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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of technology. All rights reserved.

关键词： Knowledge acquisition

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Identification of Electrical Equipment Based on V-I Trajectory, Odd Harmonic Currents 8

Identification of Electrical Equipment Based on V-I Trajecto...

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8th International Conference on Smart Grid and Smart Cities, ICSGSC 2024

作者： Fu, Yan Chen, Shengze Zhao, Fang Shi, Yvhan Ye, Siwen Jiang, Jianfeng Wang, Lei Yang, Xijun Electric Power Science Research Institute Shanghai Electric Power Company Shanghai China Software Development Center Shanghai China Shanghai Jiao Tong University Dept. of Electrical Engineering Shanghai China Beijing Zhixiang Technology Co. Ltd Beijing China

ISBN: (纸本)9798350377408

Non-intrusive load monitoring (NILM) and identification are important to smart grid construction and safe operation. A method combining mixed feature matrix and convolutional neural network based on V-I trajectory and odd harmonic components is proposed and proved for non-intrusive electrical load monitoring technology. In the paper, the specific contents are to analyze the basic characteristics of electrical equipment, including steady-state and transient characteristics of input current. Six simulation circuits of single-phase AC power supply powered electrical equipment are established by means of MATLAB/Simulink using Electrical or Electronic blockset, including heater, hair dryers LED string, adapter, variable frequency air conditioner (air-CON), and variable frequency refrigerators, as the electrical equipment to be analyzed and identified. The grid side current waveforms and spectrums, V-I trajectories, and odd harmonic current components of these six load models are extracted as basic features. The mixed feature matrix using V-I trajectory and odd harmonic features, as well as convolutional neural network (CNN), are proposed for the analysis and identification of electrical equipment. After training and testing on the PLAID dataset, the system can effectively identify electrical equipment under steady-state operation. In addition, three different detection methods are compared and analyzed to verify the superiority of the proposed method for analyzing and identifying electrical equipment based on mixed feature matrix and convolutional neural network. © 2024 IEEE.

关键词： Smart power grids

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Urdu Sentiment Analysis Using Deep Learning

Urdu Sentiment Analysis Using Deep Learning

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Imtisall Nasir Sibghat Ullah Bazai Muhammad Imran Ghafoor Shah Marjan Department of Computer Science Balochistan University of Information Technology Engineering and Management Science Quetta Pakistan Department of Computer Engineering Balochistan University of Information Technology Engineering and Management Science Quetta Pakistan Department of Engineering Pakistan Television Corporation Lahore Pakistan Department of Software Engineering Balochistan University of Information Technology Engineering and Management Science Quetta Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem.

关键词： Deep learning Training Sentiment analysis Accuracy Text analysis Social networking (online) Artificial neural networks Predictive models Electronic mail Testing

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Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System 13th

Content Prediction for Proactive Tile-Based VR Video Stream...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Liu, Qiuming Chen, Hao Zhou, Yang Wu, Dong Li, Zihui Bai, Yaxin School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China Information and Communication Branch State Grid Jiangxi Electric Power Co. Nanchang330095 China

ISBN: (纸本)9783031554704

Content prediction can avoid VR video streaming delay in mobile edge caching system. To reduce request delay, popular content should be cached on edge server. Existing work either focuses on content prediction or on caching algorithms. However, in the end-edge-cloud system, prediction and caching should be considered together. In this paper, we jointly optimize the four stages of prediction, caching, computing and transmission in mobile edge caching system, aimed to maximize the user’s quality of experience. We propose a progressive policy to optimize the four steps of VR video streaming. Since the user’s QoE is determined by the performance of the resource allocation and caching algorithm, we design a caching algorithm with unknown future request content, which can efficiently improve the content hit rate, as well as the durations for prediction, computing and transmission. We optimize the four stages under arbitrary resource allocation and simulate the proposed algorithm according to the degree of overlap, as well as completion rate. Finally, under the real scenario, the proposed algorithm is verified by comparing with several other caching algorithms, simulation results show that the user’s QoE is improved under the progressive policy and the proposed algorithm. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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Ontology Based Test Case Reduction in Regression Testing

Ontology Based Test Case Reduction in Regression Testing

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International Conference on Computer and Information technology (ICCIT)

作者： Ummer Farooq Usman Qamar Arslan Shaukat Abu Bakar Zubia Ashraf Mehwish Naz Department of Computer and Software Engineering College of Electrical and Mechanical Engineering (CEME) National University of Sciences and Technology (NUST) Islamabad Pakistan

Ontology-based test case reduction in regression testing during software testing is a sophisticated approach that leverages semantic knowledge representation to optimize the selection of test cases. By harnessing domain-specific ontologies, this methodology facilitates a more intelligent and efficient reduction process, ensuring that only the most relevant test cases are retained for subsequent testing cycles. This research paper explores the application of ontology-based technique to enhance the efficiency of regression testing in software development. By leveraging ontological knowledge representations, the study introduces a novel approach for reducing the number of test cases while preserving test coverage, thereby significantly optimizing the regression testing process. The proposed framework not only streamlines testing efforts but also enhances the overall quality assurance of software systems, making it a valuable contribution to software engineering practices.

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Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal

arXiv

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arXiv 2025年

作者： Li, Xiangyu Fan, Wanshu Shen, Yue Wang, Cong Wang, Wei Yang, Xin Zhang, Qiang Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer Science and Technology Dalian University of Technology Dalian China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University School of Computer Science and Technology Dalian University of Technology Dalian China

High-fidelity imaging is crucial for the successful safety supervision and intelligent deployment of vision-based measurement systems (VBMS). It ensures high-quality imaging in VBMS, which is fundamental for reliable visual measurement and analysis. However, imaging quality can be significantly impaired by adverse weather conditions, particularly rain, leading to blurred images and reduced contrast. Such impairments increase the risk of inaccurate evaluations and misinterpretations in VBMS. To address these limitations, we propose an Expectation Maximization Reconstruction Transformer (EMResformer) for single image rain streak removal. The EMResformer retains the key self-attention values for feature aggregation, enhancing local features to produce superior image reconstruction. Specifically, we propose an Expectation Maximization Block seamlessly integrated into the single image rain streak removal network, enhancing its ability to eliminate superfluous information and restore a cleaner background image. Additionally, to further enhance local information for improved detail rendition, we introduce a Local Model Residual Block, which integrates two local model blocks along with a sequence of convolutions and activation functions. This integration synergistically facilitates the extraction of more pertinent features for enhanced single image rain streak removal. Extensive experiments validate that our proposed EMResformer surpasses current state-of-the-art single image rain streak removal methods on both synthetic and real-world datasets, achieving an improved balance between model complexity and single image deraining performance. Furthermore, we evaluate the effectiveness of our method in VBMS scenarios, demonstrating that high-quality imaging significantly improves the accuracy and reliability of VBMS tasks. Codes are at https://***/ghfkahfk/EMResformer. Copyright © 2025, The Authors. All rights reserved.

关键词： Rain

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NLOS/LOS Classification by Constructing Indirect Environment Interaction From GNSS Receiver Measurements Using a Transformer-Based Deep Learning Model

NLOS/LOS Classification by Constructing Indirect Environment...

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2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Li, Zhenni Zeng, Kungan Wang, Lujia Kan, Xie Yuan, Rong Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Software and Engineering Sun Yat-sen University Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

The blockage of the direct signal by high-rise buildings degrades the positioning performance of the global navigation satellite system (GNSS) in urban canyons. Traditional methods usually distinguish the line-of-sight (LOS), and non-line-of-sight (NLOS) signals using the satellite’s elevation angle or signal strength to mitigate the issue, which cannot obtain satisfactory classification accuracy in complex urban environments. Recently, machine learning-based methods using GNSS measurements have been developed to improve GNSS signal classification. However, dynamic changes in environments and the limited features from the measurements raise a challenge to the classification performance of algorithms. To address the above issues, a transformer-based deep learning model in this paper is proposed for LOS and NLOS signal classification. The innovation of our proposed model is using the self-attention mechanism to construct the indirect environment interaction for learning the representation of environmental information. First, the measurements of captured satellites in the same epoch will be fed into the model. The model’s feature extraction layer can learn the fundamental representation. Then, the self-attention module can construct the indirect environment interaction by computing the relevance between the satellites. Finally, the module’s output is transferred to the output layer for classification. A comparative experimental study is conducted with real-world data collected in urban canyons. The results demonstrate that our model can keep more than 90% classification accuracy while support vector machine (SVM) and multilayer perception (MLP) decline to about 80% with the sites increasing. © 2023 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM.

关键词： Satellites

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Facial Expression Recognition Based on TripletLoss and Attention Mechanism

Facial Expression Recognition Based on TripletLoss and Atten...

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International Conference on Automation and Computing (ICAC)

作者： Rongqiang Gou Qiuyan Gai Zhijie Xu Yuanping Xu Chaolong Zhang Jin Jin Jia He Yajing Shi School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield UK

The paper proposes a facial expression recognition method called Triplet-Loss Attention Network (TAN), which aims to address the problem of large intra-class and inter-class distances in facial expression recognition. The method uses a self-attention mechanism to calculate the weight of triplet expression samples, which helps in determining the influence of key regions on expression recognition. The method uses images with attention weights above a certain threshold as training samples to form new hard triplets. The distance between high-weight samples in the three sets of samples is calculated using the Mahalanobis distance formula, and the difference in distance between high-weight groups of triplets is calculated using the Maharaja Loss function. The Triplet Loss is mainly used as the loss function in TAN, and the model is jointly optimized using Triplet Loss, Mahalanobis Loss, and Cross Entropy Loss functions to improve the performance of the model in facial expression recognition. Experimental results show that TAN performs well in alleviating the intra-class and inter-class distance problem and has good robustness and generalization performance. On the RAF-DB and FERPlus datasets, TAN achieves recognition accuracies of 88.40% and 88.73%, respectively, which is 1.37% and 0.18% higher than the previous state-of-the-art methods.

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MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

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Computer Modeling in engineering & Sciences 2021年第8期128卷 427-445页

作者： Juhong Tie Hui Peng Jiliu Zhou College of Computer Science Sichuan UniversityChengdu610065China School of Software Engineering Chengdu University of Information TechnologyChengdu610225China School of Computer Science Chengdu University of Information TechnologyChengdu610225China

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder *** used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.

关键词： MRI brain tumor segmentation U-Net dense block residual block

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Forming Circle Formations of Multi-agent Systems Under Actuator Faults 5th

Forming Circle Formations of Multi-agent Systems Under Actu...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Liu, Jiajun Ru, Xinfeng Xia, Weiguo Wang, Chen China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration Shanghai China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian China National Engineering Research Center of Software Engineering and the State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

ISBN: (纸本)9789811939976

A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial position of each agent is already on a circle and the control purpose is to form a desired formation on this circle. Based on this local information, a distributed adaptive formation control law is designed by employing the congelation of variables method to deal with time-varying actuator faults. Finally, a simulation example is given to verify the effectiveness of the adaptive fault-tolerant control design method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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