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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421-430 订阅

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A data-driven Voxel-wise White Matter Fiber Clustering Model Based on Priori Anatomical data

A Data-driven Voxel-wise White Matter Fiber Clustering Model...

引用

data driven control and learning systems (ddcls)

作者： Zhewen Cao Er Jin Siqi Zhou Ye Wu Yongqiang Li Yuanjing Feng Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou

ISBN: (纸本)9781538626191

Whole-brain fiber imaging allows nondestructive detection of human brain structural connections. the clinical application of this method is often classified as a series of fiber bundle structures of certain significance (function, structure, shape, etc.). Due to the lack of edge structure information of fiber bundles and the high variability of complex white matter structures in individual samples, fiber clustering based on anatomical information is still an open problem. In this paper, a new fiber clustering technique is proposed, which combines spatial features of whole-brain fibers and prior anatomical information as fiber similarity matching and feature extraction. In this work, we focus on the coverage of highly consistent fiber bundles in white matter structures to match anatomic features. the method is based on multiple tests of simulated data and in vivol data. the experimental results show that this method not only improves the highly consistent coverage of fiber bundles and prior anatomical knowledge, but also simplifies the fiber data space to improve the fiber clustering similarity measured population consistency. Finally, we also discuss the application of this method in clinical research.

关键词： Measurement White matter Feature extraction Grey matter Streaming media Image reconstruction Shape

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

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

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data driven control and learning systems (ddcls)

作者： Hong-wei Zhang Ling-jie Zhang Peng-fei Li De Gu Xi’an Polytechnic University Xi’an ShaanXi China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

ISBN: (纸本)9781538626191

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： Fabrics Feature extraction Convolution Predictive models Optimization Training

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the DC Bus Voltage control Based on Virtual Inertia for SST

The DC Bus Voltage Control Based on Virtual Inertia for SST

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data driven control and learning systems (ddcls)

作者： Dazhong Ma Sen Lin Qifu Cheng Qiuye Sun School of Information Science and Engineering Northeastern University Shenyang

ISBN: (纸本)9781538626191

three-stage solid state transformer adopts three independent control units, so when the system power changes, the regulation of DC bus voltage stabilization is important. A DC bus voltage control Strategy based on virtual inertia is proposed for AC/DC converter in this paper. On the basis of traditional voltage/current double closed-loop controller, the virtual inertia control loop and the DC current feed-forward loop are introduced. In order to effectively suppress the voltage fluctuation of the DC bus in the case of the fluctuation of the system power, the inertia of the DC bus is enhanced by adding virtual capacitance. In order to reduce the influence on the DC voltage caused by load current changing, the voltage stability of the DC bus is enhanced by adding the DC current feed-forward link. Single phase shift control of load current feed-forward is adopted for DAB, which decreases the fluctuation of the DC bus voltage and increases the dynamic response time of the DC bus voltage. Finally, the simulation results verify the effectiveness of the proposed control strategy.

关键词： Voltage control Topology AC-DC power converters Pulse width modulation Mathematical model Capacitors Network topology

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Anomaly Detection of Satellite Telemetry in Orbit Based on Sequence and Point Feature Combination

Anomaly Detection of Satellite Telemetry in Orbit Based on S...

引用

ieee data driven control and learning systems conference

作者： Ying Du Xin Liang Fei Wang Chao Sun XiaoFei Hua 63758 Unit of PLA Xi'an Satellite Control Center

ISBN: (纸本)9781538626191

Aiming at the detection problems of low accuracy, high false alarm rate in the detection of satellite telemetry timing data, a satellite telemetry anomaly detection model is presented based on combination of sequence and point features. the model firstly makes steady separation in telemetry data to obtain data trends and steady residual;as the data trends contain most of telemetry sequence information, the non-stationary telemetry sequence feature is reconstructed on each separation layer by performing inverse data trends transform on these coefficients. And stable residual is mainly composed of telemetry random point, the telemetry point feature is reconstructed on the final separation layer by performing inverse steady residual transform on these coefficients. Finally, the integrated anomaly detection model, that is double autoregressive combined model, is constructed by fuse the two features. the results of anomaly detection experiment in the telemetry data of a certain type of satellite power system show that the model can adapt to the complex changes of satellite telemetry data in orbit, reduce decision interference from noise and outliers effectively, reduce the false alarm rate, improve detection precision, achieve effective detection in the anomaly threshold of satellite telemetry data on orbit.

关键词： Telemetry Sequence feature Point feature Anomaly detection Feature combination Integrated detection model

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Anode Effect prediction based on Expectation Maximization and XGBoost model

Anode Effect prediction based on Expectation Maximization an...

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data driven control and learning systems (ddcls)

作者： Zhixin Zhang Gaofeng Xu Hongting Wang Kaibo Zhou School of Automation MOE Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538626191

Anode Effect Prediction problem has been drawing great research interest of scientists, due to its significant values in reducing energy consumption and improving the efficiency of aluminum electrolysis. However, a large number of missing values contained in the collected data from the aluminum reduction cell are always neglected in the works, resulting in a decline in prediction accuracy and generalization ability. To solve this problem, a combined model of Expectation Maximization and XGBoost (EM-XGBoost) is proposed. Firstly, the original incomplete samples collected from the aluminum cells are recovered by Expectation Maximization (EM) algorithm. Afterwards, the XGBoost model trains on the recovered data, and then predicts the result for new samples. the more comprehensive metrics accuracy and F1 Score are introduced for evaluation. the results in the experiment show that the proposed model improves the accuracy to 99.7% and the F1 Score can achieve 99.8% under the premise of forecasting 30 minutes in advance. the proposed model not only has a high prediction accuracy, but also owns an excellent generalization ability.

关键词： Anodes Aluminum data models Predictive models Electrochemical processes Measurement Filling

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Identifying Gait Phases from Joint Kinematics during Walking with Switched Linear Dynamical systems 7

Identifying Gait Phases from Joint Kinematics during Walking...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Drnach, Luke Essa, Irfan Ting, Lena H. Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9781538681831

Human-robot interaction (HRI) for gait rehabilitation would benefit from data-driven gait models that account for gait phases and gait dynamics. Here we address the current limitation in gait models driven by kinematic data, which do not model interlimb gait dynamics and have not been shown to precisely identify gait events. We used Switched Linear Dynamical systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill with normal gaits and with gaits perturbed by electrical stimulation. We compared the model-inferred gait phases to gait phases measured externally via a force plate. We found that SLDS models accounted for over 88% of the variation in each joint angle and labeled the joint kinematics with the correct gait phase with 84% precision on average. the transitions between hidden states matched measured gait events, with a median absolute difference of 25ms. To our knowledge, this is the first time that SLDS inferred gait phases have been validated by an external measure of gait, instead of against pre-defined gait phase durations. SLDS provide individual-specific representations of gait that incorporate both gait phases and gait dynamics. SLDS may be useful for developing control policies for HRI aimed at improving gait by allowing for changes in control to be precisely timed to different gait phases.

关键词： Legged locomotion Hidden Markov models Superluminescent diodes Kinematics data models Switches Predictive models

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Iterative learning Based Model Identification and State of Charge Estimation of Lithium-Ion Battery

Iterative Learning Based Model Identification and State of C...

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data driven control and learning systems (ddcls)

作者： Qiao Zhu Meng’en Xu Meng’qian Zheng School of Mechanical Engineering Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781538626191

this work focuses on the accurate identification of Lithium-ion battery's nonlinear parameters by using an iterative learning method. First, the 2nd-order RC model is introduced. then, when the battery repeatedly implements a discharging trial from SOC 100% to 0%, an iterative learning based recursive least square (IL-RLS) algorithm is presented to accurately identify the nonlinear parameters of the regression model. the essential idea of IL-RLS algorithm is to improve the current parameter estimations by learning the estimation errors of the previous trails. Notably, the IL-RLS algorithm needs to be implemented offline for the long-time repetitive trials, which is the price worth paying to accurately identify the nonlinear parameters. After that, the parameters are identified as the functions of SOC by using the IL-RLS, which are verified by comparing with the result of the classic identification method for current pulses. Finally, by using the classic extended Kalman filter (EKF) as well as the parameters identified by the IL-RLS to estimate the SOC, three dynamic operation conditions are given to show the efficiency of the IL-RLS, where all the SOC estimation errors are less than 2%.

关键词： State of charge Batteries Estimation Voltage measurement Electronic countermeasures Current measurement Iterative methods

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Short-Term Traffic Flow Prediction Based on XGBoost

Short-Term Traffic Flow Prediction Based on XGBoost

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data driven control and learning systems (ddcls)

作者： Xuchen Dong Ting Lei Shangtai Jin Zhongsheng Hou Advanced Control Systems Lab Beijing Jiaotong University Beijing China

ISBN: (纸本)9781538626191

Fast and accurate short-term traffic flow prediction is an important precondition for traffic analysis and control. Due to the fact that the short-term traffic flow has nonlinear characteristic and changes randomly, concurrent computation is difficult for traditional machine learning algorithms. In this paper, a traffic flow prediction model combining wavelets decomposition and reconstruction with the extreme gradient boosting (XGBoost) algorithm is proposed to predict the short-term traffic flow. First, in the training part, wavelet de-noising algorithm is utilized to obtain the high and low frequency information of target traffic flow. Secondly, the high frequency information of traffic flow is processed by threshold method. After that, the high and low frequency information is reconstituted as the training label. Finally, the de-noised target flow is sent to the XGBoost algorithm for training to predict traffic flow. In this way, the trend of the traffic flow in each sample period is retained, and the influence of the short-term high frequency noise is reduced. the proposed traffic flow prediction method is tested base on the traffic flow detector data collected in Beijing, and the proposed method is compared with support vector machine (SVM) algorithm. the result shows that the prediction accuracy of the proposed algorithm is much higher than SVM, which is of great importance in the field of traffic flow prediction.

关键词： Training Prediction algorithms Predictive models Support vector machines Linear programming Boosting

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An Intelligent Car Temperature control System

An Intelligent Car Temperature Control System

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data driven control and learning systems (ddcls)

作者： Xiongnan He Songchen Jiang Qiuye Sun College of Information Science and Engineering Northeastern University Shengyang China

ISBN: (纸本)9781538626191

Nowadays, more and more residential cars apply various of services of energy saving to help themselves improve performances and decrease cost. As for the car air conditioning, some put forward ideas that using neuron-fuzzy method can precisely control the cooling capacity, the other hold the view that power line communication based photovoltaic (PV) system can effectively manage the energy. In this paper, it aims to deal with the shortcomings that aforementioned do not take the realistic environment and the neuron-fuzzy method's disadvantages into consideration. As a result, this paper comes up an intelligent car temperature control system(ICTCS),which, comparing with conventional temperature control systems, has two main advantages--one is using three criterions, namely light intensity outside cars(I),temperature inside cars(T) and sunshine incident angle(α), to judge what kind of environment the car is in on earth and decide car cooling capacity over , the other is applying neuron-fuzzy system to train the comprehensive temperature to try its best to decrease faster. It will refrigerate in different stalls in the standard of difference between temperature inside cars and calculated most suitable temperature. Applying the above system into actual experiments, we can find under the premise that cooling effect stays nearly the same, the energy consumption gets decreased, which is to say, the ICTCS gets good results.

关键词： Automobiles Temperature sensors Batteries Air conditioning

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Dynamic State Estimation Using Event-Trigger Master-Slave Nonlinear Filter for WAMS Applications

Dynamic State Estimation Using Event-Trigger Master-Slave No...

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data driven control and learning systems (ddcls)

作者： Qing Yuan Fengdi Zhang Hengheng Gong Luyu Li Sen Li Xiaozhong Liao Zhen Li Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing P. R. China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing P. R. China Beijing Institute of Radio Measurement Beijing P. R. China

ISBN: (纸本)9781538626191

the real-time state estimation becomes greatly important with the wide application of phasor measurement unit (PMU) in distributed generation (DG) for wide-area measurement systems (WAMS). In view of estimation, particle filter (PF) is capable of providing the best performance but at the cost of heavy computation burden. Besides, the growing grid size sustainably boosts the amount of data communication from PMU, causing the congestion. An event-trigger master-slave nonlinear filter (ET-MSNF) is proposed to guarantee the estimation accuracy and get the communication bandwidth relieved. the local slave filter at the generator node carries out the local estimation and event-trigger strategy using unscented transformation, which is identical to the center slave. the master filter at the center is designed using Monte Carlo method to improve the center's estimation accuracy by the cooperation with the center slave. Such master-slave filtering structure can fully utilize the computation capability both at the center and node. Simulation on the standard ieee 39-bus system verify the performance of ET-MSNF.

关键词： Generators Monte Carlo methods Phasor measurement units Master-slave Wide area measurements State estimation

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