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An Energy optimization Management Model for Electric Vehicle Clusters Adapting to Multiple Scenarios

An Energy Optimization Management Model for Electric Vehicle...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Miao Chen Chao Liu Xiaoyan Zhao Fafu Lin Xiaoming Lin Jianlin Tang Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhuhai China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering Enterprise of Power Grid Guangzhou China

At present, research on regulation strategies for electric vehicles is mainly based on the interests of the vehicle and power grid. On the power grid side, factors such as load variance, load peak valley difference, and grid overload are generally considered, while on the user side, factors such as user charging costs and regulatory profits are considered. However, the consideration of load fluctuations on the power grid side is generally based on load variance as the calculation indicator . Although the strategy optimization goal can be achieved, there is still room for further improvement. Moreover, the universality research at the user level only considers the charging cost of users, or only considers single factors such as subsidy benefits, which cannot balance the multiple benefits of users. Therefore, this article proposes a load fluctuation rate index that jointly considers the standard deviation and mean of the load as the upper optimization objective; And propose a lower level optimization goal that considers the comprehensive satisfaction of user charging duration and charging cost. This optimizes and solves the energy regulation strategy of cluster electric vehicles.

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Research on underwater target detection technology based on deep learning

Research on underwater target detection technology based on ...

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Robotics, Automation and intelligent Control (ICRAIC), International conference on

作者： Hui Zhou Xinru Wang Rong Chen Weiheng Lu Liyan Cheng Qinghe Su Ruizhi Wang Qunhui Yang Meiwei Kong State Key Laboratory of Marine Geology Tongji University Shanghai China Department of Computer Science and Technology Tongii University Shanghai China

Underwater target detection aims to locate and identify targets in underwater scenes, which plays a significant role in the development of the marine economy and environmental protection. However, existing target detection algorithms often cannot meet people’s needs in underwater environments. In this paper, we propose an underwater target classification convolutional neural network (UTC-CNN) based on You Only Look Once Version 8. This network builds a C2f_Squeeze and Excitation module to enhance the feature extraction ability of blurred underwater targets, and adopts Bidirectional Feature Pyramid Network to replace Feature Pyramid Network to further enhance the feature fusion capability. The experiments show this model outperforms other target detection models on Underwater Robot Professional Contest 2020 dataset, with mAP0.5 reaching 84.4%, demonstrating the advantages of UTC-CNN in underwater target detection tasks.

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Radiographic Imaging and AI: A Systematic Review on Applications in Medical Imaging

Radiographic Imaging and AI: A Systematic Review on Applicat...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Partha Garai Jayashree Das Department of CA Kalyani Govt. Engg. College Kalyani India Department of CSE Kalyani Govt. Engg. College Kalyani India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The advent of deep learning has ushered in trans-formative advancements in medical imaging, particularly in radiography. This literature survey systematically reviews the application of deep learning techniques in radiographic analysis, emphasizing methodologies, outcomes, challenges, and future directions. Utilizing the PRISMA framework, this study synthesizes findings from a comprehensive range of 30 selected studies to provide an insightful overview of the current landscape and identify gaps for future research. The review examines various deep learning models and their impact on diagnostic accuracy, as well as the challenges faced in clinical integration. It also identifies emerging trends and proposes avenues for improving model robustness and interpretability in medical imaging.

关键词： Deep learning Surveys Accuracy Medical services Robustness Convolutional neural networks Diagnostic radiography X-ray imaging Biomedical imaging Systematic literature review

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Using quasi-oppositional butterfly optimization algorithm, a probabilistic optimal power flow for a combined tidal and electric vehicle renewable energy system

Using quasi-oppositional butterfly optimization algorithm, a...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Arijit Chakraborty Dibakar Sarkar Arka Ghosal Sourav Paul Sneha Sultana Susanta Dutta Provas Kumar Roy Electrical Engineering Dr. B. C. Roy Engineering College Durgapur India Electrical Engineering Dr. B. C. Roy Engineering College Electrical Engineering Kalyani Government Engineering College Kalyani India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Aiming to reduce operating expenses and emissions while satisfying system restrictions, optimal power flow, or OPF, is essential for effective and sustainable power system management. Quasi-Oppositional Based learning (QOBL) is added to the butterfly optimization algorithm in this research to improve convergence and solution accuracy when solving the OPF issue. When tested on the IEEE 57-bus system, the suggested QOBOA outperforms current optimization methods in terms of transmission loss and voltage profile improvement. Combining renewable energy sources is crucial for efficient electricity generation because fossil fuel sources are becoming more and more improved every day. The suggested solutions integrate renewable energy sources, such as tidal and electric vehicles, to reduce the demand for fossil fuels in the generation of electricity. Furthermore, the suggested approach has a great deal of promise for improving the adaptability and resilience of contemporary power grids, particularly in light of the growing integration of decentralized energy resources (DERs) and renewable energy sources. This strategy can help future smart grid systems operate more effectively, sustainably, and dependably by facilitating quicker decision-making and enhancing the coordination of dispersed assets.

关键词： Renewable energy sources Uncertainty Electricity Voltage Propagation losses Probabilistic logic Fossil fuels Smart grids Usability Load flow

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Pain Sense: intelligent Pain Mapping System for Spinal Cord Injuries

Pain Sense: Intelligent Pain Mapping System for Spinal Cord ...

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Pervasive Computing and Social Networking (ICPCSN), International conference on

作者： Kalyani Deshmukh Anurag Kule Pratik Bangal Shubham Udar Simran Khiani G.H. Raisoni College of Engineering and Management Pune

Millions of people in the world suffer from the injuries and devastating ailment of spinal cord pain. For correct diagnosis and therapy, professionals must accurately identify spinal cord pain, but this can be a difficult undertaking. The machine learning method suggested in this module uses a dataset of patient medical records, imaging data, and other pertinent data to identify spinal cord pain. To extract valuable features for analysis, the data is pre-processed by utilizing signal processing and feature engineering approaches. The next step is to train a machine learning algorithm to link the retrieved features with the presence or absence of spinal cord pain using supervised learning methods like logistic regression and neural networks. The performance of the proposed algorithm is assessed by using several criteria, including accuracy, precision, recall, and F1-score. The research findings indicate that our method successfully detects spinal cord pain with high accuracy. This project lays the groundwork for future research in this field and shows how machine learning might improve spinal cord pain diagnosis and therapy.

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Research on fire protection system based on deep learning

Research on fire protection system based on deep learning

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Robotics, Automation and intelligent Control (ICRAIC), International conference on

作者： Tiannian Zhou Ping Liang Zhi Zhang Zhiyong Dai Guangqi Li Hongyuan Guo State Key Laboratory of Disaster Prevention and Reduction for Power Grid State Grid Hunan Electric Company Limited Changsha Hunan China School of Electro-Mechanical Engineering Xidian University Shaanxi China

Flame detection systems are becoming more and more important in firefighting, and for indoor large space occasions, this paper designs a firefighting system based on computer vision. Combining the knowledge of mass kinematics and fluid dynamics, the trajectory of the firefighting water cannon jet is deduced, and the effect of different parameter conditions on the trajectory is analyzed using MATLAB simulation. The yolov4 flame detection model is trained to achieve accurate detection of the flame, and the coordinate value of the flame center position relative to the upper-left corner of the image is calculated at the same time. Then the horizontal turning angle required for the rotation of the fire gun is calculated according to the coordinate value, and the monocular visual localization technology is used to realize the localization of the fire source in the actual space, and the horizontal distance from the camera to the fire source is calculated. Substituting the horizontal distance into the jet trajectory algorithm can get the pitch angle of the fire cannon under the corresponding range.

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Exploring Student Performance Patterns Using Tree-Based Techniques

Exploring Student Performance Patterns Using Tree-Based Tech...

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International conference on Smart Cities, Automation & intelligent Computing Systems (ICON-SONICS)

作者： Felix Indra Kurniadi Meta Amalya Dewi Dina Fitria Murad Sucianna Ghadati Rabiha Awanis Romli Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Faculty of Computing Universiti Malaysia Pahang Pahang Malaysia

Due to its direct impact on the development and progress of nations, predicting student performance has acquired considerable importance in modern society. The evaluation of student performance measures the effectiveness of educational institutions and their capacity to influence the next generation. As a result, enhancing the educational process has become a necessity, compelling governments and institutions to devote significant resources to its ongoing development. Based on the Student Grade Data obtained from the Binus Online learning platform at Bina Nusantara University, this study analyzes and predicts student performance using tree-based methods, specifically Decision Tree and Random Forest. The dataset includes pupil information and variables pertaining to performance. By contrasting the performance of these tree-based models, it is possible to gain insight into their accuracy and efficacy in predicting student outcomes. The experimental results demonstrate that both the Decision Tree and Random Forest models predict student performance with high accuracy. These results demonstrate the potential of tree-based methods in educational data analysis and prediction, providing educators, administrators, and policymakers with valuable insights for identifying at-risk students and implementing timely interventions to enhance educational outcomes.

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Ensemble Resource Allocation using Optimized Particle Swarm optimization (PSO) in Cloud Computing

Ensemble Resource Allocation using Optimized Particle Swarm ...

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Sentiment Analysis and Deep learning (ICSADL), International conference on

作者： Raghunadha Reddi Dornala Sudhir Ponnapalli Kalakoti Thriveni Sai Siva Rama Krishna Reddi Rami Reddy Koteru Bhavani Koteru Cloud Architect USA Lead DevOps Engineer USA CNA USA Koteru CNA Storage Architect Humana Sr Java Devloper Humana Automation Tester

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Cloud computing has revolutionized how resources are provisioned and managed in the information technology landscape. One of the most critical challenges in cloud computing is efficient resource allocation to ensure optimal utilization of resources while meeting the performance requirements of applications. Traditional resource allocation methods often need to be revised to address the dynamic and complex nature of cloud environments. This research presents an Ensemble Resource Allocation (ERA) in cloud computing using Linear Programming (LP) and Particle Swarm optimization (PSO). PSO is a heuristic optimization technique inspired by the collective behavior of birds and fish. However, PSO, like many optimization algorithms, has its limitations, such as premature convergence and suboptimal solutions. To overcome these limitations, an optimized version of PSO is proposed that enhances its performance in cloud resource allocation. The proposed approach combines PSO with adaptive mechanisms to address the challenges specific to cloud environments. A fitness function is considered not only for resource utilization and application performance but also for cost, energy consumption, and service-level agreements. The multifaceted fitness function ensures that the allocation decisions are made with a holistic view of cloud resource management. Furthermore, the optimized PSO algorithm leverages intelligent heuristics to adjust its parameters dynamically, adapt to changing workloads, and optimize resource allocation continuously. This adaptability allows our approach to efficiently allocate resources under various operating conditions, making it well-suited for real-world cloud environments. In this work, the proposed system is evaluated through extensive simulations and practical experiments, comparing its performance against traditional resource allocation methods. The results demonstrate that the optimized PSO-based approach outperforms existing methods regarding resource ut

关键词： Cloud computing Sentiment analysis Heuristic algorithms Quality of service Organizations Linear programming Complexity theory

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Research on Task Planning and Efficiency Evaluation of Multi-Agent Collaborative Search Based on Ant Colony optimization

Research on Task Planning and Efficiency Evaluation of Multi...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), International

作者： Qian Xie Weiyi Chen Yumu Zhuang College of Electronic Engineering Naval University of Engineering Wuhan China College of Ordnance Engineering Naval University of Engineering Wuhan China

With the development of intelligent technology, multi-agent collaborative search task planning has become an important application, such as UAV swarm and robot detection. This paper proposes a method based on ant colony optimization to plan multi-agent cooperative search tasks. The global search ability and adaptability of ant colony optimization are used to find the optimal multi-agent cooperative search path. At the same time, we consider the communication and cooperation constraints between multi-agents. We used fuzzy neural network and principal component analysis to evaluate the efficiency of multi-agent collaborative search task planning. The sample library and training model are constructed to obtain the efficiency evaluation index of multi-agent collaborative search task planning. This paper uses numerical examples to verify the effectiveness and superiority of multi-agent collaborative search task planning based on ant colony optimization. It analyzes the impact of different parameters and model structures on the evaluation results. Furthermore, this paper provides new ideas and methods for multi-agent collaborative search task planning, which is of great significance in improving the performance and efficiency of multi-agent systems.

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Role of human-centred artificial intelligence in facilitating learning management system

Role of human-centred artificial intelligence in facilitatin...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International conference on

作者： Jitendra Gowrabhathini Dharam Buddhi Yedla Sai Jyothika Balla Mounika Palacharla Veera Sai Battula Anuhya Reddy KL Business School Koneru Lakshmaiah Education Foundation KL University Uttaranchal Institute of Technology Uttaranchal University Koneru Lakshmaiah Education Foundation KL University

learning management systems, often known as LMSs, represent the most desirable alternative for the provision of instructional materials in higher education since they make high-quality educational resources available to students from all over the globe. As a bridge, we utilize programs that manage the learning that occurs between students and teachers. This paradigm shift occurs as a result of the presence of intelligent agents who emulate the astute strategies utilized by the instructor and the pupils. The technologies that make up artificial intelligence are based on the reproduction of concepts that are present in human intelligence. This research gives a broad overview of the LMS environments in which AI-based techniques were used. In addition to that, the examples stress more instances of the huge range. learning management system (LMS) users, including students, professors, and managers, may all benefit from the investigation of AI-related LMS techniques, which have been investigated as potential solutions to a variety of LMS issues.

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