Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551456
Yair Herbst, Shunit Polinsky, A. Fischer, Y. Medan, R. Schneor, Joshua A. Kahn, A. Wolf
The process of fitting a prosthetic hand that is comfortable, functional, easy to use, has an acceptable appearance and overall improves the amputees' quality of life is a complex, tedious and costly process. The very high price tag due to the time spent on manually fitting the device by a trained specialist makes these devices inaccessible to large portions of the population. We present a concept and preliminary results for a fully automated fitting and manufacturing pipeline for a personalized low-cost prosthetic hand. The hand is personalized in almost every aspect, from appearance to user interface, control and feedback. The pipeline only requires a 3D printer, RealSense cameras, a few basic mechanical components, and basic tools for the model assembly. The user scan-driven data and the user preferences initiate a fully-automated pipeline which culminates in a customized, easy-to-assemble PCB design and ready to print STL files, including the optimized orientation, support and layout, such that the final parts are only one click away. We believe that the proposed pipeline and design can highly impact the accessibility of prosthetic hands and could potentially be expanded to other medical applications.
{"title":"Scan-Driven Fully-Automated Pipeline for a Personalized, 3D Printed Low-Cost Prosthetic Hand","authors":"Yair Herbst, Shunit Polinsky, A. Fischer, Y. Medan, R. Schneor, Joshua A. Kahn, A. Wolf","doi":"10.1109/CASE49439.2021.9551456","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551456","url":null,"abstract":"The process of fitting a prosthetic hand that is comfortable, functional, easy to use, has an acceptable appearance and overall improves the amputees' quality of life is a complex, tedious and costly process. The very high price tag due to the time spent on manually fitting the device by a trained specialist makes these devices inaccessible to large portions of the population. We present a concept and preliminary results for a fully automated fitting and manufacturing pipeline for a personalized low-cost prosthetic hand. The hand is personalized in almost every aspect, from appearance to user interface, control and feedback. The pipeline only requires a 3D printer, RealSense cameras, a few basic mechanical components, and basic tools for the model assembly. The user scan-driven data and the user preferences initiate a fully-automated pipeline which culminates in a customized, easy-to-assemble PCB design and ready to print STL files, including the optimized orientation, support and layout, such that the final parts are only one click away. We believe that the proposed pipeline and design can highly impact the accessibility of prosthetic hands and could potentially be expanded to other medical applications.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131216179","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551441
S. Sajjadi, M. M. H. Fallah, M. Mehrandezh, F. Janabi-Sharifi
Image-based visual predictive controllers have gained attention due to their optimality and constraint-handling capabilities. However, their performance deteriorates in presence of the modelling and measurement uncertainties. This paper presents a stochastic image-based visual predictive control method to overcome some shortcomings of the previous schemes cited in literature. In particular, the proposed approach provides a systematic solution to address the image-based constraint compliance in presence of the measurement and modelling uncertainties. The proposed method was implemented on a 6-DOF Denso robot via simulation.
{"title":"Stochastic Image-based Visual Predictive Control","authors":"S. Sajjadi, M. M. H. Fallah, M. Mehrandezh, F. Janabi-Sharifi","doi":"10.1109/CASE49439.2021.9551441","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551441","url":null,"abstract":"Image-based visual predictive controllers have gained attention due to their optimality and constraint-handling capabilities. However, their performance deteriorates in presence of the modelling and measurement uncertainties. This paper presents a stochastic image-based visual predictive control method to overcome some shortcomings of the previous schemes cited in literature. In particular, the proposed approach provides a systematic solution to address the image-based constraint compliance in presence of the measurement and modelling uncertainties. The proposed method was implemented on a 6-DOF Denso robot via simulation.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"151 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131124291","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551397
A. Ebrahimi, K. Åkesson
A challenge for highly configurable products is that the manufacturing system has to support all possible variants that can be configured. The production system is often highly automated and the link between the product and the assembly system can be expressed through operations where each operation models how a part in the bill-of-material is assembled to the final product. Typically, operations have precedence constraints that express that certain parts have to be assembled before other parts. However, it is important to make sure that all possible variants can be successfully assembled while satisfying all precedence constraints. In this paper we present fully automated analysis method that is able to analyze the existence of product configurations of that cannot be successfully assembled.
{"title":"Modelling and analysis of product platforms and assembly sequences with respect to variability","authors":"A. Ebrahimi, K. Åkesson","doi":"10.1109/CASE49439.2021.9551397","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551397","url":null,"abstract":"A challenge for highly configurable products is that the manufacturing system has to support all possible variants that can be configured. The production system is often highly automated and the link between the product and the assembly system can be expressed through operations where each operation models how a part in the bill-of-material is assembled to the final product. Typically, operations have precedence constraints that express that certain parts have to be assembled before other parts. However, it is important to make sure that all possible variants can be successfully assembled while satisfying all precedence constraints. In this paper we present fully automated analysis method that is able to analyze the existence of product configurations of that cannot be successfully assembled.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127779463","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551632
Benjamin Lutz, Dominik Kißkalt, Daniel Regulin, Burak Aybar, Jörg K.H. Franke
Microscopy is commonly used in machining to study the effects of tool wear. In modern tool condition monitoring systems, the analytical capabilities are further enhanced by machine learning, allowing for automated segmentation of the various visible defects. The prevailing challenge, however, is the divergence among different use cases, as the visual properties of cutting tool images are influenced by many domain-specific factors such as the type of the cutting tool, the respective machining process, and the image acquisition unit. Thus, we propose the usage of automated domain adaptation so that existing training data from source domains can be used effectively to train segmentation models for novel target domains, while minimizing the need for newly labelled data. This is achieved through image-to-image translation using generative adversarial networks, which generate synthetic images with similar visual characteristics as the target domain based on existing masks of the source domains. Our validation shows that with as few as ten labelled images from the target domain, a sufficient prediction performance of 0.72 mIoU can be achieved when tested on unseen images from the target domain. This corresponds to a reduction of manual labelling efforts by two-thirds compared to conventional labelling and training methods. Thus, by adapting existing data, prediction performance is increased while expensive data generation is minimized.
{"title":"Automated Domain Adaptation in Tool Condition Monitoring using Generative Adversarial Networks","authors":"Benjamin Lutz, Dominik Kißkalt, Daniel Regulin, Burak Aybar, Jörg K.H. Franke","doi":"10.1109/CASE49439.2021.9551632","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551632","url":null,"abstract":"Microscopy is commonly used in machining to study the effects of tool wear. In modern tool condition monitoring systems, the analytical capabilities are further enhanced by machine learning, allowing for automated segmentation of the various visible defects. The prevailing challenge, however, is the divergence among different use cases, as the visual properties of cutting tool images are influenced by many domain-specific factors such as the type of the cutting tool, the respective machining process, and the image acquisition unit. Thus, we propose the usage of automated domain adaptation so that existing training data from source domains can be used effectively to train segmentation models for novel target domains, while minimizing the need for newly labelled data. This is achieved through image-to-image translation using generative adversarial networks, which generate synthetic images with similar visual characteristics as the target domain based on existing masks of the source domains. Our validation shows that with as few as ten labelled images from the target domain, a sufficient prediction performance of 0.72 mIoU can be achieved when tested on unseen images from the target domain. This corresponds to a reduction of manual labelling efforts by two-thirds compared to conventional labelling and training methods. Thus, by adapting existing data, prediction performance is increased while expensive data generation is minimized.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980749","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551464
Francisco J. Huertos, Beatriz Chicote, Manuel Masenlle, Mikel Ayuso
The Hyperconnected Architecture for High Cognitive Production Plants (HyperCOG) project aims at the process industry's complete digital transformation through an advanced Industrial Cyber-Physical Infrastructure. It is based on advanced technologies that allow a hyperconnected network of digital nodes to be created improving the classic automation hierarchy of communication layers. The nodes will collect data streams in real-time, offering cognitive sensing and information along with high performance computing capabilities making the process industry businesses solid in different scenarios. The system is validated in three fields of the process industry: steel, cement and chemical where optimization in the use of energy and raw materials is obtained, among other benefits.
{"title":"A Novel Architecture for Cyber-Physical Production Systems in Industry 4.0","authors":"Francisco J. Huertos, Beatriz Chicote, Manuel Masenlle, Mikel Ayuso","doi":"10.1109/CASE49439.2021.9551464","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551464","url":null,"abstract":"The Hyperconnected Architecture for High Cognitive Production Plants (HyperCOG) project aims at the process industry's complete digital transformation through an advanced Industrial Cyber-Physical Infrastructure. It is based on advanced technologies that allow a hyperconnected network of digital nodes to be created improving the classic automation hierarchy of communication layers. The nodes will collect data streams in real-time, offering cognitive sensing and information along with high performance computing capabilities making the process industry businesses solid in different scenarios. The system is validated in three fields of the process industry: steel, cement and chemical where optimization in the use of energy and raw materials is obtained, among other benefits.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128725362","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551593
S. Thuijsman, M. Reniers, Dennis Hendriks
Given a model of an uncontrolled system and a requirement specification, a supervisory controller can be synthesized so that the system under control adheres to the requirements. There are several ways in which informal behavioral safety requirements can be formalized, one of which is using mutual state exclusion requirements. In current implementations of the supervisor synthesis algorithm, synthesis may be inefficient when mutual state exclusion requirements are used. We propose a method to efficiently enforce these requirements in supervisor synthesis. We consider symbolic supervisor synthesis, where Binary Decision Diagrams are used to represent the system. The efficiency of the proposed method is evaluated by means of an industrial and academic case study.
{"title":"Efficiently enforcing mutual state exclusion requirements in symbolic supervisor synthesis","authors":"S. Thuijsman, M. Reniers, Dennis Hendriks","doi":"10.1109/CASE49439.2021.9551593","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551593","url":null,"abstract":"Given a model of an uncontrolled system and a requirement specification, a supervisory controller can be synthesized so that the system under control adheres to the requirements. There are several ways in which informal behavioral safety requirements can be formalized, one of which is using mutual state exclusion requirements. In current implementations of the supervisor synthesis algorithm, synthesis may be inefficient when mutual state exclusion requirements are used. We propose a method to efficiently enforce these requirements in supervisor synthesis. We consider symbolic supervisor synthesis, where Binary Decision Diagrams are used to represent the system. The efficiency of the proposed method is evaluated by means of an industrial and academic case study.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124802753","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}
The many-to-many hub location routing problem (MMHLRP) has been attracting interest as a way to improve the efficiency of long-distance delivery. Since more commodities are delivered to urban areas than to rural areas, it is important in actual business to improve the efficiency of deliveries in which the commodities are unevenly distributed. In this work, we propose a network design algorithm utilizing customized hierarchical clustering for MMHLRP. The results of numerical experiments show that the proposed algorithm reduces the total cost compared to the conventional gravity rule-based clustering algorithm when the commodities are distributed unevenly.
{"title":"Hierarchical Clustering-Based Network Design Algorithm for Many-To-Many Hub Location Routing Problem","authors":"Akane Seto, Kazuya Uyama, Junko Hosoda, Naoko Miyashita","doi":"10.1109/CASE49439.2021.9551550","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551550","url":null,"abstract":"The many-to-many hub location routing problem (MMHLRP) has been attracting interest as a way to improve the efficiency of long-distance delivery. Since more commodities are delivered to urban areas than to rural areas, it is important in actual business to improve the efficiency of deliveries in which the commodities are unevenly distributed. In this work, we propose a network design algorithm utilizing customized hierarchical clustering for MMHLRP. The results of numerical experiments show that the proposed algorithm reduces the total cost compared to the conventional gravity rule-based clustering algorithm when the commodities are distributed unevenly.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128304289","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551669
Antonios E. Gkikakis, D. Kanoulas, R. Featherstone
Highly-dynamic robotic systems, such as hopping robots, require light, computationally and energy efficient on-board units for control. This paper presents such a computational unit together with a software architecture for achieving high-performance behaviors, such as balancing and hopping. These demanding behaviors require accurate dynamic calculations, high-bandwidth control, and fast real-time state estimation. The proposed system consists of cheap and off-the-shelf electronics that are detailed in this paper. The effectiveness of the presented approach is validated on a balancing machine called Tippy, which is able to achieve fast tracking of command signals while balancing. The experimental results of this paper demonstrate that reliable real-time software for demanding high-performance robotic applications, which require fast control loops and intensive calculations, can be achieved with light, low cost and energy efficient components, which can empower the widespread use and experimentation of high-performance robots worldwide.
{"title":"Autonomous Real Time Architecture for High Performance Mobile Robots","authors":"Antonios E. Gkikakis, D. Kanoulas, R. Featherstone","doi":"10.1109/CASE49439.2021.9551669","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551669","url":null,"abstract":"Highly-dynamic robotic systems, such as hopping robots, require light, computationally and energy efficient on-board units for control. This paper presents such a computational unit together with a software architecture for achieving high-performance behaviors, such as balancing and hopping. These demanding behaviors require accurate dynamic calculations, high-bandwidth control, and fast real-time state estimation. The proposed system consists of cheap and off-the-shelf electronics that are detailed in this paper. The effectiveness of the presented approach is validated on a balancing machine called Tippy, which is able to achieve fast tracking of command signals while balancing. The experimental results of this paper demonstrate that reliable real-time software for demanding high-performance robotic applications, which require fast control loops and intensive calculations, can be achieved with light, low cost and energy efficient components, which can empower the widespread use and experimentation of high-performance robots worldwide.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126477641","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551503
Luis Fernando Nazari, E. Camponogara
The impacts caused by floods years from years affect the planet's surface, therefore, grows the relevance for flood prevention studies. This field aims to reduce, or even avoid, consequences resulting from these natural events. This work seeks to contribute to the area by proposing a model predictive control of dam floodgates in a hydrographic basin. The control strategy is based on hydrological forecasting models obtained with system identification techniques. The developed methodology is applied to a real-world case, the Itajaí River Basin in Brazil, to assess its effectiveness and illustrate the results.
{"title":"Embedding Forecasting Models in Predictive Control to Minimize Flood Effects in a Real-World Hydrographic System","authors":"Luis Fernando Nazari, E. Camponogara","doi":"10.1109/CASE49439.2021.9551503","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551503","url":null,"abstract":"The impacts caused by floods years from years affect the planet's surface, therefore, grows the relevance for flood prevention studies. This field aims to reduce, or even avoid, consequences resulting from these natural events. This work seeks to contribute to the area by proposing a model predictive control of dam floodgates in a hydrographic basin. The control strategy is based on hydrological forecasting models obtained with system identification techniques. The developed methodology is applied to a real-world case, the Itajaí River Basin in Brazil, to assess its effectiveness and illustrate the results.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126407953","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551582
Ayan Dutta, Vladimir Ufimtsev, T. Said, Inmo Jang, R. Eggen
In this paper, we study the problem of allocating multiple heterogeneous robots to tasks. Due to the limited capabilities of the robots, a task might need more than one robot to complete it. The fundamental problem of optimally partitioning the set of n robots into m disjoint coalitions for allocating to m tasks is proven to be NP-hard. To solve this computationally intractable problem, we propose a distributed hedonic game formulation, where each robot decides to join or not join a team based on the other robots allocated to that particular task. It uses a bipartite matching technique to establish an initial set of coalitions before letting the robots coordinate asynchronously and change teams if desired. Our proposed solution is proved to converge to a Nash-stable solution. Results show that our proposed approach is fast and handles asynchronous robot-to-robot communication while earning more utility (up to 23%) than an existing technique in the majority of the test cases.
{"title":"Distributed Hedonic Coalition Formation for Multi-Robot Task Allocation","authors":"Ayan Dutta, Vladimir Ufimtsev, T. Said, Inmo Jang, R. Eggen","doi":"10.1109/CASE49439.2021.9551582","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551582","url":null,"abstract":"In this paper, we study the problem of allocating multiple heterogeneous robots to tasks. Due to the limited capabilities of the robots, a task might need more than one robot to complete it. The fundamental problem of optimally partitioning the set of n robots into m disjoint coalitions for allocating to m tasks is proven to be NP-hard. To solve this computationally intractable problem, we propose a distributed hedonic game formulation, where each robot decides to join or not join a team based on the other robots allocated to that particular task. It uses a bipartite matching technique to establish an initial set of coalitions before letting the robots coordinate asynchronously and change teams if desired. Our proposed solution is proved to converge to a Nash-stable solution. Results show that our proposed approach is fast and handles asynchronous robot-to-robot communication while earning more utility (up to 23%) than an existing technique in the majority of the test cases.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127653150","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：