Pub Date : 2023-02-06DOI: 10.1177/1063293x221142289
Nanyi Wang, Xinhui Kang, Qianqian Wang, Changyang Shi
Quality function deployment (QFD) is a systematic approach to transform customer requirements (CRs) into product engineering characteristics (ECs). Traditional QFD relies on market research or customer questionnaires to collect a series of ambiguous and uncertain CRs. As a result, evaluating the weighting of CRs and determining the design matrix between CRs and ECs have become the focus and difficulty of QFD. This paper proposes the grey system theory in artificial intelligence technology combined with QFD to develop grey-QFD to solve the issues mentioned before. First, collect the average evaluation values between the aesthetic images and customer satisfaction of representative products. The grey prediction GM (1, N) model is used to obtain the weight of aesthetic needs relative to customer satisfaction and import it into the left QFD. Second, the domain experts decomposed the product form into a morphological analysis table, and fuzzy Delphi screened key ECs and imported them into the ceiling of QFD. Finally, grey relationship analysis established the aesthetic product design matrix between CRs and ECs, and calculated and ranked the final weights of each ECs by using grey relationship degree. The research uses the security camera in the smart home as an experimental object. After operating the proposed grey-QFD, the aesthetic quality of the target product (lively, intelligent, friendly, personalized, and fashionable) and the optimization of the corresponding product ECs are obtained. The result provides a theoretical reference for designers and significantly improves customer aesthetic satisfaction.
{"title":"Using grey-quality function deployment to construct an aesthetic product design matrix","authors":"Nanyi Wang, Xinhui Kang, Qianqian Wang, Changyang Shi","doi":"10.1177/1063293x221142289","DOIUrl":"https://doi.org/10.1177/1063293x221142289","url":null,"abstract":"Quality function deployment (QFD) is a systematic approach to transform customer requirements (CRs) into product engineering characteristics (ECs). Traditional QFD relies on market research or customer questionnaires to collect a series of ambiguous and uncertain CRs. As a result, evaluating the weighting of CRs and determining the design matrix between CRs and ECs have become the focus and difficulty of QFD. This paper proposes the grey system theory in artificial intelligence technology combined with QFD to develop grey-QFD to solve the issues mentioned before. First, collect the average evaluation values between the aesthetic images and customer satisfaction of representative products. The grey prediction GM (1, N) model is used to obtain the weight of aesthetic needs relative to customer satisfaction and import it into the left QFD. Second, the domain experts decomposed the product form into a morphological analysis table, and fuzzy Delphi screened key ECs and imported them into the ceiling of QFD. Finally, grey relationship analysis established the aesthetic product design matrix between CRs and ECs, and calculated and ranked the final weights of each ECs by using grey relationship degree. The research uses the security camera in the smart home as an experimental object. After operating the proposed grey-QFD, the aesthetic quality of the target product (lively, intelligent, friendly, personalized, and fashionable) and the optimization of the corresponding product ECs are obtained. The result provides a theoretical reference for designers and significantly improves customer aesthetic satisfaction.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90676544","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}
Pub Date : 2022-11-10DOI: 10.1177/1063293x221138774
Song Zheng, Jun Liu, Di Wu
The optimization of production planning and scheduling is important for modern process industry. Due to different time dimensions of them, it is easy to create conflicts between the optimization results if they are solved separately, and the final results are not feasible. When facing a complex problem like production model, Collaborative optimization could be applied, but it still has shortcomings. Therefore, the improvement of collaborative optimization is proposed and improved collaborative optimization is applied to solve the uncertain production model. At last, the simulation results show that improved collaborative optimization can achieve better global optimization capability and practicability. This paper not only shows that the application scope of collaborative optimization has been expanded, but also provides a new idea for solving the uncertain production model.
{"title":"Research on uncertain integrated production planning and scheduling with risk management based on improved collaborative optimization","authors":"Song Zheng, Jun Liu, Di Wu","doi":"10.1177/1063293x221138774","DOIUrl":"https://doi.org/10.1177/1063293x221138774","url":null,"abstract":"The optimization of production planning and scheduling is important for modern process industry. Due to different time dimensions of them, it is easy to create conflicts between the optimization results if they are solved separately, and the final results are not feasible. When facing a complex problem like production model, Collaborative optimization could be applied, but it still has shortcomings. Therefore, the improvement of collaborative optimization is proposed and improved collaborative optimization is applied to solve the uncertain production model. At last, the simulation results show that improved collaborative optimization can achieve better global optimization capability and practicability. This paper not only shows that the application scope of collaborative optimization has been expanded, but also provides a new idea for solving the uncertain production model.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"274 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79999109","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}
Pub Date : 2022-11-04DOI: 10.1177/1063293x221137723
Fajun Gui, Yong Chen, Haomin Li, Chao Tang
With the popularity of IoT (Internet of Things) technology, more and more engineering systems are integrated with context-aware functionalities. It is self-evident that functional analysis is a critical process for successful development of such context-aware systems (CASs). Since existing functional analysis approaches are primarily for developing traditional engineering systems, this paper aims at developing a structured approach for assisting engineers in carrying out functional analysis of CASs. Based on the concept of context from an interactive perspective, this research analyzes what constitutes the context of a CAS and its basic features. Then a structured approach for functional analysis is proposed, which systematically elaborates how to define and analyze the context of a CAS, how to identify functionalities from the context, and how to develop the functional structure of a CAS. A case study of the functional analysis of a self-service checkout system is employed to demonstrate the proposed approach, which shows that the proposed approach can help designers carry out functional analysis of CASs in an effective manner.
{"title":"A structured approach for functional analysis of context-aware systems","authors":"Fajun Gui, Yong Chen, Haomin Li, Chao Tang","doi":"10.1177/1063293x221137723","DOIUrl":"https://doi.org/10.1177/1063293x221137723","url":null,"abstract":"With the popularity of IoT (Internet of Things) technology, more and more engineering systems are integrated with context-aware functionalities. It is self-evident that functional analysis is a critical process for successful development of such context-aware systems (CASs). Since existing functional analysis approaches are primarily for developing traditional engineering systems, this paper aims at developing a structured approach for assisting engineers in carrying out functional analysis of CASs. Based on the concept of context from an interactive perspective, this research analyzes what constitutes the context of a CAS and its basic features. Then a structured approach for functional analysis is proposed, which systematically elaborates how to define and analyze the context of a CAS, how to identify functionalities from the context, and how to develop the functional structure of a CAS. A case study of the functional analysis of a self-service checkout system is employed to demonstrate the proposed approach, which shows that the proposed approach can help designers carry out functional analysis of CASs in an effective manner.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86614829","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}
Pub Date : 2022-11-03DOI: 10.1177/1063293x221138650
Xinhui Kang, Nanyi Wang
With the improvement of manufacturing technology, the performance gap between different products has been gradually narrowed, and customers pay more and more attention to psychological feelings and aesthetic experiences in the process of purchasing and using products. Therefore, the main purpose of this research is to construct a hybrid model of aesthetic product design to develop customer satisfaction. Firstly, seven customer aesthetic needs are summarized by discussing the literature. The Kansei evaluation and customer satisfaction evaluation are investigated using Likert-scale, and the neural network is repeatedly trained and tested to obtain the weight of each aesthetic requirement. Secondly, the morphological analysis method is used to obtain the product morphological deconstruction table, and the entropy method is used to calculate the initial weights of the engineering characteristics (ECs). Finally, quality function development (QFD) is used as the platform to construct the relationship matrix between customer aesthetic needs and product ECs. Grey relationship analysis is used to calculate the final weight of ECs and obtain the priority of ECs. The research takes the side view of a car as a case. The results show that the best product form combination can provide a reference for designers and effectively improve the aesthetic experience and customer satisfaction of the product.
{"title":"A hybrid model to develop aesthetic product design of customer satisfaction","authors":"Xinhui Kang, Nanyi Wang","doi":"10.1177/1063293x221138650","DOIUrl":"https://doi.org/10.1177/1063293x221138650","url":null,"abstract":"With the improvement of manufacturing technology, the performance gap between different products has been gradually narrowed, and customers pay more and more attention to psychological feelings and aesthetic experiences in the process of purchasing and using products. Therefore, the main purpose of this research is to construct a hybrid model of aesthetic product design to develop customer satisfaction. Firstly, seven customer aesthetic needs are summarized by discussing the literature. The Kansei evaluation and customer satisfaction evaluation are investigated using Likert-scale, and the neural network is repeatedly trained and tested to obtain the weight of each aesthetic requirement. Secondly, the morphological analysis method is used to obtain the product morphological deconstruction table, and the entropy method is used to calculate the initial weights of the engineering characteristics (ECs). Finally, quality function development (QFD) is used as the platform to construct the relationship matrix between customer aesthetic needs and product ECs. Grey relationship analysis is used to calculate the final weight of ECs and obtain the priority of ECs. The research takes the side view of a car as a case. The results show that the best product form combination can provide a reference for designers and effectively improve the aesthetic experience and customer satisfaction of the product.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74287619","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}
Pub Date : 2022-10-31DOI: 10.1177/1063293x221137831
Kwansuk Oh, J. Lim, Y. Hong
One of the major challenges in variety management of modular product families is to prevent continuously generated variants of design elements. This paper aims to provide guidance on how manufacturing companies can reduce a large number of variants that have already increased from the existing product architectures. This paper introduces a new concept of architecture named variation architecture ( VA) in which relationships between variants in the market, design, and production domains are arranged explicitly for planning variety of a modular product family. Since the VA includes two perspectives which are domain mapping and variant-level planning, it can help companies to systematically establish complex relationships between variants across the domains. This paper describes elements of the three domains, relationship types between elements, and four categories of relationship rules called management rules at the variant-level planning. A framework is proposed for reducing variants through the VA to demonstrate its applicability. In the case study, we apply the framework to a front chassis family having a large number of variants and show that the VA significantly reduces unnecessary variants compared to the currently being produced.
{"title":"Variation architecture for reducing unnecessary variants in modular product family design by domain mapping and variant-level planning","authors":"Kwansuk Oh, J. Lim, Y. Hong","doi":"10.1177/1063293x221137831","DOIUrl":"https://doi.org/10.1177/1063293x221137831","url":null,"abstract":"One of the major challenges in variety management of modular product families is to prevent continuously generated variants of design elements. This paper aims to provide guidance on how manufacturing companies can reduce a large number of variants that have already increased from the existing product architectures. This paper introduces a new concept of architecture named variation architecture ( VA) in which relationships between variants in the market, design, and production domains are arranged explicitly for planning variety of a modular product family. Since the VA includes two perspectives which are domain mapping and variant-level planning, it can help companies to systematically establish complex relationships between variants across the domains. This paper describes elements of the three domains, relationship types between elements, and four categories of relationship rules called management rules at the variant-level planning. A framework is proposed for reducing variants through the VA to demonstrate its applicability. In the case study, we apply the framework to a front chassis family having a large number of variants and show that the VA significantly reduces unnecessary variants compared to the currently being produced.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77905909","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}
Pub Date : 2022-10-25DOI: 10.1177/1063293X221136215
K. Vijayakumar
The available computational facilities in recent years have helped engineers to develop new applications in the realworld problems, implement automation to reduce the complexity and improve productivity of the existing systems. The modern technologies such as remote monitoring, artificial intelligence, machine learning procedures, industrial and production automation, sustainable engineering and block chain techniques are commonly employed in concurrent engineering applications to enhance the product development/monitoring and speed-up the production capabilities. The employment of such computational facilities and modern technologies have helped companies in improving the overall performance in various sectors including, financial, medical, consumer, manufacturing, product design and implementation, manufacturing sector improvement and task scheduling applications. The main focus of this issue is to collect the cutting-edge research articles related to block chain, machine learning and other advanced virtual methods to promote concurrent engineering application in a variety of applicable domains. This issue collected the research works from different domains of the authors based on the theme of the issue. A summary of these collected articles is presented below;
{"title":"On blockchain technology and machine learning algorithms in concurrent engineering","authors":"K. Vijayakumar","doi":"10.1177/1063293X221136215","DOIUrl":"https://doi.org/10.1177/1063293X221136215","url":null,"abstract":"The available computational facilities in recent years have helped engineers to develop new applications in the realworld problems, implement automation to reduce the complexity and improve productivity of the existing systems. The modern technologies such as remote monitoring, artificial intelligence, machine learning procedures, industrial and production automation, sustainable engineering and block chain techniques are commonly employed in concurrent engineering applications to enhance the product development/monitoring and speed-up the production capabilities. The employment of such computational facilities and modern technologies have helped companies in improving the overall performance in various sectors including, financial, medical, consumer, manufacturing, product design and implementation, manufacturing sector improvement and task scheduling applications. The main focus of this issue is to collect the cutting-edge research articles related to block chain, machine learning and other advanced virtual methods to promote concurrent engineering application in a variety of applicable domains. This issue collected the research works from different domains of the authors based on the theme of the issue. A summary of these collected articles is presented below;","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"66 1","pages":"315 - 316"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74608173","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}
Pub Date : 2022-10-25DOI: 10.1177/1063293x221133344
Qinglan Chen, Liu Chen, Xiang-Ting Zeng, Chiu-Chi Wei
In a highly competitive market environment, organizations must improve their productivity, reduce production costs and improve management methods, in order to maintain a favorable competitive position. Project factors, such as time, cost and risk are closely linked. If one of those factors is out of control, the progress of the whole project will inevitably be delayed. When the progress of the project is delayed, duration compression measures such as crashing or fast tracking are usually employed to ensure the backward schedule returns to the original plan. However, the implementation of duration compression measures increases the cost and risk of the project. In most previous study of the critical path, only activity time is involved, and cost and risk are not considered. In the present study, a mathematical model is built to solve the optimal duration compression scheme objectives of the minimization of risk and cost. The model is verified by two cases, and the best solution is obtained by using LINGO software and Excel solver. The mathematical model is found to provide the best duration compression scheme for the project, with the least increases in cost and risk.
{"title":"Compressing project to minimize the increased risk and cost","authors":"Qinglan Chen, Liu Chen, Xiang-Ting Zeng, Chiu-Chi Wei","doi":"10.1177/1063293x221133344","DOIUrl":"https://doi.org/10.1177/1063293x221133344","url":null,"abstract":"In a highly competitive market environment, organizations must improve their productivity, reduce production costs and improve management methods, in order to maintain a favorable competitive position. Project factors, such as time, cost and risk are closely linked. If one of those factors is out of control, the progress of the whole project will inevitably be delayed. When the progress of the project is delayed, duration compression measures such as crashing or fast tracking are usually employed to ensure the backward schedule returns to the original plan. However, the implementation of duration compression measures increases the cost and risk of the project. In most previous study of the critical path, only activity time is involved, and cost and risk are not considered. In the present study, a mathematical model is built to solve the optimal duration compression scheme objectives of the minimization of risk and cost. The model is verified by two cases, and the best solution is obtained by using LINGO software and Excel solver. The mathematical model is found to provide the best duration compression scheme for the project, with the least increases in cost and risk.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82576668","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}
Pub Date : 2022-10-13DOI: 10.1177/1063293x221131885
Puppala Tirupathi, Polala Niranjan
In recent years, there has been a significant increase in the adaptation of current computer methodologies to tackle issues from different fields. Education, medical research, and agriculture are just a few of the fields that have seen fast development as a result of the rapid advancements in contemporary computer technology. These advancements may be seen in the form of more complex technology as well as enhanced algorithms for data processing. One such advancement is the Internet of Things (IoT)-based computing. Smart agricultural processes are being built with the use of Internet of Things (IoT) device-oriented solutions, which are becoming more popular. Nonetheless, the application of IoT devices to tackle these issues across a wide range of fields is fraught with a number of difficulties. The primary challenges are the high cost of deployment, the capacity or sustainability of the deployed device sets due to the limitations of battery technology, and, finally, the maintainability of these devices remotely due to the lack of an adequate communication infrastructure for IoT devices, all of which are significant obstacles. In particular, the adaption of Internet of Things solutions for agriculture has these previously discussed issues to a higher extent. In recent years, a slew of parallel research outputs has emerged, all of which are geared at finding solutions to these issues. Nonetheless, these parallel study outputs or current remedies have been criticized for not addressing all of the issues, but rather for focusing on just one of the three issues that have been identified as problematic. Thus, this study indicates the need, and possibility for developing a framework that may be used to address all of the challenges that have been identified. To begin, the recommended method, which is proven in the work, provides an automated procedure to assess the farm field, and then proposes the most ideal design for placing the Internet of Things devices. This study exhibits a unique application of the curve fitting approach for range, and power awareness, as well as a novel deployment of an optimization method for range, and power awareness, in order to determine the most optimum, and cost-effective deployment map or plan. Second, this study provides a technique for collecting sensor data in the most efficient manner possible, allowing any analytical engine to be constructed on top of the suggested architecture. According to the suggested framework, response time has been reduced by 15%, and average churn rates have been reduced by almost 20% when compared to the results of parallel research, resulting in increased network sustainability when compared to the results of parallel research results.
{"title":"An optimal strategy for sustainable IoT device placements for agriculture","authors":"Puppala Tirupathi, Polala Niranjan","doi":"10.1177/1063293x221131885","DOIUrl":"https://doi.org/10.1177/1063293x221131885","url":null,"abstract":"In recent years, there has been a significant increase in the adaptation of current computer methodologies to tackle issues from different fields. Education, medical research, and agriculture are just a few of the fields that have seen fast development as a result of the rapid advancements in contemporary computer technology. These advancements may be seen in the form of more complex technology as well as enhanced algorithms for data processing. One such advancement is the Internet of Things (IoT)-based computing. Smart agricultural processes are being built with the use of Internet of Things (IoT) device-oriented solutions, which are becoming more popular. Nonetheless, the application of IoT devices to tackle these issues across a wide range of fields is fraught with a number of difficulties. The primary challenges are the high cost of deployment, the capacity or sustainability of the deployed device sets due to the limitations of battery technology, and, finally, the maintainability of these devices remotely due to the lack of an adequate communication infrastructure for IoT devices, all of which are significant obstacles. In particular, the adaption of Internet of Things solutions for agriculture has these previously discussed issues to a higher extent. In recent years, a slew of parallel research outputs has emerged, all of which are geared at finding solutions to these issues. Nonetheless, these parallel study outputs or current remedies have been criticized for not addressing all of the issues, but rather for focusing on just one of the three issues that have been identified as problematic. Thus, this study indicates the need, and possibility for developing a framework that may be used to address all of the challenges that have been identified. To begin, the recommended method, which is proven in the work, provides an automated procedure to assess the farm field, and then proposes the most ideal design for placing the Internet of Things devices. This study exhibits a unique application of the curve fitting approach for range, and power awareness, as well as a novel deployment of an optimization method for range, and power awareness, in order to determine the most optimum, and cost-effective deployment map or plan. Second, this study provides a technique for collecting sensor data in the most efficient manner possible, allowing any analytical engine to be constructed on top of the suggested architecture. According to the suggested framework, response time has been reduced by 15%, and average churn rates have been reduced by almost 20% when compared to the results of parallel research, resulting in increased network sustainability when compared to the results of parallel research results.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84731469","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}
Pub Date : 2022-09-30DOI: 10.1177/1063293x221129612
N. Umasankari, B. Muthukumar
This research investigates the novel techniques which provide the detailed information on the biometric images used along with the methods applied for biometric image pre-processing. It also describes the proposed methodology which was implemented with the method of optimized Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) algorithm for classification of attributes. In the current work, a big effort has been implemented for designing an efficient technique for recognizing the biometric images, especially for the modalities like finger print and retina image. Initially, the pre-processing module used the method of histogram equalization to enhance the contrasts of entire image in order to get the best image quality. This makes the image adaptable for further processing. Next, the feature extraction module has the involvement of two image sets (finger print and retina image). The Gray Level Co-occurrence Matrix (GLCM) was used for extracting the needed features in this module. Next is Feature Based Fusion Technique (FBFT) for reducing the features for authentication purpose. This research work uses the FBFT to get fused feature vector. Finally, deals with the non-recognition and recognition of the images. The images were tested by using Artificial Neural Network (ANN). Here, the recognition is done by ANN and the optimization is done by the sophisticated function of Particle Swarm Optimization Algorithm (PSOA). ANN does the classification of images as recognized and non-recognized and yields best results.
{"title":"Evaluation of biometric communication and authenticate recognition using ANN with PSO algorithm","authors":"N. Umasankari, B. Muthukumar","doi":"10.1177/1063293x221129612","DOIUrl":"https://doi.org/10.1177/1063293x221129612","url":null,"abstract":"This research investigates the novel techniques which provide the detailed information on the biometric images used along with the methods applied for biometric image pre-processing. It also describes the proposed methodology which was implemented with the method of optimized Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) algorithm for classification of attributes. In the current work, a big effort has been implemented for designing an efficient technique for recognizing the biometric images, especially for the modalities like finger print and retina image. Initially, the pre-processing module used the method of histogram equalization to enhance the contrasts of entire image in order to get the best image quality. This makes the image adaptable for further processing. Next, the feature extraction module has the involvement of two image sets (finger print and retina image). The Gray Level Co-occurrence Matrix (GLCM) was used for extracting the needed features in this module. Next is Feature Based Fusion Technique (FBFT) for reducing the features for authentication purpose. This research work uses the FBFT to get fused feature vector. Finally, deals with the non-recognition and recognition of the images. The images were tested by using Artificial Neural Network (ANN). Here, the recognition is done by ANN and the optimization is done by the sophisticated function of Particle Swarm Optimization Algorithm (PSOA). ANN does the classification of images as recognized and non-recognized and yields best results.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89345848","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}
Pub Date : 2022-09-01DOI: 10.1177/1063293X221089089
A. Sabarivani, R. Ramadevi
In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.
{"title":"Detection and classification of epilepsy using hybrid convolutional neural network","authors":"A. Sabarivani, R. Ramadevi","doi":"10.1177/1063293X221089089","DOIUrl":"https://doi.org/10.1177/1063293X221089089","url":null,"abstract":"In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"50 1","pages":"253 - 261"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77571065","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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