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Diffusion Ranking Scores Based Improved Hypergraph Generation Method for Visual Classification Tasks

Diffusion Ranking Scores Based Improved Hypergraph Generatio...

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data driven control and learning systems (ddcls)

作者： Shuai Wang Kang Wang Xiaoli Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Visual classification has attracted considerable research attention in the last few decades. this paper presents an approach to improve the hypergraph structure using a diffusion ranking method and employs hypergraph neural networks for such tasks. Hyperedges are formed by connecting a set of vertices, and the generation of the hypergraph structure is typically task-dependent, making hypergraph generation methods less generalizable. Previous approaches have employed implicit hypergraphs for visual tasks, especially in visual classification, using attribute-based or neighborhood-based methods. However, these approaches cannot fully explore the global manifold structure of the feature space. To address this problem, this paper proposes a novel two-stage hypergraph construction method, which fully utilizes the ability of diffusion process to capture the manifold structure of feature data. Initially, the hypergraph structure is constructed using a neighbor-based approach, and subsequently, the final hypergraph structure is obtained by ranking scores of diffusion processes that reflect the degree of vertex similarity. Comparing with state-of-the-art hypergraph neural network algorithms on classical 3D visual image datasets, experimental results demonstrate the effectiveness of the proposed algorithm.

关键词： Manifolds learning systems Visualization three-dimensional displays Neural networks Diffusion processes Process control

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Slow Feature thermography for Non-Destructive Evaluation of Defects in Composite Materials

Slow Feature Thermography for Non-Destructive Evaluation of ...

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data driven control and learning systems (ddcls)

作者： Kaixin Liu Xin Zhong Ping Chen Yun Dai Yi Liu The State Key Lab for Electronic Testing Technology North University of China China Department of Information and Communication Engineering North University of China Taiyuan China College of Mechanical Engineering Nantong Vocational University Nantong China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method.

关键词： Image recognition Composite materials Time series analysis Infrared imaging Feature extraction thermal noise thermal analysis

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Semi-Supervised Domain Adaptation with Feature Separation for Wind Turbine Anomaly Detection

Semi-Supervised Domain Adaptation with Feature Separation fo...

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data driven control and learning systems (ddcls)

作者： Guangqiang Ming Shihao Ma Junyang Xu Jiaye Wang Zhenwei Jia Chunhui Zhao Powerchina Huadong Engineering Corporation Ltd. Electromechanical Engineering Institute (Huachen Electric Power) Hangzhou P. R. China College of Control Science and Engineering Zhejiang University Hangzhou P. R. China Institute of Intelligence Science and Engineering Shenzhen Polytechnic University Shenzhen P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Anomaly detection for the wind power generation process is important for the maintenance and early warning of wind turbines. Deep learning-based methods are widely used in wind turbine anomaly detection. However, the data distribution changes frequently due to the changing working conditions of wind turbines, and deep learning methods are difficult to directly apply to new working conditions with scarce labeled data. therefore, this paper proposes a semi-supervised domain adaptation method with feature separation (SSDA-FS) for wind turbine anomaly detection. First, a feature separation domain adaptation strategy is designed to eliminate inter-domain differences. the feature space is decomposed into domain-related space and anomaly-related space through the devised adversarial training. then a semi-supervised timing augment strategy is proposed, which can augment the unlabeled samples and a few labeled samples in the target domain. the proposed method is implemented and validated in a wind turbine anomaly detection dataset, and the performance is significantly improved compared with the comparison methods.

关键词： Employee welfare Training learning systems Wind power generation Wind farms Feature extraction Wind turbines

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Design and Stability Analysis of Vehicle Platooning control in Zero-Trust Environment

Design and Stability Analysis of Vehicle Platooning Control ...

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data driven control and learning systems (ddcls)

作者： Ling Zhu Darong Huang Yuhong Na Xinrong Li Institute of Information Science and Engineering Chongqing Jiao tong University Chongqing P. R. China School of Artificial Intelligence Anhui University Hefei P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Vehicle platoon control constitutes a pivotal aspect of intelligent transportation system development, most of the previous research on vehicle platoon control is carried out under mutual default trust, without considering the trust factor between interconnected vehicles, the trust factor between vehicles has a significant influence on the cooperative behavior of the vehicles, the way of information sharing, and the security of the vehicles, etc. In the actual traffic scenario, vehicle cooperative process is often In real transportation scenarios, the process of vehicle collaboration is often subject to various network attacks, so vehicle collaboration is always facing security threats. Zero-trust network is a kind of de-boundary network, which requires individuals in the network to “never trust and verify in real-time”, and vehicle platoon control can be placed in the zero-trust environment to avoid these problems. Firstly, we employ the C-F uncertainty inference method to assess the trust of nodes of interconnected vehicles. Secondly, we constructed a vehicle platoon dynamics model containing node trust, and based on this, we constructed a vehicle platoon dynamics model containing node trust, and designed a distributed controller. Finally, the effectiveness of the method proposed in this paper is verified by numerical simulation experiments.

关键词： Uncertainty Collaboration Process control Transportation Numerical simulation Stability analysis Real-time systems

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Safe Reinforcement learning for Risk-Aware UAV-Assisted Mobile Edge Computing Networks

Safe Reinforcement Learning for Risk-Aware UAV-Assisted Mobi...

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data driven control and learning systems (ddcls)

作者： Xiangpei Shi Yuanda Wang Lu Dong School of Cyber Science and Engineering Southeast University Nanjing China School of Automation Southeast University Nanjing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Mobile Edge Computing (MEC) empowers mobile users (MUs) to execute tasks with high computational demands by harnessing the computational capabilities of the network edge. Concurrently, the unmanned aerial vehicle (UAV) enhances wireless systems with adaptable deployment. Utilizing UAV s equipped with computing resources as edge nodes for ground MUs presents a highly promising paradigm. However, the presence of risk areas within the flight range poses significant challenges to flight safety during task execution. this study investigates a safe reinforcement learning (safe RL) approach specifically designed for UAV-assisted MEC networks with risk awareness. Equipped with computing resources, UAV offers offloading services to MU s while prioritizing flight safety and avoiding prolonged navigation within risk areas. Our goal is to collaboratively optimize user scheduling, UAV flight paths, and resource allocation, aiming to minimize processing latency and reduce UAV entry into risk areas whenever possible. Addressing the dynamic changes in complex environments, this paper integrates reinforcement learning algorithms with the Lagrangian method, introducing a secure computing offloading strategy based on SAC-Lagrangian. Comprehensive simulation results validate the effectiveness of the proposed algorithm in terms of latency management and risk avoidance.

关键词： Wireless communication Multi-access edge computing Navigation Heuristic algorithms Simulation Reinforcement learning Autonomous aerial vehicles

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Cooperative control of Virtual Coupled High-Speed Trains with the Consideration of Ride Comfort: A Distributed Tube-Based Model Predictive control Approach

Cooperative Control of Virtual Coupled High-Speed Trains wit...

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data driven control and learning systems (ddcls)

作者： Lei Zhu Xijuan Zhang Deqing Huang L. Xuefang School of Electrical Engineering Southwest Jiaotong University Chengdu P. R. China AVIC Chengdu Aircraft Industrial (Group) Co. Ltd Chengdu P. R. China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this work, a distributed tube-based model predictive control (TMPC) method is proposed to address the cooperative control of virtual coupled high-speed trains (HSTs), which ensures the ride comfort of passengers. Firstly, a nonlinear MPC scheme is developed based on the train nominal dynamics to address the cooperative operation among multiple HSTs with different initial states. Especially, the constraints related to the passenger comfort, including the acceleration and the acceleration change rate, are transformed into the input and the input increment constraints of the MPC respectively, which ensures the ride comfort of the HSTs. Subsequently, the recursive feasibility and stability of the MPC are proved theoretically. Secondly, an auxiliary feedback controller is devised based on the actual train dynamics of the train to deal with the external disturbances while improving the operational stability. the auxiliary feedback controller, comprising a discrete-time sliding mode control (DSMC) and a discrete-time disturbance observer (DTDO), utilizes the optimal state sequences generated by the nonlinear MPC as a reference for the feedback control. Additionally, the stability of the auxiliary feedback controller is strictly proved. Finally, the effectiveness of the proposed distributed TMPC is verified through simulations.

关键词： learning systems Optimal control Cooperative systems Predictive models Stability analysis data models Feedback control

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A Fault Diagnosis Method for Key Equipment of Bearer Network Transmission Based on Knowledge Graph and Multi-modal Neural Network

A Fault Diagnosis Method for Key Equipment of Bearer Network...

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data driven control and learning systems (ddcls)

作者： Weilin Chen Qiang Qu Huifeng Guo Haibin Li Zehui Mao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Naniing P. R. China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Aiming to address the challenge of incorporating multiple fault phenomena and utilizing accompanying information such as negation and time adverbials in the process of diagnosing faults in key equipment for bearer network transmission, this study proposes a fault diagnosis method based on knowledge graph and multi-modal neural network. By constructing a knowledge graph from a fault diagnosis knowledge base, this method retrieves candidate fault root causes based on the fault phenomena and extracts subgraphs along the reasoning paths from the fault phenomena to the candidate fault root causes in the knowledge graph. Subsequently, leveraging both the reasoning paths within the subgraphs and the fault maintenance texts, the multi-modal neural network calculates the confidence levels of the candidate fault root causes, selecting the fault root cause with the highest confidence as the reasoning conclusion. Finally, the proposed method constructs a knowledge graph based on the fault diagnosis knowledge base of key equipment for bearer network transmission provided by a domestic enterprise, and validates the efficacy of the approach using fault data employed to train the multi-modal neural network. Results demonstrate that this method effectively leverages multiple fault phenomena and their accompanying information in fault maintenance texts for accurate fault diagnosis, yielding favorable performance.

关键词： Fault diagnosis learning systems Neural networks Knowledge based systems Knowledge graphs Predictive models control systems

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Ultrasonic Signal Recognition Method Based on Single Class Attention Loss Function

Ultrasonic Signal Recognition Method Based on Single Class A...

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data driven control and learning systems (ddcls)

作者： Liwei Wang Jinhai Liu Yuan Zhong Hongyu Yang Collage of Information Science and Engineering Northeastern University Shenyang P. R. China Shenyang Branch of National Pipeline Network Group Storage and Transportation Technology Development Co. LTD. Shenyang P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Accurate identification of ultrasonic signals can effectively improve the accuracy of defect detection and inversion. However, ultrasonic signals with the different numbers of echoes in ultrasonic NDT data show extremely high similarity in high-dimensional space due to the influence of echo feature time shift and noise interference, thus affecting the classification accuracy. To address the above issue, we introduce a method of ultrasonic signal recognition based on Single Class Attention (SCA) loss function. First, SCA loss function gathers the feature vectors of positive class to an anchor point in the embedding space, and moves the feature vectors of negative class away from the anchor point. It makes the neural network focus on a single class of data in the feature extraction process, and enhances the feature extraction ability of the network for a specific class of data. In addition, we design a feature extraction network composed of three subnetworks, and use the proposed SCA loss function to optimize the network to extract more distinguishable features. the ultrasonic signal collected in real environment is adopted in the experiment, and the experimental results show that our method outperforms the other baseline methods in ultrasonic signal recognition.

关键词： learning systems Accuracy Neural networks Noise Interference Feature extraction control systems

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A Smooth Trajectory Planning for High Tibial Osteotomy Surgery with Robot-assisted Based on NURBS and S-curve Method

A Smooth Trajectory Planning for High Tibial Osteotomy Surge...

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data driven control and learning systems (ddcls)

作者： Yuan Li Guanbin Gao Yi Bao Xinkai Chen Xinyang Guo Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming China The First People's Hospital of Yunnan Province Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Compared to conventional freehand procedures, robot-assisted surgery is more efficient and minimally invasive. the planning procedures decide the quality of robot-assisted surgery. However, the traditional polynomial function method and spline method can lead the robot to oscillate when executing the trajectory for puncture or location. To generate a smooth and steady trajectory to improve the quality of robot-assisted surgery, this paper proposes a trajectory planning method for the planning of puncture points in High Tibial Osteotomy (HTO). First, the coordinates of puncture points are obtained by creating a computer-aided design (CAD) model of the leg bone with the osteotomy plate. then, the non-uniform rational B-spine (NURBS)method is applied to generate a smooth trajectory for puncture points. the S-curve method is used to plan the velocity and acceleration. Finally, simulations are conducted to verify the correctness and effect of the proposed method, which show that the proposed method can improve the trajectory of surgery in terms of smoothness and jerk reduction.

关键词： Solid modeling Surface reconstruction Design automation Trajectory planning Robot kinematics Surgery Trajectory

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Intelligent Drug Recognition System Based on Improved YOLOv7 and PP-OCRv3

Intelligent Drug Recognition System Based on Improved YOLOv7...

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data driven control and learning systems (ddcls)

作者： Zhenhao Chen Yilin Chen Zongjin Yang Na Qin Deqing Huang Institute of Systems Science and Technology Southwest Jiaotong University Chengdu P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the manual dispensing of drugs by pharmacists is time-consuming, labor-intensive, and has a low accuracy rate. In order to address this issue, this paper proposes an automated drug dispensing method based on improved object detection and text recognition. this method first embeds a Simple Attention Module (SimAM) into the network structure of YOLOv7, named YOLOv7_Sim, to achieve the localization and recognition of medicine bottles. Additionally, a lightweight PP-OCRv3 is employed for text recognition on liquid bags, and the similarity between the recognition results and the names of drugs on the prescription is used to determine a match. the novelty of this method lies in the combination of YOLOv7 and SimAM, which improves the accuracy of medicine bottle recognition and enhances the algorithm's robustness. Experimental results demonstrate that compared to YOLOv7, YOLOv7_Sim shows significant performance improvements, with a 3.12% increase in mAP, 0.69% increase in precision, and 2.83% increase in recall for the same type of medicine bottle. the proportion of fully recognized text on liquid bags is 90.6%. the drug recognition system, which combines YOLOv7_Sim and lightweight PP-OCRv3, has practical engineering significance for automating the drug dispensing process.

关键词： Drugs Location awareness learning systems Accuracy Liquids Text recognition Manuals

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