This document contains a summary of 14 core seminars presented by Nehem Tudu on tool wear monitoring and alarm systems based on pattern recognition with logical analysis of data (LAD). The seminars covered topics such as tool wear, LAD methodology, experimental design, knowledge extraction using LAD, and developing a proportional hazards model. The goal was to use data from machining titanium alloy to train LAD models to automatically detect worn tool patterns and build an online tool wear alarm system without human interference. LAD was able to accurately classify observations with a quality of 97.2%.
Automated Repair of Feature Interaction Failures in Automated Driving SystemsLionel Briand
The document describes an approach called ARIEL for automatically repairing feature interaction failures in automated driving systems. ARIEL uses a customized genetic programming algorithm that leverages fault localization weighted by test failure severity and generates patches through threshold changes and condition reordering. An evaluation on two industrial case studies found ARIEL outperformed baselines in fully repairing the systems within 16 hours. Domain experts judged the synthesized patches to be valid, understandable, useful and superior to what could be produced manually.
This document describes JTExpert, a tool for automatic test data generation. It uses static analysis and search-based techniques to generate test data that achieves high code coverage for object-oriented programs. It analyzes the source code to identify relevant methods and means of instantiating classes needed to set the class under test to a state where the test target method can be reached. JTExpert then uses this information to efficiently search the large input space to generate test data suites. An example of how JTExpert analyzes a sample class is provided to illustrate its approach.
Automatic Migration of Legacy Java Method Implementations to InterfacesRaffi Khatchadourian
Java 8 is one of the largest upgrades to the popular language and framework in over a decade. In this talk, I will first overview several new, key features of Java 8 that can help make programs easier to read, write, and maintain, especially in regards to collections. These features include Lambda Expressions, the Stream API, and enhanced interfaces, many of which help bridge the gap between functional and imperative programming paradigms and allow for succinct concurrency implementations. Next, I will discuss several open issues related to automatically migrating (refactoring) legacy Java software to use such features correctly, efficiently, and as completely as possible. Solving these problems will help developers to maximally understand and adopt these new features thus improving their software.
This document discusses control statements in Java, including if/else statements, while loops, and for loops. It provides examples of how to use these statements to make choices or repeat processes conditionally. It also covers errors that can occur in loops and debugging strategies. Additional topics include math methods, random number generation, reading/writing text files, and using dialog boxes for input/output.
The document discusses assisting designers in composing workflows through the reuse of frequent workflow fragments mined from repositories. It proposes an approach that involves mining fragments, representing workflows as graphs, homogenizing activity labels, and allowing users to search for fragments using keywords and activities from their initial workflow. Fragments are retrieved based on relevance to keywords and compatibility to specified activities, then ranked and presented to users for composition. Experiments assess different graph representations for mining fragments in terms of effectiveness, size and runtime. The approach aims to help designers reuse best practices from repositories when specifying new workflows.
A framework and approaches to develop an in-house CAT with freeware and open ...Tetsuo Kimura
The document outlines a symposium on developing in-house computerized adaptive tests (CATs) using free and open-source software. It discusses three stages of CAT development: pretesting and item analysis using the R package ltm to construct an item bank, simulating CATs with the existing item bank using the R package SimulCAT to determine specifications, and implementing CATs by publishing them on the Moodle platform using the Moodle UCAT program. The symposium will introduce various free and open-source tools that can be used at each stage of CAT development, including Exametrika, ltm, SimulCAT, catR, Moodle UCAT, and Concerto.
This document is a laboratory manual for digital signal processing experiments using MATLAB and a DSP chip. It contains an introduction to MATLAB and lists 16 experiments using MATLAB and 11 experiments using a DSP chip. The experiments cover topics like DFT, convolution, filtering, sampling, and impulse responses. The manual was prepared by faculty of the electronics and communication engineering department to help students learn and apply DSP concepts.
This document discusses advanced operations on arrays in Java, including searching, sorting, insertions, and removals. It covers linear and binary search algorithms, selection, bubble, and insertion sort methods, and how to implement searches and sorts for arrays of objects. It also introduces the ArrayList class in Java and how it provides a more flexible alternative to arrays.
Automated Repair of Feature Interaction Failures in Automated Driving SystemsLionel Briand
The document describes an approach called ARIEL for automatically repairing feature interaction failures in automated driving systems. ARIEL uses a customized genetic programming algorithm that leverages fault localization weighted by test failure severity and generates patches through threshold changes and condition reordering. An evaluation on two industrial case studies found ARIEL outperformed baselines in fully repairing the systems within 16 hours. Domain experts judged the synthesized patches to be valid, understandable, useful and superior to what could be produced manually.
This document describes JTExpert, a tool for automatic test data generation. It uses static analysis and search-based techniques to generate test data that achieves high code coverage for object-oriented programs. It analyzes the source code to identify relevant methods and means of instantiating classes needed to set the class under test to a state where the test target method can be reached. JTExpert then uses this information to efficiently search the large input space to generate test data suites. An example of how JTExpert analyzes a sample class is provided to illustrate its approach.
Automatic Migration of Legacy Java Method Implementations to InterfacesRaffi Khatchadourian
Java 8 is one of the largest upgrades to the popular language and framework in over a decade. In this talk, I will first overview several new, key features of Java 8 that can help make programs easier to read, write, and maintain, especially in regards to collections. These features include Lambda Expressions, the Stream API, and enhanced interfaces, many of which help bridge the gap between functional and imperative programming paradigms and allow for succinct concurrency implementations. Next, I will discuss several open issues related to automatically migrating (refactoring) legacy Java software to use such features correctly, efficiently, and as completely as possible. Solving these problems will help developers to maximally understand and adopt these new features thus improving their software.
This document discusses control statements in Java, including if/else statements, while loops, and for loops. It provides examples of how to use these statements to make choices or repeat processes conditionally. It also covers errors that can occur in loops and debugging strategies. Additional topics include math methods, random number generation, reading/writing text files, and using dialog boxes for input/output.
The document discusses assisting designers in composing workflows through the reuse of frequent workflow fragments mined from repositories. It proposes an approach that involves mining fragments, representing workflows as graphs, homogenizing activity labels, and allowing users to search for fragments using keywords and activities from their initial workflow. Fragments are retrieved based on relevance to keywords and compatibility to specified activities, then ranked and presented to users for composition. Experiments assess different graph representations for mining fragments in terms of effectiveness, size and runtime. The approach aims to help designers reuse best practices from repositories when specifying new workflows.
A framework and approaches to develop an in-house CAT with freeware and open ...Tetsuo Kimura
The document outlines a symposium on developing in-house computerized adaptive tests (CATs) using free and open-source software. It discusses three stages of CAT development: pretesting and item analysis using the R package ltm to construct an item bank, simulating CATs with the existing item bank using the R package SimulCAT to determine specifications, and implementing CATs by publishing them on the Moodle platform using the Moodle UCAT program. The symposium will introduce various free and open-source tools that can be used at each stage of CAT development, including Exametrika, ltm, SimulCAT, catR, Moodle UCAT, and Concerto.
This document is a laboratory manual for digital signal processing experiments using MATLAB and a DSP chip. It contains an introduction to MATLAB and lists 16 experiments using MATLAB and 11 experiments using a DSP chip. The experiments cover topics like DFT, convolution, filtering, sampling, and impulse responses. The manual was prepared by faculty of the electronics and communication engineering department to help students learn and apply DSP concepts.
This document discusses advanced operations on arrays in Java, including searching, sorting, insertions, and removals. It covers linear and binary search algorithms, selection, bubble, and insertion sort methods, and how to implement searches and sorts for arrays of objects. It also introduces the ArrayList class in Java and how it provides a more flexible alternative to arrays.
Sochi hexitex manchester 10 dec 2008 presentationTaha Sochi
The document describes EasyEDD, a software for processing powder diffraction data from tomographic energy-dispersive diffraction (TEDDI) experiments. EasyEDD allows batch processing of large quantities of TEDDI data through a graphical user interface. It supports common data formats and provides tools for data correction, visualization as color-coded grids, fitting of diffraction patterns, and analysis of results. The software combines these capabilities into an integrated environment to facilitate the analysis of data from high throughput TEDDI detectors.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
Framework defines "semi-complete" applications that embody domain-specific object structures and functionality. Frameworks avoid duplication and increase productivity. They define common loops, databases, math functions and other functionality while allowing for application-specific logic. Frameworks balance stability with allowing incremental improvements through careful management of dependencies between components.
Machine learning algorithm for classification of activity of daily life’sSiddharth Chakravarty
The document describes a machine learning approach to classify activities of daily living (ADL) using data from a wrist-worn accelerometer. The approach uses support vector machines (SVM) with feature engineering that includes vector magnitude and singular value decomposition. The model is trained on a dataset containing 11 ADLs performed by 16 volunteers. Hyperparameter tuning is performed to optimize the SVM, achieving up to 86% accuracy on test data when using both vector magnitude and SVD features compared to 71% accuracy using only vector magnitude. The results demonstrate an improved method for detecting ADLs but would benefit from testing on additional datasets.
IRJET- Fault Detection and Maintenance Prediction for Gear of an Industri...IRJET Journal
This document discusses using machine learning algorithms to detect faults and predict maintenance needs for gears in an industrial gearbox. It experiments with five machine learning approaches - K-nearest neighbors, decision trees, random forests, support vector machines, and multilayer perceptrons - on a gear dataset with 10 features and 5 class labels related to gear condition. The random forest algorithm achieved the best performance with 89.15% accuracy and the lowest root mean square error of 0.172.
This document describes a process called PREREQUIR for recovering pre-requirements for a software system via cluster analysis of stakeholder input. The process involves collecting stakeholder requirements through questionnaires, mapping the requirements to a vector space, clustering the requirements using PAM and AGNES algorithms, labeling the clusters, and manually verifying the results. As a case study, the process was used to recover requirements for a web browser by collecting input from 200 users and analyzing 433 user needs, with results showing clusters could recover common user needs but also outliers.
This document proposes a method to improve the reuse of workflow fragments by mining workflow repositories. It evaluates different graph representations of workflows and uses the SUBDUE algorithm to identify recurrent fragments. An experiment compares representations on precision, recall, memory usage, and time. Representation D1, which labels edges and nodes, performed best. A second experiment assesses how filtering workflows by keywords impacts finding relevant fragments for a user query. The method aims to incorporate workflow fragment search capabilities into the design lifecycle to promote reuse.
From Black Box to Black Magic, Pycon Ireland 2014Gloria Lovera
Machine learning algorithms in automotive field.
If you are interested in, I suggest also this presentation:
http://www.slideshare.net/bix883/machine-learning-virtual-sensors-automotive-intelligent-tire
The document discusses machine learning techniques for processing sensor data from vehicles. It describes how machine learning can be used to create virtual sensors from raw data by analyzing features, selecting relevant data, preprocessing to remove noise, and building models. Examples are provided of using support vector machines and neural networks to classify yaw rate from sensor signals. The document also introduces a tool called Distortion that manages machine learning jobs by uploading data, running algorithms, and analyzing results.
IRJET - Airplane Crash Analysis and Prediction using Machine LearningIRJET Journal
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
Matrix and Tensor Tools for Computer VisionActiveEon
The document discusses various matrix and tensor tools for computer vision, including principal component analysis (PCA), singular value decomposition (SVD), robust PCA, low-rank representation, non-negative matrix factorization, tensor decompositions, and incremental methods for SVD and tensor learning. It provides definitions and explanations of the techniques along with references for further information.
IRJET - License Plate Detection using Hybrid Morphological Technique and ...IRJET Journal
This document presents a license plate detection and recognition system using hybrid morphological techniques and neural networks. The system first uses the Viola-Jones algorithm to detect candidate license plate regions in video frames. The Kanade-Lucas-Tomasi algorithm is then used to track potential plates across frames. Candidate regions are classified using AlexNet and SVM to confirm plates. Morphological operations extract the exact plate region. Experimental results on vehicle image datasets show the approach provides improved license plate detection compared to existing methods.
AVATAR : Fixing Semantic Bugs with Fix Patterns of Static Analysis ViolationsDongsun Kim
Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the- art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.
O hst-05 design-of_the_occupant_protection_system_mahindraAnand Kumar Chinni
This document describes how design of experiments was used to optimize an occupant protection system for front-loading vehicles at the conceptual design stage. A DOE model was developed in HyperStudy to simulate different restraint system designs and vehicle crash pulses. The study varied factors like pretensioners, load limiters, seat rotations, and pulse intensities. Over 384 simulations were run to analyze the effects on occupant injuries. Results showed the level of restraint system needed varies depending on the crash pulse intensity, and sensitivity of parameters can be assessed. The DOE approach helps identify optimal restraint systems early to reduce development time and costs.
This document describes a student project to detect object movement using a webcam. The project uses Python and OpenCV libraries. Key steps include capturing video frames, comparing frames to detect differences indicating movement, and highlighting the moving regions. The project aims to build a low-cost motion detection system for home users. It analyzes image frames to detect movement and displays the regions in separate color, gray, delta, and threshold frames for clear understanding.
IRJET- Machine Learning and Deep Learning Methods for CybersecurityIRJET Journal
This document discusses the use of machine learning and deep learning methods for cybersecurity and network intrusion detection. It provides an overview of various algorithms including convolutional neural networks, support vector machines, k-nearest neighbors, decision trees, deep belief networks, and recurrent neural networks. For each algorithm, it describes the basic process and provides an example of its application to intrusion detection. It also includes a literature review summarizing research on applying these methods to intrusion detection using various datasets and evaluating their accuracy. Finally, it compares the results, limitations, and opportunities for future enhancements in using machine learning for cybersecurity.
The document discusses various technologies used in semiconductor manufacturing processes, including resolution enhancement techniques, optical proximity correction, chemical mechanical planarization, critical area analysis, scanning electron microscopy, extreme ultraviolet lithography, and sub-resolution assist features. It also covers topics like market share, reliability issues, design for manufacturability, technology roadmap challenges, and the evolution of product yields being increasingly limited by design features rather than random defects.
This document discusses the fundamentals of fuzzy logic control systems. It begins by defining fuzzy logic as a problem-solving control system methodology that uses linguistic variables and fuzzy rules to map inputs to outputs. It then outlines the typical elements of a fuzzy logic system, including fuzzy sets, linguistic variables, fuzzy rules, fuzzy inference, and defuzzification. Finally, it provides an example of applying fuzzy logic to control the temperature in a simple heating/cooling system.
Using Diversity for Automated Boundary Value TestingFelix Dobslaw
The document discusses automated boundary value testing and analysis. It defines program boundaries as places where behavior is supposed to change or actually changes. It explores using diversity to automatically test boundary cases by comparing multiple executions and observing differences. Challenges include defining appropriate diversity metrics and handling different data types and sizes.
This document discusses test automation where machines create tests rather than just running human-created tests. It describes boundary value analysis/testing to find bugs by testing program boundaries and edge cases. Boundary value analysis tests expected changes from the specification while boundary value testing explores actual program behavior. The document provides examples of testing a pizza order API and using boundary mining to generate tests from the specification and implementation. It concludes by noting that in the future, robots may take over testing.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Sochi hexitex manchester 10 dec 2008 presentationTaha Sochi
The document describes EasyEDD, a software for processing powder diffraction data from tomographic energy-dispersive diffraction (TEDDI) experiments. EasyEDD allows batch processing of large quantities of TEDDI data through a graphical user interface. It supports common data formats and provides tools for data correction, visualization as color-coded grids, fitting of diffraction patterns, and analysis of results. The software combines these capabilities into an integrated environment to facilitate the analysis of data from high throughput TEDDI detectors.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
Framework defines "semi-complete" applications that embody domain-specific object structures and functionality. Frameworks avoid duplication and increase productivity. They define common loops, databases, math functions and other functionality while allowing for application-specific logic. Frameworks balance stability with allowing incremental improvements through careful management of dependencies between components.
Machine learning algorithm for classification of activity of daily life’sSiddharth Chakravarty
The document describes a machine learning approach to classify activities of daily living (ADL) using data from a wrist-worn accelerometer. The approach uses support vector machines (SVM) with feature engineering that includes vector magnitude and singular value decomposition. The model is trained on a dataset containing 11 ADLs performed by 16 volunteers. Hyperparameter tuning is performed to optimize the SVM, achieving up to 86% accuracy on test data when using both vector magnitude and SVD features compared to 71% accuracy using only vector magnitude. The results demonstrate an improved method for detecting ADLs but would benefit from testing on additional datasets.
IRJET- Fault Detection and Maintenance Prediction for Gear of an Industri...IRJET Journal
This document discusses using machine learning algorithms to detect faults and predict maintenance needs for gears in an industrial gearbox. It experiments with five machine learning approaches - K-nearest neighbors, decision trees, random forests, support vector machines, and multilayer perceptrons - on a gear dataset with 10 features and 5 class labels related to gear condition. The random forest algorithm achieved the best performance with 89.15% accuracy and the lowest root mean square error of 0.172.
This document describes a process called PREREQUIR for recovering pre-requirements for a software system via cluster analysis of stakeholder input. The process involves collecting stakeholder requirements through questionnaires, mapping the requirements to a vector space, clustering the requirements using PAM and AGNES algorithms, labeling the clusters, and manually verifying the results. As a case study, the process was used to recover requirements for a web browser by collecting input from 200 users and analyzing 433 user needs, with results showing clusters could recover common user needs but also outliers.
This document proposes a method to improve the reuse of workflow fragments by mining workflow repositories. It evaluates different graph representations of workflows and uses the SUBDUE algorithm to identify recurrent fragments. An experiment compares representations on precision, recall, memory usage, and time. Representation D1, which labels edges and nodes, performed best. A second experiment assesses how filtering workflows by keywords impacts finding relevant fragments for a user query. The method aims to incorporate workflow fragment search capabilities into the design lifecycle to promote reuse.
From Black Box to Black Magic, Pycon Ireland 2014Gloria Lovera
Machine learning algorithms in automotive field.
If you are interested in, I suggest also this presentation:
http://www.slideshare.net/bix883/machine-learning-virtual-sensors-automotive-intelligent-tire
The document discusses machine learning techniques for processing sensor data from vehicles. It describes how machine learning can be used to create virtual sensors from raw data by analyzing features, selecting relevant data, preprocessing to remove noise, and building models. Examples are provided of using support vector machines and neural networks to classify yaw rate from sensor signals. The document also introduces a tool called Distortion that manages machine learning jobs by uploading data, running algorithms, and analyzing results.
IRJET - Airplane Crash Analysis and Prediction using Machine LearningIRJET Journal
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
Matrix and Tensor Tools for Computer VisionActiveEon
The document discusses various matrix and tensor tools for computer vision, including principal component analysis (PCA), singular value decomposition (SVD), robust PCA, low-rank representation, non-negative matrix factorization, tensor decompositions, and incremental methods for SVD and tensor learning. It provides definitions and explanations of the techniques along with references for further information.
IRJET - License Plate Detection using Hybrid Morphological Technique and ...IRJET Journal
This document presents a license plate detection and recognition system using hybrid morphological techniques and neural networks. The system first uses the Viola-Jones algorithm to detect candidate license plate regions in video frames. The Kanade-Lucas-Tomasi algorithm is then used to track potential plates across frames. Candidate regions are classified using AlexNet and SVM to confirm plates. Morphological operations extract the exact plate region. Experimental results on vehicle image datasets show the approach provides improved license plate detection compared to existing methods.
AVATAR : Fixing Semantic Bugs with Fix Patterns of Static Analysis ViolationsDongsun Kim
Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the- art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.
O hst-05 design-of_the_occupant_protection_system_mahindraAnand Kumar Chinni
This document describes how design of experiments was used to optimize an occupant protection system for front-loading vehicles at the conceptual design stage. A DOE model was developed in HyperStudy to simulate different restraint system designs and vehicle crash pulses. The study varied factors like pretensioners, load limiters, seat rotations, and pulse intensities. Over 384 simulations were run to analyze the effects on occupant injuries. Results showed the level of restraint system needed varies depending on the crash pulse intensity, and sensitivity of parameters can be assessed. The DOE approach helps identify optimal restraint systems early to reduce development time and costs.
This document describes a student project to detect object movement using a webcam. The project uses Python and OpenCV libraries. Key steps include capturing video frames, comparing frames to detect differences indicating movement, and highlighting the moving regions. The project aims to build a low-cost motion detection system for home users. It analyzes image frames to detect movement and displays the regions in separate color, gray, delta, and threshold frames for clear understanding.
IRJET- Machine Learning and Deep Learning Methods for CybersecurityIRJET Journal
This document discusses the use of machine learning and deep learning methods for cybersecurity and network intrusion detection. It provides an overview of various algorithms including convolutional neural networks, support vector machines, k-nearest neighbors, decision trees, deep belief networks, and recurrent neural networks. For each algorithm, it describes the basic process and provides an example of its application to intrusion detection. It also includes a literature review summarizing research on applying these methods to intrusion detection using various datasets and evaluating their accuracy. Finally, it compares the results, limitations, and opportunities for future enhancements in using machine learning for cybersecurity.
The document discusses various technologies used in semiconductor manufacturing processes, including resolution enhancement techniques, optical proximity correction, chemical mechanical planarization, critical area analysis, scanning electron microscopy, extreme ultraviolet lithography, and sub-resolution assist features. It also covers topics like market share, reliability issues, design for manufacturability, technology roadmap challenges, and the evolution of product yields being increasingly limited by design features rather than random defects.
This document discusses the fundamentals of fuzzy logic control systems. It begins by defining fuzzy logic as a problem-solving control system methodology that uses linguistic variables and fuzzy rules to map inputs to outputs. It then outlines the typical elements of a fuzzy logic system, including fuzzy sets, linguistic variables, fuzzy rules, fuzzy inference, and defuzzification. Finally, it provides an example of applying fuzzy logic to control the temperature in a simple heating/cooling system.
Using Diversity for Automated Boundary Value TestingFelix Dobslaw
The document discusses automated boundary value testing and analysis. It defines program boundaries as places where behavior is supposed to change or actually changes. It explores using diversity to automatically test boundary cases by comparing multiple executions and observing differences. Challenges include defining appropriate diversity metrics and handling different data types and sizes.
This document discusses test automation where machines create tests rather than just running human-created tests. It describes boundary value analysis/testing to find bugs by testing program boundaries and edge cases. Boundary value analysis tests expected changes from the specification while boundary value testing explores actual program behavior. The document provides examples of testing a pizza order API and using boundary mining to generate tests from the specification and implementation. It concludes by noting that in the future, robots may take over testing.
Similar to Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data (20)
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
6th International Conference on Machine Learning & Applications (CMLA 2024)
Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data
1. CORE SEMINAR 1
Tool Wear Monitoring and AlarmTool Wear Monitoring and Alarm
System Based on PatternSystem Based on Pattern
Recognition With LogicalRecognition With Logical
Analysis of DataAnalysis of Data
Presented By:Presented By:
Name: Nehem TuduName: Nehem Tudu
Roll No.: M150360MERoll No.: M150360ME
Branch: Manufacturing TechnologyBranch: Manufacturing Technology
2. CORE SEMINAR 2
CONTENTCONTENT
IntroductionIntroduction
Logical Analysis of DataLogical Analysis of Data
Working of LADWorking of LAD
Design of ExperimentDesign of Experiment
Knowledge Extracting and LearningKnowledge Extracting and Learning
The Statistical Proportional Hazards Model (PHM)The Statistical Proportional Hazards Model (PHM)
LAD Online Alarm System DevelopmentLAD Online Alarm System Development
Comparison With the PHM Alarm FunctionComparison With the PHM Alarm Function
DiscussionDiscussion
ConclusionConclusion
ReferenceReference
3. CORE SEMINAR 3
INTRODUCTIONINTRODUCTION
Tool wear in machining is analyzed by:Tool wear in machining is analyzed by:
1.1. Theoretical & numerical approachTheoretical & numerical approach
2.2. Data driven approachData driven approach
Our objective is to report & discuss the results obtained experimentallyOur objective is to report & discuss the results obtained experimentally
The work-piece material is TiMMCsThe work-piece material is TiMMCs
Differentiate between two types of covariates;Differentiate between two types of covariates;
1.1. Internal (diagnostic) covariates, which carry direct information about the wear processInternal (diagnostic) covariates, which carry direct information about the wear process
2.2. External (environmental or/& machining conditions) covariates, which affect the wear processExternal (environmental or/& machining conditions) covariates, which affect the wear process
Internal covariates are observed by online monitoring of time dependent factorsInternal covariates are observed by online monitoring of time dependent factors
Combination of internal & external covariates is used before in order to develop accurateCombination of internal & external covariates is used before in order to develop accurate
modelmodel
Tool wear monitoring system based on LAD during turning TiMMCs under variableTool wear monitoring system based on LAD during turning TiMMCs under variable
conditions were implementedconditions were implemented
The platform of PXI & LABVIEW were used to develop the tool wear alarm systemThe platform of PXI & LABVIEW were used to develop the tool wear alarm system
4. CORE SEMINAR 4
TOOL WEARTOOL WEAR
The rate at which the cutting edge of aThe rate at which the cutting edge of a
tool wears away during machiningtool wears away during machining
In cutting process, produced by contactIn cutting process, produced by contact
& relative sliding between& relative sliding between
Cutting tool & the work-pieceCutting tool & the work-piece
Between the cutting tool & the chipBetween the cutting tool & the chip
under the extreme conditions of cutting areaunder the extreme conditions of cutting area
Type of tool wearType of tool wear
Crater wear on rake faceCrater wear on rake face
Flank wear on flank faceFlank wear on flank face
Tool wear mechanisms in metal cuttingTool wear mechanisms in metal cutting
includeinclude
Abrasive wearAbrasive wear
Adhesive wearAdhesive wear
Diffusion wearDiffusion wear
Oxidation wear, etc.,Oxidation wear, etc.,
5. CORE SEMINAR 5
LOGICAL ANALYSIS OF DATA (LAD)LOGICAL ANALYSIS OF DATA (LAD)
LAD is a data-driven combinatorial optimization technique that allows theLAD is a data-driven combinatorial optimization technique that allows the
classification of phenomena based on pattern recognitionclassification of phenomena based on pattern recognition
LAD is applied in two consecutive stages:LAD is applied in two consecutive stages:
1.1. Training or learning stage, part of the data is used to extract special patterns of someTraining or learning stage, part of the data is used to extract special patterns of some
phenomenaphenomena
2.2. Testing or the theory formation stage, the remainder of the data is used to test theTesting or the theory formation stage, the remainder of the data is used to test the
accuracy of the previously learned knowledgeaccuracy of the previously learned knowledge
LAD is based on supervised learningLAD is based on supervised learning
In this work, we have two classes of cutting toolIn this work, we have two classes of cutting tool::
1.1. Worn-out toolWorn-out tool
2.2. A fresh toolA fresh tool
Has certain advantages over other techniques:Has certain advantages over other techniques:
It is a non-statistical approachIt is a non-statistical approach
It does not need any prior assumptions regarding the posteriori class probabilitiesIt does not need any prior assumptions regarding the posteriori class probabilities
User are able to track back any results (phenomena or effects) to its possible causesUser are able to track back any results (phenomena or effects) to its possible causes
6. CORE SEMINAR 6
Working of LADWorking of LAD
Each observation carries the values of the internal & external covariates & aEach observation carries the values of the internal & external covariates & a
labellabel
Internal covariates are the radial force (fx), the feed force (fy), and theInternal covariates are the radial force (fx), the feed force (fy), and the
cutting force (fz)cutting force (fz)
External covariates are the cutting speed (v) and the feed rate (f )External covariates are the cutting speed (v) and the feed rate (f )
After accomplishment of the two phases worn patterns & fresh patterns areAfter accomplishment of the two phases worn patterns & fresh patterns are
found by LADfound by LAD
Worn patterns are used in order to develop tool wear monitoring modelWorn patterns are used in order to develop tool wear monitoring model
Model is later incorporated in the platform of PXI & LABVIEW in order toModel is later incorporated in the platform of PXI & LABVIEW in order to
monitor the tool wear online & to give an alarm when the tool worn patternsmonitor the tool wear online & to give an alarm when the tool worn patterns
are detectedare detected
Observations are classified as either +ve (fresh, πObservations are classified as either +ve (fresh, π++
, class 1) or -ve (worn-, class 1) or -ve (worn-
out,out,ππ--
, class 2), class 2)
LAD generates collections of patterns which characterizes each classLAD generates collections of patterns which characterizes each class
Patterns represent interactions between variables separatelyPatterns represent interactions between variables separately
7. CORE SEMINAR 7
First steps involved in LADFirst steps involved in LAD
Data binarization is the process of transformation of data into a BooleanData binarization is the process of transformation of data into a Boolean
databasedatabase
This technique substitutes each numerical variable by at least one binaryThis technique substitutes each numerical variable by at least one binary
attributeattribute
For e.g, binarization of a continuous numerical variableFor e.g, binarization of a continuous numerical variable AA is done by ranking, inis done by ranking, in
ascending order, all the distinct values of the numerical variableascending order, all the distinct values of the numerical variable AA as follows:as follows:
uuAA
(1)(1)
< u< uAA
(2)(2)
<..... <u<..... <uAA
(q)(q)
(q≤Q) (1)(q≤Q) (1)
where q is the total number of distinct values of the variablewhere q is the total number of distinct values of the variable AA, &, & QQ is the totalis the total
number of observations in the training set.number of observations in the training set.
The cut-pointsThe cut-points δδA,,jA,,j, where, where jj is the number of cut-points for each variable, areis the number of cut-points for each variable, are
found between each pair of values that belong to different classes.found between each pair of values that belong to different classes.
By using Eq. (2), the cut-points are calculated as follows:By using Eq. (2), the cut-points are calculated as follows:
δδA,jA,j =(u=(uAA
(k)(k)
+u+uAA
(k+1)(k+1)
)/2 (2))/2 (2)
wherewhere uuAA
(k)(k)
πϵ πϵ ++
&& uuAA
(k+1)(k+1)
πϵ πϵ --
or vice versa.or vice versa.
A binary attributeA binary attribute bb is then formed from each cut-pointis then formed from each cut-point
Each cut-pointEach cut-point δδA,jA,j has a corresponding binary attributehas a corresponding binary attribute bbδA,jδA,j with defined valuewith defined value
bbδA,jδA,j = 1 if u= 1 if uAA ≥ δ≥ δA,jA,j
0 if u0 if uAA < δ< δA,jA,j (3)(3)
8. CORE SEMINAR 8
Second steps involved in LADSecond steps involved in LAD
Pattern generation, the key building blockPattern generation, the key building block
in LAD knowledge extractionin LAD knowledge extraction
Linear programming is used to generateLinear programming is used to generate
patternspatterns
It is assumed that each generated patternIt is assumed that each generated pattern
p is associated with a Boolean patternp is associated with a Boolean pattern
vectorvector W=(wW=(w11,w,w22,...,w,...,wqq,w,wq+1q+1,....,w,....,w2q2q )) with sizewith size
n wheren where n=2qn=2q,, qq is the size of binaryis the size of binary
observation vectorobservation vector
IfIf wwj+qj+q=1=1 then literalthen literal xxjj is included in patternis included in pattern
pp
Y=(yY=(y11, y, y22,....., y,....., yDD
++
)) is Boolean coverageis Boolean coverage
vector whose number of elements =vector whose number of elements =
number of +ve observationnumber of +ve observation DD++
& where& where yyii ==
00 if a patternif a pattern pp++
covers +ve observationcovers +ve observation ii &&
11 otherwiseotherwise
Each +ve observationEach +ve observation i πϵi πϵ ++
is representedis represented
as a Boolean observation vectoras a Boolean observation vector aai,ji,j =1=1 ifif
the binary attributethe binary attribute bbjj =1=1 && aai,j+qi,j+q=1=1 ifif bbjj =0=0
9. CORE SEMINAR 9
Third steps involved in LADThird steps involved in LAD
Theory formation or testing stage is the final step in the LAD decision modelTheory formation or testing stage is the final step in the LAD decision model
Zero value means that LAD cannot classify the observation whereZero value means that LAD cannot classify the observation where NN++
(N(N--
) is) is
the number ofthe number of +ve (-ve)+ve (-ve) patterns that are generated,patterns that are generated, ZZii
++
(o)(Z(o)(Zii
--
(o))=1(o))=1if patternif pattern
(i)(i) covers observation O, & zero otherwisecovers observation O, & zero otherwise
σσii
++
(σ(σii
--
)) is the weight of theis the weight of the +ve (-ve)+ve (-ve) patternpattern
The calculated value ofThe calculated value of Δ(o)Δ(o) of any new observation gives an indication toof any new observation gives an indication to
whether the observation belongs to fresh or worn-out classwhether the observation belongs to fresh or worn-out class
To measure the accuracy, the quality of classificationTo measure the accuracy, the quality of classification vv is usedis used
wherewhere aa && bb are the proportion of observations,are the proportion of observations, +ve+ve && -ve-ve, which are, which are
correctly classifiedcorrectly classified
cc && ee are the proportion of observations,are the proportion of observations, +ve+ve && -ve-ve, which are unclassified, which are unclassified
10. CORE SEMINAR 10
Design of ExperimentDesign of Experiment
TThe experiment was conducted in thehe experiment was conducted in the
machining laboratory at Ècolemachining laboratory at Ècole
Polytechnique de MontrèaPolytechnique de Montrèa
Cylindrical bar of Ti-6Al-4V alloyCylindrical bar of Ti-6Al-4V alloy
reinforced with 10-12% volume fractionreinforced with 10-12% volume fraction
of TiC ceramic particle is usedof TiC ceramic particle is used
The wear is measured at discreteThe wear is measured at discrete
points of time through inspections usingpoints of time through inspections using
an Olympus SZ-X12 microscopean Olympus SZ-X12 microscope
The procedure continues until the toolThe procedure continues until the tool
wear reached predefined thresholdwear reached predefined threshold
(VB(VBBmaxBmax =0.2mm)=0.2mm)
This procedure is repeated for 28 toolsThis procedure is repeated for 28 tools
11. CORE SEMINAR 11
Knowledge Extraction and LearningKnowledge Extraction and Learning
Cutting tool is failed when the tool is getting dull & no longer operates withCutting tool is failed when the tool is getting dull & no longer operates with
acceptable qualityacceptable quality
Cutting tool fails after reaching the worn-out stageCutting tool fails after reaching the worn-out stage
Classification limits is consideredClassification limits is considered
To distinguish between fresh & worn-out toolsTo distinguish between fresh & worn-out tools
In some cases where the velocity is high, progressive wear is rapidly evolving &In some cases where the velocity is high, progressive wear is rapidly evolving &
there is just one observation for wear value above 0.15mmthere is just one observation for wear value above 0.15mm
12. CORE SEMINAR 12
This classification procedure is repeated for the 28 toolsThis classification procedure is repeated for the 28 tools
Our objective is to use the data presented in Table 2 to train LAD to detectOur objective is to use the data presented in Table 2 to train LAD to detect
automatically the worn patterns & without human interferenceautomatically the worn patterns & without human interference
The software CBMLAD is used, in order to extract the knowledge from theThe software CBMLAD is used, in order to extract the knowledge from the
collected data, and then to train LADcollected data, and then to train LAD
13. CORE SEMINAR 13
Set O of the 273 observations is also divided into two sets of training, L,Set O of the 273 observations is also divided into two sets of training, L,
and testing, Tand testing, T
Tenfold cross validation procedure is conductedTenfold cross validation procedure is conducted
The quality of classification is calculated on the testing setThe quality of classification is calculated on the testing set
Thus, the learning procedure is repeated ten times with different trainingThus, the learning procedure is repeated ten times with different training
setssets
The results show that the quality of classificationThe results show that the quality of classification vv =97.2%=97.2%
The obtained five worn patterns do not cover any observation in fresh toolThe obtained five worn patterns do not cover any observation in fresh tool
spacespace
Patterns will lead us to build the online tool wear alarm systemPatterns will lead us to build the online tool wear alarm system
14. CORE SEMINAR 14
The Statistical Proportional Hazards Model (PHM)The Statistical Proportional Hazards Model (PHM)
A proportional hazards model (PHM) of the wear process is developed fromA proportional hazards model (PHM) of the wear process is developed from
the obtained experimental datathe obtained experimental data
We calculate the time to failure (TTF) by interpolating between twoWe calculate the time to failure (TTF) by interpolating between two
measurements around the failure thresholdmeasurements around the failure threshold
TTF is calculated when tool wear threshold is reachedTTF is calculated when tool wear threshold is reached
The concept of a PHM is that the failure rate of the cutting tool is not onlyThe concept of a PHM is that the failure rate of the cutting tool is not only
dependent on the age of the tool but also is affected by the internal &dependent on the age of the tool but also is affected by the internal &
external covariatesexternal covariates
15. CORE SEMINAR 15
We consider the Weibull distribution as a baseline functionWe consider the Weibull distribution as a baseline function
The failure hazard rate is written asThe failure hazard rate is written as
The conditional survival function can thus be given asThe conditional survival function can thus be given as
The conditional survival functionThe conditional survival function R(t;Y,Z)R(t;Y,Z) & its derivative& its derivative R(t;Y,Z)=R(t;Y,Z)=
h(t;Y,Z)R(t;Y,Z)h(t;Y,Z)R(t;Y,Z) are used to estimate the parametersare used to estimate the parameters (β,η,α(β,η,α11,γ,γ11,γ,γ22)) by using theby using the
maximum likelihood functionmaximum likelihood function
EXAKT software estimates the PHM parameters as shownEXAKT software estimates the PHM parameters as shown
Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)
16. CORE SEMINAR 16
Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)
The PHM model with all significantThe PHM model with all significant
variables is found by eliminatingvariables is found by eliminating
the variables whose impact on thethe variables whose impact on the
probability of failure is lowprobability of failure is low
It is concluded that the effects ofIt is concluded that the effects of
the radial force and the cuttingthe radial force and the cutting
force are higher than the effect offorce are higher than the effect of
the feed force on the progressivethe feed force on the progressive
flank tool wearflank tool wear
EXAKT produces theEXAKT produces the
Kolmogorov–Smirnov test whichKolmogorov–Smirnov test which
evaluates the model fitevaluates the model fit
17. CORE SEMINAR 17
EXAKT gives a control-limit,EXAKT gives a control-limit, d > 0d > 0 which is used in order to find the minimumwhich is used in order to find the minimum
expected machining cost per unit timeexpected machining cost per unit time
Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)
18. CORE SEMINAR 18
LAD Online Alarm System DevelopmentLAD Online Alarm System Development
The platform of PXI and LABVIEW were used to develop the online alarmThe platform of PXI and LABVIEW were used to develop the online alarm
systemsystem
For each transmitted set of measurements, the system search for wornFor each transmitted set of measurements, the system search for worn
patterns until color-coded lamp turns to red, when worn pattern is detectedpatterns until color-coded lamp turns to red, when worn pattern is detected
19. CORE SEMINAR 19
Comparison With the PHM Alarm FunctionComparison With the PHM Alarm Function
In order to compare the results, the recommended optimal time toIn order to compare the results, the recommended optimal time to
replacement is calculated by using the covariate's valuesreplacement is calculated by using the covariate's values
The recommended optimal replacement time according to certain covariate'sThe recommended optimal replacement time according to certain covariate's
values using PHM are calculated using the below equationvalues using PHM are calculated using the below equation
21. CORE SEMINAR 21
DiscussionDiscussion
PHM decisions are based on the assumption of a statistical goodness of fitPHM decisions are based on the assumption of a statistical goodness of fit
of a suitable hazard function & the cost's ratioof a suitable hazard function & the cost's ratio
LAD alarm points are based on pattern recognitionLAD alarm points are based on pattern recognition
LAD replacement decision gave warning alarm before the tool wear reachedLAD replacement decision gave warning alarm before the tool wear reached
the maximum flank wearthe maximum flank wear VBVBBmaxBmax =0.2mm=0.2mm & without losing valuable resource& without losing valuable resource
due to early replacementdue to early replacement
LAD can detect worn patterns online & in real time by monitoring covariatesLAD can detect worn patterns online & in real time by monitoring covariates
over timeover time
Important requirement for using LAD is the availability of a database thatImportant requirement for using LAD is the availability of a database that
represents accurately the phenomena under studyrepresents accurately the phenomena under study
22. CORE SEMINAR 22
ConclusionConclusion
A new online tool wear alarm system based on LAD is developedA new online tool wear alarm system based on LAD is developed
Alarm system is constructed based on data collected during turningAlarm system is constructed based on data collected during turning
TiMMCs, under changeable machining conditionsTiMMCs, under changeable machining conditions
Platform of PXI and LABVIEW were used to develop the alarm systemPlatform of PXI and LABVIEW were used to develop the alarm system
LAD alarm system is validated by comparing it to the PHM warningLAD alarm system is validated by comparing it to the PHM warning
functionfunction
Results show that the proposed alarm system detects the worn patternsResults show that the proposed alarm system detects the worn patterns
and gives “warning alarm” in order to replace the cutting tool at aand gives “warning alarm” in order to replace the cutting tool at a
working age that is relatively closer to the actual observed failure timeworking age that is relatively closer to the actual observed failure time
23. CORE SEMINAR 23
Future Scope of WorkFuture Scope of Work
The performance of the alarm system will be improved by includingThe performance of the alarm system will be improved by including
additional variables, such as vibration signal, AEs, & cutting temperaturesadditional variables, such as vibration signal, AEs, & cutting temperatures
In order to distinguish between different tool wear phases, a multiclass LADIn order to distinguish between different tool wear phases, a multiclass LAD
technique will be testedtechnique will be tested
The quality of the detected patterns will be improved, & nonpure patternsThe quality of the detected patterns will be improved, & nonpure patterns
which can cover more than one class will be used, & give more details aboutwhich can cover more than one class will be used, & give more details about
the characteristics of LAD’s patternsthe characteristics of LAD’s patterns
CBMLAD and our alarm system will be incorporated in a CNC machineCBMLAD and our alarm system will be incorporated in a CNC machine
The learning stage can be done online thereby eliminating the need forThe learning stage can be done online thereby eliminating the need for
offline analysisoffline analysis
24. CORE SEMINAR 24
ReferenceReference
Shaban, Y., Yacout, S., and Balazinski, M., 2015, "Tool Wear MonitoringShaban, Y., Yacout, S., and Balazinski, M., 2015, "Tool Wear Monitoring
and Alarm System Based on Pattern Recognition With Logical Analysis ofand Alarm System Based on Pattern Recognition With Logical Analysis of
Data," ASME J. Manuf. Sci. Eng., 137(4), p. 041004.Data," ASME J. Manuf. Sci. Eng., 137(4), p. 041004.
Li, B., 2012, “A Review of Tool Wear Estimation Using Theoretical AnalysisLi, B., 2012, “A Review of Tool Wear Estimation Using Theoretical Analysis
and Numerical Simulation Technologies,” Int. J. Refract. Met. Hard Mater.,and Numerical Simulation Technologies,” Int. J. Refract. Met. Hard Mater.,
35, pp. 143–151.35, pp. 143–151.
Ryoo, H. S., and Jang, I. Y., 2009, “MILP Approach to Pattern Generation inRyoo, H. S., and Jang, I. Y., 2009, “MILP Approach to Pattern Generation in
Logical Analysis of Data,” Discrete Appl. Math., 157(4), pp. 749–761.Logical Analysis of Data,” Discrete Appl. Math., 157(4), pp. 749–761.
Makis, V., 1995, “Optimal Replacement of a Tool Subject to RandomMakis, V., 1995, “Optimal Replacement of a Tool Subject to Random
Failure,” Int. J. Prod. Econ., 41(1), pp. 249–256.Failure,” Int. J. Prod. Econ., 41(1), pp. 249–256.
Chik, Z., Aljanabi, Q. A., Kasa, A., and Taha, M. R., 2014, "Tenfold crossChik, Z., Aljanabi, Q. A., Kasa, A., and Taha, M. R., 2014, "Tenfold cross
validation artificial neural network modeling of the settlement behavior of avalidation artificial neural network modeling of the settlement behavior of a
stone column under a highway embankment," Springer Arab J Geosci.,stone column under a highway embankment," Springer Arab J Geosci.,
7:4877–4887.7:4877–4887.