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arXiv 2024年

作者： Picard, Joseph S. Bar, Amitay Talmon, Ronen The Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa32000 Israel

Covariance fitting (CF) is a comprehensive approach for direction of arrival (DoA) estimation, consolidating many common solutions. Standard practice is to use Euclidean criteria for CF, disregarding the intrinsic Hermitian positive-definite (HPD) geometry of the spatial covariance matrices. We assert that this oversight leads to inherent limitations. In this paper, as a remedy, we present a comprehensive study of the use of various Riemannian metrics of HPD matrices in CF. We focus on the advantages of the Affine-Invariant (AI) and the Log-Euclidean (LE) Riemannian metrics. Consequently, we propose a new practical beamformer based on the LE metric and derive analytically its spatial characteristics, such as the beamwidth and sidelobe attenuation, under noisy conditions. Comparing these features to classical beamformers shows significant advantage. In addition, we demonstrate, both theoretically and experimentally, the LE beamformer’s robustness in scenarios with small sample sizes and in the presence of noise, interference, and multipath channels. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation 3

SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Zhang, Jiusi Tian, Jilun Jiang, Yuchen Luo, Hao Huang, Cong-Sheng Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States Norwegian University of Science and Technology Faculty of Engineering Trondheim7034 Norway

ISBN: (纸本)9798350324754

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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Implementation of a low-code monitoring system for solid oxide fuel cells 4

Implementation of a low-code monitoring system for solid oxi...

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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Sun, BenBen Guan, WanBing Wang, JianXin Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Advanced Fuel Cells and Electrolyzers Cell Technology of Zhejiang Province Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9781510666412

Solid oxide fuel cells (SOFCs) have the advantages of high conversion efficiency, low working noise, and environmental protection. Thus, they are widely used in large-scale power generation, combined heat power, peak load-regulating energy storage, and other fields. In the actual working process of SOFCs, the battery performance will decline sharply due to extreme conditions such as overtemperature and peroxide, and the service life will be terminated. Remote monitoring of fuel cells can detect problems in time and restore the battery’s performance by restoring the battery, thereby extending its service life. The data generated by fuel cells during operation have a strong temporal correlation. To discover the future state of fuel cells in time and solve the problem that a large number of codes are needed for monitoring, this paper designs a remote monitoring system of fuel cells with low-code based on Node-RED and Ali Cloud. The output current of fuel cells is predicted by using the improved back propagation neural network based on Levenberg-Marguardt (LM-BPNN) algorithm. The test result of LM-BPNN shows that the test samples have a root mean square error of 0.172, a mean absolute percentage error of 3.85%, and a coefficient of determination of 0.998. © 2023 SPIE.

关键词： Digital storage

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Towards a real-time control of robotic ultrasound using haptic force feedback

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Current Directions in Biomedical engineering 2022年第1期8卷 81-84页

作者： Schreiter, J. Semshchikov, V. Hanses, M. Elkmann, N. Hansen, C. University of Magdeburg Faculty of Computer Science & Research Campus Stimulate Magdeburg Germany University of Magdeburg Faculty of Electrical Engineering and Information Technology Magdeburg Germany Fraunhofer Institute for Factory Operation and Automation Iff Magdeburg Germany

Ultrasound is a widely used imaging technique and is appreciated for its non-invasiveness, absence of radiation, widespread availability, and compact equipment. Ergonomic difficulties in manual handling of the probe could be enhanced by a robotic controlled ultrasound. The paper addresses the development of such a system which enables remote operation of a ultrasound probe and includes haptic force feedback as well as video conferencing components for visual feedback. The development process followed a user-centered approach by investigating needs of potential end-users. Preliminary results demonstrated the functionality of the developed system for generating medical image data under laboratory conditions. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Probes

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

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Intelligent Automation & Soft Computing 2023年第5期36卷 2363-2378页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mofreh A.Hogo Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Electrical Engineering Department Faculty of EngineeringBenha UniversityBenha13518Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Women from middle age to old age are mostly screened positive for Breast cancer which leads to *** over the past decades,the overall sur-vival rate in breast cancer has improved due to advancements in early-stage diag-nosis and tailored *** all hospital brings high awareness and early detection technologies for breast *** increases the survival rate of *** traditional breast cancer treatment takes so long,early cancer techniques require an automation *** research provides a new methodol-ogy for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best ***,after successful learning of Convolutional Neural Network(CNN)algorithms,data augmentation is used to enhance the representation of the feature *** it uses BreastNet18 withﬁne-tuned VGG-16 model for pre-training the augmented *** feature classiﬁcation,Entropy controlled Whale Optimization Algorithm(EWOA)is *** features that have been optimized using the EWOA were utilized to fuse and optimize the *** identify the breast cancer pictures,training classiﬁers are *** using the novel probability-based serial technique,the best-chosen characteristics are fused and categorized by machine learning *** main objective behind the research is to increase tumor prediction accuracy for saving human *** testing was performed using a dataset of enhanced Breast Ultrasound Images(BUSI).The proposed method improves the accuracy com-pared with the existing methods.

关键词： Deep learning classiﬁcation data augmentation feature extraction the fusion of features breast cancer optimization classiﬁcation

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Inverse Solution Accuracy Using 12-Lead ECG vs 9 Significant Electrodes Derived by Greedy Algorithm 50

Inverse Solution Accuracy Using 12-Lead ECG vs 9 Significant...

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50th Computing in Cardiology, CinC 2023

作者： Ondrusova, Beata Tino, Peter Svehlikova, Jana Slovak Academy of Sciences Institute of Measurement Science Slovakia Slovak University of Technology in Bratislava Faculty of Electrical Engineering and Information Technology Bratislava Slovakia School of Computer Science The University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9798350382525

The inverse problem of electrocardiography is solved using potential recordings from numerous torso electrodes. However, there is a growing interest in minimizing the number of used electrodes to enhance clinical applicability. A total of 8 datasets of patients with pacemakers obtained from the EDGAR database were used. The inverse problem assuming a single dipole cardiac source was solved for 3 different electrode configurations. The configurations included all 196 pm 28 electrodes, 9 corresponding to the 12-lead ECG, and the 9 most significant electrodes. The significance of the electrodes was derived by the greedy algorithm from the singular value decomposition of the transfer matrix. The accuracy of the inverse solution was expressed as the localization error (LE) computed as the Euclidean distance between the pacemaker electrode and the inverse solution. The average LE for all 8 datasets using all torso electrodes was 26.1pm 6.1 mm, whereas using 12-lead ECG electrodes yielded a LE of 40.5pm 23.1 mm, and the greedy algorithm's selection resulted in a LE of 29.6pm 14.0 mm. The results suggest that the use of the most significant electrodes outperformed the use of the 12-lead ECG. The study emphasizes the need for back electrodes for an accurate inverse solution. © 2023 CinC.

关键词： Electrodes

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Optimizing compressive strength in sustainable concrete: a machine learning approach with iron waste integration

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Asian Journal of Civil engineering 2024年第6期25卷 4487-4512页

作者： Tipu, Rupesh Kumar Batra, Vandna Suman Panchal, V.R. Pandya, K.S. Patel, Gaurang A. Department of Civil Engineering School of Engineering and Technology K. R. Mangalam University Haryana Gurugram 122103 India Department of Computer Science and Engineering School of Engineering and Technology K. R. Mangalam University Haryana Gurugram 122103 India M. S. Patel Department of Civil Engineering Chandubhai S. Patel Institute of Technology (CSPIT) Charotar University of Science and Technology (CHARUSAT) CHARUSAT Campus Changa 388421 India Electrical Engineering Department Faculty of Engineering and Technology Parul Institute of Engineering and Technology (PIET) Parul University Gujarat Vadodara 391760 India Department of Mechanical Engineering Chandubhai S. Patel Institute of Technology (CSPIT) Charotar University of Science and Technology (CHARUSAT) CHARUSAT Campus Changa 388421 India

The current research delves into enhancing the sustainability of construction materials by incorporating iron waste into concrete mixtures. The primary aim revolves around predicting the compressive strength of such innovative concrete formulations, a critical factor in maintaining the structural integrity of constructions. By employing various machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)—the study determines the most efficacious models for predicting compressive strength. Notably, Random Forest emerges as the most proficient, as evidenced by its exceptional R² (= 0.972) and CPI score (= 0.250). A meticulous sensitivity analysis further elucidates the principal factors influencing compressive strength, notably the incorporation ratios of Iron Waste and Fine Aggregate, alongside the concrete’s age. This investigation meticulously navigates from data preprocessing to the final model selection and sensitivity analysis, ensuring the robustness of the predictive models. Moreover, the study extends its utility beyond academic realms by developing an accessible graphical user interface (GUI), hosted on GitHub, to facilitate the application of these findings. The inclusion of iron waste not only propels the construction industry towards more sustainable practices but also valorizes waste materials. Consequently, this research contributes substantially to the domain of sustainable construction by providing a reliable methodology for the integration of iron waste in concrete, thereby fostering the development of eco-friendlier construction practices. The additional creation of a GUI significantly amplifies the impact of this research, making its insights accessible to a broader audience, thus benefiting the society and construction industry at large. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Concrete compressive strength Global sensitivity analysis GUI Iron waste Machine learning Sustainable construction

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