This document discusses variable gage repeatability and reproducibility (R&R) studies in Minitab. It describes how to conduct an R&R study to evaluate measurement systems, including selecting parts, operators, and number of measurements. Key outputs from Minitab like graphs of variance components and statistics like percent tolerance and percent study variation are explained. Guidelines for interpreting R&R results are provided. Examples walking through full R&R studies in Minitab are also included to illustrate the concepts and outputs.
The document discusses a 45 second process for determining if an ANOVA or XBAR&R gauge repeatability and reproducibility (GRR) test will pass at 10% or 5% without fully developing the test program or loadboard. It offers to provide information on how instrument specifications, qualification sample location, and infrastructure can affect GRR. The tool described allows the user to input specification limits, conditions, number of instrument errors, and receive advice on measurement ranges that will achieve the desired GRR.
This document discusses gage repeatability and reproducibility (Gage R&R) studies. It defines gage R&R as a method to check if a gage is capable of precise and reliable measurements. It also defines key terms like repeatability, reproducibility, accuracy, and variations. The document provides examples of simple and more complex Gage R&R studies using a micrometer and width measurement. It analyzes the results in terms of variation contributions and control charts. Companies generally aim for gage variation to be less than 30% or 10% of total process variation depending on the gage's purpose.
This was presented at an ASQLA Section 700 monthly meeting in 2012.
This covers the basics of SPC and some of the things that need to be in place before SPC can be used effectively like a proper Gage R&R evaluation, proper specs derived and characterization of the process performed using Design of Experiments. Also covered are the main cultural barriers to implementation and some suggestions on how to proceed.
Also shown are some advanced methods of charting such as Delta from Target that allows easier use of SPC by floor shop personnel and maintains date/time sequence flow of product/measurements when there are multiple products run on a single machine.
The document discusses measurement systems analysis and gage reliability and repeatability (R&R) studies. It describes the components of a measurement system, how to conduct an R&R study to determine variability sources, and criteria for ensuring gage capability and precision. A case study example illustrates improving bore diameter measurement reliability for valve bodies by switching from dial calipers to a self-centering bore gauge.
Detailed illustration of MSA procedures both for Variable and attribute, Analysis of results and planning for MSA. Complete guidance for planning and implementation of MSA.
The document discusses process capability and statistical quality control. It provides information on different types of process variation and process capability indices. It also summarizes key concepts in statistical process control including control charts for attributes and variables as well as acceptance sampling plans. Examples are given for constructing control charts and solving acceptance sampling problems.
This document discusses variable gage repeatability and reproducibility (R&R) studies in Minitab. It describes how to conduct an R&R study to evaluate measurement systems, including selecting parts, operators, and number of measurements. Key outputs from Minitab like graphs of variance components and statistics like percent tolerance and percent study variation are explained. Guidelines for interpreting R&R results are provided. Examples walking through full R&R studies in Minitab are also included to illustrate the concepts and outputs.
The document discusses a 45 second process for determining if an ANOVA or XBAR&R gauge repeatability and reproducibility (GRR) test will pass at 10% or 5% without fully developing the test program or loadboard. It offers to provide information on how instrument specifications, qualification sample location, and infrastructure can affect GRR. The tool described allows the user to input specification limits, conditions, number of instrument errors, and receive advice on measurement ranges that will achieve the desired GRR.
This document discusses gage repeatability and reproducibility (Gage R&R) studies. It defines gage R&R as a method to check if a gage is capable of precise and reliable measurements. It also defines key terms like repeatability, reproducibility, accuracy, and variations. The document provides examples of simple and more complex Gage R&R studies using a micrometer and width measurement. It analyzes the results in terms of variation contributions and control charts. Companies generally aim for gage variation to be less than 30% or 10% of total process variation depending on the gage's purpose.
This was presented at an ASQLA Section 700 monthly meeting in 2012.
This covers the basics of SPC and some of the things that need to be in place before SPC can be used effectively like a proper Gage R&R evaluation, proper specs derived and characterization of the process performed using Design of Experiments. Also covered are the main cultural barriers to implementation and some suggestions on how to proceed.
Also shown are some advanced methods of charting such as Delta from Target that allows easier use of SPC by floor shop personnel and maintains date/time sequence flow of product/measurements when there are multiple products run on a single machine.
The document discusses measurement systems analysis and gage reliability and repeatability (R&R) studies. It describes the components of a measurement system, how to conduct an R&R study to determine variability sources, and criteria for ensuring gage capability and precision. A case study example illustrates improving bore diameter measurement reliability for valve bodies by switching from dial calipers to a self-centering bore gauge.
Detailed illustration of MSA procedures both for Variable and attribute, Analysis of results and planning for MSA. Complete guidance for planning and implementation of MSA.
The document discusses process capability and statistical quality control. It provides information on different types of process variation and process capability indices. It also summarizes key concepts in statistical process control including control charts for attributes and variables as well as acceptance sampling plans. Examples are given for constructing control charts and solving acceptance sampling problems.
The document discusses analyzing data from a nested experimental design using analysis of variance (ANOVA) methods. It compares using aov with an Error term to account for the nested structure versus using lme from the nlme package. Both methods find significant treatment effects but lme provides direct estimates of variance components and can handle unbalanced designs/complex models better while aov allows some post-hoc tests. The example analyzes data on gypsy moth larvae counts from plots with different pesticide treatments.
Identification of Outliersin Time Series Data via Simulation Studyiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document outlines statistical quality control techniques for evaluating manufacturing and service processes. It discusses measuring and controlling process variation using variables like mean, standard deviation and control charts. Key aspects covered include process capability analysis using metrics like Cpk, acceptance sampling plans to determine quality levels while balancing producer and consumer risks, and operating characteristic curves.
This document discusses evaluating meter test data that does not follow a normal distribution. It provides an overview of ANSI/ASQ Z1.9 sampling procedures and requirements for normal data. Non-normal data distributions are common for electronic and digital meter test results. Tools for assessing normality include Anderson-Darling tests and normal probability plots. If data is non-normal, transformations like Box-Cox and Johnson may be applied, but often do not work for meter data. Alternative statistical analyses may be needed for non-normal data.
A software fault localization technique based on program mutationsTao He
The document describes a new software fault localization technique called Muffler that uses program mutation analysis. Muffler aims to address the problem of coincidental correctness in existing coverage-based fault localization methods. It combines the Naish ranking function with a new metric called mutation impact, which measures the average number of test results that change from passed to failed when each statement is mutated. An empirical evaluation on seven programs shows that Muffler reduces the average code examination needed to find faults by 50% compared to the Naish technique.
Quality is defined as customers' perception of how well a product or service meets their expectations. There are three types of quality: quality of design, quality of performance, and quality of conformance. Statistical quality control uses statistical techniques to control, improve, and maintain quality. Control charts are used to determine if a process is in or out of control by monitoring for random or assignable variation. Process capability indices like Cp and Cpk compare process variability to specification limits to determine if a process is capable of meeting specifications.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
This document discusses control charts for attributes, including fraction nonconforming charts and control charts for nonconformities (defects). It covers key aspects such as:
1. The parameters, formulas, and design of fraction nonconforming charts, including sample size, frequency of sampling, and control limit width.
2. Procedures for control charts with constant and variable sample sizes, and how to estimate parameters if a standard is not given.
3. How to construct control charts to monitor nonconformities using variables like number of defects, demerit points, and Poisson distributions.
4. Guidelines for implementing control charts and determining which characteristics and processes to monitor.
DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATAIJCSEA Journal
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
This document summarizes the work done by an intern during their summer internship in the Medical Physics Department of Radiology. The intern conducted research to predict cancer outcomes based on breast lesion features. Key work included feature extraction from mammograms, analyzing features to differentiate malignant and benign lesions using ROC analysis and LDA, and exploring features to predict invasive vs. non-invasive cancer. Top predictive features were FWHM ROI, diameter, and margin sharpness. The intern gained skills in medical image analysis, statistical analysis, and evaluating results to identify trends.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
This document summarizes a study of built-in self-test (BIST) approaches for detecting single stuck-at faults in combinational logic circuits. Pseudorandom test patterns generated by a linear feedback shift register (LFSR) were applied in parallel and serially to benchmark circuits. Applying patterns in parallel via test-per-clock achieved high fault coverage but required a large LFSR for circuits with many inputs. Reseeding the LFSR improved coverage when an initial seed was ineffective. Seed selection and minimum LFSR size for different application methods were evaluated to optimize BIST fault detection.
This document provides information about measuring errors of analog and digital instruments. For analog instruments, accuracy is typically expressed as a percentage of the full scale reading. The error can be ±3% of the measured value if it is in the last third of the scale, but increases if further from the full scale value. Digital multimeters have resolution defined by the number of display digits, such as ±0.05% for 31⁄2 digits. They may also have automatic or manual range selection to ensure accurate readings in the optimal scale region.
Lecture Notes: EEEC6430312 Measurements And Instrumentation - Instrument Typ...AIMST University
This document discusses different types of instruments and their performance characteristics. It begins by distinguishing between active and passive instruments. Active instruments use an external power source to modulate their output, while passive instruments generate their own output entirely from the measured quantity. It then discusses key parameters that characterize instrument performance, such as accuracy, precision, sensitivity, resolution, and hysteresis. Different types of instruments are also covered, such as analog and digital instruments, as well as smart vs. non-smart instruments. The document provides examples to illustrate concepts like precision vs. accuracy. It concludes by discussing instrument calibration and recalibration over time.
Measurement systems analysis and a study of anova methodeSAT Journals
Abstract
Instruments and measurement systems form the base of any process improvement strategies. The much widely used QC tools like
SPC depends on sample data taken from processes to track process variation which in turn depends on measuring system itself.
The purpose of Measurement System Analysis is to qualify a measurement system for use by quantifying its accuracy, precision,
and stability and to minimize their contribution in process variation through inherent tools such as ANOVA. The purpose of this
paper is to outline MSA and study ANOVA method through a real-time shop floor experiment.
Keywords: SPC, Accuracy, Precision, Stability, QC, ANOVA
This document provides an overview of statistical process control (SPC) concepts including control charts, process capability, and applying SPC to services. It discusses control charts for attributes like p-charts and c-charts and control charts for variables like x-bar charts and R-charts. It also covers determining control limits, identifying patterns in control charts, and using Excel for SPC.
Optimization and simulation of a New Low Density Parity-Check Decoder using t...IJERA Editor
The Low Density Parity-Check codes are one of the hottest topics in coding theory nowadays equipped with
very fast encoding and decoding algorithms, LDPC are very attractive both theoretically and practically.In this
article, we present a simulation of a work that has been accepted in an international journal. The newalgorithm
allows us to correct errors quickly and without iterations. We show that the proposed algorithm simulation can
be applied for both regular and irregular LDPC codes. First, we developed the design of the syndrome Block
Second, we generated and simulated the hardware description language source code using Quartus software
tools, and finally we show low complexity compared to the basic algorithm.
This document discusses error analysis in experimental measurements. It covers two types of errors - systematic errors which affect accuracy, and random errors which affect precision. Random errors follow a Gaussian distribution, and the mean and standard deviation are used to characterize these errors. Taking more measurements reduces random errors according to the central limit theorem. The document also discusses combining measurements and calculating a weighted mean to obtain the best estimate while accounting for differences in measurement precision.
1. The document assesses various imputation methods for missing data in time series datasets. It finds that linear interpolation performs best in terms of accuracy and precision, imputing interior missing data through linear interpolation and exterior data through last observation carried forward.
2. For data where whole time series for countries or variables are missing, the "all variable multilevel" method, which uses a multilevel model trained on all available data, works best.
3. Higher order extrapolation does not increase accuracy compared to linear interpolation. For higher levels of missingness, higher order extrapolation actually decreases accuracy.
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.
The document discusses analyzing data from a nested experimental design using analysis of variance (ANOVA) methods. It compares using aov with an Error term to account for the nested structure versus using lme from the nlme package. Both methods find significant treatment effects but lme provides direct estimates of variance components and can handle unbalanced designs/complex models better while aov allows some post-hoc tests. The example analyzes data on gypsy moth larvae counts from plots with different pesticide treatments.
Identification of Outliersin Time Series Data via Simulation Studyiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document outlines statistical quality control techniques for evaluating manufacturing and service processes. It discusses measuring and controlling process variation using variables like mean, standard deviation and control charts. Key aspects covered include process capability analysis using metrics like Cpk, acceptance sampling plans to determine quality levels while balancing producer and consumer risks, and operating characteristic curves.
This document discusses evaluating meter test data that does not follow a normal distribution. It provides an overview of ANSI/ASQ Z1.9 sampling procedures and requirements for normal data. Non-normal data distributions are common for electronic and digital meter test results. Tools for assessing normality include Anderson-Darling tests and normal probability plots. If data is non-normal, transformations like Box-Cox and Johnson may be applied, but often do not work for meter data. Alternative statistical analyses may be needed for non-normal data.
A software fault localization technique based on program mutationsTao He
The document describes a new software fault localization technique called Muffler that uses program mutation analysis. Muffler aims to address the problem of coincidental correctness in existing coverage-based fault localization methods. It combines the Naish ranking function with a new metric called mutation impact, which measures the average number of test results that change from passed to failed when each statement is mutated. An empirical evaluation on seven programs shows that Muffler reduces the average code examination needed to find faults by 50% compared to the Naish technique.
Quality is defined as customers' perception of how well a product or service meets their expectations. There are three types of quality: quality of design, quality of performance, and quality of conformance. Statistical quality control uses statistical techniques to control, improve, and maintain quality. Control charts are used to determine if a process is in or out of control by monitoring for random or assignable variation. Process capability indices like Cp and Cpk compare process variability to specification limits to determine if a process is capable of meeting specifications.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
This document discusses control charts for attributes, including fraction nonconforming charts and control charts for nonconformities (defects). It covers key aspects such as:
1. The parameters, formulas, and design of fraction nonconforming charts, including sample size, frequency of sampling, and control limit width.
2. Procedures for control charts with constant and variable sample sizes, and how to estimate parameters if a standard is not given.
3. How to construct control charts to monitor nonconformities using variables like number of defects, demerit points, and Poisson distributions.
4. Guidelines for implementing control charts and determining which characteristics and processes to monitor.
DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATAIJCSEA Journal
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
This document summarizes the work done by an intern during their summer internship in the Medical Physics Department of Radiology. The intern conducted research to predict cancer outcomes based on breast lesion features. Key work included feature extraction from mammograms, analyzing features to differentiate malignant and benign lesions using ROC analysis and LDA, and exploring features to predict invasive vs. non-invasive cancer. Top predictive features were FWHM ROI, diameter, and margin sharpness. The intern gained skills in medical image analysis, statistical analysis, and evaluating results to identify trends.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
Error bounds for wireless localization in NLOS environmentsIJECEIAES
An efficient and accurate method to evaluate the fundamental error bounds for wireless sensor localization is proposed. While there already exist efficient tools like Cram ` erRao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits–CRLB and PEB–onsite without knowledge of the statistics of the ranging errors is proposed.
This document summarizes a study of built-in self-test (BIST) approaches for detecting single stuck-at faults in combinational logic circuits. Pseudorandom test patterns generated by a linear feedback shift register (LFSR) were applied in parallel and serially to benchmark circuits. Applying patterns in parallel via test-per-clock achieved high fault coverage but required a large LFSR for circuits with many inputs. Reseeding the LFSR improved coverage when an initial seed was ineffective. Seed selection and minimum LFSR size for different application methods were evaluated to optimize BIST fault detection.
This document provides information about measuring errors of analog and digital instruments. For analog instruments, accuracy is typically expressed as a percentage of the full scale reading. The error can be ±3% of the measured value if it is in the last third of the scale, but increases if further from the full scale value. Digital multimeters have resolution defined by the number of display digits, such as ±0.05% for 31⁄2 digits. They may also have automatic or manual range selection to ensure accurate readings in the optimal scale region.
Lecture Notes: EEEC6430312 Measurements And Instrumentation - Instrument Typ...AIMST University
This document discusses different types of instruments and their performance characteristics. It begins by distinguishing between active and passive instruments. Active instruments use an external power source to modulate their output, while passive instruments generate their own output entirely from the measured quantity. It then discusses key parameters that characterize instrument performance, such as accuracy, precision, sensitivity, resolution, and hysteresis. Different types of instruments are also covered, such as analog and digital instruments, as well as smart vs. non-smart instruments. The document provides examples to illustrate concepts like precision vs. accuracy. It concludes by discussing instrument calibration and recalibration over time.
Measurement systems analysis and a study of anova methodeSAT Journals
Abstract
Instruments and measurement systems form the base of any process improvement strategies. The much widely used QC tools like
SPC depends on sample data taken from processes to track process variation which in turn depends on measuring system itself.
The purpose of Measurement System Analysis is to qualify a measurement system for use by quantifying its accuracy, precision,
and stability and to minimize their contribution in process variation through inherent tools such as ANOVA. The purpose of this
paper is to outline MSA and study ANOVA method through a real-time shop floor experiment.
Keywords: SPC, Accuracy, Precision, Stability, QC, ANOVA
This document provides an overview of statistical process control (SPC) concepts including control charts, process capability, and applying SPC to services. It discusses control charts for attributes like p-charts and c-charts and control charts for variables like x-bar charts and R-charts. It also covers determining control limits, identifying patterns in control charts, and using Excel for SPC.
Optimization and simulation of a New Low Density Parity-Check Decoder using t...IJERA Editor
The Low Density Parity-Check codes are one of the hottest topics in coding theory nowadays equipped with
very fast encoding and decoding algorithms, LDPC are very attractive both theoretically and practically.In this
article, we present a simulation of a work that has been accepted in an international journal. The newalgorithm
allows us to correct errors quickly and without iterations. We show that the proposed algorithm simulation can
be applied for both regular and irregular LDPC codes. First, we developed the design of the syndrome Block
Second, we generated and simulated the hardware description language source code using Quartus software
tools, and finally we show low complexity compared to the basic algorithm.
This document discusses error analysis in experimental measurements. It covers two types of errors - systematic errors which affect accuracy, and random errors which affect precision. Random errors follow a Gaussian distribution, and the mean and standard deviation are used to characterize these errors. Taking more measurements reduces random errors according to the central limit theorem. The document also discusses combining measurements and calculating a weighted mean to obtain the best estimate while accounting for differences in measurement precision.
1. The document assesses various imputation methods for missing data in time series datasets. It finds that linear interpolation performs best in terms of accuracy and precision, imputing interior missing data through linear interpolation and exterior data through last observation carried forward.
2. For data where whole time series for countries or variables are missing, the "all variable multilevel" method, which uses a multilevel model trained on all available data, works best.
3. Higher order extrapolation does not increase accuracy compared to linear interpolation. For higher levels of missingness, higher order extrapolation actually decreases accuracy.
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.
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.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
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%.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
8. Using freely available
spreadsheet off of the Internet
for Excel analysis of GRR a
model was developed for
analysis using these 2
boundary conditions
8
9. Test Engineers
have been told to
follow 2 boundary
conditions
1. The error in the
measurement has to be 10
times less than the
measured value.
2. The error between the
USL and LSL has to be 10%
of the difference between
USL and LSL
X Instrument Error
0
2X Instrument Error
LSL
USL
ΔCV
ΔCV - Boundary Condition # 2
LCV - Boundary Condition # 1
Test Engineering Boundary Conditions
LCV
Where
USL = Upper Specifica>on Limit LSL = Lower Specifica>on Limit
LCV = Lowest Capable Value ΔCV = USL - LSL = Differen>al Capability
The variable, the RED “X”, needs to be understood
9
10. GRR Tools
Critical Evaluation with underlying Test
Engineering Boundary Conditions
The model created is using an OE (offset error of
±4mV with GE (gain error = 0%). The initial
evaluation used 10 samples, 3 tests, 3 Testers. One
tester had 0 offset, another -4mV, and the third
+4mV. Repeatability was 0.0001 to insure that just
equipment reproducibility is what is being examined.
Manage Instrument Error to achieve a passing
qualification on devices for GRR: ANOVA or XBAR&R
The experiment objective is to understand instrument
specification and how it affects GRR ALONE
10
11. GRR Tools
Manage Instrument Error to achieve a passing
qualification on devices for GRR: ANOVA or XBAR&R
GRR Calculation
# Samples
# Testers
Methods
# of Measurements
USL & LSL
Objective: Understand GRR from Instrument
Error Perspective
2-10 samples
Sample location: determines GRR
Random Selection
Intelligent selection
Plus Offset - one tester
Zero Offset - one tester
Minus Offset - one tester
ANOVA
Xbar&R
One value - Ideal
Second value: + 0.0001
Third value: - 0.0001
Note: sample location is
NOT usually looked at;
and it turns out to be
very important
11
12. How do these affect GRR
Results
• There are essentially 2 methods for doing GRR
• ANOVA
• Xbar&R
• Spreadsheets for each can be found on the internet free of
charge. Some with just one of the methods or the other, and
at least one with both methods in the spreadsheet.
• Each method was confirmed to give the same results with
identical data when comparing ANOVA to ANOVA and
Xbar&R to Xbar&R
12
13. The question is:
Are the 2 boundary conditions listed on slides 6 and 7 necesary
and sufficient to insure that the goal of GRR 10% passes?
1. Random samples within USL and LSL
2. 10 perfect evenly distributed samples within USL and LSL
3. 10 select samples to cause worst case within center ±50% of perfect samples placement
4. 10 select samples to cause worst case within center ±100% of perfect samples placement
5. 10 select samples to cause worst case within center ±150% of perfect samples placement
6. 10 select samples to cause worst case within center ±200% of perfect samples placement
6 ways to look at:
# Instrument errors range from 10X to shown on graphs
13
14. Necessary Equations
The diagram on Slide 2 shows 11X for instrument errors
to take into account the need for repeatability. This
works out to a GRR of approximately 9% for just
instrument errors, which is the objective of the exercise
USL =
LSL 1+10GE( )+ 20OE
1−10GE
USL − LSL = ΔCV
ΔCV = 10 GE i(USL + LSL)+ 2 iOE( )
ΔCV = 20 iOE +10 GE i(USL + LSL)( )
LSL = +LCV =
OE
0.1− GE
+LCV =
OE
0.1− GE
The model created us using an OE (offset error of ±4mV with GE
(gain error = 0%). The initial evaluation used 10 samples, 3 tests, 3
Testers. One tester had 0 offset, another -4mV, and the third +4mV.
Repeatability was 0.0001 to insure that just equipment reproducibility
is what is being examined.
14
15. 0
LSL
USL
±100% of perfect
samples
placement
±50% of perfect
samples
placement
0
LSL
USL
Random perfect
samples
placement
0
LSL
USL
Perfect Placement
±100 % Placement - Worst Case
0
LSL
USL
0
LSL
USL
Perfect Placement
±50 % Placement - Worst Case
15
17. Random selection of measurement for 10 DUTs with zero offset, +
offset and - offset.
1.5200 10 device randomly selected measurement values
using Monte Carlo simulations for Xbar&R from Statistical
Solutions, Tolerance and GRR results.
2.1010 10 device randomly selected measurement values
using Monte Carlo simulations using the ANOVA method
from www.dmaictools.com, Tolerance and GRR results.
Lower Specification limits started at 40mV (10X), Upper Specification limit start at 120mV (10X), ranging to
40X instrument errors. This means that the difference between USL-LSL, ranged from 10X to 40X.
Simulations were done for a process distribution width of 5.15 (encloses the central 99% of the process
distribution).
0
LSL
USL
Please note that the number of instrument
errors is the inverse of the instrument error
17
18. 409 12 14 16 18 20 22 24 26 28 30 32 34 36 38
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
(# X) Number of Instrument Errors
Percentage(%)over10%
GRR% Xbar&R
Xbar&R Monte Carlo Simula3on
in Excel. 5200 simula3ons of 10
devices.
Random devices within limits
Using Gage R&R
(XBar&R Motorola Version)
from Sta;s;cal Solu;ons
Please note that 11X and
greater is acceptable. However
neither the Xbar&R Method
nor ANOVA correctly calculate
GRR at the corners of
instrument limits
GRR% ANOVA
ANOVA Monte Carlo Simula3on
in Excel. 1010 simula3ons of 10
devices.
Random devices within limits
± 4mV offset Error
LSL starts at +40mV (10X)
USL starts at +120mV
USL-LSL starts at 80mV (10X)
Anova - www.dmaictools.com/measure/grr
30.0
Percentage of simulations (Monte Carlo) that showed the instances greater than GRR
10% for each Instrument X.
To read the graph, 20 on the X axis is the number of instrument errors. For GRR ANOVA,
approximately 0.5% of the simulation results showed greater than a GRR of 10%. For
GRR Xbar&R 1% of the simulation results showed greater than a GRR of 10%.
0
LSL
USL
Random
samples
placement
18
19. 4. 10 select perfect samples to cause worst case
within center ±50% of perfect samples placement
NOTE: solid line on graph of next slide
1. ANOVA 5.15 Standard deviations
2.Xbar&R 5.15 Standard deviations
Lower Specification limits started at 40mV (10X), Upper Specification limit
start at 120mV (10X), ranging to 40X instrument errors. This means that
the difference between USL-LSL, ranged from 10X and following.
Calculations were done for a process distribution width of 5.15 (encloses
the central 99% of the process distribution) at worst case values..
0
LSL
USL
±50% of perfect
samples
placement
Please note that the number of instrument
errors is the inverse of the instrument error
19
30. For 9% GRR the correct choices for allowable
instrument error to achieve the desired 10%
GRR with the 1% selection for repeatability are:
• Primary conclusion: Initial boundary condition is NOT SUFFICIENT to pass GRR at all!!
• Perfect parts and ONLY instrument error use 5.43% to 6.06% of the GRR 10% Goal
• Perfect parts and ONLY instrument error use 2.7% to 3.02% of the GRR 5% Goal
• Perfect parts, ONLY instrument and Sample Placement variation error use 4.22% to 5.62% of the GRR 10% Goal
• Perfect parts, ONLY instrument and Sample Placement variation error use 2.09% to 2.80% of the GRR 5% Goal
• Sample placement accounts for up to 1.3% of GRR 10% Goal for ANOVA
• Sample placement accounts for up to 0.63% of GRR 10% Goal for XBAR&R
Anova Perfect 16.5 (6.06%)
Anova ±50 17.8 (5.62%)
Anova ±100 19.3 (5.18%)
Anova ±150 21.0 (4.76%)
Anova ±200 22.9 (4.37%)
Xbar Perfect 18.4 (5.43%)
Xbar ±50 19.5 (5.13%)
Xbar ±100 20.7 (4.83%)
Xbar ±150 22.0 (4.55%)
Xbar ±200 23.7 (4.22%)
Xbar Perfect 37.1 (2.70%)
Xbar ±50 39.3 (2.54%)
Xbar ±100 41.7 (2.40%)
Xbar ±150 44.4 (2.25%)
Xbar ±200 47.8 (2.09%)
Anova Perfect 33.1 (3.02%)
Anova ±50 35.7 (2.80%)
Anova ±100 38.8 (2.58%)
Anova ±150 42.2 (2.37%)
Anova ±200 46.1 (2.17%)
9% GRR Target 4.5% GRR Target
30