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Character recognition competition for street view shop signs

National Science Review 2023年第6期10卷 27-31页

作者： Jingqun Tang Weidong Du Bin Wang Wenyang Zhou Shuqi Mei Tao Xue Xing Xu Hai Zhang Shanghai Byte Dance Technology Co. Ltd School of Control Science and Engineering Zhejiang University School of Computer Science and Technology University of Science and Technology of China School of Computer Science and Technology Tsinghua Univeristy Shenzhen Tencent Technology Co. Ltd School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognitio...

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Improving Spiking Neural Network Performance Using Astrocyte Feedback for Farsi Digit Recognition 31

Improving Spiking Neural Network Performance Using Astrocyte...

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31st International Conference on Electrical engineering, ICEE 2023

作者： Nazari, Malihe Bahrami, Fariba Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Advanced Control Systems Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Advanced Control Systems Lab Tehran Iran

ISBN: (纸本)9798350312560

The third generation of neural networks is called spiking neural networks. Spiking neural networks can not only answer all the problems that can be solved by common neural networks, they can also be computationally more powerful than them. Also, these networks are closer to the biological reality of neurons. According to these reasons, spiking neural networks have received much attention in recent years. In addition to neurons, other elements are also used in spiking neural networks, whose connection with neurons has been proven biologically. These elements are called astrocytes. In this study, we have used astrocyte local feedback in a spiking neural network to improve the accuracy of the network in a digit recognition process. To reach this goal, we use the network proposed by Diehl et al in 2015, then we added astrocyte local feedback to this network. We run the network in both states. The classification accuracy of the network in the absence of astrocytes was 88.48% on Farsi handwritten digits. After adding astrocyte local feedback, it is observed that astrocytes control the activity of neurons and modified the weight of connections based on neuron activity. In this state classification accuracy reached 90.26%. The idea was to create a neural network for learning, where, inspired by the physiological role of astrocytes at the tripartite synapse improves the performance of the neural network and we demonstrate that systems that include neurons and astrocytes perform better than systems incorporating only neurons. © 2023 IEEE.

关键词： Neurons

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Practical Realization of Only Active Floating Lossless Inductance Simulator 10

Practical Realization of Only Active Floating Lossless Induc...

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10th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2024

作者： Roongmuanpha, Natchanai Unhavanich, Sumalee Pukkalanun, Tattaya Tangsrirat, Worapong School Of Engineering Department Of Computer Engineering Bangkok10520 Thailand Faculty Of Digital Technology Bangkok10300 Thailand School Of Engineering Department Of Instrumentation And Control Engineering Bangkok10520 Thailand

ISBN: (纸本)9798350385946

This paper describes an electronically controllable floating lossless inductance simulator circuit implemented with only off-the-shelf available integrated circuits, namely LT1228 and operational amplifier (OA). The proposed design consists solely of two LT1228s and a single OA. By using an active element named LT1228, the equivalent inductance can be tuned electronically with depended on the externally supplied current applied to LT1228. The proposed simulator imposed just one component matching restriction that required the equality of two transconductances. It is easily attainable with a simple current mirror. The worthiness of the proposed floating inductance simulator has been proven and applied to the construction of the 2nd-order RLC highpass/bandpass filter as an application. PSPICE simulations are used to investigate the characteristics of the proposed simulator and its filter realization. © 2024 IEEE.

关键词： Operational amplifiers

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A novel fractional physics-informed neural networks method for solving the time-fractional Huxley equation

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Neural Computing and Applications 2024年第30期36卷 19097-19119页

作者： Shi, Jieyu Yang, Xiaozhong Liu, Xinlong School of Control and Computer Engineering North China Electric Power University Beijing102206 China Institute of Information and Computing School of Mathematics and Physics North China Electric Power University Beijing102206 China

The neural network methods in solving differential equations have significant research importance and promising application prospects. Aimed at the time-fractional Huxley (TFH) equation, we propose a novel fractional physics-informed neural networks (fPINNs) method. By integrating the physical information of the TFH equation into neural networks, the fPINNs are trained as a precise approximation model for solving the TFH equation. The fPINNs method involves calculating the Caputo fractional derivative using the L1 formula and estimating the integer-order derivative through the chain rule. The Adam algorithm is employed to optimize two kinds of loss functions constructed by hard and soft constraints, respectively. Through an analysis of the impact of fPINNs parameters on training results, we identify the optimal parameter configuration for solving both one-dimensional and two-dimensional (2D) TFH equations. Numerical examples validate the efficiency and robustness of the fPINNs method in solving TFH equations. Furthermore, numerical simulation of the 2D TFH problem demonstrates the method’s practical applicability to engineering problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Numerical methods

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Power grid fault diagnosis method based on alarm information and PMU fusion 5

Power grid fault diagnosis method based on alarm information...

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5th International Conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Liu, Jiang Zheng, Ling Zhang, Xu Guo, Zixing School of Control & Computer Engineering NCEPU Changping District Beijing102206 China

ISBN: (纸本)9781510662520

With the rapid development of smart grid technology, more and more attention has been paid to the stability of the power grid. When the power grid fails, the dispatch center will receive various types of data, including the PMU data collected by the PMU (Power Management Unit) device used by the Wide Area Monitoring System (WAMS) and the data collected by the Supervisory control System for Data Acquisition and control (SCADA). Alarm information data, they describe the same grid fault from different dimensions. At present, the power grid fault diagnosis based on PMU data and alarm information texts has been fully studied, but the information source is single, and it is difficult to ensure the accuracy of the diagnosis results when complex faults and multiple faults occur in the power grid. This paper starts with the information source of power grid fault diagnosis, and develops a power grid fault diagnosis method that integrates the dual data sources of PMU and alarm information: 1. effectively connect alarm message text and PMU based on fault events. 2. complete the fusion of PMU and alarm information to improve the scope and accuracy of power grid fault diagnosis. Finally, the simulation fault data is used to test. The experimental results show that the accuracy of fault diagnosis of dual data source fusion is higher than that of single data. © 2023 SPIE.

关键词： Failure analysis

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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Optimized shearing strategy for heavy plate based on contour recognition

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Journal of Iron and Steel Research International 2023年第9期30卷 1821-1833页

作者： Jian-zhao Cao Yu-xia Wang Shao-wen Huang Chang-tao Wang School of Computer Science and Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina Shandong Iron and Steel Co. Ltd.Jinan 271105ShandongChina School of Electrical and Control Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina

The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was ***,multi-array binocular vision linear cameras were used to complete the image ***,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour ***,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was *** practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel *** maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 *** correlation coefficient of the length prediction model based on the back propagation neural network is very *** reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.

关键词： Heavy plate Contour detection Intelligent shearing strategy Length prediction Neural network model

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DDoS Attack Detection in Smart Grid Networks Using Visual Features 2

DDoS Attack Detection in Smart Grid Networks Using Visual Fe...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali Naqvi, Sardar Shan Li, Yuancheng Uzair, Muhammad School Of Control And Computer Engineering North China Electric Power University Beijing102206 China Comsats University Islamabad Department Of Computer Engineering Wah cantt Pakistan

ISBN: (纸本)9798350394962

Smart grid networks play a key role in making electric power infrastructure more efficient and ensuring reliable energy distribution. However, the interconnected communication devices in these networks expose the system to several network threats, such as the distributed denial of service (DDoS) attacks. Timely DDoS attack detection and mitigation is an important but challenging task for maintaining service integrity. In this paper, we propose to leverage computer vision models to detect DDoS attacks in smart grid networks. After preprocessing the input network data and extracting features, we transform the features into two-dimensional images, employing them to train and fine-tune a vision MLP mixer model. Our approach offers the advantage of adaptability, as any pre-existing computer vision model can be utilized for fine-tuning purposes, providing flexibility in the absence of readily available pretrained models in the DDoS detection domain. Performance metrics, including accuracy, precision, recall, and F1 score obtained from our experiments, show promising results for DDoS attack detection in smart grid networks using the UNB-ISCX-IDS dataset, which contains real-world network traffic data containing different attack scenarios. Our approach will be beneficial for robust and secure smart grid operation in real-world scenarios. © 2024 IEEE.

关键词： Network security

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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