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Research on Adaptive Algorithms for IEEE 802.11 MAC Layer Protocol and Beacon Messages 24

Research on Adaptive Algorithms for IEEE 802.11 MAC Layer Pr...

引用

4th International conference on Internet of things and Machine learning

作者： Shi, Qi Tu, Botao Zhang, Yuejin East China Jiaotong Univ Sch Informat & Software Engn Nanchang Peoples R China Jiangxi Transportat Res Inst Co Ltd Intelligent Transportat Dept Nanchang Peoples R China

ISBN: (纸本)9798400710353

In VANETs, scalability, latency, reliability, and efficiency are critical metrics. One way to increase the duration of communication links in VANETs is to improving transmission range through increased transmission power. But, under current practical conditions, increasing the power of transmission will cause more interference and more network cost. Dynamically adjusting traffic density become more useful. For make sure normal operation for the transmission while facing emergency situation, the IEEE 802.11p for VANETs utilizes EDCA from 802.11e to support the Quality of Service for different applications. For the 802.11e, various traffic-related messages can be assigned different priorities according to the vehicle safety level. these different priorities are identified by channel access parameters, containing the size of the Contention Windows (CW).

关键词： VANET Enhanced Distributed Channel Access 802.11e Contention Window

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LexSage: Multi-Task optimization in Legal Large Language Model Applications 4

LexSage: Multi-Task Optimization in Legal Large Language Mod...

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4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Han, Lin Zhang, Yang Gao, Wei Li, Xiaowen National Supercomputing Center in Zhengzhou Zhengzhou China Zhengzhou University Zhengzhou China

ISBN: (纸本)9798331531225

With the increasing complexity of tasks in the legal domain, traditional methods struggle to meet the demands of multi-task scenarios and face significant bottlenecks in task accuracy and efficiency. To address this, we propose LexSage, a legal large language model that leverages the one-shot capability of large language models (LLMs). By utilizing different prompt templates to assist in data generation and applying data augmentation techniques to further expand the dataset, LexSage performs instruction fine-tuning on the Qwen2.5-7B model with a comprehensive legal dataset. through knowledge sharing and structural optimization across tasks, LexSage achieves a synergistic effect during multi-task fine-tuning, effectively integrating and enhancing the knowledge and expressive power across different tasks. Evaluation on LawBench shows that LexSage exhibits significant performance improvement in multiple tasks, including 25.6% improvement in the case analysis task compared to the GPT4 model, and 15.6% improvement in the law recitation task compared to the hanfei legal large language model. the experimental results show that LexSage demonstrates a strong comprehensive processing capability in legal intelligence applications, providing new ideas and technical support for the intelligent development of the legal field. © 2024 IEEE.

关键词： Multi-task learning

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2023 4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

2023 4th International Conference on Mechatronics Technology...

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4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

ISBN: (纸本)9798350314434

the proceedings contain 118 papers. the topics discussed include: research and implementation of high precision full range analog acquisition system based on zero flux current transformer;a dynamic model for the electromechanical coupling of the ball screw feed system;research on simulation design of temperature control system based on 51 single chip microcomputer;design and research of wind-solar hybrid power generation and hydrogen production system with energy storage;a two-stage optimization model for economic operation in integrated electric-gas distribution systems considering E2H conversion and uncertainties;a operation error prediction method of gateway capacitive voltage transformer based on long short-term memory neural network and multi-task learning strategy;a cable condition assessment method based on cross-recursive feature elimination;and design of downhole parameter measurement system for electric submersible pump.

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optimization of Parameters in Additive Manufacturing Processes Using Rao Algorithms 4th

Optimization of Parameters in Additive Manufacturing Process...

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4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Pabari, Miit Chetan Patel, Pooja Rao, Ravipudi Venkata Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Additive manufacturing (AM) has revolutionized the manufacturing industry by enabling the creation of complex geometries and customized parts with unprecedented speed and precision. However, to fully unlock its potential, optimizing AM processes is imperative. this paper extensively explores the utilization of Rao optimization algorithms within the domain of additive manufacturing, with the primary objective of elevating operational efficiency, mitigating production expenditures, and elevating the overall standard of manufactured products. three case studies of additive manufacturing are considered in this paper to illustrate the practical implementation of Rao algorithms, showcasing remarkable improvements in terms of material utilization, build time reduction, and structural integrity enhancement.

关键词： Rao algorithm Additive manufacturing Process parameters optimization

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FPAFS: Feature Selection Using the Flower Pollination Algorithm for Software Fault Detection System 4

FPAFS: Feature Selection Using the Flower Pollination Algori...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Rath, Pradeep Kumar Mahato, Susmita Singh, Niharika Gourisaria, Mahendra Kumar Das, Himansu School of Computer Science & Engineering Nist Institute of Science & Technology Berhampur India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

this paper suggests a suitable feature selection (FS) approach FSFPA using flower pollination algorithm (FPA). It is based on the concept of flower pollination, to choose a set of important features or variables from a vast set of characteristics present in a dataset. the objective is to choose the best suitable features for defect prediction. In this study FSFPA strategy is compared with other optimization methods such as ant colony optimization (ACO), particle swarm optimization (PSO) and evolution based algorithms differential evolution (DE), and genetic algorithm (GA). the experiment has been conducted using these five optimization strategies on nine datasets. To determine the most effective metrics for identifying error proneness, it uses five classifiers (NB, KNN, DT, LDA, QDA). It is observed that FSFPA was better considering the parameters training times and prediction accuracies as compared to the traditional GA, ACO, DE, and PSO methods. In conclusion, FPA produces better prediction outcomes in imbalanced data sets than the GA, ACO, DE, and PSO. It is necessary to correct the parameters that are responsible for software errors by utilizing optimization algorithms and appropriate classifiers. © 2024 IEEE.

关键词： Ant colony optimization

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Research on Charge and Discharge Strategies optimization based on Reinforcement learning 4

Research on Charge and Discharge Strategies Optimization bas...

引用

4th International conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Sun, Yijia Meng, Keqilao Inner Mongolia University of Technology College of Energy and Power Engineering Inner Mongolia Autonomous Region Hohhot China Inner Mongolia University of Technology College of New Energy Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798331518066

At present, it is anticipated that electric vehicles suit to address the dual challenges of energy shortage and environmental pollution, due to the utilisation of clean energy, high efficiency, and exemplary energy storage characteristics. Nevertheless, the extensive and unstructured charging of electric vehicles is likely to intensify the peak-to-valley and overall variance of the daily load curve. Although the conventional time-of-use pricing approach has served to alleviate this issue to a degree, it has simultaneously precipitated the emergence of new load peaks. In this work, we propose an optimisation method for charging and discharging strategies for electric vehicles based on reinforcement learning. A model has been developed which employs the Proximal Policy optimization (PPO) algorithm within the deep reinforcement learning framework to identify the optimal charging and discharging strategy through interaction with the grid environment. In contrast to conventional methodologies, we have incorporated a self-attention mechanism into the PPO algorithm to enhance the efficacy with which the timing characteristics of electricity prices and grid loads can be captured. the model considers a range of factors, including electricity price, grid load, vehicle usage mode and user preference, and employs a multi-objective reward function to achieve a balance between charging cost, user comfort and grid stability. By monitoring the status of the grid in real time and anticipating future developments, the model is able to make intelligent decisions regarding charging and discharging, thereby achieving the dual objectives of peak shaving and valley filling while reducing user costs. the results of the experimental analysis demonstrate that our method is markedly more effective than the traditional optimization strategy in reducing charging costs and alleviating peak loads, and has considerable practical application value. © 2024 IEEE.

关键词： State of charge

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Efficient Resource Management in IoV through Task Offloading optimization 4

Efficient Resource Management in IoV through Task Offloading...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Zhang, Qingwei Shao, Caixing Yang, Yang Zhang, Ruiqi School of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu China

ISBN: (纸本)9798331541729

With advancements in the Internet of Vehicles (IoV) and wireless technologies, intelligent vehicles are becoming increasingly prevalent, introducing applications that are computationally intensive, energy-consuming, and highly sensitive to latency. However, the limited computational capabilities of vehicles often result in excessive workloads that exceed local processing capacity, undermining real-time performance. VEC addresses this challenge by deploying computational resources at the network edge, particularly in Roadside Units (RSUs). In the field of task offloading for the IoV, recent studies have demonstrated the potential of Reinforcement learning (RL) in addressing task offloading challenges, particularly in reducing latency and energy consumption. However, research on effectively utilizing computational resources within the IoV to further optimize latency and energy efficiency remains relatively limited, making it a promising area for future exploration. In this context, we propose a reinforcement learning-based optimization algorithm, PPOH, to enhance resource utilization and performance in the IoV environments. the algorithm optimizes the transmission power between vehicles and RSU, vehicle CPU frequencies, and RSU resource allocation ratios, balancing energy consumption and latency. Simulation results validate the convergence of PPOH, demonstrating its superior performance compared to benchmark algorithms across key metrics. © 2024 IEEE.

关键词： Computation offloading

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Patterns Recognition of Sentimental Patterns from the X-Dataset using Deep learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

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4th International conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Blockchain Support for Verifiable Split learning 4

Blockchain Support for Verifiable Split Learning

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4th intelligent Cybersecurity conference, ICSC 2024

作者： Beis-Penedo, Carlos Diaz-Redondo, Rebeca P. Fernandez-Vilas, Ana Troncoso-Pastoriza, Francisco Fernandez-Veiga, Manuel Telecommunication Engineering School Universidade de Vigo Atlanttic Research Center Information & Computing Lab Vigo36310 Spain Universidade de Vigo / Escuela Naval Militar CUD Escuela Naval Militar Vigo Spain

ISBN: (纸本)9798350354775

this study introduces a decentralized learning model that combines blockchain technology with split learning to create a verifiable and secure AI training platform. the decentralization and immutability of blockchain enables to verify transactions and manage data across multiple nodes. Besides, the ability of split learning ensures that sensitive data remains local and, consequently, addresses critical issues in distributed learning environments such as data privacy and model integrity. We detail the architecture, the implementation and we provide a performance analysis of the global architecture, which offers promising results in balance data privacy, system efficiency, and computational costs. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Energy optimization Harnessing IoT and Advanced learning Techniques 4

Smart Energy Optimization Harnessing IoT and Advanced Learni...

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4th International conference on Sustainable Expert Systems, ICSES 2024

作者： Liyon Raj, S. Malleeshwaran, T. Prasanna, T. Alfred Daniel, J. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

the paper gives an extensive investigation of the research findings on the optimization of energy management in smart microgrids. the study specifically focuses on integrating renewable energy sources with the Internet of things. Smart microgrids have become an essential option due to the increasing global energy demand and environmental problems arising from the use of fossil fuels. these grids assure better efficiency in renewable energy usage energy storage, and improved user interaction using IoT-based technologies. this research investigates the application of machine learning algorithms to predict energy consumption patterns, electricity prices, and optimal energy storage management strategies. the main idea of the proposed mathematical model in this paper is to minimize the cost of energy procurement under the condition of a balanced energy supply/demand based on the optimization problem. this model is simulated with several scenarios using the linear programming tool of MATLAB software and confirms high cost savings and system efficiency. this study also investigates how EVs and BESS in the grid exert better optimization of smart energy systems. © 2024 IEEE.

关键词： Procurement Renewable energy sources Costs Machine learning algorithms Mathematical models Smart grids MATLAB Energy management optimization Energy storage

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