Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832493
Zhang jiangyi, C. Danhong, Y. yu
Internet of things technology is to collect any object or process that needs monitoring, connection and interaction in real time through various information sensing equipment and technology. At present, in the process of bee breeding, it is the most difficult problem to understand the internal situation of the beehive under the premise of least disturbance to the bee colony. The application of Internet of things technology can solve this problem. This paper discusses the design of intelligent beehive structure, and expounds the application of iot technology in beekeeping industry from two aspects: intelligent beehive and intelligent factory. This article aims to help the beekeeping industry reduce employment, increase production efficiency and improve the quality of honey.
{"title":"Design of Intelligent Hive and Intelligent Bee Farm Based on Internet of Things Technology","authors":"Zhang jiangyi, C. Danhong, Y. yu","doi":"10.1109/CCDC.2019.8832493","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832493","url":null,"abstract":"Internet of things technology is to collect any object or process that needs monitoring, connection and interaction in real time through various information sensing equipment and technology. At present, in the process of bee breeding, it is the most difficult problem to understand the internal situation of the beehive under the premise of least disturbance to the bee colony. The application of Internet of things technology can solve this problem. This paper discusses the design of intelligent beehive structure, and expounds the application of iot technology in beekeeping industry from two aspects: intelligent beehive and intelligent factory. This article aims to help the beekeeping industry reduce employment, increase production efficiency and improve the quality of honey.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117238391","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8832719
Yanni Wan, Man Li
This paper formulates a dynamic economic load dispatch (ELD) problem while considering the demand response (DR) in smart grid. To solve the novel ELD problem, an equivalent SWMP is presented first, and then a distributed Alternating Direction Method of Multipliers (ADMM) algorithm utilizing the average consensus protocol is proposed. In particular, the impact of the transmission power losses is also discussed. A sufficient condition that can ensure the convergence of the distributed algorithm is derived along with theoretical analysis. The distributed ADMM algorithm maximizes the social welfare and guarantees the instantaneous power balance. Case studies on the IEEE-39 bus system demonstrate the performance of the proposed algorithm.
{"title":"A Distributed ADMM Algorithm for Economic Load Dispatch Considering Demand Response","authors":"Yanni Wan, Man Li","doi":"10.1109/CCDC.2019.8832719","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832719","url":null,"abstract":"This paper formulates a dynamic economic load dispatch (ELD) problem while considering the demand response (DR) in smart grid. To solve the novel ELD problem, an equivalent SWMP is presented first, and then a distributed Alternating Direction Method of Multipliers (ADMM) algorithm utilizing the average consensus protocol is proposed. In particular, the impact of the transmission power losses is also discussed. A sufficient condition that can ensure the convergence of the distributed algorithm is derived along with theoretical analysis. The distributed ADMM algorithm maximizes the social welfare and guarantees the instantaneous power balance. Case studies on the IEEE-39 bus system demonstrate the performance of the proposed algorithm.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115755983","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}
This paper addresses the tracking problem for a class of nonlinear switched system with quantized input via adaptive neural method. The system is described by a set of nonlinear functions which satisfy Lipschitz conditions. A switched nonlinear observer is set up to estimate those unmeasurable state variables. Convex combination method is utilized to determine the observer gain matrix so that the effect from those nonlinear terms can be well compensated for. Then observer-based backstepping method is adopted to construct the quantized input controller. It is also proven that the tracking error converges to a small neighbourhood around the original point under the action of the suggested controller. Finally, a simulation example is studied to test the efficacy of the suggested control strategies.
{"title":"Observer-based adaptive control for switched nonlinear systems with input quantization","authors":"Zhiliang Liu, Yun Shang, Bing Chen, Chong Lin, Xin Zhao","doi":"10.1109/CCDC.2019.8832432","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832432","url":null,"abstract":"This paper addresses the tracking problem for a class of nonlinear switched system with quantized input via adaptive neural method. The system is described by a set of nonlinear functions which satisfy Lipschitz conditions. A switched nonlinear observer is set up to estimate those unmeasurable state variables. Convex combination method is utilized to determine the observer gain matrix so that the effect from those nonlinear terms can be well compensated for. Then observer-based backstepping method is adopted to construct the quantized input controller. It is also proven that the tracking error converges to a small neighbourhood around the original point under the action of the suggested controller. Finally, a simulation example is studied to test the efficacy of the suggested control strategies.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130642637","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8833054
Xiaoqian Wei, Jianying Yang, Xiangru Fan
In this paper, a distributed adaptive guidance law for multiple missiles simultaneous attack problem is investigated. The new guidance law consists of two parts. The first part focuses on consensus achieving of the system states, and the second part optimizes the objective function to get the optimal value. Adaptive parameters consisting of local state errors terms are utilized in the first part of the guidance law, while the first derivative terms, partial derivative terms and second derivative terms of the objective function are employed in the second part of the guidance law. The simulation results validated the practicability of the proposed improved guidance law.
{"title":"Optimal Design of Distributed Adaptive Guidance Laws for Simultaneous Attack","authors":"Xiaoqian Wei, Jianying Yang, Xiangru Fan","doi":"10.1109/CCDC.2019.8833054","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833054","url":null,"abstract":"In this paper, a distributed adaptive guidance law for multiple missiles simultaneous attack problem is investigated. The new guidance law consists of two parts. The first part focuses on consensus achieving of the system states, and the second part optimizes the objective function to get the optimal value. Adaptive parameters consisting of local state errors terms are utilized in the first part of the guidance law, while the first derivative terms, partial derivative terms and second derivative terms of the objective function are employed in the second part of the guidance law. The simulation results validated the practicability of the proposed improved guidance law.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130938080","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8832398
Jialing Lu, Shuming Tang, Jinqiao Wang, Haibing Zhu, Yunkuan Wang
Vehicle and pedestrian detection is significant in autonomous driving. It provides information for path planning, lane selection, pedestrian and vehicle tracking, pedestrian behavior prediction, etc. In recent years, the state-of-the-art object detection algorithms have been emerged on the base of deep convolutional neural networks, which can get higher accuracy and efficiency detection results than traditional vision detection algorithms. In this paper, we first introduce and summarize some state-of-the-date object detection algorithms based of deep convolutional neural networks and the improvement ideas of these algorithms. Their frameworks are extracted. Then, we choose several different algorithms and analyze their running results on challenging datasets, Pascal VOC and KITTI. Next, we analyze the current detection challenges as well as their solutions. Finally, we provide insights into use in autonomous driving, such as vehicle and pedestrian detection and driving control.
{"title":"A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving","authors":"Jialing Lu, Shuming Tang, Jinqiao Wang, Haibing Zhu, Yunkuan Wang","doi":"10.1109/CCDC.2019.8832398","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832398","url":null,"abstract":"Vehicle and pedestrian detection is significant in autonomous driving. It provides information for path planning, lane selection, pedestrian and vehicle tracking, pedestrian behavior prediction, etc. In recent years, the state-of-the-art object detection algorithms have been emerged on the base of deep convolutional neural networks, which can get higher accuracy and efficiency detection results than traditional vision detection algorithms. In this paper, we first introduce and summarize some state-of-the-date object detection algorithms based of deep convolutional neural networks and the improvement ideas of these algorithms. Their frameworks are extracted. Then, we choose several different algorithms and analyze their running results on challenging datasets, Pascal VOC and KITTI. Next, we analyze the current detection challenges as well as their solutions. Finally, we provide insights into use in autonomous driving, such as vehicle and pedestrian detection and driving control.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128526334","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8832836
Jian-jiang Cui, X. Jia, Pengfei Hou, Yaxu Hu, X. Lei
In the process of a thermal power generation, the prediction of the amount of NOx in contaminant has a positive effect on the elimination of NOx. In this paper, the nonlinear moving average (MA) model with time-delays is used as the prediction model to predict the process of NOx generation. Firstly, the correlation coefficient method is used to divide all variables affecting the NOx generation into several categories, and the variable whose correlation coefficient with NOx is the biggest in each category is selected as a main variable. Then BP neural network method is used to select the three variables with the greatest influence among the main variables as the input variables in the prediction model. Next, the correlation coefficient method is used to determine the time-delay parameters of the three input variables in the prediction model. What’s more, the least square method is used to estimate other parameters of the MA model to obtain a prediction model of a NOx generation process. Finally, the practical data from the generation process of a power plant are used to verify the effectiveness of the proposed prediction method.
{"title":"Prediction of NOx Generation Process Based on A Nonlinear MA model","authors":"Jian-jiang Cui, X. Jia, Pengfei Hou, Yaxu Hu, X. Lei","doi":"10.1109/CCDC.2019.8832836","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832836","url":null,"abstract":"In the process of a thermal power generation, the prediction of the amount of NOx in contaminant has a positive effect on the elimination of NOx. In this paper, the nonlinear moving average (MA) model with time-delays is used as the prediction model to predict the process of NOx generation. Firstly, the correlation coefficient method is used to divide all variables affecting the NOx generation into several categories, and the variable whose correlation coefficient with NOx is the biggest in each category is selected as a main variable. Then BP neural network method is used to select the three variables with the greatest influence among the main variables as the input variables in the prediction model. Next, the correlation coefficient method is used to determine the time-delay parameters of the three input variables in the prediction model. What’s more, the least square method is used to estimate other parameters of the MA model to obtain a prediction model of a NOx generation process. Finally, the practical data from the generation process of a power plant are used to verify the effectiveness of the proposed prediction method.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720604","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8833235
Yanan Zhu, Wenwu Yu, G. Wen, Duxin Chen
This paper studies a resource allocation problem subject to the coupling resource constraint over a strongly connected and weight-unbalanced digraph, where the global cost function is composed of a sum of the agents’s local cost functions. To solve the problem in a distributed way, we design a continuous-time algorithm by injecting a graph balancing technique into a primal-dual gradient flow algorithm. We show that the optimal variable generated by the proposed algorithm asymptotically converges to the optimal solution when the local cost functions are strongly convex and and their gradients satisfy Lipschitz conditions. A numerical simulation verifies the theoretical result.
{"title":"Continuous-time algorithm for distributed resource allocation over a weight-unbalanced digraph","authors":"Yanan Zhu, Wenwu Yu, G. Wen, Duxin Chen","doi":"10.1109/CCDC.2019.8833235","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833235","url":null,"abstract":"This paper studies a resource allocation problem subject to the coupling resource constraint over a strongly connected and weight-unbalanced digraph, where the global cost function is composed of a sum of the agents’s local cost functions. To solve the problem in a distributed way, we design a continuous-time algorithm by injecting a graph balancing technique into a primal-dual gradient flow algorithm. We show that the optimal variable generated by the proposed algorithm asymptotically converges to the optimal solution when the local cost functions are strongly convex and and their gradients satisfy Lipschitz conditions. A numerical simulation verifies the theoretical result.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133754166","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8833368
Lin Pan, Axel Dias, Jiying Wang
New technologies offer new possibilities to fight against internal traffic congestion in seaports. Only few researches have exploited this subject so far.Our study focuses on the internal traffic of vehicles. Indeed, congestion results in many little actions which lead to bigger consequences. In order to optimize the internal network, a reflexion on traffic in the stock zone will be done under schemes of Kruskal’s Theorem, which will provide us the information needed about the potential effect of autonomous vehicles on the traffic as well as their potential power in reducing the risk of congestionWith this theorem, it is essential to consider a good ponderation of unexpected events that might be encountered on roads.Moreover, a new theory of management helping to limit congestions from social problems will be highlighted.
{"title":"Internal traffic congestion: A new application of Kruskal’s Theorem","authors":"Lin Pan, Axel Dias, Jiying Wang","doi":"10.1109/CCDC.2019.8833368","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833368","url":null,"abstract":"New technologies offer new possibilities to fight against internal traffic congestion in seaports. Only few researches have exploited this subject so far.Our study focuses on the internal traffic of vehicles. Indeed, congestion results in many little actions which lead to bigger consequences. In order to optimize the internal network, a reflexion on traffic in the stock zone will be done under schemes of Kruskal’s Theorem, which will provide us the information needed about the potential effect of autonomous vehicles on the traffic as well as their potential power in reducing the risk of congestionWith this theorem, it is essential to consider a good ponderation of unexpected events that might be encountered on roads.Moreover, a new theory of management helping to limit congestions from social problems will be highlighted.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"11 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133041459","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8832519
Binbin Chi, Longhang Guo
There are many sensors used to monitor the quality of the effluent during the wastewater treatment process. So the normal monitoring of the sensor is critical to wastewater treatment. In this article, the proposed sensor fault diagnosis method is based on fault diagnosis of interval prediction which using RBF neural network with set membership estimation. After some input and output data of the WWTP are obtain, an interval containing the actual output of the system without a fault can be easily predicted. If the sensor measured is out of the predicted interval, it can be determined that a fault has occurred. This paper also establishes two independent interval diagnosis models to further make sure whether the senor is faulty or the system is faulty. The results demonstrate that the proposed sensor fault diagnosis method is effective and useful.
{"title":"Wastewater treatment sensor fault detection using RBF neural network with set membership estimation","authors":"Binbin Chi, Longhang Guo","doi":"10.1109/CCDC.2019.8832519","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832519","url":null,"abstract":"There are many sensors used to monitor the quality of the effluent during the wastewater treatment process. So the normal monitoring of the sensor is critical to wastewater treatment. In this article, the proposed sensor fault diagnosis method is based on fault diagnosis of interval prediction which using RBF neural network with set membership estimation. After some input and output data of the WWTP are obtain, an interval containing the actual output of the system without a fault can be easily predicted. If the sensor measured is out of the predicted interval, it can be determined that a fault has occurred. This paper also establishes two independent interval diagnosis models to further make sure whether the senor is faulty or the system is faulty. The results demonstrate that the proposed sensor fault diagnosis method is effective and useful.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124103435","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 : 2019-06-03DOI: 10.1109/CCDC.2019.8833364
Lei Zhang, Haijiang Zhu, Tengfei Yang
Depth learning has been applied extensively in various fields of computer vision in recent year. Although a CNN-based network structure can obtain the ideal results in many image recognition, it is rarely used to classify the ultrasonic images of the fatty liver. This is principally because the fatty liver ultrasonic image has no obvious texture features and the low resolution. In this paper, we design the network structure for the characteristics of B-mode ultrasonic images, and utilize the CNN-based model to classify fatty liver ultrasound images. The experimental results show that we achieve a satisfactory classification effect through applying the proposed CNN network and this method is better than the traditional method for classifying fatty liver ultrasonic images.
{"title":"Deep Neural Networks for fatty liver ultrasound images classification","authors":"Lei Zhang, Haijiang Zhu, Tengfei Yang","doi":"10.1109/CCDC.2019.8833364","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833364","url":null,"abstract":"Depth learning has been applied extensively in various fields of computer vision in recent year. Although a CNN-based network structure can obtain the ideal results in many image recognition, it is rarely used to classify the ultrasonic images of the fatty liver. This is principally because the fatty liver ultrasonic image has no obvious texture features and the low resolution. In this paper, we design the network structure for the characteristics of B-mode ultrasonic images, and utilize the CNN-based model to classify fatty liver ultrasound images. The experimental results show that we achieve a satisfactory classification effect through applying the proposed CNN network and this method is better than the traditional method for classifying fatty liver ultrasonic images.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129212564","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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