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2023 JSAE/SAE Powertrains, Energy and Lubricants International Meeting, PFLJAPAN 2023

作者： Eltaweel, Mahmoud Herfatmanesh, Mohammad Reza School of Physics Engineering and Computer Science Univers

During urban driving, a significant amount of energy is lost due to continuous braking, which can be recovered and stored. The flywheel energy storage system (FESS) can efficiently recover and store the vehicle's kinetic energy during deceleration. However, standby losses in FESS, primarily due to aerodynamic drag, can affect its overall efficiency. To address this issue, the flywheel rotor is typically housed in a dedicated housing maintained at a low pressure using a vacuum pump. Standby power is known as the total power used by the auxiliary systems and the power needed to overcome drag and keep the flywheel rotor at a specific state of charge. The Analysis of Variance (ANOVA) technique was combined with the computational fluid dynamics (CFD) technique in this study to determine the optimal flywheel design parameters and investigate their impact on standby power. The study's results demonstrated the optimal combination of the airgap size and the rotor's pressure cavity to achieve the lowest standby power. © 2023 SAE International. All rights reserved.

关键词： Flywheels

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Cooperative Rate Splitting Transmit Design for Full-Duplex-Enabled Multiple Multicast Communication Systems

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computer Modeling in engineering & sciences 2024年第1期138卷 619-638页

作者： Siyi Duan Mingsheng Wei Shidang Li Weiqiang Tan Bencheng Yu School of Physics and Electronic Engineering Jiangsu Normal UniversityXuzhou221116China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China School of Information Engineering Xuzhou Vocational College of Industrial TechnologyXuzhou221140China

This paper examines the performance of Full-Duplex Cooperative Rate Splitting(FD-CRS)with Simultaneous Wireless Information and Power Transfer(SWIPT)support in Multiple Input Single Output(MISO)*** a Rate Splitting Multiple Access(RSMA)multicast system with two local users and one remote user,the common data stream contains the needs of all users,and all users can decode the common data ***,each user can receive some information that other users need,and local users with better channel conditions can use this information to further enhance the reception reliability and data rate of users with poor channel *** using Cell-Center-Users(CCUs)as a cooperative relay to assist the transmission of common data can improve the average system *** maximize the minimum achievable rate,we optimize the beamforming vector of Base Station(BS),the common streamsplitting vector,the cooperative distributed beamvector and the strong user transmission power under the power budget constraints of BS and relay devices and the service quality requirements constraints of *** the whole problem is not convex,we cannot solve it ***,we propose a low complexity algorithm based on Successive Convex Approximation(SCA)technology to find the optimal solution to the problemunder *** simulation results show that FD C-RSMA has better gain andmore powerful than FD C-NOMA,HD C-RSMA,RSMA and NOMA.

关键词： Full-duplex cooperative rate segmentation SWIPT RSMA power control

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Unsupervised Learning for Insider Threat Prediction: A Behavioral Analysis Approach 17

Unsupervised Learning for Insider Threat Prediction: A Behav...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Mehmood, Rahat Singh, Priyanka Jeffery, Zoe School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798331509736

Most of the devastating cyber-attacks are caused by insiders with access privileges inside an organization. The main reason of insider attacks being more effective is that they don't have many security barriers before they get into the critical resources of the system. Different machine learning techniques have been previously utilized to identify insider threats within cy-bersecurity domain whereas research done in predicting insider attacks is not significant. Moreover, machine learning models used for prediction and detection face a critical limitation as they require training on labeled datasets, rendering them less effective for real-time data streams which lack threat presence indicators. This work presents an unsupervised machine learning approach that predicts insider threat using behavior analysis for real-time threat data. Patterns are identified in user behavior, to make predictions about benign and malicious insiders. Features are selected by analyzing activities performed. Selected features are utilized to feed machine learning model which extracts anomalous behavior among users, using anomalies in their activity patterns followed by learning methods for threat detection. A dataset that contains selected features from CERT r4.2 is used to make predictions. The performance of Isolation Forest (iForest) is compared with other algorithms of the same category including One-class SVM, Local Outlier Factor (LOF) and DBSCAN to evaluate the new approach. The iForest shows the best performance accuracy 80 percent and recall 84.2 percent. © 2024 IEEE.

关键词： Adversarial machine learning

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Porous FeP/FeO Deposited on Carbon Cloth with Accelerated Kinetics for the Hydrogen Evolution Reaction

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Journal of Wuhan University of Technology(Materials science) 2024年第6期39卷 1434-1439页

作者： YU Hai ZHAO Yujia LIU Su ZUO Lei LEI Qianqian HU Ruiling LU Jianguo ZHAO Min WANG Congrong WANG Cunyong ZHANG Miao YANG Lei ZHONG Jin CAO Chunbin School of Physics and Materials Engineering and Key Laboratory for Photoelectric Detection Science and Technology of Education Department of Anhui Province Hefei Normal UniversityHefei 230601China School of Physics and Material Science Anhui UniversityHefei 230039China Department of Chemistry and Materials Engineering Hefei UniversityHefei 230601China School of Computer Science and Technology Hefei Normal UniversityHefei 230601China School of Science Anhui Agricultural UniversityHefei 230036China

FeP/FeO was prepared on carbon cloth(CC)via hydrothermal method,heat treatment in air,and phosphorization in ***/FeO/CC presents a porous and loose morphology which is conducive to the exposure of active sites and the transfer of ***/FeO/CC requires the low overpotentials of 257 and 117 mV(*** hydrogen electrode(RHE))to achieve the current density of 10 mA_·cm^(-2)for oxygen evolution reaction(OER)and hydrogen evolution reaction(HER)in alkaline KOH solution,*** small Tafel slope values of 36.1 mV_·dec^(-1)(for OER)and 96.2 mV_·dec^(-1)(for HER)indicate that FeP/FeO/CC exhibits the fast electrocatalytic reactive kinetics for OER and *** particular,the reaction kinetics of FeP/FeO/CC accelerated with the progress of *** charge-transfer resistance(R_(ct))of FeP/FeO/CC is only 11Ω.Excellent bifunctional electrocatalytic performances of FeP/FeO/CC should be attributed to the porous morphology and the lower charge-transfer resistance.

关键词： FeP/FeO/CC OER HER water splitting

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The Method of Portrait Segmentation Based on Efficient Channel Attention Mechanism 4

The Method of Portrait Segmentation Based on Efficient Chann...

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4th International Conference on computer science and Blockchain, CCSB 2024

作者： Mo, Hanlin Yang, Zhikai Li, Pengcheng Wang, Qibiao School of Computer Science and Engineering Sichuan University of Science and Engineering Zigong China School of Physics and Electronic Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798350356502

The attention mechanism can extract task-relevant vital information while suppressing less important information, playing an increasingly critical role in deep feature representation for semantic segmentation. This research proposes a neural network model based on the widely used U-Net architecture, incorporating the ECA (Efficient Channel Attention) attention mechanism. To address the issue of edge segmentation blur, an improved module based on ASPP (Atrous Spatial Pyramid Pooling) is introduced into both the compression and expansion paths of U-Net. Additionally, to tackle the issue of neglected global context information at different scales in portrait segmentation networks, a multi-scale prediction fusion method is employed to utilize global information at various scales, and the ECA attention module is employed to aggregate attention information from each layer in the compression path. Extensive experiments demonstrate that the proposed method achieves performance comparable to or better than networks such as Deeplabv3+, FCN, and UNet++. © 2024 IEEE.

关键词： Semantic Segmentation

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Generating Synthetic EEG Data Using Generative AI for Mental States Prediction in Human-Machine Interaction 16th

Generating Synthetic EEG Data Using Generative AI for Menta...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Venugopal, Archana Faria, Diego Resende School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9789819635245

This work presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic EEG waves corresponding to concentration and relaxation mental states. By addressing the challenge of limited biophysical data, our model produces realistic synthetic brainwaves learned from real data, which is vital for studies restricted by volunteer availability and privacy concerns. Combining real and synthetic data improved classification accuracy from 92% to 98.45%, highlighting the benefit of expanding the dataset for better machine learning model performance. The WGAN-GP model generated synthetic EEG data with 96.84% accuracy for relaxation and optimum accuracy for concentration, as classified by a Convolutional Neural Network (CNN). Additionally, 50% synthetic data combined with the original dataset achieved the highest accuracy (98.48%). A comparison was made between a basic GAN and the WGAN-GP model, demonstrated that the WGAN-GP outperformed the basic GAN in this study. Techniques like discrete wavelet transform, downsampling, and upsampling were employed. This method shows potential in addressing EEG data scarcity and enhancing human-robot interaction in assistive technologies and mental health monitoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Image Enhancement using Modified Laplacian Filter, Clahe and Adaptive Thresholding 6

Image Enhancement using Modified Laplacian Filter, Clahe and...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Rehman, Sabeeh Abdul Jeffrey, Zoe Sun, Yichuang Simpson, Oluyomi University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9798350350180

In this paper, a new method for image enhancement is proposed for object recognition in autonomous vehicles. The new method involves image segmentation, Contrast-Limited Advanced Histogram Equalization (CLAHE), a Modified Laplacian filter and Adaptive Thresholding. The image is segmented into four-by-four segments. Each segment is assessed and corrected for blurriness and darkness. Adaptive Thresholding gives a unique automated threshold value for each region of the image depending upon the lighting conditions of the region of the image. The aim is to enhance the contrast and brightness of the image and increase sharpness of the image without inducing noise. The proposed method enhances the image quality that the image recognition system receives and was tested on low-light datasets. The extensive experiments show higher performance both qualitative and quantitively compared to the state of art methods. © 2024 IEEE.

关键词： Wiener filtering

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Comparison of CNN-based deep learning architectures for rice diseases classification

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Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Institute of Applied Physics Graduate School of Pure and Applied Sciences University of Tsukuba Institute of Special Environments Physical Sciences Harbin Institute of Technology School of Computer Science and Technology Harbin Institute of Technology

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction（HER） performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H2SO4,with only a 3.8% degradation in performance over a 120-h continuous *** proton exchange membrane（PEM） water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm-2,in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Acidic media Graphene encapsulation Hydrogen evolution Interface Proton exchange membrane

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An Effective Intrusion Detection System Based on the FSA-BGRU Hybrid Model

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China Communications 2025年第2期22卷 188-198页

作者： Deng Zaihui Li Zihang Guo Jianzhong Gan Guangming Kong Dejin School of Electronic and Electrical Engineering Wuhan Textile UniversityWuhan 430200China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan 430200China School of Physics and Electronic Information Engineering Hubei Engineering UniversityXiaogan 432000China

Intrusion detection systems play a vital role in cyberspace *** this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature ***,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior ***,a support vector machine(SVM)classifier is used to classify intrusion *** proposed model is verified by experiments on the NSL-KDD *** results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion *** proposed method can provide new insight into network intrusion detection.

关键词： bidirectional GRU feature selection intrusion detection system multilayer perceptron recursive feature elimination support vector machine

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