pump is on or
off). Thus we can use alarm data directly to analyze the correlation.
For a particular variable, one alarm point can be regarded
as a sample of the time series. To apply correlation analysis
to alarm occurrences, a continuous analogue signal has to be
generated from the alarm signal which is actually an event series.
By simply assigning ‘1’ or ‘0’, a time series in a continuous temporal
domain can be generated, which is simple but lacks granularity
because non-continuous (binary) values are not commensurate for
correlation analysis. In order to better estimate the time series, the
kernel method can be used in the temporal domain [15], which is a
nonparametric method, to fit the function with any shape. Here the
Gaussian kernel function is used because of its smoothness. At each
alarm point, a Gaussian kernel function is superimposed around
this time instant. The function is defined as:
K(t) =
1
√
2πσ
e− t2
2σ , (1)
where σ is the standard deviation. If all the alarm points are
considered, a continuous time series can be obtained as the
superposition of all the time-shifted Gaussian kernel functions.
Thus the resulting time series is given as
P(t) =
N
i=1
K(t − ti), (2)
where N is the number of alarm points at the time instants ti, i =
1, 2, . . . , N. This time series P(t) can be regarded as the estimation
of the corresponding process data although there may not exist
such a physical process variable; hence we call it pseudo data. Fig. 1
illustrates the principle of generating such pseudo data from binary
data.
The pseudo time series has the following properties:
• It is continuous and smooth because it is approximated by
superimposed Gaussian functions.
• For consecutive alarm points lasting a long time, the pseudo
data is also 1’s because the integral of the Gaussian kernel
function over the whole time domain is 1. This is different
from the estimation of probability density function because the
samples here are at different time instants.
• With appropriate variance of the kernel function, the magni-
tude of the kernel function is small; thus non-consecutive and
sparse alarm points cannot result in spikes in the pseudo data.
This property is particularly important for the filtering of chat-
tering alarms so that the proposed method can be used directly
on the data set with chattering alarms.
Consider the example of alarm data as shown in Fig. 2(a) in
which there is a consecutive alarm period of 400 s including a short
(5 s) break at around the 510th interval. At both the beginning and
the end of this period, there is some chattering. From this data set,
we can generate the pseudo data with standard deviation of 30 s as
shown in Fig. 2(b). Here the chattering and the gap are sufficiently
smoothened and at the same time the alarm period prevails. The
effects of missed alarms and false alarms are lessened because they
usually only exist over a short time duration.
2.2. Effect of time lags
There may exist a time lag between two correlated alarms
due to the propagation or response time. This lag reduces the
correlation coefficient and thus may mask the correlation be-
tween these variables. To eliminate this influence, different time
3. F. Yang et al. / ISA Transactions 51 (2012) 499–506 501
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17
t
16
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17
t
16
(a) Alarm data. (b) Pseudo data.
Fig. 1. Transforming alarm data (a) to pseudo data (b).
(a) Alarm data. (b) Pseudo data.
Fig. 2. (a) Original alarm data; (b) generated pseudo data by the Gaussian kernel method.
lags should be assumed and the maximal correlation coeffi-
cient can be computed; this would be regarded as the real
correlation [16].
Assume that x and y are time series of n observations with
means µx, µy and variances σx, σy respectively, then the cross-
correlation function (CCF) with an assumed lag k on y is:
φxy(k) =
E
(xi − µx)(yi+k − µy)
σxσy
, k = −n + 1, . . . , n − 1. (3)
The expectation can be estimated as the sample CCF by:
ˆφxy(k) =
1
n − k
n−k
i=1
(xi − µx)(yi+k − µy)
σxσy, if k ≥ 0,
1
n − k
n
i=1−k
(xi − µx)(yi+k − µy)
σxσy, if k < 0.
(4)
A value of the CCF is obtained by assuming a certain time lag
for one of the time series. Thus the absolute maximum value can
be regarded as the real cross-correlation and the corresponding
lag as the estimated time lag between these two variables. For
a mathematical description, one can compute the maximum
and minimum values φmax
= maxk{φxy(k), 0} ≥ 0 and φmin
=
mink{φxy(k), 0} ≤ 0, and the corresponding arguments kmax
and
kmin
. Then the estimated time delay from x to y is:
λ =
kmax
, if φmax
≥ −φmin
,
kmin
, if φmax
< −φmin
.
(5)
(corresponding to the maximum absolute value) and the actual
time delayed cross-correlation is ρ = φxy(λ) (between −1 and
1). If λ is less than zero, then it means that the actual delay is from
y to x. Thus the sign of λ provides the directionality information
between x and y. The sign of ρ indicates whether the correlation
is positive or negative. Note that this directionality does not mean
causality because there may exist another common cause of the
relationship between x and y [17].
2.3. Finding redundant alarms based on correlation
If several alarms are highly correlated, we should check
if there is redundancy in them, i.e., if some alarms can be
obtained by linearly combining other alarm signals. Singular value
decomposition (SVD) is a well-developed method to do this by
finding singular values of the set of alarm data and thus enabling
one to identify collinear columns. If there are several large singular
values that possess the dominant proportion of all the values,
then the number of large or dominant singular values can be
regarded as the number of independent alarm tags and the residual
number is the number of redundant alarms. However, SVD cannot
tell us which alarm is redundant because we are dealing with
the transformed or pseudo data to generate a set of new data.
It only provides an opportunity for one to check if there is an
alarm that can be removed or replaced by the linear combination
of other alarm signals. In addition, the independent series in the
new data may not have the approximate binary property, making
these values inappropriate to be taken as generated alarm signals.
One should also consider the physical meaning of all alarms and
reconcile this with the SVD information. This ambiguity in using
SVD is much severe if the number of alarms is large. In this case,
it is difficult to identify the independent and redundant alarms
merely based on the result of SVD. Therefore, we need to separate
the variables into several groups and perform SVD on each group
of variables. The clustering process based on visualizing the result
of the correlation analysis would help one in analyzing SVDs of
smaller groups of variables. Another point to be noted is that, for
SVD analysis, the time series should be sufficiently long to include
many alarms, otherwise it cannot reflect its true property.
3. Visualization of correlation
In order to visualize the correlation matrix, the correlation color
map is developed, in which the order of variables is rearranged
in a ranked order where variables that are highly correlated with
each other appear together with prominent colors or shades, called
clusters.
4. 502 F. Yang et al. / ISA Transactions 51 (2012) 499–506
3.1. Ordering and clustering of alarm tags
Based on the correlation coefficient, the measure to describe
the similarity between two alarms is known as the similarity
measure S [18] which has the properties of positivity (S(x, y)
≥ 0), symmetry (S(x, y) = S(y, x)), and maximality (S(x, x) ≥
S(x, y)). There are many similarity measures available for binary
data [19]; hence if we compute the similarity measures based on
original alarm data, we can choose any one of them; for example
Kondaveeti et al. [10] have used the Jaccard similarity measure.
However we are dealing with pseudo data; so generally we can
use the covariance, Euclidean distance (L2), Kendall’s τ, Pearson’s
correlation coefficient, Spearman’s rank, City-block (L1), etc. [20].
In this paper, we use the classical Pearson’s correlation coefficient
and define the following similarity measure S:
S(x, y) = |ρxy|, (6)
which lies in the interval [0, 1].
During clustering, one should employ both the similarity
measure between two alarms and also the measure between two
clusters of alarms. The latter can be defined by single-, complete-,
and average-linkage methods [20]. The definition of single-
linkage is
SS(X, Y) = min
x∈X,y∈Y
{S(x, y)}, (7)
the definition of complete-linkage is
SC(X, Y) = max
x∈X,y∈Y
{S(x, y)}, (8)
and the definition of average-linkage is
SA(X, Y) =
x∈X,y∈Y
S(x, y)
|X| |Y|, (9)
where x and y are alarm tags, X and Y are clusters of tags, and | · |
means the size of the cluster.
Using the similarity measure between each pair of alarms or
their clusters, all alarm tags can be clustered based on various clus-
tering algorithms such as agglomerative hierarchical clustering,
the methodology for which is shown as a dendrogram. This process
has been illustrated in Kondaveeti et al.’s study [10]. Other ordering
and clustering algorithms can also be used such as the ellipse or-
dering. For this purpose, the data analysis software GAP is a useful
tool [20].
3.2. Correlation color map
The correlation matrix with rearranged rows and columns is
color coded to transform it into a color map [12]. The matrix is
symmetric and both the vertical and horizontal axes are alarm
tag names. Each grid (x, y) shows the corresponding correlation
between the two alarm tags x and y. The color is coded to show
the correlation (−1 to +1) or its absolute (0–1). To explain the
meaning of each color, a color bar legend is placed beside the plot.
The order of the alarm tags is determined by the above algorithm.
The color map is symmetric about the diagonal which means the
auto-correlation coefficients are 1’s. The clusters are shown as
blocks located along the diagonal with similar color codes. From
this map, one can acquire the approximate correlation between
each pair of alarms, and easily find clusters and the corresponding
alarm tags. By matching this map with the physical meaning, one
can locate the redundant alarms.
4. Implementation issues
In the above methods there are several issues to be noted.
4.1. Sampling rates of alarm data and pseudo data
The alarm data is recorded by DCS, PLC, or other smart devices;
the sampling rate can be very high. This is necessary sometimes
because alarms occur with a very high frequency due to oscillation
or chattering. Generally speaking, we can record them in the
database as frequently as possible, say one per second. A proposed
rule of thumb is a 15 s sample interval as a lower bound [14].
It is generally unnecessary to record process data at such a
high rate because the process always has a time constant with
a magnitude of minutes or even longer. In real applications, we
often record such samples at one sample per minute. Different
from alarm data and process data, the pseudo data is generated
from alarm data but behaves as continuous process data. Thus we
can record the pseudo data at the same frequency as the original
alarm data, but we can also decrease the sampling rate to reduce
the computational burden and can also match it with the sample
rate of process data.
4.2. Variance of the Gaussian kernel function
The variance of the kernel function affects the robustness of
the pseudo data at each alarm point. If the variance is very small,
each alarm point generates a spike in the pseudo data making the
method very sensitive to an individual sample or during a short
period of alarms. On the other hand if the variance is large, the
magnitude of each kernel function is very small; hence it needs
quite a few alarm points during a period of no alarms or quite a
few ‘holes’ during a period of consecutive alarms to change the
trend of the pseudo curve. Although this can reduce the influence
of the missed alarms, false alarms, and chattering alarms due to
its robustness to individual changes, the latency increases on the
other hand, meaning that there is a longer time delay to reflect a
change. This is a trade-off between large and small variances. To
our best knowledge, most variables in the process industry have a
time constant that ranges from several seconds to several minutes
and even hours; thus the standard deviation of the Gaussian kernel
function can be chosen over this range. If there is a step change
(from no alarm to alarm for example) in the alarm data, it needs
several minutes for the pseudo data to completely change the value
(from 0 to 1).
4.3. Computational effort
Although the number of samples is generally large due to
the high frequency of sampling rate, the alarm data set includes
large amount of normal data resulting in the data set being
quite sparse. In addition, when an alarm occurs consecutively,
the corresponding pseudo time series is a set of consecutive 1’s
according to the property mentioned earlier in Section 2; such
periods can be ignored in the computation by letting them remain
unchanged. Therefore the computation only concentrates on the
non-consecutive alarm points and the boundary of the consecutive
alarm points.
In SVD, the vectors of the pseudo data can be very long, making
the computation infeasible. We can either lower the sampling rate,
or use the technique of sparse matrix analysis [21].
4.4. Selection of data range
Theoretically, a long series can result in better results, but
the computational effort is higher. In addition, in correlation
analysis we assume that the data is stationary; this is difficult to
satisfy because the operating situation or fault pattern may vary,
especially in a long series. Thus we should compromise to look
into shorter series. Based on this compromise, the computation of
correlation requires a sufficiently large data set that contains alarm
5. F. Yang et al. / ISA Transactions 51 (2012) 499–506 503
Fig. 3. High density plot of the original alarm data for case study 1.
points because the normal points do not provide information. In
practice a short time period of simultaneous alarms is a much
better data set than a long time period of rare alarms. In particular,
the alarm data during an alarm flood is an especially good source
for study.
4.5. Grouping of alarm tags of one process variable
For a typical process variable, there may be as many as four
alarms associated with it: HI, LO, HH, and LL alarms. If these alarm
tags are treated separately, the relationship between them is lost.
Instead, these four alarms can be combined together by assigning
them different values based on their directions and degrees, for
example, HI as +1, LO as −1, HH as +2, and LL as −2. This
treatment makes the alarm data have several values instead of
binary values. When generating the pseudo data, these values
should be taken as the weights of the Gaussian kernels.
4.6. Choosing color code for color map
We can either use the same color with different shades or
different colors. Nevertheless, the number of color scales is very
important, which determines the interpretability of the map. There
is a trade-off between the sensitivity and the interpretability. A
general recommendation is to use fewer number of codes first and
increase the shades gradually to suit your eyes in the visualization
process and provide more ‘granularity’ in the number of alarms for
a process. In our work we use warm colors to describe positive
correlation and cold colors to describe negative correlation. The
four bands of values are chosen as (0–0.25), (0.25–0.5), (0.5–0.75),
and (0.75–1) respectively. Please see the color bar in Fig. 4(b) for
the color assignment.
5. Case studies
To illustrate the practicality and utility of the method suggested
in this work, we consider two case studies. The first case study
is to illustrate the procedure of the proposed method and the
improvements. The second case study is an application to a real
industrial process, showing the efficacy of the method.
5.1. Case study 1
First consider data from a real industrial process including 10
analogue alarm tags with HI and LO settings that have alarms over
a period of one week. The sampling rate is one sample per second.
By assigning HI and LO alarms +1 and −1 respectively, the alarm
data is shown as a high density plot in Fig. 3 where alarm tags 3
and 7 have both HI and LO alarms. If we use the method proposed
by Kondaveeti et al. [10], the ASCM obtained is shown in Fig. 4(a)
where the padding length used is 5 s. We find that alarm tags 1 and
2 are correlated, and alarm tags 4, 5, and 6 are grouped in another
cluster.
Now we use the method proposed in this paper by setting the
standard deviation of the Gaussian kernel as 30 s. The correlation
matrix Φ (with the elements below the diagonal removed due to
symmetry) is given in Box I.
Based on this matrix, Table 1 illustrates the clustering process.
Initially each alarm tag is regarded as a cluster. In the first step,
the similarity measure (S = absolutecorrelation) of each pair
of alarm tags are compared, in which the similarity measure S
between alarm tags 4 and 5 has the largest value, meaning that
they are most similar. Therefore alarm tags 1 and 2 are grouped
into a new cluster, 11, and the old clusters 4 and 5 are removed.
Then we compute the similarity measure between each pair of
new clusters. We take alarm tags 1 and 2 in the second step, and
then 10 and 8 in the third step. In the fourth step, the measure
a b
Fig. 4. Alarm similarity color map of the original alarm data based on Kondaveeti’s scheme (a) and correlation color map of the pseudo data (b) for case study 1. (For
interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
6. 504 F. Yang et al. / ISA Transactions 51 (2012) 499–506
Φ =
1 0.97 −0.06 −0.08 −0.08 0.33 0.23 0.54 0.34 0.42
1 −0.03 −0.07 −0.07 0.34 0.23 0.55 0.35 0.43
1 0.47 0.47 −0.21 0.26 −0.08 0.04 −0.12
1 1.00 −0.20 0.10 −0.16 −0.27 −0.13
1 −0.20 0.10 −0.16 −0.27 −0.13
1 −0.25 0.64 0.34 0.67
1 0.25 0.17 0.06
1 0.60 0.79
1 0.70
1
Box I.
Table 1
Clustering process for case study 1.
Step Clusters Largest measure
Correlation Pair
0 1 2 9 10 8 6 3 4 5 7 1.00 4, 5
1 1 2 9 10 8 6 3 11 7 0.97 1, 2
2 12 9 10 8 6 3 11 7 0.79 10, 8
3 12 9 13 6 3 11 7 0.69 9, 8
4 12 14 6 3 11 7 0.67 6, 10
5 12 15 3 11 7 0.55 2, 8
6 16 3 11 7 0.47 3, 4/5
7 16 17 7 −0.27 4/5, 9
8 18 7 0.26 7, 3
9 19 – –
between clusters 9 and 13 is the largest, which is determined by
the similarity between alarm tags 9 and 8 according to the single-
linkage method. So clusters 9 and 13 become the new cluster 14
which contains three alarm tags. The algorithm continues until
only one cluster is left. The corresponding dendrogram is shown
in Fig. 5. Thus the new order of alarm tags is obtained and thereby
we have the correlation color map shown in Fig. 4(b).
Apparently, alarm tags 1, 2, 9, 10, 8, and 6 form a single cluster
with positive correlations, and alarm tags 3, 4, and 5 also have
some correlations between them. This result clearly provides more
information than the one shown in Fig. 4. Therefore the proposed
method reveals some correlations that are not uncovered in the
method proposed by Kondaveeti et al. [10], such as the correlation
between alarm tags 8 and 9. Then we take the data of alarm tags
1, 2, 9, 10, 8, and 6 to perform SVD. The six singular values are
1720, 498, 347, 302, 199, and 165. Thus it is apparent that there
exists redundancy in these tags because one or two singular values
dominate most of the collinear information. According to the user’s
preference and the corresponding physical meanings, some alarms
may be removed.
5.2. Case study 2
Next consider a hydro treater process in a refinery in Alberta,
Canada. Since there are hundreds of tags with alarms, we order
them based on the numbers of occurrence and choose 11 process
tags associated with analogue alarms (HI/LO) that contribute to
the large number of alarm counts. The data set includes one week
worth of alarm data at a rate of one sample per second, during
which there are two evident floods leading to a large number of
alarms. The above analysis is implemented on this data set and
the color map obtained is shown in Fig. 6(a). The order of tags is
determined by the same algorithm as the previous case study; the
procedure is shown in Fig. 7.
It can be observed in Fig. 6(a) that alarm tags 1, 3, 9, 10,
and 11 form a cluster with high correlation values. From process
knowledge, we know that tags 1 and 3 are both flow rates, and
tag 3 is downstream of tag 1. Therefore, when a fault occurs
Fig. 5. Dendrogram for the 10 variables in case study 1 based on the single-linkage
measure. The scale on the right indicates the similarity measure.
upstream, it can propagate downstream to other variables, which
can be reflected in the alarm data. Although they have similar
trends in this case study, they are not redundant in general
because they reveal the fault propagation and there is some time
delay as evident from the time stamps. Tags 9, 10, and 11 are
fuel gas pressures of different burners in one equipment, leading
to evident correlations. From this study, we find that they are
somewhat redundant and may be reduced to one tag. In real
engineering practice, however, we should check more data to
confirm this redundancy and resort to process knowledge to
see the physical implication. Actually, some redundancy is also
necessary because it can help improve the accuracy of detection,
especially for important variables. In this case, each burner is
placed with one sensor for monitoring its operating situation,
although they are all pressure measurements for one equipment.
In addition, the correlation between the above two groups is
also high, which shows up the alarm flood when different tags
show similar patterns due to the fault spread or interrelationship
between physical quantities. Note that tag 11 behaves differently
from tags 9 and 10 although they are highly correlated; this also
accounts for the necessity of setting an alarm on tag 11.
As a comparison, the corresponding process data is also
available, as shown in Fig. 8, based on which the correlation color
map is obtained as shown in Fig. 6(b). It is observed that the two
color maps (Fig. 6(a) and (b)) have different patterns even if they
share the same order of tags. During the floods, many process
tags are correlated, while their alarms are less correlated thanks
to the good alarm settings. For example, tags 2 and 6 show some
negative correlation because they are flow rates on upstream and
downstream variables respectively; however, the corresponding
alarms do not have an apparent correlation.
6. Concluding remarks
In this paper, the correlation matrix is plotted as a color map to
visualize the relationship between different alarm tags. Compared
to the ASCM, the clustered correlation map of pseudo data map
has the following three main advantages: (1) it is robust to missed,
false, and chattering alarms; (2) the correlation of pseudo data
provides directional (positive or negative) information in addition
7. F. Yang et al. / ISA Transactions 51 (2012) 499–506 505
a b
Fig. 6. Color maps for pseudo data (a) and process data (b) in case study 2. (For interpretation of the references to colour in this figure legend, the reader is referred to the
web version of this article.)
1.00
0.84
0.67
0.51
0.35
0.18
0.02
Fig. 7. Dendrogram for case study 2.
Fig. 8. High density plot of the process data in case study 2.
to the similarity; (3) the pseudo data used in generating the color
map can also be used in other statistical analysis that provides
more potential; and (4) no knowledge of delays between correlated
alarms is required. One disadvantage to be noted is that when
computing correlation between two time series, the simultaneous
0’s in both the series have little information because they are in the
normal region, resulting in the reduction of correlation coefficients.
Thus the period for analysis should be selected appropriately
so that there are dense alarm signals, and it is this window of
alarm data that should be converted to pseudo data for statistical
analysis. From this improved correlation analysis, we can find the
relationship and redundancy in alarm tags that can be translated
into improved alarm settings for efficient alarm rationalization.
It should be noted that the proposed method in this paper is
an improved correlation analysis based on alarm data. However,
to obtain a reasonable and effective alarm configuration, we
should also investigate process data as well as process knowledge,
as illustrated in the second industrial case study and in Fig. 6.
The analysis of alarm data is simple but is lacking in terms
of detailed information, especially the true trend information
of a process variable; as a result, we usually take this analysis
to be the first step to focus our attention on some particular
parts of the industrial process and then apply more complex
techniques. As discussed in Section 5.2, the results should be
compared to the ones based on process data to find the influence
of alarm settings. The correlation and redundancy information
captured through data analysis should be combined with process
knowledge, especially the process connectivity and causality
information between process units and variables [22,23].
The method proposed in this paper can be developed to be user-
friendly to tune the parameters. In the pseudo data generating
stage, the variance of Gaussian kernel and the sampling rate of
pseudo data are two parameters that require tuning. In the color
map plotting stage, the clustering algorithm is another option. In
the color map display period, one should be able to change the
color code and the number of scales of colors. It is important to
provide some degrees of freedom for the user because the plot is
sensitive to these tuning parameters and an appropriate choice of
parameters depends on the specific circumstances, requirements
and objectives of the analysis.
Acknowledgments
This work was supported by NSERC (SPG and IRC) in Canada,
NSFC (60736026 and 60904044), Tsinghua National Laboratory
for Information Science and Technology (TNList) Cross-discipline
Foundation, and the 973 Project (2009CB320600) in China.
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