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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是431-440 订阅

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Lightning Risk Assessment Method Considering Environmental Factors Based on Artificial Intelligence algorithms 4

Lightning Risk Assessment Method Considering Environmental F...

引用

4th international conference on Smart Grid and Energy Internet, SGEI 2024

作者： Wu, Yong Sun, Zhen Qiu, Shikun Dong, Haiqing Zhou, Qibin Chen, Kehan State Grid Jiading Power Supply Company Jiading District Shanghai201800 China Shanghai University Shanghai China

ISBN: (纸本)9798350368314

With the continuous extension of transmission lines, the risk of lightning strikes on transmission lines has significantly increased, especially in areas with complex terrain. Additionally, as the construction of transmission lines encounters more complex terrain, the influencing factors of lightning strikes have become more diverse. this paper proposes a lightning risk assessment method based on Particle Swarm optimization and Random Forest (PSO-RF). By utilizing ADTD lightning data and geographical information of the study area, the model evaluates high-risk lightning zones. the results indicate that the PSO-RF model outperforms other machine learning models in terms of accuracy, precision and recall, demonstrating excellent performance on the test set. SHAP value analysis reveals that features such as latitude, longitude, lightning frequency and building height significantly influence lightning risk. this study provides guidance for differentiated lightning protection measures in power system transmission lines. ©2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Improving DNSGA-II based on Transfer learning of Population Feature 8

Improving DNSGA-II based on Transfer Learning of Population ...

引用

8th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Xue, Tongxia Shi, Xuhua Tongxia Xue Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (纸本)9798331506230

Dynamic multi-objective optimization problems (DMOPs) are common in real-world applications. To effectively address these problems, algorithms are required to maintain solution diversity and quickly adapt to environmental changes. Transfer learning is a promising approach for tackling dynamic multi-objective optimization. In this paper, a transfer learning evolutionary optimization method based on population distribution characteristics is proposed for solving DMOPs. First, mutual information is used to calculate the correlation between the optimal solutions from two consecutive environments before the environmental change. then, the distribution characteristics of the highly correlated population individuals are extracted as valuable knowledge for the transfer process. this information is employed to guide the generation of new population individuals, which are then randomly introduced into the initial population generated by the DNSGA-II-A algorithm in the new environment, forming an improved initial population. Experimental results demonstrate that, the proposed DMOEA-TLF exhibits significant advantages in convergence and diversity in most cases. © 2024 IEEE.

关键词： optimization algorithms

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Comparison of Response Surface Methodology (RSM) and Machine learning algorithms in Predicting Tensile Strength and Surface Roughness of AA8090/B4C Surface Composites Fabricated by Friction Stir Processing 4th

Comparison of Response Surface Methodology (RSM) and Machine...

引用

4th international conference on Advanced Engineering optimization through Intelligent Techniques

作者： Adiga, Karthik Herbert, Mervin A. Rao, Shrikantha S. Shettigar, Arunkumar Shrivathsa, T., V Tapariya, Rahul Natl Inst Technol Karnataka Dept Mech Engn Surathkal India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Friction stir processing is an innovative solid-state process, widely utilized for surface composite fabrication, material property enhancement, and microstructural modification. Rotational speed, traverse speed, groove width, and axial force are key FSP parameters that improve the characteristics of surface composites (SCs). this work makes use of FSP to fabricate AA8090/B4C SCs by altering parameters within ranges. Response variables include ultimate tensile strength (UTS) and surface roughness (SR). Central composite design (CCD) of response surface methodology (RSM) leads trials, establishing a mathematical relationship between input parameters and UTS/SR. the models' adequacy is validated using ANOVA, which investigates the impact of input parameters on UTS and SR. this study also looks into machine learning regression methodologies for UTS and SR forecasting in AA8090/B4C SCs. the ML algorithms are evaluated by utilizing performance metrics like coefficient of determination (R-2) and root mean squared error (RMSE). Predicted UTS and SR values from RSM are compared with machine learning outcomes.

关键词： Machine learning Friction stir processing (FSP) Ultimate tensile strength (UTS) Surface roughness ANOVA

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4th Modeling, Estimation, and Control conference, MECC 2024 - Proceedings

4th Modeling, Estimation, and Control Conference, MECC 2024 ...

引用

4th Modeling, Estimation, and Control conference, MECC 2024

the proceedings contain 187 papers. the topics discussed include: modeling and identification of quadrotor dynamics affected by wind stress;modeling of a motor driven servo-table with significant flexible modes and nonlinear disturbances;geometric graph neural network modeling of human interactions in crowded environments;the cost of transition: modeling the swimming biomechanics of bottlenose dolphins to estimate cost of transport;machine learning-based lithium ion batteries second-life starting threshold estimation: sizing optimization for stationary applications;enhancing hip exoskeleton tuning performance with machine learning: an anthropometric data-driven approach;data-driven learning-based sensor placement and temperature distribution reconstruction in lithium-ion pouch cells;and enhancing reinforcement learning for automated driving through virtual lane logic.

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Tomato Leaf Disease Detection using Federated learning 4

Tomato Leaf Disease Detection using Federated Learning

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Lamani, Gagan Deep Gagan, T.M. Halemani, Shankaraling Fayaz, K. Raikar, Meenaxi M KLE Technological University Deparment of Computer Science Hubbali India

ISBN: (纸本)9798350349900

Early detection of leaf diseases aids in timely invention by farmers, thereby preventing the spread of the disease and minimizing crop loss. Machine learning techniques are applied to detect discoloration lesions, spots, and other abnormalities associated with the diseases. Automated leaf disease detection provides the farmer with valuable data insights that aid in yield optimization. In this paper, a federated learning technique is applied for tomato leaf disease detection to ensure data privacy while enhancing the global model by training models on numerous decentralized devices or servers that hold local tomato leaf disease samples. the proposed system addresses concerns related to security, making it suitable for agricultural applications where sensitive data is involved. the model's performance is assessed using accuracy, precision, recall, and F1 score. the findings demonstrate consistently high performance, with overall accuracy for all clients and classes ranging from 98% to 99%. the precision, recall, and F1 score values are above 94%, indicating that the model can accurately identify images of healthy and diseased tomato leaves with minor misclassification. By utilizing federated learning to create a CNN model for tomato leaf disease classification, this study demonstrates the effectiveness of federated learning in producing accurate models while protecting data privacy. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Federated learning Tomato leaf disease detection

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the 4th KDDWorkshop on Deep learning for Spatiotemporal Data, applications, and Systems (DeepSpatial'24) 24

The 4th KDDWorkshop on Deep Learning for Spatiotemporal Data...

引用

30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Jiang, Zhe Zhao, Liang Zhou, Xun Zhang, Junbo Shekhar, Shashi Ye, Jieping Univ Florida Gainesville FL 32610 USA Emory Univ Atlanta GA USA Harbin Inst Technol Shenzhen Peoples R China JD Intelligent Cities Res Beijing Peoples R China Univ Minnesota Minneapolis MN USA Alibaba Hangzhou Peoples R China Univ Michigan Hangzhou Peoples R China

ISBN: (纸本)9798400704901

Over the last decades, a rapidly growing volume of spatiotemporal data has been collected from smartphones and GPS, terrestrial, seaborne, airborne, and spaceborne sensors, as well as computational simulations. Meanwhile, advances in deep learning technologies, especially the recent breakthroughs of generative AI and foundation models such as Large Language Models (LLMs) and Large Vision Models (LVMs), have achieved tremendous success in natural language processing and computer vision applications. there is growing anticipation of the same level of accomplishment of AI on spatiotemporal data in tackling grand societal challenges, such as national water resource management, monitoring coastal hazards, energy and food security, as well as mitigation and adaptation to climate change. When deep learning, especially emerging foundation models, intersects spatiotemporal data in scientific domains, it opens up new opportunities and challenges. the workshop aims to bring together academic researchers in both AI and scientific domains, government program managers, leaders from non-profit organizations, as well as industry executives to brainstorm and debate on the emerging opportunities and novel challenges of deep learning (foundation models) for spatiotemporal data inspired by real-world scientific applications.

关键词： Spatiotemporal Data Deep learning Foundation Models

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A Fusion Algorithm of Relative Orientation for Close Range Industrial Photogrammetry 4

A Fusion Algorithm of Relative Orientation for Close Range I...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Lingbo, Jiang Shandong Vocational College of Industry Department of Electrical and Mechanical Engineering Zibo China

ISBN: (纸本)9798350359589

In order to satisfy the multiple-baselines and large-dip-angles close range photogrammetry applied in industry, a fusion algorithm of relative orientation is presented. In this algorithm, the initial values of the relative orientation parameters are established by the decomposition of the essential matrix, and then the precise solution of the parameters are calculated by a nonlinear optimization. First, the relationship between the essential matrix and the orientation parameters is derived by the determinant from the coplanarity condition, and then the rotation matrix and translation vector between two images are calculated by singular value decomposition (SVD) of the essential matrix, including the removal of the pseudo solutions. Second, according to the coplanarity condition equation, the cost function of relative orientation parameters for non-linear optimization is build. Finally, the result in comparison experiment indicates that the fusion algorithm has acquired better stabilization and high precision. © 2024 IEEE.

关键词： digital close range industrial photogrammetry essential matrix fusion algorithm of relative orientation non-linear optimization

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Efficient Resource Management in IoV through Task Offloading optimization 4

Efficient Resource Management in IoV through Task Offloading...

引用

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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Adverse Drug Reaction (ADR) Prediction using Integrated Machine learning and Deep learning Approaches 4

Adverse Drug Reaction (ADR) Prediction using Integrated Mach...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Prabhu, K. Kanagasabapathy, T. Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Pollachi India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

the prediction of adverse drug reactions (ADRs) is paramount in mitigating risks to patient safety and enhancing pharmacovigilance efforts. While both machine learning (ML) and deep learning (DL) techniques have demonstrated efficacy in individual ADR prediction tasks, they possess distinct advantages and limitations. this study proposes an integrated approach that harnesses the synergies between ML and DL to improve ADR prediction accuracy and feature representation. Our hypothesis posits that by integrating traditional ML models with DL's feature extraction capabilities, we can achieve enhanced ADR risk assessment and gain deeper insights into the complex relationships among drug-related factors. this integrated framework holds promise for revolutionizing ADR prediction and proactive pharmacovigilance initiatives. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep learning Based Modal for Predicting Flooding Pattern and Climate Shifts 4

Deep Learning Based Modal for Predicting Flooding Pattern an...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kumari, Lalita Kumar, Umesh Kumar, Niranjan Amity University Amity School of Engineering Patna India

ISBN: (纸本)9798350307757

this research presents an integrated deep learning model that combines climate pattern prediction and flood forecasting. Understanding and mitigating climate change and its impact on flooding are critical environmental concerns. Our methodology harmonizes climate and flood datasets, utilizing a neural network architecture and advanced optimization techniques. the compiled model successfully predicts climate patterns and flood events, shedding light on their potential interrelationships. this research offers a novel approach to studying climate and flooding, with significant implications for climate change mitigation and flood risk management. © 2024 IEEE.

关键词： Floods

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