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Effect of Al2O3@SiO2 core-shell nanospheres on the electrical conduction properties of polypropylene 3

Effect of Al2O3@SiO2 core-shell nanospheres on the electrica...

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3rd international conference on Power systems and Electrical technology, PSET 2024

作者： Wang, Xuening Liu, Zeqin Jiang, Wanting Liu, Xuanhe Lei, Zhipeng Men, Rujia Li, Yuanyuan Taiyuan University of Technology Shanxi Key Lab of Mining Electrical Equipment and Intelligent Control Taiyuan China

ISBN: (纸本)9798350353204

To explore the effect of Al2O3@SiO2 core-shell nanospheres on the electrical conduction of polypropylene, polypropylene samples blending different contents of core-shell nanospheres are prepared. Then, the polarization-depolarization current is measured. The experimental results show that adding Al2O3@SiO2 core-shell nanospheres can increase the number and depth of traps in the nanocomposites, increasing the threshold field strength. With the increase of nanofiller content, the threshold field strength gradually increased. © 2024 ieee.

关键词： Depolarization

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A New Transmission Cause and Effect Analysis (TCEA) Approach to Risk Management for Non-Healthcare Context: A Case Study on COVID-19

A New Transmission Cause and Effect Analysis (TCEA) Approach...

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2024 ieee international conference on systems, Man, and cybernetics, SMC 2024

作者： Kerk, Yi Wen Tay, Kai Meng Jong, Chian Haur Chai, Chee Shee Lim, Chee Peng Universiti Kebangsaan Malaysia Faculty of Information Science & Technology Bangi43600 Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering Kota Samarahan94300 Malaysia School of Engineering & Technology Universty of Technology Sarawak Sibu96000 Malaysia Universiti Malaysia Sarawak Faculty of Medicine & Health Sciences Kota Samarahan94300 Malaysia Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia

ISBN: (纸本)9781665410205

Leveraging the concept of Failure Mode and Effect Analysis (FMEA), we propose a simple and systematic approach, namely Transmission Cause and Effect Analysis (TCEA), to achieve a defined goal of reducing transmission risk through effective preventive and control actions in real-world environments. Specifically, the transmission risk of an infectious disease (e.g., COVID-19) is perceived as a combination of the presence of a transmission agent (e.g., SARS-CoV-2 virus or its variants) and the requisite factors that lead to infection of humans and the associated aftermath of infection. TCEA adopts a causal map to represent all possible transmission risks via a brainstorming process. Next, appropriate preventive and control actions associated with each transmission risk are identified. Similar to FMEA, a Risk Priority Number model with Severity, Occurrence, and Detection ratings is adopted for analysis, prioritization, and decision-making. To demonstrate the usefulness of TCEA, a real-world case study on COVID-19 is conducted. The empirical results indicate that TCEA provide a simple, systematic and easy-to-implement approach to effectively analyze and manage transmission risks of COVID-19 in non-healthcare workplaces. © 2024 ieee.

关键词： COVID-19

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Dynamic Resource Allocation for IoT-Fog Architectures Leveraging Differential Services and Adaptive QoS Models 5

Dynamic Resource Allocation for IoT-Fog Architectures Levera...

引用

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

作者： Mahavaishnavi, V. Padmavathi, V. Saminathan, R. Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai600123 India A.V.C. Engineering College Department of Information Technology Mayiladuthurai609305 India Annamalai University Department of Computer Science and Engineering Annamalainagar608002 India

ISBN: (纸本)9798331518097

The constant changes in IoT-Fog architectures, allocating resources plays a vital role in overall resource management, which includes several different requests for applications. This paper proposes a dynamic resource allocation framework based on adaptive QoS and differential service models for IoT-Fog networks. Consequently, the proposed model sorts IoT applications according to the necessary QoS for their implementation, thus improving resource use and satisfying end-users. In addition, the framework complements machine learning algorithms to forecast the resources' needs and dynamically allocate the resources in response to the changing requirements, thereby increasing responsivity and decreasing resource rivalry. In this paper, to conduct several simulations and real-world evaluations of this adaptive QoS-based approach, showing that it improves service delivery, especially for latency-sensitive and high-priority applications. Thus, this research presents a method for large-scale optimization of resource allocation in the IoT-fog environment and supports reliable and efficient service deployment in distributed and often limited-resource settings. © 2024 ieee.

关键词： Quality of service

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intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sarathkumar D Anil. A. R J Sivadasan T. Jayakumar Murugesan Manivel Anandhakumar C Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Computer Science &Engineering Sree Buddha College of Engineering Pattoor Alappuzha Kerala India Department of Electronics and Communication Engineering PSR Engineering College Sivakasi Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronic Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem.

关键词： Fuzzy logic automation Machine learning algorithms Neural networks Prediction algorithms Real-time systems Optimization intelligent control Industrial Internet of Things Predictive control

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Interaction-Aware Hierarchical Representation of Multi-Vehicle Reinforcement Learning for Cooperative control in Dense Mixed Traffic 27

Interaction-Aware Hierarchical Representation of Multi-Vehic...

引用

27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Cai, Yuxin Liu, Zhengxuan He, Xiangkun Zuo, Zhiqiang Yau, Wei-Yun Lv, Chen School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin300072 China

ISBN: (纸本)9798331505929

Improving traffic performance in dense mixed traffic scenarios such as bottlenecks, presents significant challenges due to complex interactions and the unpredictable behaviors of human drivers. These challenges are compounded by varying human driving styles and different proportions of Connected and Automated Vehicles (CAVs) within the traffic flow. Our research focuses on developing cooperative control strategies for CAVs to enhance generalization across diverse traffic scenarios. To address these challenges, we introduce an Interaction-Aware Hierarchical Representation (IAHR) module, integrated into Multi-Agent Reinforcement Learning (MARL) framework. The IAHR module hierarchically processes interactions between CAVs and Human-Driven Vehicles (HDVs), effectively extracting essential features to facilitate generalization across various traffic scenarios. Additionally, we design an effective reward function that balances individual interests with overall traffic performance, guiding CAVs to improve their driving efficiency and safety while also enhancing overall traffic flow. The model is rigorously trained and zero-shot evaluated in various bottleneck scenarios. Results demonstrate the model's capability to significantly improve traffic performance under dense conditions and generalize across different CAV penetration rates, vehicle numbers, and HDV driving style distributions. © 2024 ieee.

关键词： Reinforcement learning

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The fusion-based Path Planning for Near Seabed Autonomous Underwater Vehicles 20

The fusion-based Path Planning for Near Seabed Autonomous Un...

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

作者： Yang, Tianlong Wei, Yanhui Hou, Yongkang Yu, Tian Harbin Engineering University College of Intelligent Systems Science and Engineering No. 145 Nantong Street Harbin China Nanhai Institute Harbin Engineering University Sanya China

ISBN: (纸本)9798350320831

Autonomous underwater vehicles (AUVs) have played a huge role in deep-sea operations and exploration. However, with the limited energy, AUV faces obstacles in complex underwater environments such as seamounts and trenches during near bottom operations. Therefore, planning the reasonable and efficient path is one of the important conditions for AUV to achieve underwater operations[1]. A new path planning method that combines improved RRT strategy and dynamic window analysis (DWA) is proposed to address the impact of underwater hydrodynamic forces and complex underwater environments on AUV during near bottom navigation. This proposed path planner has the following two advantages: one is to improve the global planning performance compared with the traditional RRT algorithm, and the other is to guarantee the real-time local planning capability via the DWA method. The superior performance of the developed planning algorithm is verified in the numerical examples. © 2023 ieee.

关键词： Autonomous underwater vehicles

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

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

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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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Predictive Maintenance of AC using Multiple Parameters 2

Predictive Maintenance of AC using Multiple Parameters

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

作者： Bhanuse, Vijaykumar Gangurde, Nachiket Dube, Suhas Devi, Jay Doifode, Hritik Vishwakarma Institute of Technology Department of Ins Trumentation and Control Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350327533

This research work focuses on the implementation of a predictive maintenance system for air conditioning (AC) units, utilizing critical parameters such as temperature and humidity levels. The primary objective is to proactively monitor the health of AC systems and predict potential issues before they lead to failures. Data collected from sensors embedded in the AC units is analyzed using data analytics techniques to identify patterns indicative of deteriorating performance or imminent faults. The thresholds for high temperature and humidity levels are defined, and instances exceeding these thresholds trigger maintenance alerts. The system employs a user-friendly interface for real-Time monitoring and feedback, allowing maintenance personnel to take timely actions. The expected outcomes include reduced downtime, increased energy efficiency, and an extended lifespan for AC equipment, contributing to improved reliability and user satisfaction. The predictive maintenance approach offers a more proactive and cost-effective strategy compared to traditional reactive maintenance practices. © 2024 ieee.

关键词： Downtime Equipment etc Humidity Predictive analysis Temperatures

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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...

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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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A Survey of Deep Learning for intelligent Feeding in Smart Fish Farming 5

A Survey of Deep Learning for Intelligent Feeding in Smart F...

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5th international conference on Internet of Things, automation and Artificial Intelligence, IoTAAI 2023

作者： Peng, Xiaohong Zhou, Tianyu Wu, Zhenlu Li, Zhao Faculty of Mathematics and Computer Guangdong Ocean University Guangdong Zhanjiang524088 China

ISBN: (纸本)9798400716485

The rapid development of deep learning has been successfully applied in various fields, including aquaculture, providing new methods and ideas to realize unmanned and intelligent aquaculture. This paper focuses on the technology and research methods used in deep learning for intelligent feeding in fish farming in the past decade, and discusses the application of deep learning in intelligent feeding of fish in detail, including feeding behavior analysis, detection and tracking of live fish, growth state monitoring, residual bait identification and counting, water quality prediction, etc., and summarizes and evaluates the methods, at the same time, analyzes the technical details of the deep learning applied to intelligent precision feeding is analyzed in technical details, including data, algorithms, and evaluation performance indexes. The summarized results show that the advantage of deep learning lies in the automatic extraction of features, which also provides technical support for the construction of intelligent feeding system. However, due to the large differences in fish species, aquaculture environments and data acquisition methods, the technology is less portable, and it is still in the stage of weak artificial intelligence, which requires a large amount of data to train the model, and the cost is high, which has become a bottleneck that restricts the further development of aquaculture. Nevertheless, deep learning has still made breakthroughs in processing complex data. In summary, the purpose of this review is to provide researchers and producers with a better understanding of the current research status of deep learning in intelligent precision feeding of fish, and to provide strong theoretical support for the production process. © 2023 ACM.

关键词： Feeding

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