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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource allocation method with good robustness is urgently needed under complex industrial environments. This paper considers the distributed constraint- coupled resource allocation problem with noisy information exchange over an undirected network, where each agent holds a private cost function and obtains the solution via only local communications. Communication noise poses a challenge to gradient-tracking based algorithm as the impact of noise will accumulate and its variance tends to infinity when the noise is persistent. Adopting noise-tracing scheme, we propose an exact noise-robust distributed gradient-tracking algorithm to achieve cost-optimal distribution of resources, which can avoid noise-accumulation in the tracking step. Moreover, noise suppression parameters are introduced to further attenuate the impact of noise. With diminishing suppression parameters, it is theoretically proved that the proposed algorithm is able to achieve exact convergence to the optimal solution. Finally, a numerical example is provided for verification.

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A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

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IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

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A Missing Data Imputation Method for Electricity Consumption Data Based on TCN-Attention with Mask Tokens

A Missing Data Imputation Method for Electricity Consumption...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yifeng Mao Ming Yang Peng Li Zhujian Ou Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Nantong Power Supply Company State Grid Jiangsu Electric Power CO. LTD Nantong China

With the popularization of smart meters, power companies can collect massive amounts of data from users for non-invasive load detection, electricity theft detection, etc. However, due to faults in smart meters and abnormal communication, the dataset often has missing values, making it difficult for data-driven methods to be widely applied. In this paper, we propose an unsupervised learning method based on TCN-Attention to fill in customers' electricity consumption data. Firstly, we extract electricity consumption features through TCN. In order to improve the feature extraction capability, we introduce a channel attention mechanism to TCN. The attention mechanism makes TCN focus on the effective features in the data by assigning weights. Meanwhile, we use mask tokens to cover a certain proportion of data, forcing the proposed model to learn potential feature representations of the data during the training process. Finally, the features are restored to data through feedforward neural networks. An example is given to verify the effectiveness of the proposed method.

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Brillouin optical correlation domain analysis based on a phase-chaos laser

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Advanced Photonics Nexus 2024年第6期3卷 106-116页

作者： Lintao Niu Yahui Wang Jing Chen Haochen Huang Lijun Qiao Mingjiang Zhang Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control SystemMinistry of EducationTaiyuanChina Taiyuan University of Technology College of Physics and OptoelectronicsTaiyuanChina Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering TaiyuanChina

The physical mechanism of gain motivation is the main theoretical bottleneck that restricts the signal-to-noise ratio(SNR)and results in a mono-merit implementation for the existing stimulated Brillouin scattering-based fiber sensors.A phase-chaos laser(PCL)is proposed and introduced in the Brillouin optical correlation domain analysis(BOCDA)scheme to promote the SNR and achieve a high-accuracy *** PCL characteristics are presented,and a theoretical model of chaos gain accumulation and extraction is ***,the simulation results reveal that the SNR is improved by 5.56 dB,and the signal-to-background noise ratio(SBR)of the Brillouin gain spectrum(BGS)is promoted by 8.28 dB with a 100-km sensing ***,the PCL is experimentally *** the proof-of-concept experiment,the accuracy of the Brillouin frequency shift is upgraded to 0.64 MHz,and the SBR of BGS is improved by 10.77 *** PCL provides a new research direction for optical chaos,and the PCL-BOCDA showcases a promising future for optimal-merit-coupling sensing and its application.

关键词： phase-chaos laser distributed fiber sensing stimulated Brillouin scattering high signal-to-noise ratio

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Tightly-Coupled Lidar-Visual-Inertial Odometry and Mapping in Real Time

Tightly-Coupled Lidar-Visual-Inertial Odometry and Mapping i...

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Chinese control Conference (CCC)

作者： Wenyu Ma Bailing Tian Hanchen Lu Hongming Shen School of Electrical and Information Engineering Tianjin University Tianjin P. R. China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing P. R. China

We proposed a tightly-coupled Lidar-visual-inertial odometry and mapping method, which takes advantage of measurement of Lidar, visual and inertial sensors to achieve highly accurate, real-time 6DoF state estimation and map-building in GNSS-denied environments. The proposed odometry is a tightly-coupled optimization-based method, obtains robust and low drift odometry by fusing pre-integrated IMU measurements, visual features from the image, and geometric features from Lidar data. Further, we adapt an online method to mitigate degeneracy in optimization problems to improve robustness in environmentally degenerate cases. Simulation and real-world experiments show that the proposed method exhibits similar or better robustness and accuracy with the state-of-the-art SLAM methods.

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Study on a plug and play control method for renewable energy source

Study on a plug and play control method for renewable energy...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xiongwei Hu Fang Shi Guiting Xue Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan P.R. China Mentougou Branch of Beijing Electric Power Company Beijing P.R China

ISBN: (纸本)9781509054190

This paper proposes a new power supply module combining the solar panels with an energy storage device, which can achieve the plug-and-play function. Photovoltaic (PV) cells supply power to the micro-grid via an inverter controlled by maximum power point tracking (MPPT) strategy to extract the maximum power from the PV arrays. The energy storage system is used to maintain the voltage and frequency via an inverter governed by droop-control to maintain the stable operation of the system even under disturbances. This power supply module will have a droop characteristic when connected to a variable load. The combined module is modelled and simulated with MATLAB/SIMULINK for different working conditions, such as connecting to different load and working in the daytime or at night. The simulation results show the rationality and feasibility of the proposed module.

关键词： Inverters Energy storage Power supplies Power generation Renewable energy sources Plugs Reactive power

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Adaptive Backstepping Sliding Mode control for Underwater Glider Trajectory Tracking Based on Nonlinear Disturbance Observer

Adaptive Backstepping Sliding Mode Control for Underwater Gl...

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Chinese Automation Congress (CAC)

作者： Zhenjia Li Weihua Cao Chao Gan Mingming Wen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Guangzhou Marine Geological Survey Guangzhou China

An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's control system is analyzed. In the end, the proposed control strategy is compared with other approaches.

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Research on Depth Estimation Method of Single Aerial Image

Research on Depth Estimation Method of Single Aerial Image

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IEEE Information Technology, Networking, Electronic and Automation control Conference

作者： Wang Gao Lin Pu Lei Li Fei Deng Lingzhi Bie Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Institute of Mechanical and Electrical Engineering Beijing China School of Geodesy and Geomatics Wuhan University Wuhan China Wuhan Tianjihang Information Technology Co. Ltd Wuhan China

Aiming at the problem that the current depth estimation of single image mostly uses the ground public data set, and there is less research on aerial images, this paper uses the collected visible and infrared aerial image data sets to study the depth estimation. We used FCRN and LapDRN to extract the depth estimation results of visible aerial image and infrared aerial image under global and single object respectively, and compared the results extracted from the two types of networks by qualitative and quantitative methods. The results show that the depth estimation accuracy of visible aerial image is higher than that of infrared image, but the increase rate of infrared image accuracy index is higher than the increase rate of image depth range. The image depth estimation results extracted by LapDRN method are superior to FCRN in accuracy, structure consistency and edge clarity, which indicates that LapDRN is more effective in the estimation of aerial image depth information.

关键词： Automation Image edge detection Estimation Data mining Indexes

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Wind and Storage Cooperative Scheduling Strategy Based on Deep Reinforcement Learning Algorithm 2

Wind and Storage Cooperative Scheduling Strategy Based on De...

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2019 2nd International Conference on Advanced Algorithms and control Engineering, ICAACE 2019

作者： Qin, Jingtao Han, Xueshan Liu, Guojing Wang, Shang Li, Wenbo Jiang, Zhe Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong Province Jinan250061 China State Grid Jiangsu Electric Power Company Economic Research Institute Jiangsu Province Nanjing210008 China Tianjin Chengdong Power Supply Company Hedong District Tianjin300250 China State Grid Shandong Electric Power Research Institute Jinan 250003 Shandong Province China

Wind energy has become one of the most promising new energy sources in the context of the global energy interconnection. However, the inherent uncertainty of wind power and the volatility of its output make it difficult to join the internet for scheduling. The uncertainty of the output must be reduced or even eliminated by certain methods. In this paper, wind power and energy storage are coordinated to eliminate the uncertainty of wind power output, and to reduce the burden on the grid while ensuring the long-term operation of wind farms. This paper first introduces Q-learning in reinforcement learning as a controller. Through a large number of historical wind power data training, the controller has good decision-making ability, so as to reduce the punishment caused by wind power uncertainty;then the Q-learning is improved aiming at the maximum average income of the stage. Finally, the Q-value network is established with conventional Q-learning and improved Q-learning. The DQN algorithm in the deep reinforcement learning algorithm is introduced for deep training and decision-making and the three algorithms are verified. The result proves that the deep reinforcement learning algorithm can achieve better control effect than Q-learning. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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