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IEEE Transactions on Green Communications and Networking 2025年

作者： Chen, Siya Tan, Chee Wei Zhai, Xiangping Poor, H. Vincent City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Nanyang Technological University Singapore Singapore Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep-learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements. © 2017 IEEE.

关键词： Convex optimization

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Smart Gradational Vehicle Disassembling and Troubleshooting Support Assistant

Smart Gradational Vehicle Disassembling and Troubleshooting ...

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Advancements in Computing (ICAC), International Conference on

作者： Suvin Tennakoon Pamuditha Wickramasinha Hasini Ranathunga Chathuranga Ulpathakumbura Dharshana Kasthurirathna Udara Srimath S. Samaratunge Arachchillage Department of Computer Science and Software Engineering Sri Lanka Institute Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute Information Technology Malabe Sri Lanka

The world of vehicle service and troubleshooting can be daunting for individuals without specialized training. Vehicle manuals are often complex and challenging to comprehend, while relying on experienced mechanics for assistance can be inconvenient and expensive. To address these challenges, this research paper presents the development of an interactive vehicle service assistance system that empowers vehicle owners to independently service and troubleshoot their vehicles. The proposed system encompasses four key components: a knowledgebase construction, write semantic rules (SWRL), real-time object identification using YOLO model, audio feedback and stepwise guidance for customer. The knowledgebase is used for stepwise guidance for disassembling engine through employing marker-less Augmented Reality (AR). The knowledgebase component offers user-friendly access to troubleshooting techniques based on insights from experienced mechanics. By providing stepwise instructions accompanied by AR based visualization, users can effectively learn the process of deconstructing and assembling different components of their automobiles. The proposed system enhances vehicle troubleshooting experiences by enabling owners to acquire the necessary skills to self-troubleshooting without involving external assistance and empower individuals to take control of their own vehicles.

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Robust Partitioning Scheme for Accelerating SQL Database

Robust Partitioning Scheme for Accelerating SQL Database

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2021 IEEE International Conference on Emergency Science and Information technology, ICESIT 2021

作者： Khan, Wisal Zhang, Cheng Luo, Bin Kumar, Teerath Ahmed, Ejaz School of Computer and Application Technology Anhui University Hefei230039 China School of Computing National University of Computer and Emerging Sciences Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665435314

SQL Databases have shown tremendous performance in the last four decades. Data consistency, isolation, and durability are the main strengths of SQL databases. However, in relational databases, computing joins are time-consuming at run time. To address this issue, we propose to utilize the Oracle partitioning technique to enhance specific query performance. Physical database tuning approaches of SQL databases are being used to speed up each SQL statement such as partitioning and indexing. With the partitioning technique, the Oracle 11g RDBMS efficiency performance improves by approximately absolute 50%. Additionally, we compare its performance with the state-of-the-art NoSQL graph database (Neo4j). © 2021 IEEE.

关键词： Graph Databases

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BedIDS: An Effective Network Anomaly Detection Method by Fusing Behavior Evolution characteristics

BedIDS: An Effective Network Anomaly Detection Method by Fus...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhen Liu Changzhen Hu Chun Shan Junkai Yi Computer School Beijing Information Science & Technology University Beijing China Beijing Key Laboratory of Software Security Engineering Technology School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Automation Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Leveraging artificial intelligence models to enhance the performance of intrusion detection systems has become an important component in the field. However, as the scale of networks continues to expand, the structure of networks becomes more complex, and the amount of data in the networks grows larger. Existing methods are facing numerous challenges, including difficulties in constructing training datasets for models, challenges in transferring and reusing models, and high costs associated with model training. This paper introduces a novel approach named BedIDS. This method involves constructing the evolutionary process of network behavior and calculating the evolutionary characteristics of network behavior. Using only the most fundamental five network traffic features, including IP addresses, BedIDS achieves rapid and accurate detection performance on a device equipped with a 3060ti graphics card. We conducted tests using the CICIDS2017 and UNSW-NB15 datasets to evaluate its performance. Experimental results demonstrate that BedIDS maintains high detection accuracy and improves detection speed while requiring a relatively low AI computing force.

关键词： Training Performance evaluation Radio frequency Privacy Costs Accuracy Intrusion detection Telecommunication traffic Feature extraction Security

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Research on GAN-based Text Effects Style Transfer 4

Research on GAN-based Text Effects Style Transfer

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4th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2021

作者： Liu, Yinquan Chen, Zhuang School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Engineering Chongqing University of Technology Chongqing400065 China

ISBN: (纸本)9781665441247

With the development of neural style transfer and generative adversarial network, the research of text effect style transfer has appeared. The text effect style transfer aims to render text images with style images to produce text effects images. However, for more complex text, the existing methods will generate unrecognizable font images. Therefore, we propose to add morphological methods to the glyph transformation to limit the degree of glyph transformation, and add distance transformation loss when training the texture network to limit the texture transfer, so as to improve the overall transformation effect. Experiments show that, compared with other existing technologies, our proposed method is more suitable for stylizing complex glyph images. © 2021 IEEE.

关键词： Generative adversarial networks

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Multi Model Approach to Evaluate and Enhance Student - Teacher Interactions

Multi Model Approach to Evaluate and Enhance Student - Teach...

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Advancements in Computing (ICAC), International Conference on

作者： Nisal Daksith Sathsara Wanaguru Uresha Kolonne Amaya Dolawatta Lakmini Abeywardhana Dharshana Kasthurirathna Department of Information Technology Sri Lanka Institute of Inoformation Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Inoformation Technology Malabe Sri Lanka

Advancements in artificial intelligence (AI) and machine learning (ML) have enabled the development of tools to address issues in Virtual Classrooms. This research focuses on utilizing AI and ML to monitor student behavior in virtual classrooms by analyzing facial expressions, background noise, and keystroke patterns. An AI based approach is proposed to analyze real-time webcam data, detecting patterns of inattention, and notifying teachers accordingly. The application also monitors background noise levels, identifying non-conducive learning environments, and analyzes keystroke patterns to detect unrelated activities. The research draws from academic literature to evaluate the effectiveness of the application while considering ethical implications such as potential bias and student privacy protection. Overall, this study contributes to the existing literature on AI and ML-based applications in education, demonstrating how these technologies can revolutionize student behavior monitoring, leading to enhanced outcomes for virtual classroom learning.

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Facial Skincare Product Suggestion with Product Popularity and Post Recommendation Care

Facial Skincare Product Suggestion with Product Popularity a...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： H. M. W. T. C. B. Samarakoon A. U Ovitigala G. A. T. T. Wijesinghe K. M. L. P. Weerasinghe G. K. B. V. Karunathilake Ishara Weerathunga Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

Skin health significantly affects a person's overall wellbeing, which includes both physical and emotional aspects. The objective of this study is to develop an integrated system for recommending skincare products that integrates image processing, machine learning, sentiment analysis, and user feedback analysis to deliver individualized skincare advice and boost user satisfaction. Utilizing deep learning algorithms and face cues, the system uses user image analysis to detect important skin features, skin type, acne severity, and dark spots with extraordinary accuracy. Using sentiment analysis and clustering, the program identifies popular skincare products and tailors’ recommendations to user preferences. Recommendations are further personalized by collaborative filtering. By using image analysis to identify allergies and other negative effects, user safety is increased. The technique prioritizes data safety and includes model training, sentiment analysis, image processing, and user input analysis. Initial findings demonstrate the excellent accuracy of skin issue detection and severity prediction. By allowing users from a variety of user demographics to make informed skincare decisions, this complete framework has the potential to change the skincare industry.

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Improved Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Considering Hazardous Tasks

Improved Proximal Policy Optimization Algorithm for Multi-ob...

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IEEE International Conference on Networking, Sensing and Control

作者： Zeyu Guo Xiwang Guo Jiacun Wang Shujin Qin Liang Qi Qi Kang Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA Economics and Management Shangqiu Normal University Shangqiu China Computer Science Department Shandong Technology University Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

As disassembly lines evolve, keeping workers safe from all potential dangers has become paramount. This work proposes an improved approach, Proximal Policy Optimization for Disassembly Line(PPO-DL), to address Multi-objective Dis-assembly Line Balancing Problem(MDLBP) that integrates task switching time and hazardous tasks. The MDLBP model aims to optimize disassembly net profit, balance workstation utilization, and mitigate penalties from hazardous tasks. The proposed PPO-DL modifies the action space of PPO to accommodate MD LBP characteristics, ensuring efficient learning by restricting actions to valid ranges through action masking. Comparative experiments with PPO, Q-learning, and A2C demonstrate PPO-DL's superiority in finding near-optimal solutions across various scenarios.

关键词： Q-learning Switches Fatigue Control systems Workstations Sensors Optimization

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Infinity-Norm Weighted Low-Order Sparse Iterative DOA Estimation under Impulsive Noise

Infinity-Norm Weighted Low-Order Sparse Iterative DOA Estima...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Zhang, Yongqing Liu, Zhiqiang Wang, Weidong Li, Hui Shi, Wentao School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454003 China School of Computer Science and Technology Henan Polytechnic University Jiaozuo454003 China School of Software Henan Polytechnic University Jiaozuo454003 China Ocean Institute Northwestern Polytechnical University Taicang215400 China

ISBN: (纸本)9798350316728

This paper investigates the subject of the direction of arrival (DOA) estimation with acoustic vector sensor array (AVSA) under impulsive noise. A low-order-based variant of the typical sparse reconstruction method is designed to address this challenge. The proposed scheme successively weights the array output snapshots using the infinity norm to remove high outliers. Subsequently, the Frobenius norm based approach is extended to lower order, and a new cost function is created to address the impulse characteristics of the residual. Finally, the expression of the signal power is quantified, and DOA is approximated on the signal power by spectral peak search mechanism. Numerical results demonstrate that the proposed algorithm offers significant performance enhancements over some current techniques in impulsive noise scenarios. © 2023 IEEE.

关键词： Impulse noise

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection

Evaluation and Validation of Design Metrics in Machine Learn...

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International Conference on Emerging Technologies, ICET

作者： Sahar Ajmal Muhammad Usman Abqa Javed Dept. of Software Engineering National University of Computer and Emerging Sciences Chiniot-Faisalabad Pakistan Dept. of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Pakistan Dept. of Computer Science Univ. of Engineering and Technology Lahore Pakistan

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain.

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