The control system is a system that delivers the desired response by controlling the output. The control system manages the behavior of other devices or systems using control rings, executes or regulates commands. It is used to control the first processes of a home heat controller that uses a thermostat to control the boiler in the home. Maybe up to large industrial control systems. Control system a set of mechanical or electronic devices that control other devices or systems through control loops. Typically, control systems are computerized. Control systems are an integral part of industry and automation. Continuous modulated control is a feedback controller used to automatically control a process or process. The control system uses the plant process variable a to compare the process value variable or position (PV) with the desired value or set point (SP). Control signal. Output to the same value. A variable size or set variable sizes are made according to the recommended rule. This will keep the values of the controlled quantities constant or vary as recommended. A control system can be operated by electric, mechanical mixing mechanisms, liquid pressure (liquid or gas) or mechanisms. Although interruptions are most common when a computer is engaged in control circuits, it is generally more convenient to operate all control systems on electricity.
{"title":"Exploring Various Control Systems and Its Application","authors":"","doi":"10.46632/eae/1/1/7","DOIUrl":"https://doi.org/10.46632/eae/1/1/7","url":null,"abstract":"The control system is a system that delivers the desired response by controlling the output. The control system manages the behavior of other devices or systems using control rings, executes or regulates commands. It is used to control the first processes of a home heat controller that uses a thermostat to control the boiler in the home. Maybe up to large industrial control systems. Control system a set of mechanical or electronic devices that control other devices or systems through control loops. Typically, control systems are computerized. Control systems are an integral part of industry and automation. Continuous modulated control is a feedback controller used to automatically control a process or process. The control system uses the plant process variable a to compare the process value variable or position (PV) with the desired value or set point (SP). Control signal. Output to the same value. A variable size or set variable sizes are made according to the recommended rule. This will keep the values of the controlled quantities constant or vary as recommended. A control system can be operated by electric, mechanical mixing mechanisms, liquid pressure (liquid or gas) or mechanisms. Although interruptions are most common when a computer is engaged in control circuits, it is generally more convenient to operate all control systems on electricity.","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128951752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Review on Embedded System, Design and Simulation","authors":"","doi":"10.46632/eae/1/1/9","DOIUrl":"https://doi.org/10.46632/eae/1/1/9","url":null,"abstract":"","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128139015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring Various Applications of Micro Controller","authors":"","doi":"10.46632/eae/1/1/8","DOIUrl":"https://doi.org/10.46632/eae/1/1/8","url":null,"abstract":"","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"30 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113980987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, Software development has been considerably grown. Fault in the software causes fault and interrupts the output. Characteristics like these make it much challenging to avert software flaws. Spontaneously forecasting the amount of flaws within the software modules is essential and also can assist developers to proficiently allot restricted resources. Recently, numerous Software Defect Prediction (SDP) techniques are developed. But, the accuracy and time consuming challenges still remain to be solved. Also, a few top-notch techniques don't properly classify the software whereas it is a needed metric to ensure quality standards. This work proffers a novel Decaying Learning Rate – Learning vector Quantization (DLR-LVQ) classifier to forecast the software defect. The proposed methods consist of the following steps: redundant data removal, feature extraction (FE), feature oversampling, data normalization, defect prediction (DP), and quality prediction. The proposed DLR-LVQ’s attained outcome is assessed with the existent methodologies. The outcomes exhibit that the methodology proposed attains efficient classification outcomes are examined. Keywords: Software Defect Prediction (SDP), Non defective software quality prediction, BM-SMOTE, Decaying Learning Rate, Learning Vector Quantization, Fuzzy rules, HDFS and Map Reduce.
{"title":"Software Defect Prediction and Software Quality Assessment Using Dlr-Lvq and Fuzzy Rules","authors":"V. S. Prasad, K. Sasikala","doi":"10.46632/eae/1/1/4","DOIUrl":"https://doi.org/10.46632/eae/1/1/4","url":null,"abstract":"Recently, Software development has been considerably grown. Fault in the software causes fault and interrupts the output. Characteristics like these make it much challenging to avert software flaws. Spontaneously forecasting the amount of flaws within the software modules is essential and also can assist developers to proficiently allot restricted resources. Recently, numerous Software Defect Prediction (SDP) techniques are developed. But, the accuracy and time consuming challenges still remain to be solved. Also, a few top-notch techniques don't properly classify the software whereas it is a needed metric to ensure quality standards. This work proffers a novel Decaying Learning Rate – Learning vector Quantization (DLR-LVQ) classifier to forecast the software defect. The proposed methods consist of the following steps: redundant data removal, feature extraction (FE), feature oversampling, data normalization, defect prediction (DP), and quality prediction. The proposed DLR-LVQ’s attained outcome is assessed with the existent methodologies. The outcomes exhibit that the methodology proposed attains efficient classification outcomes are examined. Keywords: Software Defect Prediction (SDP), Non defective software quality prediction, BM-SMOTE, Decaying Learning Rate, Learning Vector Quantization, Fuzzy rules, HDFS and Map Reduce.","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126448466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distributed Cloud Computing Storage has come up as a service that can expedite data owners (DO) to store their data remotely according to their application or data or file environment. However, insecure data storage, high uploading bandwidth, integration issues of DCS has breached the trustworthiness of the user to store data. In order to conquer the challenge, the work has developed a data Deduplication and portability-based secure data storage in DCS. The work aids to remove unwanted data and selects the most relevant features to avoid data loss by using GK-QDA Feature Reduction Method and HFG feature selection method. The selected cloud server for the respective data or application is analyzed for redundant data by data duplication using a whirlpool hashing algorithm followed by a hash chaining algorithm. Finally, to minimize the integration issues while moving the encrypted data between the DCS, the work has developed an LF-WDO technique. An experimental analysis has showed an enormous result by achieving a computation time of 2987 ms as compared to the existing methods
{"title":"An Efficient Secure Data Deduplication and Portability In Distributed Cloud Server Using Whirlpool-Hct And Lf-Wdo","authors":"A. Athira, P. Sasikala","doi":"10.46632/eae/1/1/5","DOIUrl":"https://doi.org/10.46632/eae/1/1/5","url":null,"abstract":"Distributed Cloud Computing Storage has come up as a service that can expedite data owners (DO) to store their data remotely according to their application or data or file environment. However, insecure data storage, high uploading bandwidth, integration issues of DCS has breached the trustworthiness of the user to store data. In order to\u0000conquer the challenge, the work has developed a data Deduplication and portability-based secure data storage in DCS. The work aids to remove unwanted data and selects the most relevant features to avoid data loss by using GK-QDA Feature Reduction Method and HFG feature selection method. The selected cloud server for the respective data or application is analyzed for redundant data by data duplication using a whirlpool hashing algorithm followed by a hash chaining algorithm. Finally, to minimize the integration issues while moving the encrypted data between the DCS, the work has developed an LF-WDO technique. An experimental analysis has showed an enormous result by achieving a\u0000computation time of 2987 ms as compared to the existing methods","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128206289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. J. Wilson, D. Pallavi, M Ramachandran, Sathiyaraj Chinnasamy, S. Sowmiya
: A memetic algorithm in computer science and functional research an extension of the traditional genetic algorithm. Multiple target Memetic Algorithm for Design Improvement. The study of memes sees magical ideas as a kind of virus that sometimes spreads beyond fact and logic. Its pronunciation: Survival beliefs do not have to be true, survival rules are not fair, and survival rituals are not necessary. The term mimetic algorithm was first coined by Moscow (1989) to describe population-based hybrid evolutionary mechanisms integrated with local purification techniques. Magic the study of information and culture in terms of its analogy with Darwinian evolution. Spiritualists describe this as an approach to evolutionary models of cultural interactions. Mimetic describes how to successfully propagate an idea, but it may not be true. Evolutionary methods are Based on the concepts of biological evolution. The 'population' of possible solutions to the problem will be created first, and each solution will be evaluated using a 'fitness function'. The population develops over time and identifies the best solutions. Differential evolution is a population-based Meet Heuristic search algorithm that improves the problem by repeatedly improving a candidate solution based on the evolutionary process. Such algorithms make little or no assumption about the basic optimization problem, and genetic programming is a domain-independent system that quickly explores enormous design gaps and builds genetically multiple computer programs to solve a problem. In particular, genetic programming converts the population of a computer program into new generation programs using analogies of naturally occurring genetic functions. My metric algorithm in computer science and functional research is an extension of traditional genetics. Algorithm this will provide a good enough solution to an optimization problem. This reduces the chance of pre-joining using local search technology. Gene algorithms are commonly used to develop advanced solutions for biologically motivated operators, i.e. mutations, shortcuts and selective updates and search issues. Starting with the basic process of a genetic algorithm - creating an initial population estimate - we evaluate each member to calculate ‘fitness’ for population and personal preference - we want to continue to improve our overall fitness. The study of population memes sees magical ideas as a kind of virus that sometimes spreads beyond fact and logic. Its pronunciation is that survival beliefs do not have to be true, survival rules are not fair, and survival rituals are not required. The advantages of genetic systems integration are global optimization. A large package solution provides many solutions that require less information in space. Probability in nature is the genetic representation using chromosomes. Biometric algorithms are one of the latest research areas in evolution. The term MA is now used in conjunction with evolution or a po
{"title":"A Review On Memetic Algorithms and Its Developments","authors":"A. J. Wilson, D. Pallavi, M Ramachandran, Sathiyaraj Chinnasamy, S. Sowmiya","doi":"10.46632/eae/1/1/2","DOIUrl":"https://doi.org/10.46632/eae/1/1/2","url":null,"abstract":": A memetic algorithm in computer science and functional research an extension of the traditional genetic algorithm. Multiple target Memetic Algorithm for Design Improvement. The study of memes sees magical ideas as a kind of virus that sometimes spreads beyond fact and logic. Its pronunciation: Survival beliefs do not have to be true, survival rules are not fair, and survival rituals are not necessary. The term mimetic algorithm was first coined by Moscow (1989) to describe population-based hybrid evolutionary mechanisms integrated with local purification techniques. Magic the study of information and culture in terms of its analogy with Darwinian evolution. Spiritualists describe this as an approach to evolutionary models of cultural interactions. Mimetic describes how to successfully propagate an idea, but it may not be true. Evolutionary methods are Based on the concepts of biological evolution. The 'population' of possible solutions to the problem will be created first, and each solution will be evaluated using a 'fitness function'. The population develops over time and identifies the best solutions. Differential evolution is a population-based Meet Heuristic search algorithm that improves the problem by repeatedly improving a candidate solution based on the evolutionary process. Such algorithms make little or no assumption about the basic optimization problem, and genetic programming is a domain-independent system that quickly explores enormous design gaps and builds genetically multiple computer programs to solve a problem. In particular, genetic programming converts the population of a computer program into new generation programs using analogies of naturally occurring genetic functions. My metric algorithm in computer science and functional research is an extension of traditional genetics. Algorithm this will provide a good enough solution to an optimization problem. This reduces the chance of pre-joining using local search technology. Gene algorithms are commonly used to develop advanced solutions for biologically motivated operators, i.e. mutations, shortcuts and selective updates and search issues. Starting with the basic process of a genetic algorithm - creating an initial population estimate - we evaluate each member to calculate ‘fitness’ for population and personal preference - we want to continue to improve our overall fitness. The study of population memes sees magical ideas as a kind of virus that sometimes spreads beyond fact and logic. Its pronunciation is that survival beliefs do not have to be true, survival rules are not fair, and survival rituals are not required. The advantages of genetic systems integration are global optimization. A large package solution provides many solutions that require less information in space. Probability in nature is the genetic representation using chromosomes. Biometric algorithms are one of the latest research areas in evolution. The term MA is now used in conjunction with evolution or a po","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114967055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Subash, M. Ramachandran, Vimala Saravanan, Vidhya Prasanth
: Tabu Search is one of the local search methods used for mathematical optimization Metaheuristics search method. It was founded in 1986 by Fred W. Developed by Clover and in 1989 Formalized. Local (nearby) searches take a potential solution to a problem and its immediate neighbor Check countries (i.e., similar solutions except for very small details). Improved solution Diagnosis. Local search methods on plateaus where subdivisions or multiple solutions are equally applicable Tend to get entangled. Tabu Search is the local search by relaxing its basic rule Improves performance. First, any moves that get worse with each step Will be accepted if not upgraded (if the search is stuck in a strict local minimum). In addition, obstacles (hereinafter referred to as taboo) prevent the return to previously visited solutions Introduced in the category. The implementation of the tab search, the solutions visited or the user Uses memory systems that describe sets of rules provided. [2] A certain short If the possible solution within the period has been visited before or if it violates a rule, it is Will be referred to as "taboo" (blocked) so that the algorithm does not reconsider that possibility.
{"title":"An Investigation on Tabu Search Algorithms Optimization","authors":"N. Subash, M. Ramachandran, Vimala Saravanan, Vidhya Prasanth","doi":"10.46632/1/1/3","DOIUrl":"https://doi.org/10.46632/1/1/3","url":null,"abstract":": Tabu Search is one of the local search methods used for mathematical optimization Metaheuristics search method. It was founded in 1986 by Fred W. Developed by Clover and in 1989 Formalized. Local (nearby) searches take a potential solution to a problem and its immediate neighbor Check countries (i.e., similar solutions except for very small details). Improved solution Diagnosis. Local search methods on plateaus where subdivisions or multiple solutions are equally applicable Tend to get entangled. Tabu Search is the local search by relaxing its basic rule Improves performance. First, any moves that get worse with each step Will be accepted if not upgraded (if the search is stuck in a strict local minimum). In addition, obstacles (hereinafter referred to as taboo) prevent the return to previously visited solutions Introduced in the category. The implementation of the tab search, the solutions visited or the user Uses memory systems that describe sets of rules provided. [2] A certain short If the possible solution within the period has been visited before or if it violates a rule, it is Will be referred to as \"taboo\" (blocked) so that the algorithm does not reconsider that possibility.","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128225987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Bharathi, D. Pallavi, M. Ramachandran, Kurinjimalar Ramu, Chinnasami Sivaji
. Evolutionary methods are a horror-based approach to solving problems that are not easily solved in polynomial time, for example, classical NP-heart problems and take longer to complete. Evolutionary methods are commonly used to provide good approximate solutions to problems that cannot be easily solved using other techniques. Many optimization issues fall into this category. It can be very calculated- finding a suitable solution is serious but sometimes the optimal solution is enough. Major classes of contemporaries (in the order of popularity) E.A. Genetic algorithms (GAs), evolutionary strategies (ESs), differential evolution (DE) and distribution algorithm evaluation (EDAs) are used. Evolutionary methods are based on the concepts of biological evolution. The 'population' of possible solutions to the problem will be created first, and each solution will be evaluated using a 'fitness function'. The population develops over time and (hopefully) identifies the best solutions.
{"title":"A Study on Evolutionary Algorithms and Its Applications","authors":"P. Bharathi, D. Pallavi, M. Ramachandran, Kurinjimalar Ramu, Chinnasami Sivaji","doi":"10.46632/eae/1/1/1","DOIUrl":"https://doi.org/10.46632/eae/1/1/1","url":null,"abstract":". Evolutionary methods are a horror-based approach to solving problems that are not easily solved in polynomial time, for example, classical NP-heart problems and take longer to complete. Evolutionary methods are commonly used to provide good approximate solutions to problems that cannot be easily solved using other techniques. Many optimization issues fall into this category. It can be very calculated- finding a suitable solution is serious but sometimes the optimal solution is enough. Major classes of contemporaries (in the order of popularity) E.A. Genetic algorithms (GAs), evolutionary strategies (ESs), differential evolution (DE) and distribution algorithm evaluation (EDAs) are used. Evolutionary methods are based on the concepts of biological evolution. The 'population' of possible solutions to the problem will be created first, and each solution will be evaluated using a 'fitness function'. The population develops over time and (hopefully) identifies the best solutions.","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125387237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evaluation of Histopathology images are a vital approach that is used for the breast cancer detection. To build up the efficiency of breast cancer detection and to reduce the burden of doctors and specialists, we layout various Deep Learning algorithms to recognize most cancers with the usage of histopathology scans. This paper follows several deep learning models like Convolutional Neural network (CNN) and Vgg16 for the recognition method. The dataset we used for class manner is Breast Histopathology Images which contain positive and negative images. We examined breast Histopathology images of 2,77,524 patients of which 198,748 images are IDC (-) and 78,786 images are IDC (+). This shows the deep learning algorithms can greatly facilitate the breast cancer detection, improving the accuracy and speed of detection. One of the most common cancers is Invasive Ductal Carcinoma (IDC). To determine the aggressiveness score to whole-mount specimen, doctors typically focus on areas containing IDC. Therefore, one of the common pre-processing steps for automatic aggressive categorization is to identify the exact region of IDC along the mounting side.
{"title":"Detection of Breast Cancer Using Deep Learning Techniques","authors":"G. S. Chandrasekhar, N. Thirupathi Rao","doi":"10.46632/eae/2/1/9","DOIUrl":"https://doi.org/10.46632/eae/2/1/9","url":null,"abstract":"Evaluation of Histopathology images are a vital approach that is used for the breast cancer detection. To build up the efficiency of breast cancer detection and to reduce the burden of doctors and specialists, we layout various Deep Learning algorithms to recognize most cancers with the usage of histopathology scans. This paper follows several deep learning models like Convolutional Neural network (CNN) and Vgg16 for the recognition method. The dataset we used for class manner is Breast Histopathology Images which contain positive and negative images. We examined breast Histopathology images of 2,77,524 patients of which 198,748 images are IDC (-) and 78,786 images are IDC (+). This shows the deep learning algorithms can greatly facilitate the breast cancer detection, improving the accuracy and speed of detection. One of the most common cancers is Invasive Ductal Carcinoma (IDC). To determine the aggressiveness score to whole-mount specimen, doctors typically focus on areas containing IDC. Therefore, one of the common pre-processing steps for automatic aggressive categorization is to identify the exact region of IDC along the mounting side.","PeriodicalId":446446,"journal":{"name":"Electrical and Automation Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117271453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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