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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Rabbiun, Hossein Mohammadi, Jamal Shirazi, Mahmoud Kargahi, Mehdi Department of Computer Science and Information Technology Zanjan Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran

ISBN: (纸本)9798331529284

This paper advances the schedulability analysis of the Adaptive Mixed-Criticality for Weakly Hard Real-Time Systems (AMC-WH) which allows a specified number of consecutive low-criticality (LO) jobs of tasks to be skipped when the system operates in the high-criticality (HI) mode. The current AMC-WH schedulability test, based on the skip-over model, in which low tasks are guaranteed to meet m-s out of m consecutive deadlines, where s is the number of allowed skips and m is the cycle length, only calculates the worst-case response times (WCRT) for low tasks under a fixed execution pattern post-mode switch. To address this limitation, we propose a new sufficient schedulability test for AMC-WH using the (m,k)-firm model, which allows various execution patterns for low tasks after a mode switch, enhancing the flexibility and applicability. Additionally, we develop an exact schedulability test for AMC-WH based on response time analysis (RTA). This test modifies the existing exact tests for the AMC algorithm, incorporating updates to state-space exploration and pruning rules to account for low task execution after a mode switch. Simulation results validate the effectiveness of our tests, demonstrating enhanced schedulability analysis and optimized resource allocation. © 2024 IEEE.

关键词： Space research

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A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection

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computers, Materials & Continua 2025年第1期82卷 521-542页

作者： Xiaoyun Chen Lanyao Zhang Xiaoling Chen Yigang Cen Linna Zhang Fugui Zhang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Technology Beijing Jiaotong UniversityBeijing100044China

Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective ***,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective *** paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these *** network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation ***,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information *** order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target ***,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter ***,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and *** and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon *** results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.

关键词： Defect segmentation multi-scale feature fusion multi-scale attention depthwise separable residual block

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A new passive transfemoral prosthesis mechanism based on 3R36 knee and ESAR foot providing walking and squatting

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Theoretical & Applied Mechanics Letters 2023年第5期13卷 399-407页

作者： Amer Imran Borhan Beigzadeh Mohammad Reza Haghjoo School of Mechanical Engineering Iran University of Science and TechnologyTehranIran Faculty of Mechanical and Energy Engineering Shahid Beheshti University(SBU)TehranIran

Researchers have proposed various linkage mechanisms to connect knee and ankle joints for above-knee prosthe-ses,but most of them only offer natural ***,studies have shown that people assume a squatting posture during daily *** paper introduces a novel mechanism that connects the knee joint with the foot-ankle joint to enable both squatting and *** prosthetic knee used is the well-known 3R36,while the energy storing and return(ESAR)prosthetic foot is used for the ankle-foot *** coordinate knee and ankle joint movements,a six-bar linkage mechanism structure is *** results demonstrate that the proposed modular transfemoral prosthesis accurately mimics the motion patterns of a natural human leg during walking and *** instance,the prosthesis allows a total knee flexion of more than 140°during *** new prosthesis design also incorporates energy-storing mechanisms to reduce energy expenditure during walking for amputees.

关键词： Transfemoral leg amputation Passive prosthesis Squatting Walking Six-bar mechanism Energy saving

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A Study on the Inter-Pretability of Network Attack Prediction Models Based on Light Gradient Boosting Machine(LGBM)and SHapley Additive exPlanations(SHAP)

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computers, Materials & Continua 2025年第6期83卷 5781-5809页

作者： Shuqin Zhang Zihao Wang Xinyu Su School of Computer Science Zhongyuan University of TechnologyZhengzhou450000China School of Cyberspace Security Information Engineering UniversityZhengzhou450000China

The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats *** recent years,artificial intelligence has achieved significant progress in the field of network ***,many challenges and issues remain,particularly regarding the interpretability of deep learning and ensemble learning *** address the challenge of enhancing the interpretability of network attack prediction models,this paper proposes a method that combines Light Gradient Boosting Machine(LGBM)and SHapley Additive exPlanations(SHAP).LGBM is employed to model anomalous fluctuations in various network indicators,enabling the rapid and accurate identification and prediction of potential network attack types,thereby facilitating the implementation of timely defense measures,the model achieved an accuracy of 0.977,precision of 0.985,recall of 0.975,and an F1 score of 0.979,demonstrating better performance compared to other models in the domain of network attack *** is utilized to analyze the black-box decision-making process of the model,providing interpretability by quantifying the contribution of each feature to the prediction results and elucidating the relationships between *** experimental results demonstrate that the network attack predictionmodel based on LGBM exhibits superior accuracy and outstanding predictive ***,the SHAP-based interpretability analysis significantly improves the model’s transparency and interpretability.

关键词： Artificial intelligence network attack prediction light gradient boosting machine(LGBM) SHapley Additive exPlanations(SHAP) interpretability

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Advancing differential diagnosis: a comprehensive review of deep learning approaches for differentiating tuberculosis, pneumonia, and COVID-19

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Multimedia Tools and Applications 2025年第13期84卷 11871-11906页

作者： Kansal, Kajal Chandra, Tej Bahadur Singh, Akansha School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating between illnesses or ailments with comparable symptoms is essential, deep learning has gained importance. Recent developments in deep learning have demonstrated considerable promise for revolutionizing medical diagnostics by using the ability of artificial intelligence (AI) to accurately interpret radiological images. We examine the most cutting-edge deep learning techniques currently being utilized for the differential diagnosis of tuberculosis, pneumonia, and COVID-19 in this in-depth review. The study presents an in-depth critical review of several SOTA (state-of-the-art) studies used for differential diagnosis of different respiratory abnormalities like TB, Pneumonia, and COVID-19. In addition, an overview of various approaches, datasets employed in each method, various diagnosis tests, used assessment measures, and obtained performance is summarized and comprehensively compared to assist future research. We suggest a pathway for future research and development of deep learning solutions for differential diagnosis by critically analyzing the current literature and outlining the limitations and potential in this sector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Transparent and trustworthy interpretation of COVID-19 features in chest X-rays using explainable AI

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Multimedia Tools and Applications 2025年第18期84卷 19853-19881页

作者： Kinger, Shakti Kulkarni, Vrushali Computer Engineering & Technology School of Computer Science & Engineering Dr. Vishwanath Karad MIT World Peace University Pune India

The potential of AI-based disease prediction models for assessing COVID-19 patients outperforms conventional methods. However, their black-box nature has limited their applicability. This study explores the approach for COVID-19 identification by integrating Artificial Intelligence (AI) methods with Deep Neural Networks (DNNs), such as EfficientNet and DenseNet, applied for medical imaging. To address the black-box challenge, the study incorporates eXplainable AI (XAI) techniques, including LIME, Grad-CAM, and a novel variant of Grad-CAM++, termed "Modified Grad-CAM++ ". The modification aims to enhance explanations for COVID-19 predictions, improving the interpretability and transparency of the model’s decision-making process. Collaborating with expert radiologists, the study validates the Modified Grad-CAM++ using a separate dataset of radiologist-validated chest X-ray (CXR) images. The Integrated Uncertainty Calculation (IUC) metric is introduced as an evaluation measure for the Modified Grad-CAM++. Remarkably, both EfficientNet and DenseNet achieve high diagnostic precision, with accuracy rates of 98% and 97%, respectively. The integration of XAI algorithms enhances the interpretability and transparency of predictions, ensuring clinical relevance and validity. These precise AI models offer valuable support to healthcare professionals in decision-making and resource allocation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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MotionUVRNN: A Motion Capture Recurrent Neural Network for Wind Field Forecast Correction via U and V Components Modeling

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 791-800页

作者： Gu, Zhan-Xin Wu, Jie Jin, Wei Han, Guo-Jing School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Anshan Meteorological Service Anshan114004 China

Wind field forecasting is crucial for human activities, but numerical weather prediction still has room to improve accuracy. In this paper, we formalize wind field forecast correction as a spatiotemporal sequence prediction task and propose MotionUVRNN, a recurrent neural network model suitable for wind field correction. The model consists of three elements: a feature decoupling module, an encoder, and a corrector. The feature decoupling module decouples the wind field into U-Wind (Wind component in x/longitude-direction) feature, V-Wind (Wind component in y/latitude direction) feature, and spatiotemporal feature. The encoder unifiedly models the motion trend and transient variation of the wind field. A novel long short-term memory dynamic capture unit (RS-LSTM) is proposed as the core part of the encoder to capture the different dynamic patterns of U-Wind and V-Wind. The corrector extracts and integrates multi-scale wind field information to capture regional features. By correcting the 3-12 hour wind field forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF;EC for short), the proposed model reduced the wind speed prediction error by 53.37% to 46.52%, and lowered the wind direction error by 21.99% to 13.26%. Moreover, this model surpasses the existing leading spatiotemporal sequence prediction models in wind field correction. © (2024) International Association of Engineers. All rights reserved.

关键词： Signal encoding

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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