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SSRN 2023年

作者： Wang, Zhenchun Wang, Nianguo School of Electrical Engineering Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

This paper investigates the model predictive control (MPC) of a point-absorption sea wave energy converter (WEC). The dynamic model and information about upcoming waves serve as the foundation for MPC implementation, but building the plant model and forecasting waves will invariably introduce errors. As a result of this fact and the multi-step predictive optimization feature of MPC, a novel objective function based on the weighting factor is adopted in the MPC design, which has the following benefits: First, the weighting factor penalizes the objective function with errors at various steps in time, which can limit the adverse effects of uncertainties on the controller. Second, the weighting factor can better constrain the float displacement, ensuring the device’s safe operation. In addition, the weighting factor is optimized offline using a genetic algorithm, and its value changes depending on the sea state. The simulation results show the effectiveness of the proposed MPC strategy. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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3D printed-electrospun PCL/hydroxyapatite/MWCNTs scaffolds for the repair of subchondral bone

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Regenerative Biomaterials 2023年第1期10卷 294-305页

作者： Yanyan Cao Lei Sun Zixian Liu Zhizhong Shen Wendan Jia Peiyi Hou Shengbo Sang College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China

Osteochondral defect caused by trauma or osteoarthritis exhibits a major challenge in clinical treatment with limited symptomatic effects at *** regeneration and remodeling of subchondral bone play a positive effect on cartilage regeneration and further promotes the repair of osteochondral *** use of the strengths of each preparation method,the combination of 3D printing and electrospinning is a promising method for designing and constructing multi-scale scaffolds that mimic the complexity and hierarchical structure of subchondral bone at the microscale and nanoscale,*** this study,the 3D printed-electrospun poly(ɛ-caprolactone)/nano-hydroxyapatites/multi-walled carbon nanotubes(PCL/nHA/MWCNTs)scaffolds were successfully constructed by the combination of electrospinning and layer-by-layer 3D *** resulting dual-scale scaffold consisted of a dense layer of disordered nanospun fibers and a porous microscale 3D scaffold layer to support and promote the ingrowth of subchondral ***,the biomimetic PCL/nHA/MWCNTs scaffolds enhanced cell seeding efficiency and allowed for higher cell-cell interactions that supported the adhesion,proliferation,activity,morphology and subsequently improved the osteogenic differentiation of bone marrow mesenchymal stem cells in ***,this study elucidates that the construction of 3D printed-electrospun PCL/nHA/MWCNTs scaffolds provides an alternative strategy for the regeneration of subchondral bone and lays a foundation for subsequent in vivo studies.

关键词： additive manufacturing 3D printing electrospinning subchondral bone

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Optimal Time and Power Allocation in Full-Duplex Wireless-Powered Cognitive Radio Networks

Optimal Time and Power Allocation in Full-Duplex Wireless-Po...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Zhenyu Feng Ming Su Meihua Zhou Zhixin Liu School of Electrical Engineering Yanshan University Qinhuangdao China Training Center of Liren College Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao Hebei Province China

In this paper, we integrate wireless powered communication networks (WPCNs) with cognitive radio networks (CRNs) in order to develop wireless powered CRNs (WP-CRNs). The hybrid access point (HAP) is used to broadcast radio-frequency signals to the devices at the downlink stage, and to receive information from these devices. The HAP has two antennas to achieve full-duplex: one is for downlink wireless power transfer and the other is for receiving uplink information transmission from the users simultaneously in the uplink. In the underlay paradigms. The SUs can harvest energy to transmit data when the interference to primary users (PUs) does not exceed a tolerable threshold. To tackle the above network issues, an optimal resource allocation method is proposed to maximize the secondary system uplink throughput. The optimization problem with respect to time and power allocation is formulated including the constraint of energy causality and interference level. The non-convex problem is transformed into an equivalent convex optimization problem, then the joint optimal power and time allocation (JOPTA) algorithm is presented to determine the optimal solutions. The effectiveness of the proposed algorithm is verified in the simulations.

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Distributed Fault-Tolerant Secondary control For Dc Microgrids Against False Data Injection Attacks

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SSRN 2022年

作者： Xie, Zongkui Wu, Zhongqiang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

This paper focuses on the issue of unknown false data injection attacks (FDIA) of controllers in DC microgrid, and proposes a distributed fault-tolerant secondary controller for DC microgrid to ensure that the control objectives of voltage regulation and current sharing can be achieved when the system is subject to FDIA. Firstly, the secondary control problem in DC microgrid with FDIA is transformed into a first-order leader-following multi-agent system fault-tolerant consensus problem, and the impact of FDIA signals on the microgrid system is analyzed. Then, a distributed extended state observer (DESO) is designed based on the derived multi-agent system, which observes the injected FDIA signals using the relative output error of each distributed generators and its neighbours. Further, based on the observed FDIA signals, a fault-tolerant secondary controller is designed to eliminate the adverse effects of the attack signals on the system. Particularly, the stability analyses for the designed DESO and secondary controller prove that the control system is global asymptotically stable. Finally, the effectiveness and superiority of the proposed fault-tolerant secondary control method for DC microgrids with FDIA are verified by simulation analyses of four cases. © 2022, The Authors. All rights reserved.

关键词： Network security

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Contragan-Acl:Contrastive Learning for Image-to-Image Translation with Adversarial Comsistency Loss

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SSRN 2022年

作者： Li, HaiBing ChengXin, Liang Li, YaQian School of Industrial Computer Control Engineering Yanshan University China Key Laboratory of Industrial Computer Control Engineering of Hebei Yanshan University China

Generative adversarial networks (GANs) are a hot research topic recently. So unsupervised image-to-image translation become a central task in computer vision. In this paper, we propose contrastive learning training strategy that obtain more significant features of true and false samples to improve the ability of distinguishing true and false samples. Then we design adversarial consistency loss that the generator and discriminator are added to the reconstruction process to further deal with the details and texture of the generated image to stabilize training strategy. Experiments on CelebA-HQ and a AFHQ,the FID score about reduce by more than 10% and LPIPS score increase by 10%. © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Configuration Synthesis of Weakly Coupled Parallel Hip Exoskeletons With Large Workspace

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Journal of Mechanisms and Robotics 2025年第7期17卷 074502页

作者： Xu, Jilong Liu, Fucai Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao 066004 China Key Lab of Industrial Computer Control of Heibei Province Yanshan University Qinhuangdao 066004 China

To address the issues of strong motion coupling, high control difficulty, and insufficient degrees-of-freedom (DoFs) in existing bio-syncretic parallel hip exoskeletons, a design scheme characterized by weak coupling is proposed. A configuration synthesis method for weakly coupled parallel hip exoskeletons is introduced by deriving the conditions that the transmission wrench screws (TWSs) in each limb must satisfy. Furthermore, to tackle the problem of limited workspace in parallel hip exoskeletons, a design method aimed at increasing the operational workspace is presented. By applying the proposed methods to the configuration synthesis of the hip exoskeleton, several weakly coupled configurations with expanded workspaces are generated. Finally, the correctness and effectiveness of the proposed methods are validated through case analyses.

关键词： weakly coupled hip exoskeleton configuration synthesis parallel robots Screws mechanism synthesis and analysis wearable devices parallel mechanism Exoskeleton devices workspace mechanisms and robots

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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm

Trajectory Optimization of Manipulator for Loading and Unloa...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Dongliang Zhao Yiming Fang Chen Yang Jingliang Xin Key Lab of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao Hebei Province China Hebei Iron and Steel Group Tangshan Hebei Province China

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter.

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Deep Learning based Defect Detection Algorithm for Solar Panels 5

Deep Learning based Defect Detection Algorithm for Solar Pan...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Li, Jiaqi Li, Hongxu Wu, Yifan Zhou, Hailiang Manfredi, Luigi Li, Peng Zhang, Hong Hebei Province Yanshan University Department of Key Lab of Industrial Computer Control Engineering Qinhuangdao066004 China University of Alberta Department of Computing Science EdmontonABT6G 2R3 Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. China United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350307320

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots. © 2023 IEEE.

关键词： Solar panels

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Event-Triggered-Based Robust control of Heterogeneous Connected Vehicles Platoon with Communication Delay and Random Packet Loss

Event-Triggered-Based Robust Control of Heterogeneous Connec...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Chen Liu Shuangkun Ru Lin Qi Jiayi Fang Ting Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

With the development of vehicle autonomous driving technology and wireless network communication technology, the mix platoon of connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs) is becoming inevitable. The abnormal V2V communication and uncertain vehicle dynamics may affect the CAV control performance. Considering time-varying communication delays and random packet loss, an event-trigger-based robust control strategy is proposed based on the stability theory of stochastic time-delay systems. The Bernoulli packet loss model is constructed to solve the random packet loss problem. The robust controller with the event-triggered mechanism designed using the Lyapunov functional stability theory of stochastic systems ensures the stability of the mix platoon with vehicle heterogeneity and the stability of CAV. The simulation comparison has verified that the proposed control strategy can better suppress the fluctuation of the preceding vehicle and more quickly reach the desired value when both time delay and packet loss are present.

关键词： Robust control Fluctuations Wireless networks Packet loss Vehicular ad hoc networks Stability analysis Delays Vehicle dynamics Autonomous vehicles Time-varying systems

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Fast 3d-Graph Convolutional Networks for Skeleton-Based Action Recognition

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SSRN 2022年

作者： Zhang, Guohao Wen, Shuhuan Li, Jiaqi Che, Haijun Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Research on human action recognition based on skeleton has received a lot of attention. But most of the research focuses on improving the generalization ability of the model, while ignoring significant efficiency issues. This leads to the development of heavy models with poor scalability and cost-effectiveness in practical use. In this paper, we investigate the under-studied but practically critical recognition model efficiency problem. To this end, we present a new Fast Recognition Distillation (FRD) model learning strategy. Specifically, FRD trains a lightweight recognition neural network structure that can be quickly executed at low computational cost. It can be achieved by effectively disseminating the identification probability information of the teacher network to the lightweight network. We call the probability information of the teacher network as soft-target, and FRD can learn more potential information from soft-target. In addition, we also used a special loss function for soft-target. Through the FRD network, while basically maintaining the recognition accuracy, we minimized the network structure. Extensive experiments on the two large-scale datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate that our model (FRD) is more lightweight and refined than other models. Therefore, our model FRD is very practical. © 2022, The Authors. All rights reserved.

关键词： Distillation

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