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2021 International Conference on computer Vision and Pattern Analysis, ICCPA 2021

作者： Zhang, Yutong Li, Su Wang, Haorui School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781510651937

ISBN: (纸本)9781510651920

Music is an indispensable part of human society since ancient times. Its evolution is affected by the development of human history and has a wide impact on humans. This paper establishes a musical impact model based on factor analysis and a music similarity model based on the European distance model to quantify and analyze music evolution. First, we use Graph Theory, connect all artists, establish a network and draw an image. In order to measure the influence of each musicians, we selected three indicators, using TOPSIS to establish a factor-based music influence model. Finally, a subnet connected to the network is generated with five artists for the parent node. Next, we have retained 14 music characteristics in the complete music data related to the similarity of artist. We use factor analysis to reduce these 14 music features to obtain five representative main components. Finally, the SPSS software has established a music similarity model based on the Euclidean distance model, and the similarity of artists within the genre and the artists is calculated, 0.7842 and 0.7804, respectively. It can be seen that the artists of the same flow are more similar © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Factor analysis

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Cluster-Refined Optimal Transport for Unsupervised Action Segmentation

Cluster-Refined Optimal Transport for Unsupervised Action Se...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shijie Zhang, Jinrong Liu, Yule Li, Shiyao Feng, Lin School of Computer Science and Technology Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China School of Innovation and Entrepreneurship Dalian University of Technology Dalian China

ISBN: (纸本)9798350368741

Action segmentation in untrimmed videos is essential for comprehensive video understanding. Despite significant progress in unsupervised methods, capturing both long-range dependencies and short-duration actions simultaneously remains a challenging task. To address this challenge, this paper introduces the Cluster-Refined Optimal Transport (CROT) method, combining hierarchical clustering and optimal transport for unsupervised action segmentation. We first hierarchically cluster video frame representations to capture long-range dependencies and generate pseudo-boundaries. Initial pseudo-labels are then obtained via optimal transport, ensuring short-duration actions are recognized. Finally, these pseudo-labels are refined using the pseudo-boundaries, resulting in the final segmentation output. Extensive experiments on three public datasets, i.e., YouTube Instructions, Breakfast, and 50Salads, demonstrate that our method performs on par with or better than previous approaches. © 2025 IEEE.

关键词： action segmentation hierarchical clustering optimal transport unsupervised learning

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Layer normalized OpenGAN framework for human intrusion activity recognition in a φ-OTDR optical fiber sensing system

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Optics Express 2025年第10期33卷 20272-20295页

作者： Zhou, Zhengxian Wang, Xiankun Xie, Tianchang Qu, Jun Shi, Jianping Zhang, Dawei Peng, Jiansheng Zhuang, Songlin Anhui Province Key Laboratory for Control and Applications of Optoelectronic Information Materials Anhui Normal University Wuhu241000 China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Artificial Intelligence and Manufacturing Hechi University Guangxi546300 China

Distributed optical fiber vibration sensing systems (DVS) are widely employed in perimeter security for their high sensitivity, simplicity, and strong immunity to electromagnetic interference. However, these systems are facing with two serious challenges: accurately classifying closed-set signals (known events) and detecting open-set signals (unknown events). To address this, we propose an open-set recognition framework, ResEff-OpenGAN-LN. By integrating layer normalization into the OpenGAN architecture, this framework mitigates instability caused by input feature variations while leveraging ResEff for efficient feature extraction to enhance closed-set classification. Experimental results show that ResEff achieves 99.92% accuracy on closed-set tasks, and ResEff-OpenGAN-LN obtains an AUROC of 0.9900 with 96.63% overall accuracy on mixed datasets containing open-set and closed-set signals, validating its potential to improve intrusion detection and reduce false alarms. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Light interference

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Performance Comparison of Kalman Filter and Extended Kalman Filter for Human Tracking and Prediction with Particle Swarm Optimisation 1

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2022

作者： Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan Department of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Electronics and Computer Engineering Lagos State University Epe Campus Lagos Nigeria

ISBN: (数字)9789811987038

ISBN: (纸本)9789811987021

In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted in a broad variety of applications, including surveillance, biometric identification or biological imaging, human-machine interactions, traffic control, and intelligent vehicles, all of which have benefited from tracking applications. This paper presents an IoT-based human tracking system that employs the Kalman filter (KF) and extended Kalman filter (EKF) algorithms. However, the performance of the filters is impacted by noise. Therefore, instead of manually tuning the KF and EKF’s process noise and measurement error, particle swarm optimisation (PSO) is utilised to optimise them. Four 2-dimensional models (namely conventional KF and EKF, optimised KF-PSO, and EKF-PSO) were developed and evaluated using ten distinct human sets of data containing 100 samples each. The object’s tracked positions are estimated in horizontal and vertical directions. Accuracy analysis was used to compare the four models’ quality performance. With an average mean square error of 5.99 mm (or 0.599%), the EKF-PSO model outperformed the conventional EKF model, which had an error of 7.18 mm (or 0.718%). A 17.2 mm (or 1.72%) error was found in the KF-PSO model. The last one is the traditional KF model, yielding an error of 22.4 mm (or 2.24%). As a result of its higher accuracy, the EKF-PSO model outperforms the other three models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Influence of the load variations on the PSS performance during steady state and transient state 20

Influence of the load variations on the PSS performance duri...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Hashim, Wassan Adnan Hamad Hameed, Jawad Derbel, Nabil Al-Qalam University College Department of Technical Computer Engineering Kirkuk Iraq University of Sfax National Engineering School of Sfax Control & Energy Management Lab. Tunisia College of Petroleum Processes Engineering Tikrit University Department of Petroleum Systems Control Engineering Iraq University of Sfax National Engineering School of Sfax Control & Energy Management Lab. Sfax Tunisia

ISBN: (纸本)9798350332568

The load on a power station varies from time to time due to uncertain demands of the consumers and is known as variable load on the station. Also it is known to have an effect on the performance of a power system stabilizer while damping inter-area oscillation. In this paper conventional lead lag PSS and PSS2B are assessed on real case studies;Peerdawod Gas power station in north of Iraq- Erbil. The PSS utilizes the local rotor speed and power as its input. To investigate the effects of dynamic loads on power systems stability, the effectiveness of PSS is tested and compared with the variation of loads during and after sadden faults. The simulation software that used MatlabTM/Simulink;and results of this paper contribute to the understanding of how load variations influence the system damping based on PSS. © 2023 IEEE.

关键词： Voltage regulators

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On non-von Neumann flexible neuromorphic vision sensors

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npj Flexible Electronics 2024年第1期8卷 638-663页

作者： Hao Wang Bin Sun Shuzhi Sam Ge Jie Su Ming Liang Jin Institute for Future Shandong Key Laboratory of Industrial Control TechnologySchool of AutomationQingdao UniversityQingdao 266071China College of Electronic and Information Engineering Qingdao UniversityQingdao 266071China Department of Electrical and Computer Engineering and Institute for Functional Intelligent MaterialsNational University of SingaporeSingapore 117583Singapore

The structure andmechanismof thehuman visual system contain rich treasures,and surprising effects can be achieved by simulating the human visual *** this article,starting from the human visual system,we compare and discuss the discrepancies between the human visual system and traditional machine vision *** the wide variety and large volume of visual information,the use of nonvon Neumann structured,flexible neuromorphic vision sensors can effectively compensate for the limitations of traditional machine vision systems based on the von Neumann ***,this article addresses the emulation of retinal functionality and provides an overview of the principles and circuit implementation methods of non-von Neumann computing ***,in terms of mimicking the retinal surface structure,this article introduces the fabrication approach for flexible sensor ***,this article analyzes the challenges currently faced by non-von Neumann flexible neuromorphic vision sensors and offers a perspective on their future development.

关键词： Neumann system starting

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A Self-Supervised Contrastive Learning Method for Radar Signal Modulation Recognition

A Self-Supervised Contrastive Learning Method for Radar Sign...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Shiya Li Xiaolin Du School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Effective implementation of supervised learning-based radar signal modulation recognition (RSMR) techniques is heavily dependent on the quantity and quality of labeled datasets. However, the high cost and difficulty involved in analyzing and labeling radar signal samples limit its development. To address this issue, a RSMR system that utilizes self-supervised contrastive learning (SSCL) methodology is proposed. In the classical contrastive learning framework MoCo V2, a custom data augmentation method is employed to capture time-frequency features of the radar signal. Furthermore, the feature extraction network ResNet50 is enhanced by separating spatial and channel filters, resulting in increased sensitivity to time-frequency features. To improve recognition accuracy, two loss functions, alignment and uniformity, are employed in place of the info noise contrastive estimation (InfoNCE) loss, and both loss functions are optimized directly. The experiments demonstrate the effectiveness of the proposed system.

关键词： Time-frequency analysis Accuracy Sensitivity Noise Modulation Radar Feature extraction

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Attribute Reduction in Multi-scale Fuzzy Decision Systems 11th

Attribute Reduction in Multi-scale Fuzzy Decision Systems

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Liu, Hengke Zhang, Nan School of Computer and Control Engineering Yantai University Yantai 264005 China

ISBN: (纸本)9783031927461

The theory of rough sets provides a valuable approach in artificial intelligence and data mining. Optimal scale selection and attribute reduction are meaningful problems in rough set theory. Many related studies have been done in recent years. However, optimal scale selection based on the fuzzy conditional entropy and attribute reduction in multi-scale fuzzy decision systems have not been discussed yet. Aiming at this problem, we develop a novel algorithm based on the fuzzy conditional entropy to determine the optimal scale in a multi-scale fuzzy decision system. An attribute reduction algorithm is also proposed to reduce the redundant attributes in a fuzzy decision system that contains the optimal scale. Experimental results demonstrate that the proposed algorithms achieve higher classification accuracy and efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attribute reduction Multi-scale fuzzy decision system Rough sets

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Anomaly Detection for Bolt Tightening in Automotive Engine Assembly Based on Machine Learning

Anomaly Detection for Bolt Tightening in Automotive Engine A...

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computer engineering and Intelligent control (ICCEIC) International Conference on

作者： Dong Zhang Zhendong Cui School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

With the advancement of industrial automation, the quality requirements for automotive engine assembly bolt tightening have become increasingly stringent, as they are directly related to engine performance and vehicle safety. Traditional monitoring methods for bolt tightening rely on preset torque criteria, which often overlook anomalies such as material defects and tool wear. To address the issue of limited negative samples in industrial data, this study proposes an innovative positive sample boundary modelling strategy based on artificial intelligence, combined with a random forest algorithm for anomaly detection. By transforming time-series data into high-dimensional feature data, an efficient classifier is constructed through the integration of multiple decision trees. Experimental results from real engine assembly line data demonstrate that the proposed method can quickly and accurately identify abnormal conditions without increasing computational burden, significantly improving assembly quality and reducing failure rates.

关键词： Adaptation models Automation Accuracy Forestry Fasteners Decision trees Random forests Engines Assembly Anomaly detection

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Fast sequence to graph alignment based on graph folding

Fast sequence to graph alignment based on graph folding

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanfei Deng Jinjun Kang Zhuang Liu Xiao Zhu Wei Quan School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sequence-to-graph alignment is a critical component of pan-genome-based read alignment and represents the most computationally intensive step in this process. To address this challenge, we have introduced a sequence-to-graph alignment algorithm based on graph folding, which minimizes the size of the graph structure and enhances alignment efficiency while identifying the optimal path. Experiments on both simulated and real datasets demonstrate that our algorithm significantly improves the speed of sequence-to-graph alignment. The source code is available at Github: https://***/zzzerd/fgpoa

关键词： Source coding Bioinformatics Software development management

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