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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是151-160 订阅

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Sequential Graphical Lasso for Fault Detection and Isolation 7

Sequential Graphical Lasso for Fault Detection and Isolation

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Liu, Yi Zeng, Jiusun Xie, Lei Luo, Shihua Su, Hongye Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310627 Zhejiang Peoples R China China Jiliang Univ Coll Metrol & Measurement Engn Hangzhou 310018 Zhejiang Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9781538626184

this article proposes a sequential graphical Lasso based approach for monitoring of complex industrial systems. the graphical Lasso is a widely used algorithm to estimate the precision matrix (inverse covariance matrix), which encodes the conditional relationship between pairs of variables given other entities. Based on the estimated precision matrix, a graphical model can be constructed to represent the structured correlation information between process variables. the proposed approach utilizes the graphical model to localize anomalous variables. Different from the conventional graphical Lasso approach, the proposed method considers an additional fusseed lasso term and a similarity term in the objective function and the optimization problem can be solved by the alternative direction method of multiplier (ADMM). Using a moving window approach, the proposed method generates a sequence of sparse Gaussian graphs and a new monitoring statistic based on penalized likelihood ratio and matrix norm is constructed. Once a fault is detected, the problem of fault isolation becomes a graph matching problem and a fault score index is calculated for each variable. the validity of proposed method in fault detection and isolation is illustrated by a typical fault observed in the Tennessee Eastman (TE) process.

关键词： Graphical Lasso Penalized Likelihood Ratio Fault Score Moving Window

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A Novel System Decomposition Method Based on Pearson Correlation and Graph theory 7

A Novel System Decomposition Method Based on Pearson Correla...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Jin, Jing Zhang, Shu Li, Lijuan Zou, Tao Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Jiangsu Peoples R China Chinese Acad Sci Shenyang Inst Automat Ind Control Networks & Syst Dept Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538626184

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In the traditional system decomposition methods based on graph theory, the weight on each edge of the graph is set by state space equation to reflect the mutual influence of variables in the system. But in the actual industrial process, the acquisition of state space equation is more difficult. In this paper, a system decomposition method based on Pearson correlation coefficient and graph theory is proposed to avoid the use of state space equations. At first, a directed graph is established to represent the actual process of the industrial system and the weights on corresponding edges in the directed graph are set by the Pearson correlation coefficients between two nodes connected by these edges. then the directed graph is decomposed into several initial subgraphs and the subgraphs will be fused according to a certain rule. Here, a fusion index is defined to select the optimal fusion results in each fusion process. After each fusion process, the termination condition is required to determine whether to continue the next round of fusion process. When the fusion process ends, the subsets obtained at this time are the results of the system decomposition. When the system decomposition is finished, the online subsystems modeling will be carried out by RPLS algorithm. Finally, the proposed algorithm is applied in the Tennessee Eastman process to verify the validity.

关键词： System decomposition Pearson correlation graph theory

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Subject Features and Hash Codes for Multi-label Image Retrieval 7

Subject Features and Hash Codes for Multi-label Image Retrie...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Xiong, Changzhen Shan, Yanmei North China Univ Technol Beijing Key Lab Urban Intelligent Control Beijing 100144 Peoples R China

ISBN: (纸本)9781538626184

In order to solve the problem that existing hashing methods cannot describe multi-label images accurately, this paper proposes a multi-label image retrieval method based on hashing codes and main part detection of an image. this method adds a fully connected layer after the region proposal network (RPN) in Faster-RCNN to learn the binary hash codes of the detected region proposals. through the network, the region proposals in the query image and their class probabilities, spatial positions and hash codes can be obtained. then, the main instance of query and its hash codes are extracted according to the class probability and the spatial relations of the proposals. Finally, the images with the same class label of the query are retrieved from the database and ranked by the Hamming distance of hash codes. the top N images with the highest score are returned as the final results. Experimental verification is carried out on 2000 query images selected randomly from VCO2007 dataset, the results show that the NDCG(normalized discounted cumulative gain) is 0.8967 and the ACG(average cumulative gain) is 0.7970 when the top 1000 images selected in retrieval result. Compared with IAH(Instance aware hashing) method, they respectively increased by 1.02 and 3.04 percent. the proposed method has a good retrieval effect on the multi-label query images.

关键词： Multi-label Image retrieval Hashing Object detection

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Wind Turbine Unit Power Prediction Based on Wavelet Neural Network Optimized by Brain Storm Optimization Algorithm 7

Wind Turbine Unit Power Prediction Based on Wavelet Neural N...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Guo, Qiang Xue, Zhiwei Zhang, Longying Lu, Xiaohui Yin, Yue Huang, Congzhi State Grid Shanxi Elect Power Res Inst Taiyuan 030001 Shanxi Peoples R China State Grid Shanxi Elect Power Co Taiyuan 030001 Shanxi Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781538626184

the construction of the wind power curve is of great significance to the wind turbines. Based on the accurate model of wind power curve developed, it can be employed for the wind power prediction and fault diagnosis. Normally, the wind turbine manufacturer provides the standard wind power curve, which is measured at standard conditions. However, the actual situation of the wind turbine is different from the standard state and is constantly changing. the wind power curve needs to be modified. the wind power curve essentially establishes a functional relationship between wind speed and active power. the neural networks have the ability to approximate function. In this paper, based on the actual data from a wind farm in Shanxi Province, the wavelet neural network is used to model the wind power curve, and the initial parameters are determined by using the brain storm optimization algorithm. the probability of the non-convergence in the learning process of the wavelet neural network is greatly reduced. Extensive experimental results are presented to validate the effectiveness of the proposed approach.

关键词： Wind power curve Wavelet neural network Brain storm optimization algorithm Power prediction

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the Equivalence Induced by Unifying Fitness Mappings in Frequency-Dependent Moran Process 7

The Equivalence Induced by Unifying Fitness Mappings in Freq...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Huang, Feng Chen, Xiaojie Wang, Long Peking Univ Coll Engn Ctr Syst & Control Beijing 100871 Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781538626184

As one of the most prevalent microscopic mechanisms, frequency-dependent Moran process is widely adopted to model the traits 'evolution of agents in a well-mixed population, where an individual is chosen for reproduction proportional to its fitness. Using a pair of specific fitness mappings, it has been demonstrated that the Moran process leads to identical evolutionary outcomes under weak selection. But does the equivalent relation hold for any two fitness functions? In this paper, by introducing a general mapping that fitness is an arbitrary non-negative function of payoff and selection intensity, the fitness mappings is unified. And accordingly we investigate that whether the unifying fitness mappings can lead to equivalent evolutionary outcomes in frequency-dependent Moran process. By calculating fixation probabilities and fixation times under weak selection, we find that the effect of different fitness mappings on these two quantities just embody in a constant factor under mild conditions. In particular, this constant factor can be absorbed into the selection intensity by proper rescaling or by making the payoff matrix change a scale. thus, in spite of the scaling factor, any two fitness functions are equivalent under weak selection for frequency-dependent Moran process.

关键词： Stochastic game dynamics Frequency-dependent Moran process Fitness function

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the Methods of Extracting Spatiotemporal Characteristics of Travel Based on Mobile Phone data 7

The Methods of Extracting Spatiotemporal Characteristics of ...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Tan, Jiyuan Dong, Luxi Gao, Jian Guo, Weiwei Li, Zhengxi North China Univ Technol Beijing Key Lab Urban Intelligent Traff Control T Beijing 100144 Peoples R China Minist Transport Beijing Res Inst Highway Beijing Peoples R China

ISBN: (纸本)9781538626184

With the rapid development of urbanization in China, the problem of traffic congestion is mainly due to the rapid increase in traffic demand. Compared with a variety of travel behaviors and origin-destination spatiotemporal distribution, which is helpful for us to explore the cause of traffic congestion. Traditionally, travel surveys are time consuming and huge economic investment. the accuracy of the results were existed large errors. In recent years, data acquisition techniques and storage capabilities are developed rapidly, more and more human travel related data have been collected. these "Big data" is brought both opportunities and challenges for extracting valid travel information. In this paper, the different trajectories of travel mode are match with traffic analysis zones through using geography information system. And then stay points are identified by clustering spatiotemporal characteristics of trajectories. Moreover, the OD matrix is established by different stay regions. the indices of travel and OD desire lines are chosen to analyze travel behaviors. Meanwhile, the OD volume distribution in rush hours are used to explain traffic demand in different urban area. the findings could be helped government make the appropriate decision of urban traffic system and made residents the better daily travel planning, which has significant reference value.

关键词： Travel behaviors trajectories traffic demand od matrix

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data-driven Sliding-Mode Decoupling control with Time-Varying Sliding Surface for Nonlinear Discrete-Time Processes 7

Data-Driven Sliding-Mode Decoupling Control with Time-Varyin...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Weng, Yongpeng Wang, Ning Li, Shaowu Liu, Xinming Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China Hubei Univ Nationalities Coll Informat Engn Enshi 445000 Peoples R China Liaoning Tech Univ Coll Elect & Control Engn Huludao 125105 Peoples R China

ISBN: (纸本)9781538626184

In this paper, a novel data-driven time-varying sliding surface (TVSS) for second-order sliding-mode decoupling control (SSDC) law is proposed to improve tracking control performance of nonlinear discrete-time processes. First, using the extended state observer (ESO) and the non-parametric dynamic linearization technique (NDLT), a decoupled data-driven sliding surface (DSS) is firstly proposed to facilitate the SSDC law design. then, inspired by the fuzzy logic control (FLC) approach, a TVSS is proposed to further enhance the SSDC law's performance, where the sliding surface slope is updated by the proposed rule base of FLC. In light of the developed sliding surface, a less overshoot and faster dynamic response are obtained without deteriorate the original discontinuous control term. In addition, the chattering phenomenon is also alleviated under this control scheme. Finally, a numerical example is given to evaluate the proposed approach.

关键词： Time-varying sliding surface Chattering Decoupling Fuzzy logic control Second-order-sliding mode control

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Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks 7

Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep ...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang, Hong-wei Zhang, Ling-jie Li, Peng-fei Gu, De Xian Polytech Univ JinHua South Rd Xian 710048 Shaanxi Peoples R China Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China

ISBN: (纸本)9781538626184

To reduce labor costs for manual extract image features of yarn-dyed fabric defects, a method based on YOLOV2 is proposed for yarn-dyed fabric defect automatic localization and classification. First, 276 yarn-dyed fabric defect images are collected, preprocessed and labelled. then, YOLO9000, YOLO-VOC and Tiny YOLO are used to construct fabric defect detection models. through comparative study, YOLO-VOC is selected to further model improvement by optimize super-parameters of deep convolutional neural network. Finally, the improved deep convolutional neural network is tested for yarn-dyed fabric defect detection on practical fabric images. the experimental results indicate the proposed method is effective and low labor cost for yarn-dyed fabric defect detection.

关键词： yarn-dyed fabric deep convolutional neural networks defect detection YOLOV2

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Design of Regional Logistics System Based on Unmanned Aerial Vehicle 7

Design of Regional Logistics System Based on Unmanned Aerial...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Ni, Haoyuan Deng, Xiaohui Gong, Bo Wang, Pangwei North China Univ Technol Beijing Key Lab Urban Intelligent Traff Control T Beijing 100144 Peoples R China China Highway Engn Consultants Corp Beijing 100089 Peoples R China

ISBN: (纸本)9781538626184

With the development of electronic commerce, a large number of labors are demanded to complete the express delivery, because of the complexity of labor management, the increasing amount of labors will result in increasing complication of the express distribution process. In addition, the express also has great limitations in mountainous areas, disaster areas and other special areas of harsh conditions. To solve the problem, this paper presents an intelligent regional logistics transportation system based on unmanned aerial vehicle (UAV). Firstly, this paper designs the application (APP) for mobile phone to send coordinate information and delivery route to the UAV. then the GPS and visual identification location module of UVA help implement the delivery for the residential area. this system can help achieve a safer, cheaper, less delayed, more orderly and unmanned regional logistics system in residential neighborhoods or other similar areas. Finally, the problem of "last kilometer logistics" will be solved to some extent by this method.

关键词： Intelligent transportation system(ITS) unmanned aerial vehicle (UAV) regional logistics system

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Finite Time State Estimation and Fault Detection for Linear Switched systems with Unknown Inputs 7

Finite Time State Estimation and Fault Detection for Linear ...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Yang, Junqi Wu, Chen Cui, Lizhi Chen, Yantao Henan Polytech Univ Coll Elect Engn & Automat Jiaozuo 454000 Peoples R China

ISBN: (纸本)9781538626184

In this paper, we consider using finite time switched observer to estimate the states and detect the faults for a type of linear switched system with unknown inputs. We first design the finite time switched observer by a reduced-order switched system which originates from the primary switched system, where the unknown inputs are removed with the help of state and output transformations. A cluster of finite time observers are presented for all of the reduced-order subsystems. then, by choosing any small time parameters, we can get the finite time state estimation of the reduced-order switched system by finite time switched observers. And the finite time state estimation of the primary switched system is acquired by state equivalent transformations. Next, we put forward a fault detection method using output residual. Finally, a MATLAB simulation result is presented to confirm the reliability of the method we put forward.

关键词： Finite time observer Switched system State estimation Fault detection

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