Pub Date : 2022-04-15DOI: 10.1177/1063293X221088353
Kanimozhi Soundararajan, M. T.
Computer vision in sport is a very interesting application. People spend a lot of time watching sports videos because this is one of the best field of entertainment. Sports video broadcasts generally take a lot of time, ranging from two to four hours. However, the interesting part happens for just a few minutes. Detecting the highlighted event in a sport will be useful for people who like to watch only the prominent events section instead of watching the whole video broadcast. Event detection will give precise details about the action that occurred for a particular time, but the detection of highlighted events is more complex. This is due to the fact that a sports video contains collections of events. Among them, segregation of the required event is a time-consuming process but it requires more knowledge about the sport as well as processing time. Hence, a novel work is proposed focused on identifying the location of the functional object using agglomerative clustering and annotating the event highlights automatically by means of the rule inference mechanism. The SHRED (Sports Highlight Recognition and Event Detection) system achieves an overall accuracy of about 97.38% relative to other state-of-art methods in event class annotation.
计算机视觉在体育运动中的应用非常有趣。人们花很多时间看体育视频，因为这是最好的娱乐领域之一。体育视频转播通常需要很长时间，从2小时到4小时不等。然而，有趣的部分只发生在几分钟内。对于那些只喜欢观看突出事件部分而不是观看整个视频广播的人来说，检测一项运动中突出的事件将是有用的。事件检测将给出特定时间发生的动作的精确细节，但是对突出显示的事件的检测更为复杂。这是由于体育视频包含事件集合的事实。其中，所需项目的分离是一个耗时的过程，但它需要更多的运动知识和处理时间。为此，本文提出了一种基于聚类的功能对象位置识别方法和基于规则推理机制的事件亮点自动标注方法。相对于其他最先进的事件类标注方法，SHRED (Sports Highlight Recognition and Event Detection)系统的总体准确率约为97.38%。
{"title":"Sports highlight recognition and event detection using rule inference system","authors":"Kanimozhi Soundararajan, M. T.","doi":"10.1177/1063293X221088353","DOIUrl":"https://doi.org/10.1177/1063293X221088353","url":null,"abstract":"Computer vision in sport is a very interesting application. People spend a lot of time watching sports videos because this is one of the best field of entertainment. Sports video broadcasts generally take a lot of time, ranging from two to four hours. However, the interesting part happens for just a few minutes. Detecting the highlighted event in a sport will be useful for people who like to watch only the prominent events section instead of watching the whole video broadcast. Event detection will give precise details about the action that occurred for a particular time, but the detection of highlighted events is more complex. This is due to the fact that a sports video contains collections of events. Among them, segregation of the required event is a time-consuming process but it requires more knowledge about the sport as well as processing time. Hence, a novel work is proposed focused on identifying the location of the functional object using agglomerative clustering and annotating the event highlights automatically by means of the rule inference mechanism. The SHRED (Sports Highlight Recognition and Event Detection) system achieves an overall accuracy of about 97.38% relative to other state-of-art methods in event class annotation.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"31 1","pages":"206 - 213"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87239522","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 : 2022-04-14DOI: 10.1177/1063293X221080518
J. Dafni Rose, K. Vijayakumar, Laxman Singh, S. Sharma
Globally, breast cancer is considered a major reason for women’s morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.
{"title":"Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model","authors":"J. Dafni Rose, K. Vijayakumar, Laxman Singh, S. Sharma","doi":"10.1177/1063293X221080518","DOIUrl":"https://doi.org/10.1177/1063293X221080518","url":null,"abstract":"Globally, breast cancer is considered a major reason for women’s morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"34 1","pages":"181 - 189"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77295025","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 : 2022-03-01DOI: 10.1177/1063293X221082331
K. Vijayakumar, V. Rajinikanth
In a generation of the rapid evolution of technological enhancements, intelligent computing helps to produce solutions for every elementary problem with the help of computing technology. It is the process of developing the algorithm for a program to provide a solution for any task. It helps to break down any task into specific functions to get better solutions. Computing intelligence must be able to transform the data obtained into a function that produces a beneficial solution. Global Positioning Systems (GPS), mobile phones, Email, the internet, and the development of various mobile apps are some examples of constant improvement in computing. Computing has made regular work simple and has proved to be suitable and user-friendly without any adverse effects. Intelligent Computing has proved to be an area of constant research leading to the discovery of efficient ideas and implementation. Computing when coupled with intelligent communication systems helps in the effective transfer of information in a network that aids to generate and implement new services efficiently and economically. Enhanced next-generation algorithms for communication systems can be utilized to make accomplishments easy. It provides a comprehensive vision that opens up numerous industrial, management, and research opportunities. The exceptions to convey, categorize and utilize the data created and transmitted by connected objects are enormous. Concurrent engineering is a process that can use intelligent computing in the design and development of various engineering products and services. Concurrent Engineering is acknowledged to an extent of 52% in well-established companies, 45% in mid-sized companies, and 39% in small companies. Intelligent Computing and Communication in Concurrent Engineering helps to reduce the time taken to complete a task also reduces the time taken for product development. It helps to increase productivity, faster design process and also decreases the cost of production. It encourages multidisciplinary collaboration amongst concurrent teams. However, there are some challenges faced in concurrent design that includes skill development, insufficient knowledge, and support from management, early design reviews, efficient communication between team members, software compatibility, and lack of IT tools. New Intelligent Computing and Communication in Concurrent Engineering strategies can be analyzed and developed to overcome drawbacks. This special issue aims to collect high quality research works related to intelligent computing and communication associated with concurrent engineering. We request original works that have not been published nor under consideration in other publication venues. Topics of interest for this special issue include;
{"title":"Special issue on “intelligent computing and communication in concurrent engineering”","authors":"K. Vijayakumar, V. Rajinikanth","doi":"10.1177/1063293X221082331","DOIUrl":"https://doi.org/10.1177/1063293X221082331","url":null,"abstract":"In a generation of the rapid evolution of technological enhancements, intelligent computing helps to produce solutions for every elementary problem with the help of computing technology. It is the process of developing the algorithm for a program to provide a solution for any task. It helps to break down any task into specific functions to get better solutions. Computing intelligence must be able to transform the data obtained into a function that produces a beneficial solution. Global Positioning Systems (GPS), mobile phones, Email, the internet, and the development of various mobile apps are some examples of constant improvement in computing. Computing has made regular work simple and has proved to be suitable and user-friendly without any adverse effects. Intelligent Computing has proved to be an area of constant research leading to the discovery of efficient ideas and implementation. Computing when coupled with intelligent communication systems helps in the effective transfer of information in a network that aids to generate and implement new services efficiently and economically. Enhanced next-generation algorithms for communication systems can be utilized to make accomplishments easy. It provides a comprehensive vision that opens up numerous industrial, management, and research opportunities. The exceptions to convey, categorize and utilize the data created and transmitted by connected objects are enormous. Concurrent engineering is a process that can use intelligent computing in the design and development of various engineering products and services. Concurrent Engineering is acknowledged to an extent of 52% in well-established companies, 45% in mid-sized companies, and 39% in small companies. Intelligent Computing and Communication in Concurrent Engineering helps to reduce the time taken to complete a task also reduces the time taken for product development. It helps to increase productivity, faster design process and also decreases the cost of production. It encourages multidisciplinary collaboration amongst concurrent teams. However, there are some challenges faced in concurrent design that includes skill development, insufficient knowledge, and support from management, early design reviews, efficient communication between team members, software compatibility, and lack of IT tools. New Intelligent Computing and Communication in Concurrent Engineering strategies can be analyzed and developed to overcome drawbacks. This special issue aims to collect high quality research works related to intelligent computing and communication associated with concurrent engineering. We request original works that have not been published nor under consideration in other publication venues. Topics of interest for this special issue include;","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"14 1","pages":"128 - 130"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75329924","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 : 2022-03-01DOI: 10.1177/1063293X221085830
K. Vijayakumar
In recent years, concurrent engineering (CE) has played an essential role in providing relevant and optimal solutions for multi-disciplinary problems. These are closely associated with various vital tasks, such as product design, manmachine interface for product automation, and achieving the overall performance of the product integrated with cognitive ergonomics. Concurrent Engineering aids in the development of feasible and cost-effective product solutions to ensure the complete satisfaction of the consumer in comparison with their product competitors. The product/ methodology developed with CE helps in achieving (i) enhanced quality, (ii) improved productivity, (iii) optimized design for x-abilities outcomes (like DFM, DFA, and DFX), and (iv) enhanced performance objectives. Concurrent Engineering techniques also help to reduce the gap between the physical and functional arrangement of a successful product. Furthermore, CE-enhanced schemes add to improved efficiency and flexibility. Recently, advents in computerized techniques during the automation of process monitoring and decision-making have been found to be quite useful in a variety of domains. Likewise, the machine-learning (ML) algorithm has supported development of systems with monitoring and decision-making capabilities. Such knowledge-based systems are widely employed in the medical science domain to automate various processes ranging from screening to treatment implementation. When ML schemes are applied in the medical domain, it supports early detection, disease diagnosis, automatic report generation, and treatment planning processes. Such schemes help reduce the diagnostic burden when an extensive number of patients are to be screened. When the ML is associated with CE, the system’s capability, accuracy, and speed automatically increase and the resulting outcome becomes clinically significant. The ML approach helps to examine a considerable number of diseases including, retinal peculiarity, COVID-19, and associated abnormalities. Concurrent Engineering in combination with ML schemes helps to provide better results during patient screening treatment.
{"title":"Concurrent engineering and machine learning techniques in medical science","authors":"K. Vijayakumar","doi":"10.1177/1063293X221085830","DOIUrl":"https://doi.org/10.1177/1063293X221085830","url":null,"abstract":"In recent years, concurrent engineering (CE) has played an essential role in providing relevant and optimal solutions for multi-disciplinary problems. These are closely associated with various vital tasks, such as product design, manmachine interface for product automation, and achieving the overall performance of the product integrated with cognitive ergonomics. Concurrent Engineering aids in the development of feasible and cost-effective product solutions to ensure the complete satisfaction of the consumer in comparison with their product competitors. The product/ methodology developed with CE helps in achieving (i) enhanced quality, (ii) improved productivity, (iii) optimized design for x-abilities outcomes (like DFM, DFA, and DFX), and (iv) enhanced performance objectives. Concurrent Engineering techniques also help to reduce the gap between the physical and functional arrangement of a successful product. Furthermore, CE-enhanced schemes add to improved efficiency and flexibility. Recently, advents in computerized techniques during the automation of process monitoring and decision-making have been found to be quite useful in a variety of domains. Likewise, the machine-learning (ML) algorithm has supported development of systems with monitoring and decision-making capabilities. Such knowledge-based systems are widely employed in the medical science domain to automate various processes ranging from screening to treatment implementation. When ML schemes are applied in the medical domain, it supports early detection, disease diagnosis, automatic report generation, and treatment planning processes. Such schemes help reduce the diagnostic burden when an extensive number of patients are to be screened. When the ML is associated with CE, the system’s capability, accuracy, and speed automatically increase and the resulting outcome becomes clinically significant. The ML approach helps to examine a considerable number of diseases including, retinal peculiarity, COVID-19, and associated abnormalities. Concurrent Engineering in combination with ML schemes helps to provide better results during patient screening treatment.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"2 1","pages":"3 - 4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88737570","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 : 2022-02-05DOI: 10.1177/1063293X211072192
Carsten Keinicke Fjord Christensen, N. Mortensen
The purpose of this paper is to address a gap of missing modularization methods for engineer-to-order (ETO) companies. The research project was initiated by clarifying the challenges facing ETO companies, based on these challenges synthesis of existing methods was done to conceptualize a method. This article presents the modular candidate identification (MCI) method aimed at identifying modular candidates in ETO companies. The method analyzes five dimensions, namely, market segments, customer requirements, product architectures, cost and lead time to find modular candidates. The method was applied in a Danish ETO company and shown to be successful in identifying two modular candidates. Both were recognized by management and redesigned in modular product development projects.
{"title":"Identifying modular candidates in engineer-to-order companies","authors":"Carsten Keinicke Fjord Christensen, N. Mortensen","doi":"10.1177/1063293X211072192","DOIUrl":"https://doi.org/10.1177/1063293X211072192","url":null,"abstract":"The purpose of this paper is to address a gap of missing modularization methods for engineer-to-order (ETO) companies. The research project was initiated by clarifying the challenges facing ETO companies, based on these challenges synthesis of existing methods was done to conceptualize a method. This article presents the modular candidate identification (MCI) method aimed at identifying modular candidates in ETO companies. The method analyzes five dimensions, namely, market segments, customer requirements, product architectures, cost and lead time to find modular candidates. The method was applied in a Danish ETO company and shown to be successful in identifying two modular candidates. Both were recognized by management and redesigned in modular product development projects.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"56 1","pages":"159 - 170"},"PeriodicalIF":0.0,"publicationDate":"2022-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81453476","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 : 2022-02-02DOI: 10.1177/1063293X211069193
Altaf Alam, Z. Jaffery, Himanshu Sharma
Vehicle detection plays an important role in the development of an autonomous driving system. Fast processing and accurate detection are two major aspects of generating the autonomous vehicle detection system. This paper proposes a novel computer vision-based cost-effective vehicle detection system. Here, a Gentle Adaptive Boosting algorithm is trained with Haar-like features to generate the hypothesis of vehicles. Haar-like feature generates hypotheses very fast but may detect false vehicle candidates. The support vector machine algorithm is trained with the histogram of oriented gradient features to filter out the generated false hypothesis. The histogram of oriented gradients descriptor utilizes the shape and outlines of the vehicles, hence detects vehicles more accurately. Haar-Likes features and histogram of oriented gradients features are organized to accomplish the aspects of autonomous driving. The performance of the proposed vehicle detector is evaluated for day time and night time captured images and compared with three different existing vehicle detectors. The average precision of the proposed system for day time captured image is 0.97 and for night time captured image is 0.94. The proposed system requires 15 times less training time as compared to the existing technique for the same number of image data and on the same CPU.
{"title":"A cost-effective computer vision-based vehicle detection system","authors":"Altaf Alam, Z. Jaffery, Himanshu Sharma","doi":"10.1177/1063293X211069193","DOIUrl":"https://doi.org/10.1177/1063293X211069193","url":null,"abstract":"Vehicle detection plays an important role in the development of an autonomous driving system. Fast processing and accurate detection are two major aspects of generating the autonomous vehicle detection system. This paper proposes a novel computer vision-based cost-effective vehicle detection system. Here, a Gentle Adaptive Boosting algorithm is trained with Haar-like features to generate the hypothesis of vehicles. Haar-like feature generates hypotheses very fast but may detect false vehicle candidates. The support vector machine algorithm is trained with the histogram of oriented gradient features to filter out the generated false hypothesis. The histogram of oriented gradients descriptor utilizes the shape and outlines of the vehicles, hence detects vehicles more accurately. Haar-Likes features and histogram of oriented gradients features are organized to accomplish the aspects of autonomous driving. The performance of the proposed vehicle detector is evaluated for day time and night time captured images and compared with three different existing vehicle detectors. The average precision of the proposed system for day time captured image is 0.97 and for night time captured image is 0.94. The proposed system requires 15 times less training time as compared to the existing technique for the same number of image data and on the same CPU.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"23 1","pages":"148 - 158"},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80632030","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 : 2022-01-31DOI: 10.1177/1063293X211071044
K. Ilayarajaa, E. Logashanmugam
Diabetic Retinopathy (DR) is considered to be the leading cause for preventive blindness in humans, the DR is sighted with a diabetic stage of progression and hence the patient is required to undergo regular health checkups on DR formation and detection. In this paper, the objective is to extract and detect the patterns of DR with respect to the propagation stages using Recursive Neural Network (RNN). In this work, we have developed and validated a novel Inter-Correlated Attribute Coordination (ICAC) Technique for attribute based feature mapping and feature inter-dependent cluster generation. The ICAC technique generates a series of standard dataset attributes ( S D ) A for process alignment towards the generation of feature set (f). The proposed technique has validated the categorization of DR into grade 1 and grade 0 patients for an unambiguous decision making. The technique’s trained datasets provide a self-learning RNN for multidimensional tomography dataset processing. The ICAC technique has developed a detection rate of 97.3% for the 276 feature set clusters.
{"title":"Embedded mobile computational framework for multidimensional diabetic retinopathy extraction and detection technique using recursive neural network approach for unstructured tomography datasets","authors":"K. Ilayarajaa, E. Logashanmugam","doi":"10.1177/1063293X211071044","DOIUrl":"https://doi.org/10.1177/1063293X211071044","url":null,"abstract":"Diabetic Retinopathy (DR) is considered to be the leading cause for preventive blindness in humans, the DR is sighted with a diabetic stage of progression and hence the patient is required to undergo regular health checkups on DR formation and detection. In this paper, the objective is to extract and detect the patterns of DR with respect to the propagation stages using Recursive Neural Network (RNN). In this work, we have developed and validated a novel Inter-Correlated Attribute Coordination (ICAC) Technique for attribute based feature mapping and feature inter-dependent cluster generation. The ICAC technique generates a series of standard dataset attributes ( S D ) A for process alignment towards the generation of feature set (f). The proposed technique has validated the categorization of DR into grade 1 and grade 0 patients for an unambiguous decision making. The technique’s trained datasets provide a self-learning RNN for multidimensional tomography dataset processing. The ICAC technique has developed a detection rate of 97.3% for the 276 feature set clusters.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"70 1","pages":"93 - 102"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79942731","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}
Aiming at improving assembly line efficiency and flexibility, a balancing method of parallel U-shaped assembly line system is proposed. Based on the improved product priority diagram, the bidirectional priority value formula is obtained. Then, assembly lines are partitioned into z-q partitions and workstations are defined. After that, the mathematical model of the parallel U-shaped assembly line balancing problem is established. A heuristic algorithm based on bidirectional priority values is used to solve explanatory examples and test examples. It can be seen from the results and the effect indicators of the assembly line balancing problem that the heuristic algorithm is suitable for large balancing problems. The proposed method has higher calculation accuracy and shorter calculation time. The balancing effect of the parallel U-shaped assembly line is better than that of single U-shaped assembly line, which verifies the superiority of the parallel U-type assembly line and effectiveness of the proposed method. It provides a theoretical and practical reference for parallel U-type assembly line balancing problem.
{"title":"Balancing of parallel U-shaped assembly lines with a heuristic algorithm based on bidirectional priority values","authors":"Yuling Jiao, Xue Deng, Mingjuan Li, Xiaocui Xing, Binjie Xu","doi":"10.1177/1063293X211065506","DOIUrl":"https://doi.org/10.1177/1063293X211065506","url":null,"abstract":"Aiming at improving assembly line efficiency and flexibility, a balancing method of parallel U-shaped assembly line system is proposed. Based on the improved product priority diagram, the bidirectional priority value formula is obtained. Then, assembly lines are partitioned into z-q partitions and workstations are defined. After that, the mathematical model of the parallel U-shaped assembly line balancing problem is established. A heuristic algorithm based on bidirectional priority values is used to solve explanatory examples and test examples. It can be seen from the results and the effect indicators of the assembly line balancing problem that the heuristic algorithm is suitable for large balancing problems. The proposed method has higher calculation accuracy and shorter calculation time. The balancing effect of the parallel U-shaped assembly line is better than that of single U-shaped assembly line, which verifies the superiority of the parallel U-type assembly line and effectiveness of the proposed method. It provides a theoretical and practical reference for parallel U-type assembly line balancing problem.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"21 1","pages":"80 - 92"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83113038","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 : 2021-12-12DOI: 10.1177/1063293X211047290
Cuiqing Jiang, Abdullah Alqadhi, Mahmood Almesbahi
Due to the massive number of products being produced every year in every industry, firms have witnessed a tremendous growth in innovation of methods to create a sustainable competitive advantage. For the past decade and with the availability of online consumer reviews, companies and researchers have developed many approaches utilizing electronic Word-of-Mouth to improve and develop products and services to outperform competitors. The purpose of this study is to construct an effective method to perform a better product comparative analysis based on online consumer reviews. We propose a novel framework called Teardown Joint Sentiment-Topic analysis model consisting of a combination of text analytical approaches incorporated with a developed method of the traditional teardown analysis product comparative approach. The proposed approach is fully unsupervised model that employs Latent Dirichlet Allocation topic modeling to form topics which are classified according to their sentiments. Topics are then analyzed against competitive products and critical topics are identified using a developed teardown method. A case study analyzing online customer reviews of competing products in two domains (i.e., mobile phones and surveillance cameras) is conducted. The identified critical topics are further analyzed in view of products’ specifications perspective. We found that the detected aspects of the selected products are indeed critical, and hence, they need to be improved in order to gain a competitive advantage. The significant result of this study shows that the proposed method is effective in conducting products comparative analysis and provides valuable insights into utilizing the consumer reviews for product development.
{"title":"Conducting product comparative analysis to outperform competitor’s product using Teardown JST Model","authors":"Cuiqing Jiang, Abdullah Alqadhi, Mahmood Almesbahi","doi":"10.1177/1063293X211047290","DOIUrl":"https://doi.org/10.1177/1063293X211047290","url":null,"abstract":"Due to the massive number of products being produced every year in every industry, firms have witnessed a tremendous growth in innovation of methods to create a sustainable competitive advantage. For the past decade and with the availability of online consumer reviews, companies and researchers have developed many approaches utilizing electronic Word-of-Mouth to improve and develop products and services to outperform competitors. The purpose of this study is to construct an effective method to perform a better product comparative analysis based on online consumer reviews. We propose a novel framework called Teardown Joint Sentiment-Topic analysis model consisting of a combination of text analytical approaches incorporated with a developed method of the traditional teardown analysis product comparative approach. The proposed approach is fully unsupervised model that employs Latent Dirichlet Allocation topic modeling to form topics which are classified according to their sentiments. Topics are then analyzed against competitive products and critical topics are identified using a developed teardown method. A case study analyzing online customer reviews of competing products in two domains (i.e., mobile phones and surveillance cameras) is conducted. The identified critical topics are further analyzed in view of products’ specifications perspective. We found that the detected aspects of the selected products are indeed critical, and hence, they need to be improved in order to gain a competitive advantage. The significant result of this study shows that the proposed method is effective in conducting products comparative analysis and provides valuable insights into utilizing the consumer reviews for product development.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"67 1","pages":"21 - 31"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77460809","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 : 2021-12-09DOI: 10.1177/1063293X211054132
Mo Chen, G. Fadel, Ivan Mata
Affordance-based design (ABD) plays an important role in identifying interactions, especially effortless ones, between users and artifacts. Cognitive ergonomics extends our understanding of this effortless interaction. This study combines the two design methodologies together in order to reduce cognitive friction in using digital products. The design process of a compact digital camera is selected as a case study that includes the design of the physical shape for a camera and of its user interface. In designing a product shape, a design toolbox was developed that integrated a modified multi-objective genetic algorithm and the ABD, which was named as affordance-based interactive genetic algorithm. Using this toolbox and interactive user feedback, the camera design evolves toward a product that better satisfies the users. User interfaces (UIs) including linear and elliptic layouts were subsequently designed based on cognitive ergonomics. A predictive tool of UI, the Cog Tool, was used to evaluate the performance of skilled users on a given task by correlating the overall task completion time. Finally, this research has the potential to not only effectively address the shortcomings of the design of consumer electronics but also enrich the generation of design solutions during the preliminary design phase of such products.
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