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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Li Qin Lihuan Liang Fucai Liu Zhenlin Jin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University YSU Qin-Huangdao China Department of Mechanical Engineering Yanshan University YSU Qin-Huangdao China

This paper presents a methodology of the dynamic analysis and control for a novel hybrid humanoid robot arm. The hybrid humanoid robot arm under consideration consists of a spherical parallel manipulator (SPM) connecting two revolute pairs in series form. The dynamic model of the hybrid humanoid robot arm has been set up based on the lie group and lie algebra combining with the principle of virtual work which can avoid processing of constraint reaction and the division of logic open-chains, as well as a great quantity of differential operation. Aiming at the parameter uncertainties and disturbances, an adaptive backstepping sliding mode controller is developed. Compared with PD control in trajectory tracking simulation, the results show the advanced of the controller.

关键词： Process control Trajectory Actuators Complexity theory TV

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Fuzzy Neural Network algorithm based on the delay compensation force/position control structure of a redundant actuation parallel robot *

Fuzzy Neural Network algorithm based on the delay compensati...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuhuan Wen Yanfang Zha Haiyang Yu Luigi Manfredi Xiongfei Li Sen Wang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom

In this paper, a novel control method of the redundant force branch based on the force/position hybrid control structure of Smith predictor compensation is proposed. A fuzzy PI controller is designed based on Smith predictor compensation structure and it is included in the redundant force branch. This method can obtain good tracking and dynamic performance. However, fuzzy control doesn't have self-learning and adaptive ability, so fuzzy neural network (FNN) controller is used in the redundant force branch. The simulation results show that the proposed FNN algorithm based on delay compensation force/position hybrid control structure can improve the adaptability and the control accuracy of driving force of redundant branch.

关键词： Force Fuzzy control PI control Fuzzy neural networks Parallel robots Mathematical model Artificial neural networks

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Improved Lightweight YOLOv5 Based on GhostNet and Its Application in Target Board Detection for Ladle Nozzle Positioning

Improved Lightweight YOLOv5 Based on GhostNet and Its Applic...

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第43届中国控制会议

作者： Fengfei Si Yiming Fang Hao Ju Dongliang Zhao Jingliang Xin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Tangshan Branch HBIS Company Limited

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The detection of the target board of ladle nozzle positioning is the basis of automatic loading and unloading of ladle nozzle by ladle robot in continuous casting. Due to the complex situation of high temperature, dust, backlight and so on, the target board of ladle nozzle positioning is prone to false detection or low detection accuracy, and it is difficult to take into account the contradiction between rapidity and detection accuracy. Therefore, this paper proposes an improved lightweight algorithm GVP-YOLOv5(YOLOv5 with GhostNet, VoVGSCSP and Pruning), which takes into account both detection accuracy and rapidity, to detect the target board for ladle nozzle positioning. Firstly, Ghost module is used to improve the backbone network of YOLOv5 as the backbone feature extraction network to reduce the number of parameters and calculation of the backbone. Then in the neck, the GSConv module is used to design a thin neck structure, which can ensure the multi-scale feature fusion capability and further reduce the complexity of the network. Finally, the model pruning technology is used to prune the whole model, and the channels with small scaling factor are removed to compress the model to the greatest extent, so as to meet the requirements of industrial field application deployment. The experimental results show that the lightweight GVP-YOLOv5 algorithm proposed in this paper can complete the real-time detection of the target board in complex industrial scenarios. Compared with the original YOLOv5s algorithm, the number of parameters in the model is reduced by 82.6%, and the size of the model is significantly reduced to 19.4%. At the same time, the detection accuracy is maintained at more than 95% on mAP@50:95 and the detection speed is effectively improved.

关键词： Target board detection Ladle nozzle positioning YOLOv5 lightweight GhostNet thin neck structure channel pruning

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Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems

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Mathematical Biosciences and engineering 2024年第2期21卷 2608-2625页

作者： Xu, Wanying Yang, Xixin Guan, Yuanlin Cheng, Xiaoqing Wang, Yu College of Computer Science & Technology Qingdao University Qingdao266071 China School of Automation Qingdao University Qingdao266071 China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of Education Qingdao University of Technology Qingdao266520 China School of Mechanical & Automotive Engineering Qingdao University of Technology Qingdao266520 China

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI’s overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://***/licenses/by/4.0)

关键词： Learning systems

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Adaptive backstepping control of hydraulic servo system with input saturation for rolling mill based on multi-model switching

Adaptive backstepping control of hydraulic servo system with...

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Chinese control Conference (CCC)

作者： Yehong Li Yiming Fang Jianxiong Li Shengli Shi Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Qinhuangdao Qinhuangdao China

For the problems of parameters change abruptly and input saturation of the rolling mill hydraulic servo systems, an adaptive backstepping control method based on multi-model switching is presented in this paper. Firstly, multiple models of the rolling mill hydraulic servo systems under different operating modes are established. Secondly, an auxiliary system is introduced to reduce the effects of input saturation, and the adaptive backstepping controllers are designed for all subsystem. With the help of common Lyapunov theory, the proposed method can guarantee stability of the multi-model switched system under arbitrary switching strategy. Finally, the simulation results demonstrate the effectiveness of the proposed control method.

关键词： Servomotors Switches Backstepping Adaptive systems Valves Actuators

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Stochastic predictive control for energy-efficient cooperative wireless cellular networks

Stochastic predictive control for energy-efficient cooperati...

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IEEE International Conference on Communications (ICC)

作者： Shuhuan Wen F. Richard Yu Jinsong Wu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Bell Laboratories Shanghai P.R. China

Energy efficiency has become an increasingly important aspect of wireless communications. Coordinated multipoint (CoMP) processing may be considered as a promising approach to improve the energy efficiency, extend the cell coverage, and increase the communication capacity in wireless cellular networks. However, the CoMP communication systems may require a large amount of channel state information (CSI) feedbacks which would be constrained by the wireless backhaul networks. This paper proposes a control theoretical approach to improve the energy efficiency in cooperative wireless cellular networks with CoMP communications. In particular, we propose a stochastic predictive control algorithm to achieve energy-efficient CoMP transmissions through optimal base station grouping. Moreover, since the channel state information in CoMP is often outdated/lost due to the feedback delays in the backhaul network, our proposed approach is based on a new discrete time predictive function control model to mitigate the impacts of packet delay or loss of channel state information. Simulation results show the effectiveness of the proposed scheme.

关键词： Base stations Wireless communication Channel state information Delays Predictive models Predictive control Prediction algorithms

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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly labeled and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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Adaptive Particle Swarm-Based Fuzzy Clustering Algorithm in the Application of Steam Drum Pulverized Coal Fired Boiler

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International Journal of Advancements in Computing Technology 2011年第11期3卷 444-452页

作者： Ma, Yunfei Niu, Peifeng Zhao, Yantao Ma, Xiaofei Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 Hebei Province China Key Laboratory of Hebei Province Industrial Computer Control Engineering Yanshan University Qinhuangdao 066004 China Northeastern University at Qinhuangdao Qinhuangdao 06600 China

The Pulverized Coal Fired Boiler is a control object with time-varying parameters, large delay and multivariate tight-coupling. This paper, aiming at improving the actualities of excessive parameters and suboptimal data clustering effect in the Steam Drum Pulverized Coal Boiler running, proposes a new algorithm - Fuzzy Clustering Algorithm (APFM) to fulfill the efficiency optimization of the boiler combustion. Based on the Adaptive Particle Swarm Optimization Algorithm (APSO), this new algorithm overcomes the disadvantages of FCM algorithm such as the sensitivity to initial value and the easy involvement in partial optimum, and enhances the abilities of APSO algorithm such as global search and escape from partial optimum. Finally, the feasibility and effectiveness of this clustering method is demonstrated through the simulation experiment.

关键词： Fuzzy clustering

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Reliability Optimization of Multi-state Hydraulic System Based on T-S Fault Tree and Extended PSO Algorithm

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IFAC Proceedings Volumes 2013年第5期46卷 463-468页

作者： Chengyu Yao Bin Wang Dongning Chen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China (Tel: ***) Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao China Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University) Ministry of Education of China Qinhuangdao China

In order to overcome the difficulty of constructing the multi-state system's reliability optimization model, and the shortage of premature convergence of PSO (Particle Swarm Optimization) algorithm, a new reliability optimization method based on T-S fault tree and EPSO (Extended PSO) algorithm is proposed. According to the T-S fault tree and T-S rules of the events, the multi-state system reliability optimization model is constructed, at the same time, given constraint conditions on the system cost, weight and volume, and combined with the death penalty to construct the fitness function. Furthermore, EPSO algorithm based on the attraction and repulsion forces in the artificial physics is applied to the reliability optimization of multi-state hydraulic system. Comparing with PSO algorithm, the results of EPSO algorithm are in a better performance in obtaining global optimal solution.

关键词： Hydraulic system Reliability optimization Multi-state T-S fault tree EPSO algorithm

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Reliability Prediction Method of Hydraulic System by Fuzzy Theory

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IFAC Proceedings Volumes 2013年第5期46卷 457-462页

作者： Dongning Chen Chengyu Yao Zhongkui Feng Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao China (Tel: ***) Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University) Ministry of Education of China Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

The hydraulic system's reliability is a key factor to ensure the reliable operation of equipment and it is very important to evaluate reliability of hydraulic system. The paper describes a reliability prediction method that may be used to evaluate the reliability of hydraulic systems for industrial applications. The proposed method takes advantage of fuzzy set theory. The aim of this method is to minimize the deficiencies of the traditional reliability prediction methods treating a failure rate as fixed value due to insufficiency practice data. Triangular fuzzy number and Gaussian fuzzy number are used in the application of this prediction method, where environmental factor, derating factor, and other factors are considered. Finally, an example of type crane's hydraulic system is given to verify the feasibility and practicability of the method.

关键词： Type crane Hydraulic system Reliability prediction Fuzzy theory Reliability logic diagram

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