Pub Date : 2016-07-10DOI: 10.1109/SOLI.2016.7551692
Hui Li, Hongqiang Lv, Q. Lin, Jianwen Zhang
Among the mathematical optimization algorithms, simplex algorithm is a popular and practical algorithm which was listed as one of the top 10 algorithms of the twentieth century by the journal Computing in Science and Engineering. Although simplex algorithm is efficient in the linear programming, the quality of convergence is unacceptable in practice as a numerical breakdown of the algorithm, even for smooth and well-behaved functions. On the other hand, full convergence might be seen in genetic algorithms (GA) using only crossover. So in this paper we combine the GA and simplex algorithm by initializing simplex from the final individual in GA and getting the converged result through simplex algorithm thereafter. A case study in estimating gas emission shows noteworthy improvement of efficiency and stability, compared with GA or simplex algorithm.
{"title":"GA-simplex algorithm and its application: A case study of gas emission estimation","authors":"Hui Li, Hongqiang Lv, Q. Lin, Jianwen Zhang","doi":"10.1109/SOLI.2016.7551692","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551692","url":null,"abstract":"Among the mathematical optimization algorithms, simplex algorithm is a popular and practical algorithm which was listed as one of the top 10 algorithms of the twentieth century by the journal Computing in Science and Engineering. Although simplex algorithm is efficient in the linear programming, the quality of convergence is unacceptable in practice as a numerical breakdown of the algorithm, even for smooth and well-behaved functions. On the other hand, full convergence might be seen in genetic algorithms (GA) using only crossover. So in this paper we combine the GA and simplex algorithm by initializing simplex from the final individual in GA and getting the converged result through simplex algorithm thereafter. A case study in estimating gas emission shows noteworthy improvement of efficiency and stability, compared with GA or simplex algorithm.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125878892","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551673
Y. Pang, G. Lodewijks
Belt conveyors are important equipment for large-scale bulk material transport and logistic distribution in very wide industry fields. In line with the variance of material loading degree, belt conveyor speed control has been proved as an effective way to reduce power consumption and to improve the service of the equipment with respect to system lifetime and maintenance. Since discrete control is preferred to regulate the speed of conveyor, control methods based on fuzzy logic can be applied. However, speed control is not always applicable for all conveyor operation scenarios. The stress cycles generated during fuzzy speed control in transient operations may lead to extra degradation of the equipment and should be limited. This paper presents the determination of the stress cycles during the fuzzy control for belt conveyor transient operations. After categorizing the loading scenarios that can be normally found in different application fields, the outputs of modeling and simulating a fuzzy speed control system display the effect of stress cycles on belt conveyor operations. The determination of minimum stress cycles along with maximum energy savings can be used to improve the algorithm and settings of belt conveyor speed control systems.
{"title":"Determining stress cycles for belt conveyor speed control in transient operations","authors":"Y. Pang, G. Lodewijks","doi":"10.1109/SOLI.2016.7551673","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551673","url":null,"abstract":"Belt conveyors are important equipment for large-scale bulk material transport and logistic distribution in very wide industry fields. In line with the variance of material loading degree, belt conveyor speed control has been proved as an effective way to reduce power consumption and to improve the service of the equipment with respect to system lifetime and maintenance. Since discrete control is preferred to regulate the speed of conveyor, control methods based on fuzzy logic can be applied. However, speed control is not always applicable for all conveyor operation scenarios. The stress cycles generated during fuzzy speed control in transient operations may lead to extra degradation of the equipment and should be limited. This paper presents the determination of the stress cycles during the fuzzy control for belt conveyor transient operations. After categorizing the loading scenarios that can be normally found in different application fields, the outputs of modeling and simulating a fuzzy speed control system display the effect of stress cycles on belt conveyor operations. The determination of minimum stress cycles along with maximum energy savings can be used to improve the algorithm and settings of belt conveyor speed control systems.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130007513","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551663
Zhenliang Ni, Zhen Shen, Chao Guo, Gang Xiong, T. Nyberg, Xiuqin Shang, Shuangshuang Li, Yiming Wang
In recent years, the Information Technology develops rapidly. With the development of the Information Technology, the social manufacturing, a new mode of manufacturing, has been proposed. The social manufacturing combines the Internet technology, the three-dimensional scanning technology and the virtual reality technology, and is used in the apparel industry and other fashion industries for the mass customization. The three-dimensional scanning technology and the virtual fitting mirror technology are inseparable from the depth camera. Depth cameras play an important role in the social manufacturing. Manufacturers use depth cameras to obtain three-dimensional models of the body of customers to design suitable clothes. Customers can visually see the effect of clothes by using a virtual fitting mirror which is based on the depth camera. The social manufacturing makes mass customization possible, and the depth camera help develop the social manufacturing. This paper introduces the principles of depth cameras, and the application of depth cameras in the social manufacturing.
{"title":"The application of the depth camera in the social manufacturing: A review","authors":"Zhenliang Ni, Zhen Shen, Chao Guo, Gang Xiong, T. Nyberg, Xiuqin Shang, Shuangshuang Li, Yiming Wang","doi":"10.1109/SOLI.2016.7551663","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551663","url":null,"abstract":"In recent years, the Information Technology develops rapidly. With the development of the Information Technology, the social manufacturing, a new mode of manufacturing, has been proposed. The social manufacturing combines the Internet technology, the three-dimensional scanning technology and the virtual reality technology, and is used in the apparel industry and other fashion industries for the mass customization. The three-dimensional scanning technology and the virtual fitting mirror technology are inseparable from the depth camera. Depth cameras play an important role in the social manufacturing. Manufacturers use depth cameras to obtain three-dimensional models of the body of customers to design suitable clothes. Customers can visually see the effect of clothes by using a virtual fitting mirror which is based on the depth camera. The social manufacturing makes mass customization possible, and the depth camera help develop the social manufacturing. This paper introduces the principles of depth cameras, and the application of depth cameras in the social manufacturing.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130041911","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551689
Hui Li, Jianwen Zhang, Hongqiang Lv, Q. Lin
Proven to be a viable and cost effective solution for ubiquitous internet access, wireless access network have proliferated at homes, offices, and cafes all over the globe. Being placed at the very edge of the computer network, with increasingly powerful computational ability, wireless access points are more and more suitable for deploying configurable and personalized services for mobile clients. In this paper we propose an intelligent wireless service framework over enhanced wireless access points (referred to as Smart Wireless Access Point) that prefetch and aggregate web contents on the wired side based on user profiles and delivery it to the user in a wireless network environment. We modify the source code of browser to enhance its data collection capabilities and develop some practical smartphone applications on the mobile client-side. Additionally, we perform some caching strategy and algorithms when a user have created considerable usage data or rated enough information. Measuring the download latency shows that DNS caching and content caching yields significant improvements in page load time. Prefetching mechanism, recommend system and some rich featured wireless local applications all benefit users much.
{"title":"Web prefetching of smart wireless access point","authors":"Hui Li, Jianwen Zhang, Hongqiang Lv, Q. Lin","doi":"10.1109/SOLI.2016.7551689","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551689","url":null,"abstract":"Proven to be a viable and cost effective solution for ubiquitous internet access, wireless access network have proliferated at homes, offices, and cafes all over the globe. Being placed at the very edge of the computer network, with increasingly powerful computational ability, wireless access points are more and more suitable for deploying configurable and personalized services for mobile clients. In this paper we propose an intelligent wireless service framework over enhanced wireless access points (referred to as Smart Wireless Access Point) that prefetch and aggregate web contents on the wired side based on user profiles and delivery it to the user in a wireless network environment. We modify the source code of browser to enhance its data collection capabilities and develop some practical smartphone applications on the mobile client-side. Additionally, we perform some caching strategy and algorithms when a user have created considerable usage data or rated enough information. Measuring the download latency shows that DNS caching and content caching yields significant improvements in page load time. Prefetching mechanism, recommend system and some rich featured wireless local applications all benefit users much.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126507825","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551655
J. Karjalainen, Xiong Gang
We have witnessed the emergence of a new set of corporations that have been able to grow faster than the competition with fewer resources. Corporations such as Uber and AirBnb have grown to become one of the largest of their kind in less than ten years: Uber was established in 2009 and is currently the world's biggest taxi company. Interestingly, it does not own any vehicles. Similarly, AirBnb was established in 2008 and is currently is the world's largest accommodation provider. Again, interestingly, it does not own any real estate. What is happening here, and more importantly, what kind of business model innovation has taken place, if any? This paper aims to shed light on these questions by analyzing the business models of these companies and contrasting them with those of their respective industry incumbents. The results highlight the importance of software in automatizing and outsourcing otherwise capital and labor intensive activities as well as enabling the business model to build upon logical resources composed of decentralized, transient slack resources of individuals and companies.
{"title":"Social manufacturing and business model innovation","authors":"J. Karjalainen, Xiong Gang","doi":"10.1109/SOLI.2016.7551655","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551655","url":null,"abstract":"We have witnessed the emergence of a new set of corporations that have been able to grow faster than the competition with fewer resources. Corporations such as Uber and AirBnb have grown to become one of the largest of their kind in less than ten years: Uber was established in 2009 and is currently the world's biggest taxi company. Interestingly, it does not own any vehicles. Similarly, AirBnb was established in 2008 and is currently is the world's largest accommodation provider. Again, interestingly, it does not own any real estate. What is happening here, and more importantly, what kind of business model innovation has taken place, if any? This paper aims to shed light on these questions by analyzing the business models of these companies and contrasting them with those of their respective industry incumbents. The results highlight the importance of software in automatizing and outsourcing otherwise capital and labor intensive activities as well as enabling the business model to build upon logical resources composed of decentralized, transient slack resources of individuals and companies.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128116693","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551693
Yuqiang Liu, T. Shi, Xiaojun Lv, Rui Zan, Long Wang, Yawei Zhang, X. Ye
The train reception and departure time directly affects the performance of the passenger service system. However, the system in existing stations sometimes cannot accurately obtain the train arrival time because of train delay. This paper proposed a kind of informative linkage mechanism for train reception and departure system by fusing intelligent video processing techniques. By using surveillance cameras deployed in track turnout region, we can obtain the real-time image for monitoring the status of train operation. The images can be used for the further analysis for acquiring the position, arrival time, type and velocity of the train. Then the information can be sent to the passenger service system and fused with the information from the transporting dispatching management information system. The system proposed in this paper presents a kind of low cost solution for railway monitoring, which can effectively solve the problem of how to accurately get the arrival time through the linkage of the video monitoring system and of the passenger service system.
{"title":"Research on train reception and departure system based on intelligent video analysis","authors":"Yuqiang Liu, T. Shi, Xiaojun Lv, Rui Zan, Long Wang, Yawei Zhang, X. Ye","doi":"10.1109/SOLI.2016.7551693","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551693","url":null,"abstract":"The train reception and departure time directly affects the performance of the passenger service system. However, the system in existing stations sometimes cannot accurately obtain the train arrival time because of train delay. This paper proposed a kind of informative linkage mechanism for train reception and departure system by fusing intelligent video processing techniques. By using surveillance cameras deployed in track turnout region, we can obtain the real-time image for monitoring the status of train operation. The images can be used for the further analysis for acquiring the position, arrival time, type and velocity of the train. Then the information can be sent to the passenger service system and fused with the information from the transporting dispatching management information system. The system proposed in this paper presents a kind of low cost solution for railway monitoring, which can effectively solve the problem of how to accurately get the arrival time through the linkage of the video monitoring system and of the passenger service system.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623344","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551675
Shuai Zeng, Jie Zhang
Student motivation is one of the most important individual characters for explaining student performance in class. Existing researches show that it can be affected largely by social-contextual factors. Rather than focusing on social relationship, in this paper we construct social activity networks for analyzing the impact of collective behaviors on student motivation. We conduct field experiments to compare social activity networks with relationship networks by analyzing the structural characteristics. We also investigate the dynamic of social activities, and validate the effectiveness of social activity network in reflecting social influence on student motivation. Our results show that social activities are important factors affecting student motivation.
{"title":"Analyse social influence on student motivation based on social activity network","authors":"Shuai Zeng, Jie Zhang","doi":"10.1109/SOLI.2016.7551675","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551675","url":null,"abstract":"Student motivation is one of the most important individual characters for explaining student performance in class. Existing researches show that it can be affected largely by social-contextual factors. Rather than focusing on social relationship, in this paper we construct social activity networks for analyzing the impact of collective behaviors on student motivation. We conduct field experiments to compare social activity networks with relationship networks by analyzing the structural characteristics. We also investigate the dynamic of social activities, and validate the effectiveness of social activity network in reflecting social influence on student motivation. Our results show that social activities are important factors affecting student motivation.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122765224","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551691
Y. Duan, Yisheng Lv, Feiyue Wang
Traffic flow prediction is very important in the deployment of intelligent transportation system. Based on our previous research on deep learning approach for traffic data prediction, we further evaluates the performance of the SAE model for traffic flow prediction at daytime and nighttime. Through 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3 different criteria, we obtain the best combination of hyper parameters for each criterion at different times on weekday and non-weekday, respectively. Experimental results show that the MAE and RMSE at daytime are larger than that at nighttime, while the MRE at daytime are smaller than that at nighttime. For different criteria, the hyper parameters of the SAE model should vary accordingly. The results in this paper indicate that in real applications, traffic flow prediction using the deep learning approach can be a combination of multiple SAE models with different parameters suitable for different periods, which is of significance in future research.
{"title":"Performance evaluation of the deep learning approach for traffic flow prediction at different times","authors":"Y. Duan, Yisheng Lv, Feiyue Wang","doi":"10.1109/SOLI.2016.7551691","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551691","url":null,"abstract":"Traffic flow prediction is very important in the deployment of intelligent transportation system. Based on our previous research on deep learning approach for traffic data prediction, we further evaluates the performance of the SAE model for traffic flow prediction at daytime and nighttime. Through 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3 different criteria, we obtain the best combination of hyper parameters for each criterion at different times on weekday and non-weekday, respectively. Experimental results show that the MAE and RMSE at daytime are larger than that at nighttime, while the MRE at daytime are smaller than that at nighttime. For different criteria, the hyper parameters of the SAE model should vary accordingly. The results in this paper indicate that in real applications, traffic flow prediction using the deep learning approach can be a combination of multiple SAE models with different parameters suitable for different periods, which is of significance in future research.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127276100","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551682
Wenbo Jiang, Huaqi Chai
Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, data mining perspectives are pointed out about how business model innovation is driven by “key data” based on illustrations about big data. Nine basic factors can be represented as business model innovation. GA-BP model is constructed by the combination of genetic algorithm and BP algorithm to extract the knowledge from the data in data mining environment and to find associations, patterns by analyzing the big data sets. Finally, “key data” that affects the consequence significantly can be grabbed to explore entry points for business model innovation in the era of big data, and to offer enterprises and executives for business model innovation from a new version.
{"title":"Research on big data in business model innovation based on GA-BP model","authors":"Wenbo Jiang, Huaqi Chai","doi":"10.1109/SOLI.2016.7551682","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551682","url":null,"abstract":"Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, data mining perspectives are pointed out about how business model innovation is driven by “key data” based on illustrations about big data. Nine basic factors can be represented as business model innovation. GA-BP model is constructed by the combination of genetic algorithm and BP algorithm to extract the knowledge from the data in data mining environment and to find associations, patterns by analyzing the big data sets. Finally, “key data” that affects the consequence significantly can be grabbed to explore entry points for business model innovation in the era of big data, and to offer enterprises and executives for business model innovation from a new version.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126075045","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 : 2016-07-10DOI: 10.1109/SOLI.2016.7551677
Hongli Peng, Xiuqin Shang, Chao Guo, Gang Xiong, T. Nyberg, Dong Fan, Yiming Wang
In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of IoT, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.
{"title":"A survey on big data for human body shape","authors":"Hongli Peng, Xiuqin Shang, Chao Guo, Gang Xiong, T. Nyberg, Dong Fan, Yiming Wang","doi":"10.1109/SOLI.2016.7551677","DOIUrl":"https://doi.org/10.1109/SOLI.2016.7551677","url":null,"abstract":"In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of IoT, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.","PeriodicalId":128068,"journal":{"name":"2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129922639","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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