Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442229
Fan Zhang, V. Lau, Gong Zhang
In this paper, we consider a MIMO cyber physical system (CPS) in which a sensor amplifies and forwards the observed MIMO plant state to a remote controller via a MIMO fading channel. We focus on the MIMO amplify-and-forward (AF) precoding design at the sensor to minimize a weighted average state estimation error at the remote controller subject to an average communication power gain constraint of the sensor. The MIMO AF precoding design is formulated as an infinite horizon average cost Markov decision process (MDP). To deal with the curse of dimensionality associated with the MDP, we propose a novel continuous-time perturbation approach and derive an asymptotically optimal closed-form priority function for the MDP. Based on this, we derive a closed-form first-order optimal dynamic MIMO AF precoding solution, and the solution has an event-driven control structure. Specifically, the sensor activates the strongest eigenchannel to deliver a dynamically weighted combination of the plant states to the controller when the accumulated state estimation error exceeds a dynamic threshold. We further establish technical conditions for ensuring the stability of the MIMO CPS.
{"title":"An Event-Driven MIMO Amplify-and-Forward Precoding for Cyber Physical Systems","authors":"Fan Zhang, V. Lau, Gong Zhang","doi":"10.1109/INDIN45582.2020.9442229","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442229","url":null,"abstract":"In this paper, we consider a MIMO cyber physical system (CPS) in which a sensor amplifies and forwards the observed MIMO plant state to a remote controller via a MIMO fading channel. We focus on the MIMO amplify-and-forward (AF) precoding design at the sensor to minimize a weighted average state estimation error at the remote controller subject to an average communication power gain constraint of the sensor. The MIMO AF precoding design is formulated as an infinite horizon average cost Markov decision process (MDP). To deal with the curse of dimensionality associated with the MDP, we propose a novel continuous-time perturbation approach and derive an asymptotically optimal closed-form priority function for the MDP. Based on this, we derive a closed-form first-order optimal dynamic MIMO AF precoding solution, and the solution has an event-driven control structure. Specifically, the sensor activates the strongest eigenchannel to deliver a dynamically weighted combination of the plant states to the controller when the accumulated state estimation error exceeds a dynamic threshold. We further establish technical conditions for ensuring the stability of the MIMO CPS.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132824402","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 : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442243
Guoqing Zhang, Wei Yu, Jiqiang Li
This note focuses on the application of the event-triggered mechanism into the formation control system. For this purpose, a novel fleet control model is established in the Cartesian coordinate system. Through this structure, a model-based event-triggered control (ETC) is designed by utilizing the radial basic function neural networks (RBF NNs) and the minimum learning parameter (MLP) technique. Thus, the continuous acquisition of the formation state does not take longer, and the communication load of the resource-limited fleet is largely reduced. In addition, the semi-global uniformly ultimately bounded (SGUUB) of all signals are proved by the Lyapunov candidate function. And the corresponding simulation results can be used to verify the effectiveness and robustness of the proposed control scheme.
{"title":"Robust Adaptive Formation Control of USVs with the Event-Triggered Mechanism","authors":"Guoqing Zhang, Wei Yu, Jiqiang Li","doi":"10.1109/INDIN45582.2020.9442243","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442243","url":null,"abstract":"This note focuses on the application of the event-triggered mechanism into the formation control system. For this purpose, a novel fleet control model is established in the Cartesian coordinate system. Through this structure, a model-based event-triggered control (ETC) is designed by utilizing the radial basic function neural networks (RBF NNs) and the minimum learning parameter (MLP) technique. Thus, the continuous acquisition of the formation state does not take longer, and the communication load of the resource-limited fleet is largely reduced. In addition, the semi-global uniformly ultimately bounded (SGUUB) of all signals are proved by the Lyapunov candidate function. And the corresponding simulation results can be used to verify the effectiveness and robustness of the proposed control scheme.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130828027","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 : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442249
S. Wein, C. Fimmers, S. Storms, C. Brecher, Marlene Gebhard, M. Schluse, J. Roßmann
In many sectors of the economy, modularization and linkage of distributed structures has significantly increased flexibility. Thereby, individual customer demands and changing conditions can be adressed. While this concept is often applied to closed systems whose control is coordinated by central components or can follow defined processes, concepts for the implementation in decentralized and open systems are missing. Such processes can be found in different domains of application, such as logistics, the construction industry or forestry. The use of active asset administration shells for modularized objects enables elements of a system or products to take over parts of the control and coordination. Production systems are enabled to act autonomously. A service infrastructure provides the necessary directory services to provide the asset administration shells with an entry point and to offer suitable routing services. The overall structure is demonstrated and evaluated using a representative example from forestry.
{"title":"Embedding Active Asset Administration Shells in the Internet of Things using the Smart Systems Service Infrastructure","authors":"S. Wein, C. Fimmers, S. Storms, C. Brecher, Marlene Gebhard, M. Schluse, J. Roßmann","doi":"10.1109/INDIN45582.2020.9442249","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442249","url":null,"abstract":"In many sectors of the economy, modularization and linkage of distributed structures has significantly increased flexibility. Thereby, individual customer demands and changing conditions can be adressed. While this concept is often applied to closed systems whose control is coordinated by central components or can follow defined processes, concepts for the implementation in decentralized and open systems are missing. Such processes can be found in different domains of application, such as logistics, the construction industry or forestry. The use of active asset administration shells for modularized objects enables elements of a system or products to take over parts of the control and coordination. Production systems are enabled to act autonomously. A service infrastructure provides the necessary directory services to provide the asset administration shells with an entry point and to offer suitable routing services. The overall structure is demonstrated and evaluated using a representative example from forestry.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132388675","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 : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442161
C. Alexakos, Andreas Komninos, C. Anagnostopoulos, G. Kalogeras, A. Kalogeras
The manufacturing environment undergoes a disruptive evolution due to the Fourth Industrial Revolution driven by the Industrial Internet of Things and Cyber Physical Systems technologies. This evolution is applicable to further sectors comprising similar requirements, involving large numbers of devices that need to interoperate, exchange their data and be controlled. Integration at the manufacturing environment remains a challenge taking into account the diversity of equipment / devices, the existence of legacy systems, and the need to integrate IoT devices participating in the production paradigm. This paper presents an AutomationML based approach for this integration, modeling the industrial manufacturing environment, and enabling its emulation through a 3D Virtual Environment.
{"title":"IoT Integration in the Manufacturing Environment Towards Industry 4.0 Applications","authors":"C. Alexakos, Andreas Komninos, C. Anagnostopoulos, G. Kalogeras, A. Kalogeras","doi":"10.1109/INDIN45582.2020.9442161","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442161","url":null,"abstract":"The manufacturing environment undergoes a disruptive evolution due to the Fourth Industrial Revolution driven by the Industrial Internet of Things and Cyber Physical Systems technologies. This evolution is applicable to further sectors comprising similar requirements, involving large numbers of devices that need to interoperate, exchange their data and be controlled. Integration at the manufacturing environment remains a challenge taking into account the diversity of equipment / devices, the existence of legacy systems, and the need to integrate IoT devices participating in the production paradigm. This paper presents an AutomationML based approach for this integration, modeling the industrial manufacturing environment, and enabling its emulation through a 3D Virtual Environment.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125125758","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 : 2020-07-20DOI: 10.1109/indin45582.2020.9442345
{"title":"Efficient Multimedia Sensing and Computing for Smart City","authors":"","doi":"10.1109/indin45582.2020.9442345","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442345","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133067","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 : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442158
Zehao He, Liangcai Cao
A spatial bandwidth product expansion method based on diffraction superposition is proposed. Different from time-multiplexing techniques and spatial light modulator array based techniques, the proposed method divides the target 3D scene into multiple parts according to their positions. Computer-generated holograms (CGHs) are calculated from corresponding parts. Each part of the 3D scene is contributed by the corresponding CGH. By superposing reconstructions of all CGHs, the entire 3D scene can be perceived. The evaluation of peak signal to noise ratio has verified the effectiveness of the proposed method.
{"title":"Spatial bandwidth product expansion method by diffraction superposition for holographic display","authors":"Zehao He, Liangcai Cao","doi":"10.1109/INDIN45582.2020.9442158","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442158","url":null,"abstract":"A spatial bandwidth product expansion method based on diffraction superposition is proposed. Different from time-multiplexing techniques and spatial light modulator array based techniques, the proposed method divides the target 3D scene into multiple parts according to their positions. Computer-generated holograms (CGHs) are calculated from corresponding parts. Each part of the 3D scene is contributed by the corresponding CGH. By superposing reconstructions of all CGHs, the entire 3D scene can be perceived. The evaluation of peak signal to noise ratio has verified the effectiveness of the proposed method.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123785035","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 : 2020-07-20DOI: 10.1109/indin45582.2020.9442175
{"title":"Informatics Methods in Internet of Things enabled Healthcare","authors":"","doi":"10.1109/indin45582.2020.9442175","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442175","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127359165","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 housing market is increasing huge, predicting housing prices is not only important for a business issue, but also for people. However, housing price fluctuations have a lot of influencing factors. Also, there is a non-linear relationship between housing prices and housing factors. Most econometric or statistical models cannot capture non-linear relationships yet. Therefore, we propose housing price prediction models based on deep learning methods, which can capture non-linear relationships. In this work, we construct a dataset, including the housing attributes data and macroeconomic data in Taiwan from January 2013 to December 2018. The housing attributes data includes two types of housing transactions, which are “land + building” (Type1) and “land + building + park” (Type2). Macroeconomic data includes housing investment demand ratio, owner-occupier housing ratio, housing price to income ratio, housing loan burden ratio, and housing bargaining space ratio. Then, this dataset is utilized to evaluate the prediction methods based on deep learning algorithms BPNN and CNN to predict housing prices. Experimental results show that CNN with housing features has the best prediction effect. This study can be used to develop targetted interventions aimed at the housing market.
{"title":"Housing prices prediction with deep learning: an application for the real estate market in Taiwan","authors":"Choujun Zhan, Zeqiong Wu, Yonglin Liu, Zefeng Xie, Wangling Chen","doi":"10.1109/INDIN45582.2020.9442244","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442244","url":null,"abstract":"The housing market is increasing huge, predicting housing prices is not only important for a business issue, but also for people. However, housing price fluctuations have a lot of influencing factors. Also, there is a non-linear relationship between housing prices and housing factors. Most econometric or statistical models cannot capture non-linear relationships yet. Therefore, we propose housing price prediction models based on deep learning methods, which can capture non-linear relationships. In this work, we construct a dataset, including the housing attributes data and macroeconomic data in Taiwan from January 2013 to December 2018. The housing attributes data includes two types of housing transactions, which are “land + building” (Type1) and “land + building + park” (Type2). Macroeconomic data includes housing investment demand ratio, owner-occupier housing ratio, housing price to income ratio, housing loan burden ratio, and housing bargaining space ratio. Then, this dataset is utilized to evaluate the prediction methods based on deep learning algorithms BPNN and CNN to predict housing prices. Experimental results show that CNN with housing features has the best prediction effect. This study can be used to develop targetted interventions aimed at the housing market.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366565","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 : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442148
Cheng-Geng Huang, Hongzhong Huang, Yanfeng Li, W. Peng
This article develops a generalized deep convolutional neural network (DCNN)-Bootstrap-based prognostic approach for remaining useful life (RUL) prediction of rolling bearing. The proposed architecture includes two main parts: first, a hybrid DCNN model is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and predict RUL of bearing; second, the proposed hybrid DCNN model is embedded into the Bootstrap-based implementation framework for quantification of RUL prediction interval. Unlike other deep learning (DL)-based prognostic approaches, the proposed DCNN-Bootstrap method has two innovative features: first, both time series-based and image-based features of bearings, which can multi-dimensionally characterize the degradation of bearing, are comprehensively leveraged by the proposed hybrid DCNN model; second, the RUL prediction interval can be effectively quantified without relying on any bearing's physical and statistical prior information recurring to Bootstrap implementation paradigm. Moreover, the proposed approach is experimentally validated with a case study on rolling element bearings, and comparisons with other popular techniques widely employed in this field are also presented.
{"title":"Fault prognosis using deep convolutional neural network and bootstrap-based method","authors":"Cheng-Geng Huang, Hongzhong Huang, Yanfeng Li, W. Peng","doi":"10.1109/INDIN45582.2020.9442148","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442148","url":null,"abstract":"This article develops a generalized deep convolutional neural network (DCNN)-Bootstrap-based prognostic approach for remaining useful life (RUL) prediction of rolling bearing. The proposed architecture includes two main parts: first, a hybrid DCNN model is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and predict RUL of bearing; second, the proposed hybrid DCNN model is embedded into the Bootstrap-based implementation framework for quantification of RUL prediction interval. Unlike other deep learning (DL)-based prognostic approaches, the proposed DCNN-Bootstrap method has two innovative features: first, both time series-based and image-based features of bearings, which can multi-dimensionally characterize the degradation of bearing, are comprehensively leveraged by the proposed hybrid DCNN model; second, the RUL prediction interval can be effectively quantified without relying on any bearing's physical and statistical prior information recurring to Bootstrap implementation paradigm. Moreover, the proposed approach is experimentally validated with a case study on rolling element bearings, and comparisons with other popular techniques widely employed in this field are also presented.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125436196","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 : 2020-07-20DOI: 10.1109/indin45582.2020.9442120
{"title":"Resilience, Reliability, and Security of Smart Mobility of Connected Automated Vehicles","authors":"","doi":"10.1109/indin45582.2020.9442120","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442120","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116765081","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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