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16. The Ninth Visual Object Tracking VOT2021 Challenge Results

Author

Kristan, Matej, primary, Matas, Jiri, additional, Leonardis, Ales, additional, Felsberg, Michael, additional, Pflugfelder, Roman, additional, Kamarainen, Joni-Kristian, additional, Chang, Hyung Jin, additional, Danelljan, Martin, additional, Zajc, Luka Cehovin, additional, Lukezic, Alan, additional, Drbohlav, Ondrej, additional, Kapyla, Jani, additional, Hager, Gustav, additional, Yan, Song, additional, Yang, Jinyu, additional, Zhang, Zhongqun, additional, Fernandez, Gustavo, additional, Abdelpakey, Mohamed, additional, Bhat, Goutam, additional, Cerkezi, Llukman, additional, Cevikalp, Hakan, additional, Chen, Shengyong, additional, Chen, Xin, additional, Cheng, Miao, additional, Cheng, Ziyi, additional, Chiu, Yu-Chen, additional, Cirakman, Ozgun, additional, Cui, Yutao, additional, Dai, Kenan, additional, Dasari, Mohana Murali, additional, Deng, Qili, additional, Dong, Xingping, additional, Du, Daniel K., additional, Dunnhofer, Matteo, additional, Feng, Zhen-Hua, additional, Feng, Zhiyong, additional, Fu, Zhihong, additional, Ge, Shiming, additional, Gorthi, Rama Krishna, additional, Gu, Yuzhang, additional, Gunsel, Bilge, additional, Guo, Qing, additional, Gurkan, Filiz, additional, Han, Wencheng, additional, Huang, Yanyan, additional, Lawin, Felix Jaremo, additional, Jhang, Shang-Jhih, additional, Ji, Rongrong, additional, Jiang, Cheng, additional, Jiang, Yingjie, additional, Juefei-Xu, Felix, additional, Jun, Yin, additional, Ke, Xiao, additional, Khan, Fahad Shahbaz, additional, Hak Kim, Byeong, additional, Kittler, Josef, additional, Lan, Xiangyuan, additional, Lee, Jun Ha, additional, Leibe, Bastian, additional, Li, Hui, additional, Li, Jianhua, additional, Li, Xianxian, additional, Li, Yuezhou, additional, Liu, Bo, additional, Liu, Chang, additional, Liu, Jingen, additional, Liu, Li, additional, Liu, Qingjie, additional, Lu, Huchuan, additional, Lu, Wei, additional, Luiten, Jonathon, additional, Ma, Jie, additional, Ma, Ziang, additional, Martinel, Niki, additional, Mayer, Christoph, additional, Memarmoghadam, Alireza, additional, Micheloni, Christian, additional, Niu, Yuzhen, additional, Paudel, Danda, additional, Peng, Houwen, additional, Qiu, Shoumeng, additional, Rajiv, Aravindh, additional, Rana, Muhammad, additional, Robinson, Andreas, additional, Saribas, Hasan, additional, Shao, Ling, additional, Shehata, Mohamed, additional, Shen, Furao, additional, Shen, Jianbing, additional, Simonato, Kristian, additional, Song, Xiaoning, additional, Tang, Zhangyong, additional, Timofte, Radu, additional, Torr, Philip, additional, Tsai, Chi-Yi, additional, Uzun, Bedirhan, additional, Van Gool, Luc, additional, Voigtlaender, Paul, additional, Wang, Dong, additional, Wang, Guangting, additional, Wang, Liangliang, additional, Wang, Lijun, additional, Wang, Limin, additional, Wang, Linyuan, additional, Wang, Yong, additional, Wang, Yunhong, additional, Wu, Chenyan, additional, Wu, Gangshan, additional, Wu, Xiao-Jun, additional, Xie, Fei, additional, Xu, Tianyang, additional, Xu, Xiang, additional, Xue, Wanli, additional, Yan, Bin, additional, Yang, Wankou, additional, Yang, Xiaoyun, additional, Ye, Yu, additional, Yin, Jun, additional, Zhang, Chengwei, additional, Zhang, Chunhui, additional, Zhang, Haitao, additional, Zhang, Kaihua, additional, Zhang, Kangkai, additional, Zhang, Xiaohan, additional, Zhang, Xiaolin, additional, Zhang, Xinyu, additional, Zhang, Zhibin, additional, Zhao, Shaochuan, additional, Zhen, Ming, additional, Zhong, Bineng, additional, Zhu, Jiawen, additional, and Zhu, Xue-Feng, additional

Published
2021
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19. Research on Teaching Reform of 'Motor and Drive'Based on Matlab Simulation

Author

Liang Yihua, Shengyi Yang, Zhang Chengwei, and Chao Liu

Subjects
Information management, Enthusiasm, business.industry, Computer science, media_common.quotation_subject, Teaching method, Automation, Assessment methods, ComputingMilieux_COMPUTERSANDEDUCATION, Mathematics education, Matlab simulation, Quality (business), MATLAB, business, computer, media_common, computer.programming_language
Abstract

“Motor and Drive” is a professional basic course with strong theory and wide application in practical engineering. Its theory is abstract and formula is complex, which is considered by teachers and automation students as one of the professional courses difficult to teach and learn. In order to better teaching and learning, teaching reform is provided. The teaching content is optimized, the teaching methods, experimental methods and assessment methods are reformed based on matlab simulation. It is helpful to improve the teaching quality, stimulate students' learning enthusiasm and solve practical problems. The teaching reform foundation of “motor and drive” is provided in this paper.

Published
2021

20. Data Integrity Attack in Dynamic State Estimation of Smart Grid: Attack Model and Countermeasures.

Author

An, Dou, Zhang, Feiye, Yang, Qingyu, and Zhang, Chengwei

Subjects
DATA integrity, PARTIALLY observable Markov decision processes, REINFORCEMENT learning
Abstract

A smart grid integrates advanced sensors, efficient measurement methods, progressive control technologies, and other techniques and devices to achieve safe, efficient and economical operation of the grid system. However, the diversified and open environment of a smart grid makes energy and information of the smart grid vulnerable to malicious attacks. As a representative cyber-physical attack, the data integrity attack has an extremely severe impact on the grid operation for it can bypass the traditional detection mechanisms by adjusting the attack vector. In this paper, we first present the attack strategy against dynamic state estimation of power grid in the perspective of adversary and formulate the data integrity attack detection problem that has the characteristic of sequential decision making as a partially observable Markov decision process. Then, a deep reinforcement learning-based approach is proposed to detect against data integrity attacks, which utilizes the Long Short-Term Memory layer to extract the state features of previous time steps in determining whether the system is currently under attack. Moreover, the noisy networks are employed to ensure effective agent exploration, which prevents the agent from sticking to the non-optimal policy. The principle of a multi-step learning is adopted to increase the estimation accuracy of Q value. To address the sparse rewards problem, the prioritized experience replay is proposed to increase training efficiency. Simulation results demonstrated that the proposed detection approach surpasses the benchmarks in the comparison metrics: delay error rate and false rate. Note to Practitioners—In this paper, we present a deep reinforcement learning-based algorithm to defend against the data integrity attacks of smart grid. Most of the previous works discretized the system states and utilized the current state information to identify whether the system is under attack. For this reason, the detection policy may totally ignored the continuously changing characteristics of the grid states, which will lead to poor detection performance. Moreover, the attacked system states only accounts for a small part of the entire grid operation states, the probability of sampling the experience containing the attack state is extremely small, which limits the learning efficiency of previous RL-based detection approaches. In order to increase the accuracy of detection, we first present the attack strategy against power grid’s dynamic state estimation in the perspective of adversary and formulate the partially observable Markov decision process model of attack detection problem. Moreover, we propose a deep reinforcement learning-based detection approach combining the LSTM network to extract the system state features of the previous time steps to determine whether the system is currently being attacked. To address the sparse rewards problem, the prioritized experience replay is used to increase learning efficiency. The experiments demonstrate the effectiveness of proposed detection scheme compared with benchmarks in terms of detection delay as well as accuracy. In conclusion, the proposed detection scheme is helpful in defending against the data integrity attacks without obtaining the opponent’s strategy in advance and can be conveniently applied to the real-world security management system of smart grid. [ABSTRACT FROM AUTHOR]

Published
2022
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23. State Monitoring and Early Warning Technology of Gas Turbine Based on Mixed Gaussian distribution under Complex Working Conditions

Author

Luan Tianyu, Tian Tian, Xu Mengmeng, Zhang Yufeng, and Zhang Chengwei

Subjects
Data set, Nonlinear system, symbols.namesake, Warning system, Computer science, Control theory, Gaussian, Expectation–maximization algorithm, symbols, Probability distribution, Residual, Least squares
Abstract

Aiming at the problem that existing monitoring and early warning methods can not effectively realize gas turbine state monitoring and early warning under complex operating conditions, a monitoring and early warning technology based on nonlinear fitting and mixed Gaussian distribution is proposed, and a detailed analysis is carried out by taking the average gas temperature as an example. Firstly, based on the least squares nonlinear fitting method, the nonlinear relationship between the average gas temperature and power is obtained as the baseline of the normal state. Then through the statistical analysis of the probability distribution of the residual after fitting, it is found that it meets the two Weighted Gaussian mixture distribution model. Finally, the EM algorithm is used to estimate the parameters of the two-weighted mixed Gaussian distribution of the residuals, and the upper and lower thresholds are set according to the three principles of the two-weighted Gaussian distribution. The verification results of the normal data set and the abnormal data set showed that the set threshold value of the sub-operating conditions is reasonable and credible, and can effectively identify the abnormal state of the average gas temperature under variable operating conditions. The comparison and analysis of abnormal data sets with the existing monitoring and warning methods highlights the superiority of the monitoring and warning methods mentioned in this article, and also has a certain reference value for the monitoring and warning of other equipment under complex working conditions.

Published
2020

24. Object-Aware Ghost Identification and Elimination for Dynamic Scene Mosaic.

Author

Zhang, Zhe, Ren, Xuhong, Xue, Wanli, Zhang, Chengwei, Guo, Qing, and Chen, Shengyong

Subjects
IMAGE segmentation
Abstract

Composite ghost is a common phenomenon that widely exists in dynamic scene image mosaic and significantly affects the naturalness of mosaic. To remove the ghost effectively and produce visually natural mosaic, we propose a novel image mosaic method by jointly identifying composite ghost and eliminating ghost regions without distorting, splitting, and duplicating objects. Specifically, our main contributions are three-fold: First, we propose the motion-aware composite ghost identification to localize the potential composite ghosts in the mosaic region (i.e., overlapping area between two images to be stitched) by detecting the salient-moving objects in two stitched images. Second, we design the object-aware alternative region selection strategy to produce ghostless regions that can replace the localized composite ghosts while avoiding object distortion, object separation, and object repetition. Third, we realize the image interpolation-based composite ghost elimination that can generate natural stitched image by eliminating the composite ghost of the initial blending result with the selected image source. We validate the proposed method on challenging datasets and show that our method outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2022
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39. Research on Overvoltage Protection Strategy of Oil-Gas Pipeline Based on Non-metallic Grounding Material

Author

Zhang Chengwei, Zhen Xiang, Xun Li, Qishen Lv, Jingwen Ai, and Liu Shungui

Subjects
Materials science, business.industry, Ground, 020209 energy, Electrical engineering, Hardware_PERFORMANCEANDRELIABILITY, 02 engineering and technology, Pulsed power, law.invention, Pipeline transport, Electric power transmission, Hardware_GENERAL, law, Overvoltage, Hardware_INTEGRATEDCIRCUITS, 0202 electrical engineering, electronic engineering, information engineering, Resistor, business, Transformer, Voltage
Abstract

High power pulsed power supply is the basis of electromagnetic emission. In this paper, the capacitor voltage limit mode and non-linear resistor voltage limit mode power pulsed power supply are common models. In this paper, two circuit modes based on high temperature superconducting (HTS) pulsed transformers were introduced and the working principle were analyzed respectively. In accordance with the actual parameters of a small experimental superconducting pulsed transformer, the discharging process of two modes was simulated and the characteristics of outputted current pulse were analyzed at different limited primary voltage conditions. The results show that both two circuit modes can effectively limit the primary voltage. However, the overall performance by the capacitor voltage limit mode is better than the use of non-linear resistor voltage limit mode.

Published
2019

40. Optimization Research on Grounding Resistance of Power Transmission Line Tower Foundation

Author

Zhang Chengwei, Zhen Xiang, Qishen Lv, Xiao Chen, Xun Li, and Jingwen Ai

Subjects
Power transmission, geography, geography.geographical_feature_category, business.industry, Ground, 020209 energy, Foundation (engineering), Aquifer, 02 engineering and technology, Structural engineering, Transmission line, 0202 electrical engineering, electronic engineering, information engineering, Environmental science, Reduction (mathematics), business, Tower, Voltage
Abstract

Improving the current divergence characteristics of the tower foundation can effectively reduce the tower grounding resistance and reduce the cost of secondary artificial grounding. At present, it is necessary to further study the grounding of the tower foundation. In this paper, the finite element simulation software COMSOL is used to build single-pile concrete foundation simulation models and multi-pile concrete foundation simulation models. Five kinds of tower foundation external resistance reduction methods based on flexible planar composite grounding materials are proposed, such as full-laying, lower-half-laying, upper-laying, middle-laying and lower-laying. The effects of external radius, tower depth and groundwater layer position on grounding resistance are investigated. The effects of the aquifer on the current density distribution are compared. The effects of aquifers on current density distribution and the effects of three multi-pile resistance reduction schemes on ground potential and current density are compared. The simulation results show that increasing the external radius and tower depth is beneficial to improve the efficiency of reducing the resistance. From the big to the small, the efficiency of resistance reduction by external application of pile-by-pile is full application, lower half application, lower, middle and upper. The aquifer reduces the grounding resistance of the foundation, and when the aquifer is closer to the surface, the effect of reducing the resistance is more obvious. When the multi-pile foundation with higher voltage level adopts external application to reduce resistance, the efficiency of external application of ring wrap is higher than that of pile-by-pile application. The conclusions of this paper can provide reference for the actual transmission line design and lightning protection grounding construction.

Published
2019

41. Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem.

Author

Zhang, Chengwei, Jin, Shan, Xue, Wanli, Xie, Xiaofei, Chen, Shengyong, and Chen, Rong

Subjects
*REINFORCEMENT learning, *TRAFFIC engineering, *TRAFFIC signs & signals, *GROUP work in education, *PROBLEM solving, *CITY traffic
Abstract

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We model ATSC problem as a partially observable weak cooperative traffic model (PO-WCTM). Different from a traditional IRL task that averages the returns of all agents in fully cooperative games, the learning goal of each intersection in PO-WCTM is to reduce the cooperative difficulty of learning, which is also consistent with the traffic environment hypothesis. To achieve the optimal cooperative strategy of PO-WCTM, we propose an IRL algorithm called Cooperative Important Lenient Double DQN (CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the forgetful experience mechanism and the lenient weight training mechanism. The former mechanism decreases the importance of experiences stored in the experience reply buffers, while the latter mechanism increases the weight experiences with high estimation and ‘leniently’ trains the DDQN neural network. Experiments in two real traffic scenarios and one simulated traffic scenarios show that, CIL-DDQN outperforms other methods in almost all performance indicators of ATSC. [ABSTRACT FROM AUTHOR]

Published
2021
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50. Learnable Optical Flow Network for Radar Echo Extrapolation.

Author

Zhang, Chengwei, Zhou, Xudong, Zhuge, Xiaoyong, and Xu, Meng

Abstract

The impact of extreme weather on maintaining flight schedules is becoming more pronounced. Currently, radar echo extrapolation technology is widely used in the nowcasting of severe convection, in which the optical flow method is a representative example of traditional extrapolation algorithms. By training a large number of known samples to find the optimal solution, the deep extrapolation models have gradually become better than the traditional algorithms in recent years. In this study, after examining the optical flow method and other deep learning models, a learnable optical flow deep model with a fully convolutional structure is proposed. Using the convolutional deep learning of optical flow information, this new model can overcome the kernel size limitation of traditional convolutional neural networks, and it can correlate the data history further in time and space. The six-year radar mosaics of Guangdong Province, China, were used as the data set to independently train and verify the new model. The results reveal that the new model outperformed the traditional optical flow method and it is also better than other deep learning models. [ABSTRACT FROM AUTHOR]

Published
2021
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