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Modeling and Fault Characteristics Analysis of Multi-Energy Complementary Microgrids 4

Modeling and Fault Characteristics Analysis of Multi-Energy ...

引用

4th international conference on intelligent Power and systems, ICIPS 2024

作者： Li, Chunyan Huang, Huimin Chen, Yan Li, Wenyan Chongqing University of Technology School of Electrical and Electronic Engineering Chongqing China Chongqing Communication Design Institute Co. Ltd Chongqing China

ISBN: (纸本)9798331532765

To promote the achievement of the 'dual carbon' goals and address the threats posed by the uncertainties of new energy sources to grid operation, it is urgent to study the operational characteristics of multi-energy microgrids that include wind, solar, and storage systems. The doubly-fed induction generator (DFIG) is the mainstream model for wind energy utilization due to its wider speed regulation range and higher energy conversion efficiency. The stator is directly linked to the grid, while the rotor excitation voltage is supplied by converters connected on both the rotor side and the grid side. This configuration enables variable speed constant frequency operation through converter control. This paper first proposes the control approach for the converters: the grid-side converter (GSC) maintains a constant DC capacitor voltage with good dynamic response capability, while the rotor-side converter (RSC) controls the speed to achieve maximum power tracking of wind energy. To improve the wind turbine's fault ride-through capability, a new crowbar protection strategy is proposed for the rotor side by incorporating the instantaneous rotor current, system voltage, and a fixed time delay as key features. Secondly, the control strategy for the photovoltaic power generation system inverter is studied, also adopting maximum power tracking to achieve efficient energy utilization. Then, a control strategy for the storage battery as the main control micro-source is proposed: during grid-connected operation, fixed active and reactive power output is used;during islanded operation, droop control is employed to maintain the stability of the microgrid's voltage and frequency. To ensure a seamless transition from isolated operation to grid-connected operation, a synchronization module is integrated into the battery storage control system. Subsequently, a microgrid model is developed using Matlab/Simulink to simulate the complementary operational characteristics of various micro-sou

关键词： Wind power

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A Single-Frame Face Anti-Spoofing Algorithm With Circular CDC and Multi-Scale Spatial Attention 20

A Single-Frame Face Anti-Spoofing Algorithm With Circular CD...

引用

20th ieee international conference on Mechatronics and automation, ICMA 2023

作者： He, Shan Dong, Enzeng Tong, Jigang Yang, Sen Liang, Xuehui Li, Wenyu Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Nankai University College of Artificial Intelligence 94 Weijin Road Tianjin China

ISBN: (纸本)9798350320831

Face anti-spoofing is to prevent face images with attack properties from entering face recognition and causing confusion or spoofing of the face recognition function. Since most face presentation attacks do not possess true deep information, some recent works use this information as supervisory signal. In this paper, we continue such a pattern by proposing a circular CDC (Central Difference Convolution) with larger sensory field and contextual semantic awareness, and use it in a backbone network for improving the network model's ability to characterize texture features and multi-scale information. Combined with the spatial attention method concat multiple scale feature maps are used to fuse the feature map information from multiple scales. We conduct a series of experiments on OULU-NPU and demonstrate that our network accuracy is better than the original algorithm, especially in terms of generalizability across devices and different environments. © 2023 ieee.

关键词： Face recognition

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Boosting Robot Intelligence in Practice: Enhancing Robot Task Planning with Large Language Models 8

Boosting Robot Intelligence in Practice: Enhancing Robot Tas...

引用

8th international conference on Robotics and automation Engineering, ICRAE 2023

作者： Zhang, Yisheng Wang, Zhigang Zhang, Shengmin Peng, Yanlong Chen, Ming School of Mechanical Engineering Shanghai Jiaotong University Shanghai China Intel Labs China Beijing China

ISBN: (纸本)9798350327656

Task planning capabilities are crucial for intelligent robots to operate autonomously in the physical world. However, traditional Planning Domain Definition Language (PDDL) based methods often suffer from combinatorial explosion and unsat-isfactory planning time. In this paper, we propose enhancing robot task planning with large language models (LLMs) in an innovative way - using LLMs to guide the search process of PDDL planners rather than replacing PDDL planning completely. The LLMs guide the search process of PDDL planners with learned heuristics and provide constraint reasoning to reduce the search space. To address potential pitfalls of LLMs, a verification mechanism is added at the execution stage to validate plan correctness. We evaluated our method on a real scenario, end-of-life vehicle battery disassembly. Experimental results demonstrate that incorporating LLMs into the planning pipeline can significantly improve planning efficiency and scalability while maintaining plan validity. This research provides a promising direction towards integrating language models with classical approaches to boost robot intelligence for practical applications. The proposed framework makes a solid step forward in en-hancing the task planning capability of future intelligent robotic systems. © 2023 ieee.

关键词： intelligent robots

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Fruit Weight Prediction and Cost Estimation Using YOLOv9-based Deep Learning 5

Fruit Weight Prediction and Cost Estimation Using YOLOv9-bas...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Yashas Kumar, S. Mishra, Atharv Nambiar, Radhika Nekar, Akhilesh Benedict, Shajulin Indian Institute of Information Technology Kottayam Dept. of CSE Kerala Kottayam India

ISBN: (纸本)9798331518097

Accurate fruit weight and ripeness measurements play a crucial role in optimizing yield and maximizing profits in agriculture. This paper introduces a methodology that uses image recognition to detect fruit weights and deep learning-based YOLOv9 to classify the level of ripeness. The real-world spatial scales for fruit dimensions are calculated by extracting Exchangable Image File Format (EXIF) metadata from captured images. The proposed approach uses an estimation of fruit weight from specific height factors and fruit density based on the Ground Sampling Distance (GSD) and real-world area to predict weight. A module that estimates costs analyzes prices across regions to recommend the most profitable selling points based on transportation costs. Experimental results show high precision of over 95% accuracy in estimating both weight and ripeness classification, such that farmers can use this insight to their advantage for yield forecasting and market optimization. This integrated approach applies computer vision and market analytics using advanced modes to boost agricultural productivity and economic returns. © 2024 ieee.

关键词： Cost estimating

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Detecting Network Attack using Federated Learning for IoT Devices

Detecting Network Attack using Federated Learning for IoT De...

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2024 international conference on intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Thakur, Akshit Tyagi, Ronit Tripathy, Hrudaya Kumar Yang, Tiansheng Rathore, Rajkumar Singh Mo, Danyu Wang, Lu Kalinga Institute of Industrial Technology Bhubaneswar India University of South Wales Pontypridd United Kingdom Cardiff Metropolitan University Cardiff School of Technologies Cardiff United Kingdom Beijing Normal University China Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798350360660

This study examines the utilization of federated learning to improve security within Internet of Things (IoT) environments, tackling issues such as data privacy and scalability which are intrinsic to centralized approaches. The IoT connects a wide range of devices, necessitating strong security measures to protect sensitive data and maintain system integrity. Federated learning presents a decentralized remedy by allowing model training directly on edge devices, reducing data transmission to centralized servers and upholding information confidentiality. A primary emphasis is on creating a federated learning-based Intrusion Detection System (IDS) which is specifically designed for the IoT networks, with the aim of effectively detecting and mitigating network attacks while decreasing susceptibilities to data breaches. Experimental validation confirms the system's adaptability to various IoT data distributions and changing network conditions, affirming its practical effectiveness in realworld settings. Improvement of federated learning algorithms for real-time anomaly detection will, therefore, be the subject of further research efforts and the introduction of many emerging more sophisticated encryption techniques in attempts to further bolster the evolving threats against data protection mechanisms. Advanced federated learning towards robust security for IoT contributes towards network resilience that guards sensitive information within interconnected IoT systems providing insights necessary for safe implementations in health care, smart cities, and industrial automation. © 2024 ieee.

关键词： Network intrusion

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Investigation on Application of Novel Converters for Hybrid Renewable Energy systems 2

Investigation on Application of Novel Converters for Hybrid ...

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2nd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Thangasankaran, R. Patnaik, Tapaswini Bhuvanesh, A. Geetha, M. Raj, T. Reena Alagu Rasa, V. KLN College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Madurai India C. V. Raman Global University Department Electronics and Communication Engineering Odisha Bhubaneswar India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Lord Jegannath College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798350327533

The proposed three input step up DC-DC converter controlled by adaptive neuro-fuzzy interference system (ANFIS) for renewable hybrid energy resources inputs is presented in this study. ANFIS, a control algorithm technique by combining both fuzzy and artificial neural network operates in many electrical systems. ANFIS is developed in this converter to provide soft switching and the voltage at the output is regulated. Multi-input converters are the best alternative for implementing the hybrid sources and they utilize the entire usage of batteries and ultra-capacitors. The proposed converter fed from renewable energy resources operates efficiently with a load and provides higher voltage gain. The results are implemented in Simulink. Therefore, the proposed work system efficiency is about 98.21 % when compared to other converters fed from Renewable Energy Sources. © 2024 ieee.

关键词： Fuel Stack Grid Hybrid Renewable energy resources Photovoltaic Three Input Step-up DC-DC converter Wind Turbine

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Collaborative Autonomous 3D Reconstruction for Heterogeneous Multiple UGVs in Complex Environments

Collaborative Autonomous 3D Reconstruction for Heterogeneous...

引用

2024 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Li, Yuxiang Chen, Kun Chen, Haoyao School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (纸本)9798331504755

Unmanned Ground Vehicles (UGVs), with the ad- vantages of endurance, payload capacity, and high autonomy, are widely utilized in search and rescue, surveillance, and exploration. The 3D reconstruction of unknown environments by multiple UGVs is a fundamental task in multi-robot systems. However, diverse terrains and complex surface structures of en- vironmental objects pose significant challenges to efficient multi- robot collaborative reconstruction. To address these challenges, it is essential to adopt a heterogeneous multi-robot system equipped with various sensor types and chassis configurations to traverse and cover the environment efficiently. This work focuses on map reconstruction and collaborative planning for heterogeneous multi-robot systems to enable real-time exploration of complex environments. We propose an incomplete surface element (ISE) extraction method based on terrain awareness and observation quality to support collaborative map fusion and view planning. A view generation technique employing minimal view set sampling is introduced to reduce the number of task views while adapting to the motion and sensory constraints of various robots. Addition- ally, a hierarchical view planning framework is designed with a clustering-based matching algorithm to achieve near-optimal task allocation, effectively coordinating the view tasks of robots with different capabilities. The feasibility of the proposed method is validated through a simulation scenario, demonstrating efficient environment exploration and high-completeness surface coverage. © 2024 ieee.

关键词： Multipurpose robots

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light controlling and Monitoring System using intelligent Sensors 9

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Saranya, G. Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India

ISBN: (纸本)9798350365092

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability. © 2024 ieee.

关键词： Internet of things

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Research on the Generation Method of Power Grid Partitioning Schemes under the Background of High Proportion of New Energy Integration 6

Research on the Generation Method of Power Grid Partitioning...

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6th international conference on Energy systems and Electrical Power, ICESEP 2024

作者： Lu, Zijing Lil, Zishou Tang, Jun Chen, Hongkun State Grid Hubei Electric Power Co. Ltd State Grid Corporation of China Hubei Wuhan430077 China School of Electrical Engineering and Automation Wuhan University Hubei Wuhan430072 China Wuhan University Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network Hubei Wuhan430072 China

ISBN: (纸本)9798350353563

Power grid partition is utilized widely for simplifying network structure, facilitating system monitoring and scheduling, and is an inevitable trend in development of large power grid. However, in the context of large-scale new energy integration, the uncertainty and volatility of its output have a significant impact on the operational characteristics of the power grid. Aiming at the shortcomings of traditional power grid partitioning methods that do not consider the impact of new energy integration, a generation method of power grid partitioning schemes considering the background of new energy integration is proposed. Firstly, a typical set of new energy output scenarios is obtained based on scenario generation and scenario reduction;Afterwards, the definition of electrical distance is given, the electrical distance matrix of the network is constructed, and a correction coefficient is defined based on the typical scenario set of new energy output to modify the electrical distance matrix. Then, the Dijkstra algorithm is used to expand it to the full dimensional electrical distance matrix;Finally, affinity propagation algorithm is used to complete the power grid partitioning, and a case study is conducted on an improved ieee 39 node system to verify the effectiveness and rationality of the proposed method. © 2024 ieee.

关键词： Power grids

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Design and Network Security Evaluation of Campus One Card intelligent System

Design and Network Security Evaluation of Campus One Card In...

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2023 ieee international conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Yang, Zili Guo, Jiajia Huaqiao University Department of Informationization Construction and Management Quanzhou China

ISBN: (纸本)9798350300826

The campus one card system is a web-based, efficient and reliable control and management of school card information, achieving various functions from identity authentication to management status query. This article analyzed and studied the system, mainly using hash algorithm to determine the parameter values and related technical indicators in various hardware devices and communication protocols. In this article, Hash algorithm was used to achieve secure and confidential calculation methods for data transmission between the main station and non-sub stations. Then, through experiments, the security performance of the system was verified to be over 95%, and the vast majority of basic security vulnerabilities can be detected. The research on campus one card intelligent system has found that in the design of campus one card intelligent system, the system provides diverse functions to meet the needs of students, faculty, and management personnel, and has good scalability to adapt to the increasing number of users and service scope. © 2023 ieee.

关键词： intelligent systems

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