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Partial Discharge recognition in Real Switchgear Based on soft Scoring Mechanism and Multi-Mode Fusion

Partial Discharge Recognition in Real Switchgear Based on So...

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China international Electrical and Energy conference (CIEEC)

作者： Tianyi Zhang Zhenguo Di Simeng Li Zewei Bu Hao Wu Sisi Hui Shengchang Ji Offshore Oil Engineering Co. Ltd. Tianjin China Offshore Oil Engineering Co. Ltd Tianji China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an China

Partial discharge (PD) in switchgear is closely related to its premature insulation degradation, and accurate identification of PD types is an important means to improve its operation condition and to ensure safety of power system. In this paper, four typical PD types are designed in real switchgear: point discharge, suspension potential discharge, strong vertical component along the surface discharge and weak vertical component along the surface discharge. UHF sensor, TEV sensor, AE sensor and HFCT are combined for detection of switchgear PD, and the detection sensitivity of different sensors under the same defect and the spectral characteristics of different defect types are analyzed. Five characteristic parameters of discharge density distribution and correlation coefficient of contour of the extraction spectrum are combined to form a new feature system. A soft scoring method is proposed to identify the PD pattern of switchgear. This method solves the issues associated with traditional recognition methods that rely solely on single spectral characteristics, resulting in low recognition accuracy and inaccurate recognition types. Moreover, it offers a theoretical framework for online monitoring and fault diagnosis of switchgear equipment.

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Differential Residual Learning for Facial Expression recognition 21

Differential Residual Learning for Facial Expression Recogni...

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5th international conference on Machine Learning and soft computing, ICMLSC 2021

作者： Pu, Lipeng Zhu, Lingyun Chongqing University of Technology Liangjiang International College China

ISBN: (纸本)9781450387613

The facial expression recognition algorithm based on convolution neural network (Convolutional Neural Network) still has the problem of imperfect feature point extraction. Therefore, a facial expression recognition algorithm based on the combination of the detail feature extraction network and pre-training model is proposed. According to the emotional face and neutral face of the residual of the images, learn the residual to extract features, and make the feature information accurate. Then, the face feature markers generated by the feature extraction network are loaded into the pre-training model respectively for classification and recognition. By grouping and crossing experiments on datasets CK+ and FER2013, the average accuracy of face recognition is 95.74% and 73.11%, respectively. Compared with the state-of-the-art recognition model, this method is effective in facial expression recognition to some extent. © 2021 ACM.

关键词： Feature extraction

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Seeing the World in Dots: A Machine Learning Adventure in Colorblind Perception

Seeing the World in Dots: A Machine Learning Adventure in Co...

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computing, Applications and Systems (COMPAS), IEEE international conference on

作者： Shah Rafatur Rahman Md Rashedul Islam Aditya Chakrabortty Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh School of Information Technology Deakin University Melbourne Australia

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Color is a vital communication element, conveying the message’s meaning and differentiating the contents. Nevertheless, color vision deficit (CVD), called colorblindness, affects 8% of the global population. This impairment alters color perception, making reading and understanding the message difficult. This issue becomes increasingly significant with the expanding accessibility of multimedia content in digital environments, including web browsers, digital images, entertainment media, and social media. One of the methods of identifying CVD is the Ishihara Color-blind Test. Ishihara images consist of a red-green background and a hidden number. Color-blind persons won’t recognize the number as they cannot recognize the red and green colors. Using color glasses or contact lenses this deficiency can be overcome. The main point is that there is no cure for this deficiency. This research explores machine learning's potential to overcome this challenge. We trained a neural network to detect and classify numbers from Ishihara images, successfully "passing" the test. With an impressive 96.42% accuracy, this technology offers promising hope for bridging the communication gap for colorblind individuals.

关键词： Visualization Image recognition Accuracy Image color analysis Social networking (online) Transfer learning Robustness pattern recognition Noise measurement Vision defects

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Modem Myoelectric Control – Is it Time to Change the Algorithmic Focus?

Modem Myoelectric Control – Is it Time to Change the Algori...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Rami N. Khushaba Oluwarotimi W. Samuel Ali H. Al-Timemy Guanglin Li Transport for NSW Alexandria NSW Australia School of Computing and Data Science Research Centre University of Derby Derby United Kigndom Department of Biomedical Eng. Al-Kkwarizmi College of Eng University of Baghdad Baghdad Iraq Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper explores the evolving landscape of Electromyogram (EMG) signal analysis, focusing on the growing prominence of deep learning (DL) algorithms for hand, wrist, and finger movement recognition. Such algorithms often come with high computational costs, potentially limiting the clinical translation on resource-limited devices and igniting more research on reduced complexity models. This prompts the question: is it time to shift the algorithmic focus in EMG pattern recognition, given the reported performance of some light-weight traditional or hybrid methods emphasizing synergy between different EMG signals? A comparative study is implemented between state-of-the-art deep learning extension for time series classification, denoted as Random Convolutional Kernel Transform (ROCKET), and simple, yet effective pattern recognition methods tailored to exploit basic forms of EMG signal synergies— Waveform Length Phasors (WLPHASOR), Root-Mean-Squared Phasor (RMSPHASOR), and the proposed novel Multi-Signal Waveform Length (MSWL). Tests are conducted on EMG data from 22 participants performing 11 hand and wrist movements using two EMG armbands (10 and 8 channels, respectively), utilizing the open-source LibEMG toolbox. Preliminary findings suggest that, while DL algorithms exhibit formidable capabilities, the performance gap with traditional EMG feature extraction methods may not be as substantial as anticipated. The observations of this study revealed no significant differences in average accuracies between ROCKET, WLPHASOR, and RMSPHASOR (87% average across participants). Furthermore, MSWL significantly enhances performance to 90%, and the combination of ROCKET+MSWL achieves 91% on average across all subjects. These findings challenge the narrative of DL dominance in EMG pattern recognition, urging a re-evaluation of the algorithmic focus and contributing valuable insights to the debate on information extraction from EMG signals.

关键词： Wrist Deep learning Rockets Convolution Time series analysis Transforms Feature extraction Electromyography pattern recognition Kernel

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Quantum One-class Classification With a Distance-based Classifier

Quantum One-class Classification With a Distance-based Class...

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international Joint conference on Neural Networks (IJCNN)

作者： de Oliveira, Nicolas M. de Albuquerque, Lucas P. de Oliveira, Wilson R. Ludermir, Teresa B. da Silva, Adenilton J. Univ Fed Pernambuco Ctr Informat Recife PE Brazil Univ Fed Rural Pernambuco Dept Estat & Informat Recife PE Brazil

ISBN: (纸本)9780738133669

The advancement of technology in Quantum computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of available qubits make it necessary to use solutions that use fewer qubits and fewer operations, mitigating such obstacles. Hadamard Classifier (HC) is a distance-based quantum machine learning model for pattern recognition. We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a quantum device and show that QOCC has advantages over HC.

关键词： quantum machine learning quantum computing pattern classification

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Interpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning Techniques

Interpretable Anomaly Detection: A Hybrid Approach Using Rul...

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international conference for Convergence of Technology (I2CT)

作者： Rahul Mohite Lahcen Ouarbya Department of Computing Goldsmiths University of London

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Anomaly detection is a critical aspect of ensuring the security and reliability of various systems in diverse domains, including cybersecurity, finance, and industrial processes. Traditional black-box anomaly detection methods often lack interpretability, making it challenging for users to understand the reasoning behind the detection of anomalies. In this research, we propose a novel hybrid approach that combines rule-based and machine learning techniques to enhance the interpretability of anomaly detection systems. Our method integrates a rule-based system that generates interpretable anomaly detection rules with machine learning components that leverage complex pattern recognition and classification capabilities. We evaluate the proposed approach on a diverse set of real-world datasets, demonstrating its effectiveness in identifying anomalies while providing transparent explanations of the detection process. Through comprehensive experimentation and comparative analysis with existing state-of-the-art methods, we showcase the superior interpretability and performance of our hybrid approach. Our findings highlight the significance of interpretability in anomaly detection systems and underscore the potential of the proposed approach for enhancing transparency and trust in critical decision-making processes. This research contributes to the advancement of interpretable anomaly detection techniques and opens avenues for future research in the domain of transparent and reliable anomaly detection systems.

关键词： Decision making Finance Machine learning Robustness Cognition pattern recognition Fraud

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Synaptic Weight Optimization for Oscillatory Neural Networks: A Multi-Agent RL Approach

Synaptic Weight Optimization for Oscillatory Neural Networks...

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IEEE international conference on Agents (ICA)

作者： Shuhao Liao Xuehong Liu Wenjun Wu Rongye Shi Junyu Zhang Haopeng Wang School of Artificial Intelligence Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China

ISBN: (数字)9798331539917

ISBN: (纸本)9798331539924

The Oscillatory Neural Network (ONN) presents itself as a promising architecture model for pattern recognition (PR), based on which advanced neuromorphic computing and integrated circuit designs are implemented. The core of the ONN’s PR capability lies in the synaptic weight design, i.e., how the neurons are connected to each other. Conventional design methods, like the Hebbian rule, are able to store only a limited number of patterns. In this paper, we propose a strategy to leverage the Multi-Agent Reinforcement Learning (MARL) for acquiring the optimal synaptic weights that can efficiently store more patterns into the ONN system as stable quilibria. To obtain the synaptic weights in a more efficient manner and further increase the number of patterns to be stored, we additionally propose a method to leverage Curriculum Learning (CL) to optimize the learning process of the policy. Experimental results demonstrate that the proposed MARL-based method outperforms baseline methods in terms of storing more patterns as stable equilibria in ONN.

关键词： Integrated circuit synthesis Neuromorphic engineering Design methodology Neurons Reinforcement learning Computer architecture pattern recognition Integrated circuit modeling Biological neural networks Optimization

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Electric Vehicle Parking Monitoring System Based on Object Detection Algorithm

Electric Vehicle Parking Monitoring System Based on Object D...

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Electronic Technology and Information Science (ICETIS), international conference on

作者： Yixin Xu Heyang Peng Lvzhuoyu Zhou Yudan Yan Jingu Xie Yixin Hou Harbin Economics and Management Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Science Harbin Institute of Technology Harbin China School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

With the continuous development of society and the increase of urbanization level, electric vehicles have gradually become one of the necessary means of transport for people's daily travel. However, the surge in the number of EVs has also triggered corresponding safety issues, including causing fires and obstructing traffic. The traditional manual supervision method is difficult to meet the needs of detection and alarm, and has low efficiency and high false alarm rate. Therefore, it is important to design a real-time detection system for illegal parking of electric vehicles. In this paper, focusing on the actual scenario of random parking of electric vehicles, we develop an original system for detecting illegal parking of electric vehicles, and in response to the safety problems that may be caused, we use the YOLOv8 algorithm to achieve fast and accurate detection and alarm with the help of computer vision, artificial intelligence, and pattern recognition technology, which realizes the innovative application of the algorithm in the field of electric vehicle monitoring.

关键词： Accuracy Vehicle detection Urban areas Object detection Electric vehicles Real-time systems pattern recognition

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Optical Observation for Body Angles of Cricket Players’ Motion to Identify the Batting Shots

Optical Observation for Body Angles of Cricket Players’ Mot...

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Advanced Research in computing (ICARC), international conference on

作者： Ishan Jayaweerage Ashan Induranga Kaveendra Maduwantha Samantha Kumara Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Faculty of Technology Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Cricket, one of the world’s most popular sports, has developed considerably during the past few decades. The competition and complexity of this sport increased largely during the past few years, which brought the attention of the researchers to develop technical solutions to improve the quality of the sport by improving both the on-field decision-making and practicing methods. There are several kinds of high-end equipment available in the market that can be used to improve these skills. Even though such equipment can be used at the national level, it is very difficult to use them at school and club levels due to their higher cost. This research focused on enhancing the batting skills of cricket batsmen using a low-cost setup that uses webcams to capture the footage and the MediaPipe library, which was developed by Google to analyze motion data. This research analyzed two cricket shots, the front foot defence, and the pull shot, using four web cameras. The received footage was analyzed using MediaPipe to identify the variations in the angles of the player’s left elbow and left shoulder while playing the shot. These pattern identifications can help successfully understand the angle variation of the forward foot defence and the pull shots by generating an average angle value graph using four web cameras. These graphs show the special patterns for each batting shot. This methodology and the results are valuable for developing a successful player analyzing system by considering the other important cricket shots at a very low cost with affordable 2D web cameras.

关键词： Integrated optics Costs Webcams Ultraviolet sources Lighting Machine learning Optical imaging pattern recognition Sports Foot

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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Smart Devices (ICSD), international conference on

作者： Amit Kumar Mishra Neeraj Kumar Pandey Manoj Diwakar Nilesh Bhaskarrao Bahadure Ankur Dumka Jagdish Chandra Patni Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dept. of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Computer Science & Engineering Women Institute of Technology Dehradun India Dept. of CSE Alliance School of Advance Computing Alliance University Bengaluru Karnataka India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate.

关键词： Training Accuracy Supervised learning Radial basis function networks Real-time systems pattern recognition Object tracking Object recognition Smart devices Logistics

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