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arXiv 2025年

作者： Guo, Yixuan Xia, Shuaifan Xiong, Mingliang Liu, Qingwen Zhou, Shengli Fang, Wen Jiang, Qingwei Yan, Gang Mu, Jiangchuan The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The College of Electronics and Information Engineering Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China The Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States The School of Physics Science and Engineering Tongji University Shanghai200092 China

To address the challenge of complex beam control in traditional multiple-input multiple-output (MIMO) systems, research has proposed establishing adaptive beam alignment by utilizing retro-directive antenna (RDA) arrays to create echo resonance between the base station (BS) and user equipment (UE), thereby reducing system computational load. However, in conventional resonant beam systems (RBS), the use of the same frequency for uplink and downlink inevitably leads to echo interference issues. Therefore, this paper proposes an innovative design for an resonance beam-based integrated sensing and communication (RB-ISAC) system to achieve efficient passive sensing and bidirectional communication. In this system, the UE does not actively transmit signals but instead relies on a passive phase conjugation and frequency conversion structure to separate the uplink and downlink carrier frequencies. Additionally, through effective compensation for signal propagation loss, resonance is achieved after multiple iterations, at which point the beam’s field distribution becomes a low-diffraction-loss, high-focusing pattern, automatically aligning the transmitter with the receiver. This enables high-precision passive positioning while facilitating uplink and downlink communication. Simulation results demonstrate that the proposed system can achieve resonance after multiple iterations, and can support uplink and downlink communication within a range of 5 meters while achieving passive direction of arrival (DOA) estimation with an error of less than 2 degrees. Copyright © 2025, The Authors. All rights reserved.

关键词： Directive antennas

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Bi-Directional Power Control in Grid-Connected Electric Vehicle On-Board Chargers using Spider Wasp Optimization

Bi-Directional Power Control in Grid-Connected Electric Vehi...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Cuddapah Anitha G Vipashi Kansal Swapnil Uttamrao Deokar Kamal Sutaria Ravi Kumar V Natrayan L Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science & Engineerin Graphic Era Deemed to be University Dehradun Department of Mechanical Engineering Smt. Kashibai Navale College of Engineering Pune Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The bi-directional power control in electric vehicle (EV) on-board chargers aims to maximize charging performance while minimizing size and cost of the system. This presents an increasing technical challenge that further complication of thermal management through greater integration of power conversion components may impact system reliability, efficiency, and longevity. By address these issues this paper proposes a Spider Wasp Optimization (SWO) for enhancing stability in bi-directional power control in EV on-board chargers. The objective is to minimize Total Harmonic Distortion (THD) in EV on-board chargers. By optimizing the converter's parameters, SWO effectively reduces harmonic distortions, leading to improved power quality and smoother operation of bi-directional power control in EV on-board chargers. By then the proposed SWO method is implemented in MATLAB platform and evaluated their performance with various existing methods such as Improved Particle Swarm Optimization (IPSO), Intelligent Neural Herd based Water Drop Optimization (INHWDO), Squirrel Wolf Optimization algorithm (SWOA), Wild Horse-Particle Swarm Optimization (WHPSO) and Assailant Inspired Chimp Optimization Algorithm (AICOA). The proposed SWO method outperforms all the others with the lowest THD of 2.008%, indicating its superior performance in reducing harmonic distortion.

关键词： Total harmonic distortion Power control Power quality Bidirectional control Electric vehicles Real-time systems Vehicle dynamics Particle swarm optimization Optimization Thermal stability

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Multi-Label Movie Genre Classification with Attention Mechanism on Movie Plots

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computers, Materials and Continua 2025年第3期83卷 5595-5622页

作者： Faheem Shaukat Naveed Ejaz Rashid Kamal Tamim Alkhalifah Sheraz Aslam Mu Mu Department of Computing and Technology Iqra University H-9 Campus Islamabad 04436 Pakistan School of Computing Queens University Kingston ON K7L2N8 Canada School of Computing Ulster University Belfast BT15 1ED Northern Ireland UK Faculty of Arts Science and Technology University of Northampton Waterside Campus Northampton Northamptonshire NN1 5PH UK Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science CTL Eurocollege Limassol 3077 Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol 3036 Cyprus

Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), A Lite BERT (ALBERT), Distilled BERT (DistilBERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA), eXtreme Learning Network (XLNet) and Decoding-enhanced BERT with Disentangled Attention (DeBERTa). Experimental results demonstrate the superior performance of DeBERTa-based GAM, which employs a two-tier hierarchical attention mechanism: word-level attention highlights key terms, while sentence-level attention captures critical narrative segments, ensuring a refined and interpretable representation of movie plots. Evaluated on three benchmark datasets Trailers12K, Large Movie Trailer Dataset-9 (LMTD-9), and MovieLens37K. GAM achieves micro-average precision scores of 83.63%, 83.32%, and 83.34%, respectively, surpassing state-of-the-art models. Additionally, GAM is computationally efficient, requiring just 6.10 Giga Floating Point Operations Per Second (GFLOPS), making it a scalable and cost-effective solution. These results highlight the growing potential of text-based deep learning models in genre classification and GAM’s effectiveness in improving predictive accuracy while maintaining computational efficiency. With it

关键词： Multi-label classification artificial intelligence movie genre classification hierarchical attention mechanisms natural language processing content recommendation text-based genre classification explainable AI (Artificial Intelligence) transformer models BERT

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LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving

arXiv

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arXiv 2025年

作者： Kong, Lingdong Xu, Xiang Liu, Youquan Cen, Jun Chen, Runnan Zhang, Wenwei Pan, Liang Chen, Kai Liu, Ziwei The School of Computing Department of Computer Science National University of Singapore CNRS@CREATE Singapore The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Hochschule Bremerhaven Germany Shanghai AI Laboratory Shanghai China The Hong Kong University of Science and Technology Hong Kong The University of Hong Kong Hong Kong S-Lab Nanyang Technological University Singapore

Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous driving applications, remains underexplored. In this paper, we introduce LargeAD, a versatile and scalable framework designed for large-scale 3D pretraining across diverse real-world driving datasets. Our framework leverages VFMs to extract semantically rich superpixels from 2D images, which are aligned with LiDAR point clouds to generate high-quality contrastive samples. This alignment facilitates cross-modal representation learning, enhancing the semantic consistency between 2D and 3D data. We introduce several key innovations: i) VFM-driven superpixel generation for detailed semantic representation, ii) a VFM-assisted contrastive learning strategy to align multimodal features, iii) superpoint temporal consistency to maintain stable representations across time, and iv) multi-source data pretraining to generalize across various LiDAR configurations. Our approach delivers significant performance improvements over state-of-the-art methods in both linear probing and fine-tuning tasks for both LiDAR-based segmentation and object detection. Extensive experiments on eleven large-scale multi-modal datasets highlight our superior performance, demonstrating the adaptability, efficiency, and robustness in real-world autonomous driving scenarios. The project page can be accessible at: https://***/LargeAD. © 2025, CC BY-SA.

关键词： Superpixels

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Real-Time Object Detection Using Convolutional Neural Networks for Autonomous Vehicles 3

Real-Time Object Detection Using Convolutional Neural Networ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Premalatha, K.P. Divyarani, M.S. Chaudhary, Pavan Dev, Anoop Patra, Narayan Ghosal, Shreya Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India

ISBN: (纸本)9798331542375

This is where timely and accurate detection by autonomous cars becomes vital. Due to this, Convolution Neural Networks have gained the utmost popularity in object detection and are showing great potential in performing all sorts of sort's r vision tasks. In this project, we propose a Convolution Neural Networks based real-time object detection system for autonomous vehicles. Our system detects and localizes real-time objects based on deep neural network architecture. The function of our software is to take an image from the sensors, like cameras or Liar, on board this car. This image is further input to CNN, which takes these features, extracts the object, and classifies them into a scene. As we previously mentioned, CNN is trained with a large dataset of labeled images, allowing it to recognize hundreds or thousands of different objects accurately. This is then computed further in the CNN and attempts to find bounding boxes around detected objects, giving where that object exists spatially in a scene. Our system's response time has been designed for real-time analysis and should be able to analyze images and detect objects in about milliseconds. This, in return, enables autonomous vehicles to make immediate decisions when considering these objects and navigate safely. Our system allows for real-time object detection, opening the door to high-performance and safe autonomous vehicles that bring us closer than ever before to achieving full autonomy driving. © 2025 IEEE.

关键词： Vehicle detection

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Uplink Rate-Splitting Multiple Access for Mobile Edge Computing with Short-Packet Communications

arXiv

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arXiv 2025年

作者： Xu, Jiawei Zhang, Yumeng Xu, Yunnuo Clerckx, Bruno Imperial College London United Kingdom Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Information Science and Engineering Shandong University Qingdao266237 China

In this paper, a Rate-Splitting Multiple Access (RSMA) scheme is proposed to assist a Mobile Edge Computing (MEC) system where local computation tasks from two users are offloaded to the MEC server, facilitated by uplink RSMA for processing. The efficiency of the MEC service is hence primarily influenced by the RSMA-aided task offloading phase and the subsequent task computation phase, where reliable and low-latency communication is required. For this practical consideration, short-packet communication in the Finite Blocklength (FBL) regime is introduced. In this context, we propose a novel uplink RSMA-aided MEC framework and derive the overall Successful Computation Probability (SCP) with FBL consideration. To maximize the SCP of our proposed RSMA-aided MEC, we strategically optimize: (1) the task offloading factor which determines the number of tasks to be offloaded and processed by the MEC server;(2) the transmit power allocation between different RSMA streams;and (3) the task-splitting factor which decides how many tasks are allocated to splitting streams, while adhering to FBL constraints. To address the strong coupling between these variables in the SCP expression, we apply the Alternative Optimization method, which formulates tractable subproblems to optimize each variable iteratively. The resultant non-convex subproblems are then tackled by Successive Convex Approximation. Numerical results demonstrate that applying uplink RSMA in the MEC system with FBL constraints can not only improve the SCP performance but also provide lower latency in comparison to conventional transmission scheme such as Non-orthogonal Multiple Access (NOMA). © 2025, CC BY.

关键词： Iterative methods

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A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph 25

Way to Specialist: Closing Loop Between Specialized LLM and ...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Yutong Zhang Lixing Chen Shenghong Li Nan Cao Yang Shi Jiaxin Ding Zhe Qu Pan Zhou Yang Bai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering Central South University Changsha Hunan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400712456

Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge, e.g., emotional sociology and medicine. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial efforts in domain data acquisition and model parameter fine-tuning. To address these challenges, this paper proposes the Way-to-Specialist (WTS) framework, which synergizes retrieval-augmented generation with knowledge graphs (KGs) to enhance the specialized capability of LLMs in the absence of specialized training. In distinction to existing paradigms that merely utilize external knowledge from general KGs or static domain KGs to prompt LLM for enhanced domain-specific reasoning, WTS proposes an innovative ''LLM↻KG'' paradigm, which achieves bidirectional enhancement between specialized LLM and domain knowledge graph (DKG). The proposed paradigm encompasses two closely coupled components: the DKG-Augmented LLM and the LLM-Assisted DKG Evolution. The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG. WTS closes the loop between DKG-Augmented LLM and LLM-Assisted DKG Evolution, enabling continuous improvement in the domain specialization as it progressively answers and learns from domain-specific questions. We validate the performance of WTS on 7 datasets (e.g., TweetQA, ChatDoctor5k) spanning 6 domains, e.g., emotional sociology, medical, ect. The experimental results show that WTS surpasses the previous SOTA in 5 specialized domains, and achieves a maximum performance improvement of 11.3%.

关键词： domain knowledge graph

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Stock Market Growth and Decline Using ARIMA Model and Sentiment Analysis

Stock Market Growth and Decline Using ARIMA Model and Sentim...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sandipan Biswas Bhaskar Roy Sujit Kumar Singh Nitin Nitin Saroj Kumar Sahoo Sanjaya Kumar Sarangi Dept. of CSE-AI Brainware University Kolkata India Dept. of CSE(AIML) Asansol Engineering College Asansol India Faculty of Information Technology and Computer Science Parul University Vadodara India Department of CSE Akhilesh Das Gupta Institute of Professional Studies Delhi India Department of CSE Centurion University of Technology and Management Bhubaneswar India Dept. of Computer Science Utkal University Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Researchers have been developing and entering the technological era since the 18th century. Before everyone else, social networks brought people from distant nations together. Social network applications such as Reddit, Twitter, Facebook, Instagram, LinkedIn, and others are examples of associate degree programs. Each application has a unique purpose. Investors who want to buy stocks take use of this social networking information. They stay up with a few companies thanks to this information. In order to assess people's sentiments regarding a certain stock, we will be using stock-related news data in this instance. Using TEXTBLOB sentiment analysis, this analysis will provide feedback on the corporate so that we may be prepared to see a rise or fall in relation to investors or firm performance.

关键词： Sentiment analysis Analytical models Social networking (online) Time series analysis Predictive models Data models Web sites Multimedia communication Stock markets Intelligent systems

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