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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11563 条记录，以下是321-330 订阅

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Deep learning Radar Point Cloud Input Improvement Method 4

Deep Learning Radar Point Cloud Input Improvement Method

引用

4th IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wan, Qingmian Peng, Hongli Su, Shixian Liao, Xing Liu, Kuayue Li, Hanchen Shanghai Jiao Tong University State Key Laboratory of Radio Frequency Heterogeneous Integration Shanghai China

ISBN: (纸本)9798350361643

Radar point cloud is used as the basic input data for deep learning, in order to improve the accuracy of radar data output, this paper will present the point cloud optimization algorithm for high-precision 4D radar, using the dynamic adjustment of spatial area echoes, and proposes a point cloud generation optimization method. At the same time, in the subsequent AOA angle resolution, echo space positioning can be further integrated with further matching optimization according to downstream needs. Finally, our detection data collected for the 4D radar in real road scenarios and results show that this 4D radar has significant performance improvements in terms of clutter suppression and density generation. It is known that the signal echo threshold is the product of spatial inversion after judgment. therefore, in order to obtain a more accurate multi-channel the point cloud echo signal, the target detection of the echo needs to be optimized first. © 2024 IEEE.

关键词： Deep learning

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Distributed Power System Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MADRL 4

Distributed Power System Simulation and Monitoring Supported...

引用

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

作者： Luo, Hui Zhang, Jie Hu, Yufeng Zhang, Jingyue Guo, Haiping Huang, Libin Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

this study explores the optimization of Distributed Generation Systems (DGS) within a Multi-Agent Deep Reinforcement learning (MADRL) framework. the focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. the results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. the cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. the scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources. © 2024 IEEE.

关键词： Cloud platforms

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Research on Auxiliary Control Strategy for Large-scale Power Grid Based on Deep Reinforcement learning 4

Research on Auxiliary Control Strategy for Large-scale Power...

引用

4th IEEE International conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Liu, Shiqi Wang, Jian Qiu, Shengmin Li, Zhizhong Zhang, Kun Lou, Nan China Southern Power Grid Co. Ltd Dispatch Control Center GuangZhou China

ISBN: (纸本)9798350349252

Large-scale power grid sections often exceed their limits due to the participation of power generation units in the frequency regulation auxiliary service market. To address this issue, this paper proposes an auxiliary control strategy for large-scale power grid sections based on deep reinforcement learning. the Deep Deterministic Policy Gradient (DDPG) algorithm is applied to construct an intelligent agent for power grid section control, providing real-time control strategies in complex power grid environments, while considering both the safety and economy of power grid operations. On this basis, a two-stage optimization strategy considering sensitivity is proposed. In cases where the section limit cannot be eliminated through real-time control, the optimal market intervention plan is provided for operators to eliminate the section limit. Finally, case studies are designed to verify the effectiveness of real-time control strategies and market intervention plans. Under different types of section limits caused by power generation units participating in the market, the method proposed in this paper reduces the clearing price by an average of 1.2% and the average adjustment amount by 37.6% compared to the current rules, effectively improving the system's economy. © 2024 IEEE.

关键词： Deep reinforcement learning

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ICDAR 24: intelligent Cross-Data Analysis and Retrieval 24

ICDAR 24: Intelligent Cross-Data Analysis and Retrieval

引用

4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Dao, Minh-Son Riegler, Michael Alexander Dang-Nguyen, Duc-Tien Tran, Hanh-Nhi Kiran, Rage Uday Komamizu, Takahiro Natl Inst Informat & Commun Technol Big Data Integrat Res Ctr Tokyo Japan Simula Metropolitan Ctr Digital Engn Oslo Norway Bergen Univ Bergen Norway French Mil Acad St Cyr Coetquidan Paris France Univ Aizu Aizu Wakamatsu Japan Nagoya Univ Nagoya Aichi Japan

ISBN: (纸本)9798400706028

Our workshop aims to provide a platform for both academic and industrial professionals engaged in the analysis and retrieval of cross-data from diverse perspectives, with a particular emphasis on wearable and ambient sensors, lifelog cameras, social networks, and surrounding sensors. Despite numerous studies exploring individual viewpoints, there remains a significant gap in the analysis and retrieval of cross-data to maximize benefits for humanity. Additionally, challenges such as data security and distributed learning for cross-modal model training and inference arise when dealing with large and distributed datasets. We invite researchers to contribute to this initiative, with the overarching goal of fostering the development of a smart and sustainable society through the efficient utilization of intelligent cross-data analysis and retrieval techniques.

关键词： cross-data analysis privacy-preserving prediction model time-series data forecast cross-modal models image clustering and segmentation social multimedia hidden knowledge reveal transformer federated learning

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Enhancing Dust Detection on Photovoltaic Panels with PP-YOLO: A Deep learning Approach 4

Enhancing Dust Detection on Photovoltaic Panels with PP-YOLO...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Sun, Tianyi Gao, Han Fan, Siyuan Hu, XiaXia Northeast Elect Power Univ Sch Automat Engn Jilin Jilin Peoples R China Northeast Elect Power Univ Sch Energy & Power Engn Jilin Jilin Peoples R China State Nucl Elect Power Planning Design & Res Inst Beijing Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Atmospheric dust deposition on photovoltaic panels leads to dust accumulation, impairing heat dissipation and significantly reducing both the power generation efficiency and system safety. this paper explores a detection method for dust accumulation on photovoltaic panels using a deep learning algorithm. To precisely determine the degree and location of dust accumulation, a detection model based on the PP-YOLO algorithm has been developed. Utilizing the YOLOv5 algorithm, this model incorporates the lightweight PP-LCNet backbone network and H-Swish activation function, enhanced with the CA attention mechanism, to elevate both detection accuracy and speed. Comparative analysis using the photovoltaic panel dust accumulation dataset reveals that the proposed PP-YOLO model outperforms the SSD, Faster-RCNN, and YOLOv5 models in terms of precision and recognition speed, achieving precision and recall rates of 89.71% and 90.23%, respectively.

关键词： Photovoltaic panel identity method dust accumulation YOLO

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Comparative Analysis of Machine learning Algorithms for Traffic Flow Prediction in intelligent Transportation Systems 4

Comparative Analysis of Machine Learning Algorithms for Traf...

引用

4th International conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Yadav, Mahima Kumar, Bijendra Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350387490

As human society advances, the limitations of current transportation systems become more apparent, prompting a growing desire to leverage advanced technology for intelligent transportation solutions. However, low identification rates and complicated processing plague many of the vehicle detection techniques used in video analytics today. this paper gives a comparative analysis of multiple machine learning algorithms for traffic flow prediction in order to improve intelligent Transportation Systems (ITS). Using an extensive dataset of historical traffic flow and environmental variables, we evaluate the performance of several methods: Linear Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests (RF) and deep Neural Networks (DNN). Numerous Variables are taken into account in the evaluation, including scalability, computing efficiency, and forecast accuracy. the findings show that while algorithms like RF and SVM give good performance with less computational overhead, DNN obtain the maximum accuracy. the work offers valuable information on the disadvantages of various machine learning (ML) techniques, which may be used to select suitable models for the prediction of traffic movement in real-world ITS. © 2024 IEEE.

关键词： Random forests

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A Taxonomy for Low Computational Complexity of Energy optimization in Smart Building 4

A Taxonomy for Low Computational Complexity of Energy Optimi...

引用

4th International Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Ngouessy Ep Ovono, Bertille Auyane Otunniyi, Temidayo Vaal University of Technology Faculty of Engineering Gauteng South Africa

ISBN: (纸本)9798350387988

the growing energy demand and the need for sustainable solutions have led to the rise of smart buildings equipped with innovative technologies aimed at optimizing energy use. Machine learning (ML) algorithms play a pivotal role in these buildings by predicting energy consumption trends, detecting anomalies, and automating control systems. this review provides a thorough analysis of the most advanced machine learning algorithms used for energy optimization, while also offering a new perspective by categorizing these algorithms based on their commercial viability, computational efficiency, and applicability to different building types. Additionally, the paper synthesizes recent progress and highlights key research gaps, offering practical recommendations for future investigations. © 2024 IEEE.

关键词： Adversarial machine learning

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A Trusted Mechanism for Participant Screening and Verification in Federated learning 4

A Trusted Mechanism for Participant Screening and Verificati...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Xie, Yun Wu, Yucheng Wang, Hongyan Li, Ang Nanjing Vocat Coll Informat Technol Jiangsu Prov Serv Customizat Network Applicat Eng Nanjing Peoples R China Nanjing Vocat Coll Informat Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Nanjing Univ Sci & Technol Zijin Coll Nanjing Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Federated learning(FL) solves the problem of "Data Silos" achieving the dual-purpose of data retention and remote sharing, which is widely applied in fields such as healthcare, transportation, and manufacturing. Local participants (LPs) are the main entities in FL, contributing resources such as data, computing, communication, and energy. the actual contribution of LPs directly affects the performance of federated learning. Existing research has mostly focused on how to design efficient algorithms for LPs, while neglecting the credibility of them. Obviously, highly trusty LPs will provide high-quality data sources and model medium parameters, which are the core factors affecting the performance of FL. this paper designs a new mechanism based on the joining protocol to verify the legitimacy of LPs, and combines subjective logical models to evaluate the reputation of participants. It solves the credibility evaluation and screening problems of participants, as well as the fairness of rewards.

关键词： Federated learning Reputation mechanism Subject logic Join protocol

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Data-Driven Battery Health optimization in Electric Vehicles: A Machine learning Approach 4

Data-Driven Battery Health Optimization in Electric Vehicles...

引用

4th IEEE International conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Pujari, Pradyumna Gnanasambanthan, Lakshya Rubi Sharma, Prabhav Sriramalakshmi, P. School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383997

the burgeoning electric vehicle (EV) market hinges on maximizing battery health and lifespan. this research investigates the efficiency of machine learning (ML) in optimizing battery health through a data-driven approach. this article deals with a novel framework that harnesses battery and heating data in real driving cycles gleaned from the battery management system (BMS) and other vehicle components. this rich data undergoes rigorous processing and analysis via advanced ML algorithms such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and Support Vector Regression (SVR), aiming to achieve precise prediction of battery degradation, unveiling key health determinants and personalized battery management. Implementing this data-driven paradigm aims to yield substantial advancements in EV battery health, resulting in extended range and lifespan, enhanced safety, reliability and optimized charging strategies. this research stands as a significant contribution to the evolution of intelligent BMS systems, paving way for smarter, more efficient and sustainable EV battery management system. © 2024 IEEE.

关键词： Support vector regression

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SwinDCA-Net: Leveraging Swin Transformer and Dynamic Connection Attention for Medical Image Segmentation 4

SwinDCA-Net: Leveraging Swin Transformer and Dynamic Connect...

引用

4th International conference on intelligent Communications and Computing

作者： Wang, Xinhui Zhang, Min Zhao, Xiuniao Gannan Univ Sci & Technol Coll Informat Engn Ganzhou Peoples R China

ISBN: (纸本)9798331530372;9798331530365

this paper introduces SwinDCA-Net, a novel architecture tailored for medical image segmentation, with a specific emphasis on vessel segmentation. the model incorporates Swin Transformer Modules into the U-Net framework, blending local feature extraction with global context modeling to enhance the segmentation of complex structures. Additionally, a Dynamic Connection Attention Module is implemented within the skip connections, allowing for adaptive re-weighting of features across different scales, thereby improving feature fusion. through extensive experiments, SwinDCA-Net consistently demonstrates superior performance, surpassing existing models in metrics such as F1-Score, sensitivity, and accuracy. these results confirm the model's ability to accurately segment thin and complex structures.

关键词： deep learning vessel segmentation swin transformer dynamic connection attention

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