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检索条件"任意字段=2nd Electronics System-Integration Technology Conference"

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The Informatization Construction and Intelligent Development of the Power Logistics system in Enterprises 24

The Informatization Construction and Intelligent Development...

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2nd International conference on Artificial Intelligence, systems and Network Security, AISNS 2024

作者： Jiang, Yan Dai, Sumin Wang, Xu Sun, Lei BEIJING FIBRLINK COMMUNICATIONS CO. LTD. Beijing China

ISBN: (纸本)9798400711237

In recent years, the rapid development of information technology has made the informatization and intelligent development of logistics management systems in global power enterprises increasingly significant. While the application of information technology has significantly improved the management efficiency of power enterprises, challenges remain in optimizing logistics resource allocation, achieving real-time monitoring, and supporting decision-making. To address these challenges, this paper aims to further enhance the efficiency and service quality of power enterprise logistics management by introducing modern information technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The study adopts the design and integration of intelligent management systems, and through case studies, demonstrates how these technologies can improve the optimization of logistics resources and achieve efficient real-time monitoring. This research not only helps to improve the efficiency of logistics management in power enterprises but also provides important technical support for the continuous, stable, and healthy development of China’s power industry. © 2024 Copyright held by the owner/author(s).

关键词： Informatization

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Yoga Pose Recognition and Correction Feedback system Using Deep Learning and Computer Vision 2

Real-Time Yoga Pose Recognition and Correction Feedback Syst...

引用

2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Chauhan, Vishal Aggarwal, Mukul Singhal, Yash Shukla, Swapnil Department of IT KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9798331530389

Yoga pose detection is a rapidly evolving field that integrates advanced technology with wellness, addressing the need for accurate and adaptable systems for diverse user groups. This paper proposes a novel framework that combines RGB images, depth maps, and skeletal joint data to enhance multi-modal feature extraction and improve spatial-temporal analysis for tasks such as action recognition and human activity modeling. The framework overcomes challenges such as pose variations and occlusions through techniques like data augmentation, model optimization, and robust performance evaluation. Experimental results demonstrate the system's high accuracy of 91.7%, successfully detecting six distinct yoga pose sequences in real time and providing self-correction feedback to users, thereby improving performance. The practical applications of this approach are significant, with potential use cases in yoga studios, fitness centers, and home environments, promoting scalable, precise, and globally accessible yoga practices. © 2025 IEEE.

关键词： Accuracy AR/VR integration Deep learning F1-score Machine learning Precision Yoga pose recognition

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Analysis of Distributed Photovoltaic integration Impact on Distribution Network Using MATLAB-Based Newton-Raphson Simulation 24

Analysis of Distributed Photovoltaic Integration Impact on D...

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2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Li, Yangfan Zhang, Xin Yang, Peihong Zhang, Jihong Zhang, Xiaoming Zhang, Zilei Inner Mongolia University of Science and Technology Key Laboratory of Synthetical Automation for Process Industries Baotou Inner Mongolia Autonomous Region China Inner Mongolia University of Science and Technology Inner Mongolia Autonomous Region Baotou China

ISBN: (纸本)9798400710681

This paper delves into the mathematical foundations of the Newton-Raphson load flow analysis and its implementation within a MATLAB framework for simulating a 33-node distribution network. An admittance matrix is meticulously constructed, encapsulating the impedance characteristics of each branch and the power specifications of generators, thereby laying the groundwork for iterative computational processes. The nodes and branches are precisely defined by their respective contributions in terms of active and reactive power, which facilitates detailed simulations. The study intensively examines the repercussions of integrating distributed photovoltaic (PV) systems into the distribution network. It addresses three distinct dimensions of PV integration: the effects of varying capacities, the impact of different locational deployments within the network, and the influence of diverse power factors. By conducting robust simulation analyses, the research provides both a theoretical framework and a practical toolkit for anticipating and validating the operational performance of power systems under varied configurations and conditions. The insights derived from this analysis prove indispensable for the strategic planning and optimization of power systems, enhancing decision-making capabilities pertinent to system enhancements and the integration of renewable energy sources. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Power allocation and voltage control strategies for islanded microgrids 24

Power allocation and voltage control strategies for islanded...

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2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhang, Shuai Zhang, Jihong Zhang, Xin Inner Mongolia University of Science and Technology Inner Mongolia Autonomous Region China

ISBN: (纸本)9798400710681

Addressing the bus voltage stabilization issue arising from renewable energy fluctuation and load stochasticity in DC microgrids, we have developed an advanced hybrid energy storage dual-loop control strategy. This strategy integrates predictive control with feed-forward regulation to significantly enhance system performance. In the design of this strategy, a fuzzy PI controller is employed to establish the outer power loop, complemented by a current inner loop without differential beat control, ensuring precise and stable control. The strategy meticulously divides PV output and load variations into average power, error power, and transient power components. By leveraging the unique differentiated capabilities of the hybrid energy storage system, these power components are precisely compensated for, achieving stable bus voltage. To verify the effectiveness of this strategy, MATLAB/Simulink simulation experiments were conducted. The experimental results demonstrate that this strategy excels in balancing bus voltage, optimizing the distribution of stored power, and enhancing the overall energy utilization of the system. Consequently, it not only meets national standards for voltage fluctuation but also ensures robust and reliable system operation. The successful application of this strategy undoubtedly provides powerful technical support and guarantee for the stable operation of DC microgrids. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Deep learning based multiple regression analysis approach for designing medical image recognition system 2nd

Deep learning based multiple regression analysis approach fo...

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2nd International conference on Next-Generation Computing and Information systems, ICNGCIS 2023

作者： Singh, Shweta Jagadish, A.N. Vyas, Bhuman Wasim Bhatt, Mohammed Sameen, Aws Zuhair Degadwala, Sheshang Electronics and Communication Department IES College of Technology Bhopal India Department of Computer Science Bapuji Institute of Engineering and Technology Karnataka Davanagere India Credit Acceptance Canton United States Model Institute of Engineering and Technology J&K Jammu India College of Medical Techniques Al-Farahidi University Baghdad Iraq Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

Machine learning models are optimization models that make it possible to gather data, evaluate it, and provide the experts and management with the reports they need to make the best decisions. This study uses quantitative research, as it seeks to gather information and analyse it with statistical models such as regression analysis and analysis of variance. Researchers want to use statistical techniques such as descriptive analysis, regression analysis, and ANOVA (analysis of variance) to analyse the hypothesis. The purposive sample technique is used by the authors to choose respondents who work in the healthcare sector. A non-probability sampling technique called purpose sampling is used. The R squared value is 0.744, or 74.4 percent, according to the analysis. According to the investigation, there is a strong correlation between improved picture quality and ML-based techniques in digital image recognition. © 2025 The Author(s).

关键词： Regression analysis

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The application of GIS technology in accident prevention and management in intelligent traffic management systems 2

The application of GIS technology in accident prevention and...

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2nd International conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yu, Xintong Shenzhen University Shenzhen China

ISBN: (纸本)9781510689053

This study investigates the integration of Geographic Information system (GIS) technology with Intelligent Traffic Management systems (ITMS) for accident prevention and management, addressing the limitations of traditional systems in processing real-time data and spatial analysis. The research implements a comprehensive GIS-based framework combining distributed computing, machine learning algorithms, and WebGIS technologies. The system architecture incorporates improved Hidden Markov Models for GPS trajectory mapping, Graph Neural Networks for traffic flow prediction, and enhanced Ordering Points to Identify the Clustering Structure (OPTICS) algorithms for spatial analysis. Experimental results demonstrate significant improvements in system performance, with the accident prevention model achieving 89.7% accuracy and an AUC value of 0.94. The accident response time was reduced by 42%, from 15 minutes to 8.7 minutes, while the secondary accident rate decreased from 7.2% to 3.1%. The system successfully handled peak concurrent users of 10 million with 99.9% of requests responding within 200 milliseconds, leading to a 12.3% reduction in average commuting time and 24.6% decrease in congestion duration. This study confirms that the integration of GIS technology with intelligent traffic management systems significantly enhances traffic safety and efficiency while providing valuable insights for future smart city initiatives. © 2025 SPIE.

关键词： Graph neural networks

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Multi-Robot Cooperative Navigation in Dynamic Environments using Deep Reinforcement Learning in ROS 24

Multi-Robot Cooperative Navigation in Dynamic Environments u...

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2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Wu, Shuangshuang Wu, Jianchuang Chen, Wenbai Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798400710681

In the rapidly advancing field of robotics, multi-robot cooperative navigation stands out as a critical area with applications ranging from indoor environmental monitoring to logistics. However, achieving autonomous navigation in dynamic settings without preexisting maps poses considerable challenges, notably in terms of efficiency. This study applies Deep Reinforcement Learning (DRL) within the Robot Operating system (ROS) framework to achieve the autonomous navigation and overcome resource planning in such environments. By incorporating DRL into the global path planner of ROS, robots are able to learn and execute cooperative navigation strategies in real-time, without relying on prior environmental knowledge. Our simulation experiments revealed that this integration not only improves navigation efficiency but also minimizes resource consumption. The results underscore the trans-formative potential of DRL in optimizing individual and collective robot navigation strategies. It offers new insights and approaches for the application of multi-robot systems in complex dynamic environments, with significant theoretical and practical implications. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Dynamic reconfiguration of new urban distribution networks based on risk-aversion reinforcement learning 24

Dynamic reconfiguration of new urban distribution networks b...

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2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Song, Zhigao Shi, Yuntao Qi, Yushuai Zhang, Shufeng North China University of Technology Beijing China North China Electric Power University Beijing China

ISBN: (纸本)9798400710681

To address the issues of large voltage fluctuations and high local voltages caused by reverse power flow, due to large-scale integration of distributed renewable energy sources, like wind and solar power, into the urban distribution network, this paper proposes a dynamic reconfiguration strategy based on conditional value at risk (CVaR) aversion. First, wind power generation, photovoltaic generation, and electric vehicle loads are considered as stochastic uncertainties, and the voltage deviation is used as the loss function to construct the CVaR voltage fluctuation risk index. Then, a Q-learning framework is proposed to solve the distribution network reconfiguration strategy, with the objective of minimizing voltage deviation and network losses. Finally, based on the monotonicity and magnitude changes of distributed generation and loads, reconfiguration periods are divided, and risk assessment is conducted within each period. The decision to reconfigure in each period is made based on the CVaR index, resulting in the final risk-aversion dynamic reconfiguration strategy. Simulation tests on the IEEE 33-bus distribution system demonstrate the effectiveness and superiority of the proposed method. © 2024 Copyright held by the owner/author(s).

关键词： Power distribution networks

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2nd International conference on Intelligent Manufacturing and Robotics, ICIMR 2024

2nd International Conference on Intelligent Manufacturing an...

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2nd International conference on Intelligent Manufacturing and Robotics, ICIMR 2024

ISBN: (纸本)9789819639489

The proceedings contain 70 papers. The special focus in this conference is on Intelligent Manufacturing and Robotics. The topics include: Research on Abrasive-Workpiece Interaction Mechanism in 2.5D Needle-Punched-Cf/C-SiC Composites Scratching Tests;A Bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN);advancing In-Situ Bioprinting Through Ant Colony Optimisation: Resolving Dead Zone Challenges in Path Planning;autonomous Real-Time Human-Robot Emotional Interaction Through Facial Recognition;machine Learning for Predictive Risk Analytics in Industry 4.0: A Comprehensive Random Forest Evaluation;Enhancing Deepfake Detection: A Weighted Summation Model of CNN Approach with Local and Global Analysis;virtual Streamer Features: Unveiling the Influence on Online Purchase Intentions in the TikTok Era;Green Threads and Digital Dreams: Exploring the Influence of AI-Mediated Experiences on Purchase Intentions in Sustainable Apparel Markets;the Implications of Platform Algorithms on Platform Worker’s Job Security;The Role of Entrepreneurs’ Hidden Competencies and AI integration in Shaping Entrepreneurial Success;Empowering Entrepreneurship: Exploring the Mediating Role of AI integration Between SMA and Performance;Leadership in the Digital Age: Leveraging AI-Enhanced Training for Peak Performance;Mediating Role of AI-Based Social Activities in Enhancing Elderly Quality of Life: Exploring the Impact of Social Support and Environmental Influences;cost-Benefit Analysis and technology Transition Management of Automation Adoption in Tea Plucking Based on system Dynamics Model Design;enhancing Efficiency and Profit Growth in Automotive Final Assembly: A system Dynamics Approach to Automated Feeding and Fastening systems;facial Recognition Educational Attendance system;creative Robotics: Designing Waving-Hand Dancing Robots.

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