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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Shi, Yuehong Zhao, Yanan Zhou, Wenjiang Zhang, Rufei Guan, Hongli Xu, Haitao University of Science and Technology TravelSky Mobile Technology Limited Beijing China China Academy of Information and Communications Technology Beiing China University of Science and Technology Beijing Beijing China TravelSky Mobile Technology Limited Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350355253

PWC-Net is a convolutional neural network for optical flow estimation that is superior in both inference speed and accuracy. However, the detection accuracy of this network for small moving objects is poor, and there is a problem of overlapping blur caused by occluded areas during the warping process. This paper proposes an improved PWC-Net network, this method can meet the real-time requirements, and the accuracy has been greatly improved compared with PWC-Net. Compared with PWC-Net, RAONet has a 22.1% lower error on Flying Chairs validation dataset, 12.3% lower error on MPI Sintel Clean dataset, and 12.3% lower error on MPI Sintel Final dataset and KITTI dataset by about 9%. Under the hardware environment of a 12- core CPU and RTX 2080 Ti GPU, RAONet can run at about 50fps on 1024 × 436 resolution images. And through the visual analysis of optical flow, it is concluded that RAONet can improve the detection accuracy of small moving objects, and can effectively solve the overlapping blur problem caused by the warping process. © 2024 IEEE.

关键词： Convolutional neural networks

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Novel soft robotic finger model driven by electrohydrodynamic(EHD)pump

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Journal of Zhejiang university-science A(Applied Physics & engineering) 2024年第7期25卷 596-604页

作者： Xuehang BAI Yanhong PENG Dongze LI Zhuochao LIU Zebing MAO Department of Mechanical Automotive and Materials EngineeringFaculty of EngineeringUniversity of WindsorWindsorN9B 3P4Canada Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya464-8601Japan Department of Intelligent Science and Technology College of Computer Science and TechnologyQingdao UniversityQingdao266071China Design and Research Institute of University of Science and Technology Beijing Co.Ltd. Beijing100083China Faculty of Engineering Yamaguchi UniversityYamaguchi755-8611Japan

1Introduction In the field of robotic-human interactions,soft robotics offers enhanced safety and adaptability.A major challenge in this area is the integration of soft actuators with pump systems,which often increases the system volume and *** study presents the development and testing of a robotic finger powered by electrohydrodynamic(EHD)*** leveraging the electric field-induced flow of dielectric fluids.

关键词： robotic complexity. pump

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An FPGA Prototype for Parkinson’s Disease Detection using Machine Learning on Voice Signal

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IEEE Access 2025年 13卷 91113-91128页

作者： Khan, Mujeev Moiz, Abdul Khan, Gani Nawaz Wajid, Mohd Usman, Mohammed Ali, Jabir Z.H.C.E.T. Aligarh Muslim University Department of Electronics Engineering India Bennett University Department of Electronics and Communication Engineering India Bennett University School of Computer Science Engineering and Technology India

Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms—Chi-squared (χ2), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) — and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. The Quadratic SVM, optimized for minimal power consumption, is implemented on a Nexys A7 FPGA. This setup achieves a dynamic power consumption of just 23 mW and delivers a performance acceleration of 155× compared to equivalent computations on a 6th-generation Intel i5 processor. By combining a high-accuracy machine learning model with an energy-efficient FPGA implementation, our approach offers a powerful and portable solution for real-time PD detection. © 2013 IEEE.

关键词： Neurodegenerative diseases

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Bioinspired Double-stranded Yarn with Alternating Hydrophobic/Hydrophilic Patterns for High-efficiency Fog Collection

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Chinese Journal of Polymer science 2024年第7期42卷 968-975,I0008页

作者： Lan-Lan Hou Meng-Na Qiu Ya-Qiong Wang Tong-Hua Bai Zhi-Min Cui Jing-Chong Liu Ying-Qun Qi Nü Wang Yong Li Yong Zhao Beijing Institute of Graphic Communication Beijing102600China Key Laboratory of Bioinspired Smart Interfacial Science and Technology of Ministry of Education Beijing Key Laboratory of Bioinspired Energy Materials and DevicesSchool of ChemistryBeijing Advanced Innovation Center for Biomedical EngineeringBeihang UniversityBeijing100191China Key Laboratory of Cryogenics Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190China Beijing Key Laboratory for Bioengineering and Sensing TechnologyDaxing Research InstituteSchool of Chemistry and Biological EngineeringUniversity of Science and Technology BeijingBeijing100083China

Efforts to develop innovative water harvesting strategies offer powerful solutions to alleviate the water crisis,especially in remote and arid *** by the hydrophobic/hydrophilic pattern of desert beetles and water self-propulsion property of spider silks,a double-strand hydrophobic PVDF-HFP/hydrophilic PAN nanofibers yarn is proposed by electrospinning and twisting *** double-strand cooperation approach allows for water deposition on hydrophobic PVDF-HFP segment and transport under the asymmetric capillary driving force of hydrophilic PAN segment,thus speeded up the aggregation and growth of *** effects of the composition and the diameter ratio of the two primary yarns were studied and optimized for boosting fog collection *** double-strand anisotropic yarn not only provide an effective method for water harvesting,but also hold the potential to inspire innovative design concepts for fog collection materials in challenging environments.

关键词： Fog collection Wettability Electrospinning Nanofibers Spider silk

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Nonlinear Backstepping Trajectory Tracking Control for a Quadcopter with Experimental Validation

Nonlinear Backstepping Trajectory Tracking Control for a Qua...

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2024 China Automation Congress, CAC 2024

作者： Xiang, Siwei Yang, Ziying Yu, Yao Shi, Yi Duan, Kairong Zhang, Weidong Xie, Wei Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9798350368604

This paper addresses the nonlinear control problem for a quadcopter. The proposed control strategy ensures effective trajectory tracking with ultimately bounded errors. A nonlinear controller employing a backstepping framework is designed to stabilize the quadcopter with damping effects. Simulation and experimental results demonstrate the controller’s capability to maintain stability and accurately follow reference trajectory, validating the effectiveness of the proposed method. © 2024 IEEE.

关键词： Trajectories

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Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 480-491页

作者：王丽张旭毅姚亚兵尉雪龙 Library Northwest Normal UniversityLanzhou 730070China Department of Criminal Science and Technology Henan Police CollegeZhengzhou 450046China School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Northeastern UniversityShenyang 110169China

This study mainly focused on the dynamic self-similar k_(c)-center network as a result of information distribution through social *** attraction with various preferences was characterized in the model as a result of reciprocal attraction among individuals and human ***,the model incorporated the community network structure and network evolution mechanism,and a dynamic self-similar k_(c)-center network generation model was *** with the classical scale-free network generation algorithm,the generated network embodied not only the characteristics of the small-world and scale-free,but also the characteristics of dynamic self-similar k_(c)-center *** experimental results were verified by comparing the real data with the experimental *** results showed that there are dynamic self-similar k_(c)-center networks and their internal network relationship dynamics in the micro scale,meso scale and global perspective based on information dissemination.

关键词： edge state evolutionary network self-similarity ke-center network

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WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning

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Chinese Journal of Electronics 2022年第2期31卷 354-366页

作者： LEI Tianwei XUE Jingfeng WANG Yong NIU Zequn SHI Zhiwei ZHANG Yu School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23%compared with TCA+ algorithm on AEEEM datasets,and an average improvement of 5% on ReLink datasets.

关键词： feature engineering cross-project defect prediction software defect prediction project management feature selection learning (artificial intelligence) software maintenance WCM-WTrA distance-weight transfer learning software quality distance-weight instance transfer multisource model MultiWCM-WTrA

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An eBPF-Based Trace-Driven Emulation Method for Satellite Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第3期6卷 188-192页

作者： Tian, Weibiao Li, Ye Zhao, Jinwei Wu, Sheng Pan, Jianping Nantong University School of Information Science and Technology Nantong226019 China Nantong Research Institute for Advanced Communication Technologies Nantong226019 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Victoria Faculty of Engineering and Computer Science VictoriaBCV8P 5C2 Canada

System-level performance evaluation over satellite networks often requires a simulated or emulated environment for reproducibility and low cost. However, the existing tools may not meet the needs for scenarios such as the low-earth orbit (LEO) satellite networks. To address the problem, this letter proposes and implements a trace-driven emulation method based on Linux's eBPF technology. Building a Starlink traces collection system, we demonstrate that the method can effectively and efficiently emulate the connection conditions, and therefore provides a means for evaluating applications on local hosts. © 2019 IEEE.

关键词： Satellite communication systems

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An Offloading Scheme for Diverse Inter-Dependent Tasks in Vehicle Edge Computing 10

An Offloading Scheme for Diverse Inter-Dependent Tasks in Ve...

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10th IEEE Smart World Congress, SWC 2024

作者： Cao, Dun Cai, Dan Peng, Bo Wang, Jin Changsha University of Science and Technology School of Computer and Communication Engineering China Sanya Institute of Hunan University of Science and Technology China

ISBN: (纸本)9798331520861

Vehicle Edge Computing (VEC) is a promising paradigm for efficiently processing massive amounts of data from sensors and stable responses to diverse applications from vehicles. In the usual scenario, the data needs to be transmitted between different nodes and the applications can be represented as inter-dependent tasks. Moreover, the subtasks in each task have diverse interdependencies and each task has a different deadline. Therefore, in order to make the task offloading decision, two questions must be answered: when and where each subtask should be offloaded according to its dependencies. This paper formulates the execution sequence of subtasks by modeling the relationships between subtasks with Directed Acyclic Graph (DAG). Meanwhile, to satisfy the deadline of applications, the computing resources of idle vehicles are sufficiently utilized, and the computing resources on the VEC server are efficiently allocated to tasks. To optimize the overall performance of the system, this paper constructs an optimization problem of minimizing average task completion delay, and proposes the Optimal Offloading Strategy and Resource Allocation for Multi-Dependent Tasks (OSRA-MDT) scheme to solve this optimization problem. Simulation results show that our proposed OSRA-MDT scheme has achieved up to 24 % improvement in reducing the average task completion latency of the system compared to the baseline scheme. © 2024 IEEE.

关键词： Resource allocation

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Developing Lexicons for Enhanced Sentiment Analysis in Software engineering:An Innovative Multilingual Approach for Social Media Reviews

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computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam Fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

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