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IEEE Canada Electric Power Conference, EPEC

作者： Md Rezwan Parvez Wenkai Hu Tongwen Chen University of Alberta Edmonton Alberta Canada School of Automation and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China

ISBN: (数字)9781728164892

ISBN: (纸本)9781728164908

Alarm floods are considered to be the major obstacles that prevent the smooth process operations of large-scale industrial facilities. During an alarm flood situation, industrial operators often get confused by too many alarms and thus have difficulties in observing and handling critical alarms. In recent years, sequence alignment based similarity analysis has emerged as an effective way to handle alarm floods. Alarm floods caused by the same fault are very likely to consist of the same group of alarms in a certain sequential order. Conducting realtime sequence alignment of industrial alarm floods can help operators to quickly recall the root cause and make prompt corrective actions. This paper proposes the online similarity analysis of alarm floods based on the Smith-Waterman and Needleman-Wunsch algorithms, and compares their differences and application conditions. Case studies are provided to illustrate the proposed online similarity analysis methods and the differences of the two sequence alignment algorithms.

关键词： Heuristic algorithms Indexes Prediction algorithms Power systems Pattern matching Frequency measurement Conferences

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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Engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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Improving Rate-of-Penetration and Reducing State Fluctuation of Drill-String System Based on Multiobjective optimization

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IEEE Transactions on Industrial Informatics 2025年

作者： Yang, Xiao Wu, Min Lu, Chengda Huang, Hengyu Wu, Xiao Li, Wangnian Zhang, Youzhen China University of Geosciences School of Future Technology Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Company Ltd. Xi'an710077 China

This article is concerned with the balance between improving rate-of-penetration and suppressing drill-string vibrations by optimizing weight-on-bit and rotational speed. The main contribution of this article lies in three points. First, due to the lack of downhole data, the problem of suppressing drill-string vibrations is transformed into reducing state fluctuation of drill-string system. Meanwhile, a multiobjective optimization scheme is established. Second, the temporal relations are revealed, including modeling time, optimization time, resampling time, controller response time, buffer time, and optimization interval. They act as constraints for subsequent modeling and optimization. Third, the improved extreme learning machine methods are developed to build the calculation models of rate-of-penetration and state fluctuation of drill-string system, separately. The nondominated sorting genetic algorithm II, technique for order preference by similarity to an ideal solution method, and moving window are used to obtain the optimal weight-on-bit and rotational speed. The effectiveness of the approach is demonstrated through an industrial case study based on the actual drilling data. © 2005-2012 IEEE.

关键词： Multiobjective optimization

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Survey on 6D Pose Estimation of Rigid Object

Survey on 6D Pose Estimation of Rigid Object

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第三十九届中国控制会议

作者： Jiale Chen Lijun Zhang Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd.

Estimating 6 D pose of rigid objects has gained increasing attention as it has become an curcial problem in rapidly growing technology related to robotics, augmented reality and autonomous driving. Therefore, the research on 6 D pose estimation technology is of great significance. In this paper, firstly, current position of the field is summarized regarding object pose estimation. We found that deep learning combined with traditional methods can produce better results. Then, pose ambiguity which is an open problem needed further study is raised. Finally, the main problems of the current research and possible development directions are identified.

关键词： Pose Estimation Feature Detection Pose Ambiguity

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Noise Modeling and Data Augmentation Using Conditional Adversarial Autoencoder

Noise Modeling and Data Augmentation Using Conditional Adver...

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第三十九届中国控制会议

作者： Yunkai Jiang Yuan Zhu Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd.

Noising image recognition is an important but challenging task. The images collected by the camera are often accompanied by a variety of noise, which will cause the loss of image information to make recognition difficult. With powerful feature extraction, neural network models can be used to process these complex noising images. However, when the noise model to be learned is complex and the number of learning samples is limited, this may lead to overfitting of the network model. In order to increase the diversity of noising images in limited learning samples and improve the generalization ability of the recognition model, we propose a noise modeling method based on CVAE-GAN. First, the encoder maps a real noising image to hidden layer vectors, which contains noise distribution, image category, and pose information. Then the decoder receives these hidden layer vectors to generate an synthetic image of the specified category and noise level. Finally, during the training process, we combine adversarial loss and smoothl1 loss to make the generated noising image more realistic. Experiments show that compared with other current generative models, our method can more effectively learn the distribution rules of complex noise in real images and generate synthetic images more similar to real noise. These synthetic images generated by our method can can alleviate the overfitting problem caused by insufficient data volume and enhance the generalization ability of image recognition models. By this way, we obtain the state-of-the-art recognition performance on the challenging noising image dataset.

关键词： Noising Image Data Augmentation Recognition Noise Modeling Weak Supervision

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Adaptive Backstepping Sliding Mode control for Fractional Order systems

Adaptive Backstepping Sliding Mode Control for Fractional Or...

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第三十九届中国控制会议

作者： Xiang Chen Shiqi Zheng Feng Liu Xiongliang Zhang Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

This paper concentrates on the control problem of a class of fractional order nonlinear systems. A new adaptive backstepping sliding mode control is proposed, which inherits both the advantages of backstepping and sliding mode ***, a fractional order sliding mode disturbance observer is proposed to estimate unknown external disturbances. Meanwhile, RBF neural network is adopted to approximate unknown nonlinear terms. Finally, an illustrative simulation result based on a practical fractional order permanent magnet synchronous motor model is presented to verify the validity of proposed controller.

关键词： Fractional order systems adaptive sliding mode control disturbance observer backstepping

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Solving A Class of Nonsmooth Resource Allocation Problems with Directed Graphs though Distributed Smooth Multi-Proximal Algorithms

arXiv

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arXiv 2020年

作者： Wei, Yue Shang, Chengsi Fang, Hao Zeng, Xianlin Dou, Lihua Pardalos, Panos Peng Cheng Laboratory China The Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China Graduate School at Shenzhen Tsinghua University China Industrial and Systems Engineering University of Florida United States

In this paper, two distributed multi-proximal primal-dual algorithms are proposed to deal with a class of distributed nonsmooth resource allocation problems. In these problems, the global cost function is the summation of local convex and nonsmooth cost functions, each of which consists of one twice differentiable function and multiple nonsmooth functions. Communication graphs of underling multi-agent systems are directed and strongly connected but not necessarily weighted-balanced. The multi-proximal splitting is designed to deal with the difficulty caused by the unproximable property of the summation of those nonsmooth functions. Moreover, it can also guarantee the smoothness of proposed algorithms. Auxiliary variables in the multi-proximal splitting are introduced to estimate subgradients of nonsmooth functions. Theoretically, the convergence analysis is conducted by employing Lyapunov stability theory and integral input-to-state stability (iISS) theory with respect to set. It shows that proposed algorithms can make states converge to the optimal point that satisfies resource allocation conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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Robotic arm calibration and teaching method based on binocular vision

Robotic arm calibration and teaching method based on binocul...

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第三十九届中国控制会议

作者： Ming Chen Xin Hong Longsheng Wei Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd.

Aiming at the errors caused by the production and assembly of the robotic arm and the problem of low absolute positioning accuracy, a method of robotic arm calibration and teaching based on binocular vision is proposed in this paper. First,the joints of the robot arm is controled to rotate separately, and the motion trajectory data of the end of the robot arm is recorded by the binocular vision. Then the circle center and circle plane normal vector of each joint axis motion is optimized using Adam algorithm. After identification and calculation, accurate kinematic parameters are obtained, and then kinematically model the manipulator once again. After the calibration is completed, the user can use the teaching rod to define the trajectory under the camera, and the defined trajectory is converted to the robot arm coordinate system for inverse kinematics solution. Finally, the robotic armis controled to reproduce the trajectory. Experimental results show that the proposed method can complete the precise calibration of the kinematic parameters of the robotic arm. After calibration, the average error of the end of the robotic arm is within 1 mm, and the absolute positioning accuracy is improved by more than 80%. The method proposed in this paper has low cost and simple operation, and it is easy to be popularized and applied in industrial and desktop robotic arm calibration and teaching.

关键词： Binocular vision Calibration Kinematic modeling Teaching Positioning accuracy

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Adaptive control for Stochastic Nonlinear systems With Quantized States

Adaptive Control for Stochastic Nonlinear Systems With Quant...

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第三十九届中国控制会议

作者： Mingyuan Zhao Shiqi Zheng Feng Liu Xiang Chen Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, adaptive control problem is investigated for stochastic nonlinear systems with quantized states. Compared with the existing works, the considered system is more general which contains a deterministic nonlinear term and a stochastic disturbance in each channel. In order to handle the high order nonlinearities, we present a new technique which makes the partial derivative of virtual control law become a constant, this makes the adaptive control scheme of state quantization feasible. Then, a new adaptive law is proposed to deal with some completely unknown parameters, which includes an interesting third-order term of the parameters. Finally, simulation results demonstrate the efficacy of the proposed schemes.

关键词： adaptive control backstepping state quantization stochastic system

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A Parallel Medical Diagnostic and Treatment System for Chronic Diseases

A Parallel Medical Diagnostic and Treatment System for Chron...

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Chinese Automation Congress (CAC)

作者： Jing Wang Xiao Wang Yuanyuan Guo Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Smart Healthcare Systems Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In order to improve the accuracy of diagnosis and the effectiveness of treatment on chronic diseases, a parallel medical diagnostic and treatment system for chronic diseases is proposed in this paper. The system consists of the artificial system, computational experiments and parallel execution. Generally, the actual medical system includes real doctors, real patients, and the process of real doctors diagnosing and creating a treatment plan. Based on the components in actual medical system, the virtual doctors and virtual patients are created in artificial system. The computational experiments are to exhaustively explore and evaluate reasonable diagnoses and treatment options, so that the most accurate diagnosis and the best treatment plan are configured. The parallel execution between the artificial and the actual system includes the interaction between virtual doctors and real doctors as well as that between the virtual patients and actual patients. The parallel execution, on the doctors' end, is to ensure the precise diagnosis of diseases and the effectiveness of the treatment plan. On the other hand, the parallel execution, on the patients' end, is to provide patients with more personal guidance on following the treatment and to assist sending feedback to the system regarding the treatment effectiveness. As the chronic diseases generally require a long-term treatment, this parallel system can better accommodate the process by continuously monitoring the treatment and sending feedback to the model so as to actively optimize the treatment plan.

关键词： Medical diagnostic imaging Medical services Diseases Medical diagnosis Deep learning Neural networks Knowledge engineering

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