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Industrial Control intelligent Operation and Maintenance System Based on Zigbee Wireless Communication Technology 3

Industrial Control Intelligent Operation and Maintenance Sys...

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3rd International conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Fan, Jiaqi College of Electronic and Information Southwest Minzu University Sichuan Chengdu China

ISBN: (纸本)9781665417327

In order to solve a series of problems of the existing industrial control equipment based on the traditional operation and maintenance system, based on Zigbee networking technology, artificial intelligence and heartbeat detection technology, a set of intelligent operation and maintenance management system of automated industrial control system has been constructed. Through the mutual detection of equipment network and hardware, data analysis and machine learning, the shortcomings of traditional operation and maintenance systems are supplemented, and the cost of manpower and material resources for maintaining equipment and testing equipment operation status is reduced. Improve the reliability of industrial automation operation and maintenance. © 2021 IEEE.

关键词： Zigbee

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Improving Load Profile Through Optimal Load Scheduling Using Genetic Algorithms

Improving Load Profile Through Optimal Load Scheduling Using...

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International conference on Advanced Electrical Engineering (ICAEE)

作者： Rihab Belhabri Sid-Ahmed Zidi Samir Hadjeri Halima Boubekeur Electrical Engineering Department Intelligent Control & Electrical Power Systems Laboratory (ICEPS) Djillali Liabès University Sidi-bel-abbés Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

This paper presents an innovative methodology based on a Genetic Algorithm (GA) to optimize Demand-Side Management (DSM) in the energy domain. The proposed approach utilizes a GA to analyze appliance consumption profiles and identify load shifting opportunities for controllable appliances from peak hours to off-peak hours, defined by Demand Response (DR) programs such as Time-of-Use (TOU) pricing. This optimization enables the reduction of electricity consumption during peak hours, lowers consumer bills, and improves the efficiency of the power grid, contributing to more sustainable and economical energy management.

关键词： Electrical engineering Energy consumption Costs Electricity Pricing Scheduling Power grids Energy efficiency optimization Genetic algorithms

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Inclusive and intelligent Traffic Management System for the Physically Impaired Pedestrians

Inclusive and Intelligent Traffic Management System for the ...

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Advancement in Computation & Computer Technologies (InCACCT), International conference on

作者： Enock Isack Mawanda Vincent Prasant Kumar Dash Kwizera Jean Bosco Mingma Kanchhi Sherpa Ujjwal Kumar Bhadani Computer Science and Engineering C.V. Raman Global University Bhubaneswar India Infocommunication Technology University of Science and Technology MISIS Moscow Russia

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

This study investigates the consideration of physically impaired pedestrians in the existing traffic management system. Although IoT-driven intelligent systems have shown promise in managing traffic flow and incident detection, they overlook the specific needs of physically impaired individuals, leading to longer waiting times and safety. First, an Inclusive and intelligent Traffic Management System (IITMS) is proposed, designed to support the independent mobility of physically impaired pedestrians by dynamically adjusting traffic signals and incorporating Vehicle to Everything (V2X). The framework integrates CCTV cameras with the YOLOv9 model for detecting individuals with mobility aids including crutches, wheelchairs, and walking frames, at pedestrian crossings. Integrated with V2X for communication amongst vehicles and pedestrians, an action selection strategy algorithm for enhanced optimization of real-time processing by maintaining optimal traffic flow for all users to enhance operational efficiency is then introduced. The simulation results show that integrating IITMS, V2X and an action selection strategy algorithm achieves 94% detection accuracy across various physical impairment categories and enhances operational efficiency compared to non-optimized models.

关键词： Pedestrians Accuracy Wheelchairs Simulation Real-time systems Safety Reliability Vehicle-to-everything optimization Meteorology

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Smart Grid Resource Scheduling Algorithm Based on Reinforcement learning for Edge Computing

Smart Grid Resource Scheduling Algorithm Based on Reinforcem...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Ran Zhao Jiaming Mao Jinghang Yu Lei Fan Information and Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

Smart grid is a power system that enables intelligent management and optimization. Network virtualization technology can effectively improve the resource utilization and reliability of smart grid and meet the differentiated needs of different users. In the case of limited resources, traditional virtual network mapping algorithms cannot dynamically adjust the allocation and mapping of virtualized resources based on the resource usage and user demands of the power system. To address this issue, we combined edge computing and virtualization technology, and introduced a reinforcement learning-based virtual network resource scheduling algorithm. Simulation results show that our virtual resource scheduling algorithm performs better than the other three scheduling algorithms in improving the reliability and resource utilization of the power grid.

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Deep learning-based Alzheimer's disease prediction for smart health system

Deep learning-based Alzheimer's disease prediction for smart...

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The 3rd International conference on Distributed Sensing and intelligent Systems (ICDSIS 2022)

作者： S. Hlawa N. B. Romdhane Higher Institute of Computer Science and Communication Techniques of Hammam Sousse 4011 University of Sousse Tunisia

Smart Health (s-Health) is used in improving healthcare by providing many services such as patient monitoring and early diagnosis of diseases. In this context, Alzheimer's disease (AD) begins years before the first clinical symptoms emerge. Early diagnosis is essential to ensure adequate patient care. This work aims to propose a new method to predict this disease. Our method is based on deep learning which combines the techniques of data augmentation, the pretrained “VGG-16” model and transfer learning. In order to evaluate the performance of our method, we conducted a series of experiments on Kaggle Alzheimer's dataset. The proposed solution offers an accuracy rate of 91.75%.

关键词： deep learning (artificial intelligence) diseases health care patient care patient monitoring

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Predictive Modeling of Dysmenorrhea Onset and Severity in Adolescent Girls

Predictive Modeling of Dysmenorrhea Onset and Severity in Ad...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Hemalatha Raavi Keerthi Sujana Korukonda Department of Computer Science & Engineering Vishnu Institute of Technology (Affiliated to JNTUK Kakinada) Bhimavaram India Department of Artificial Intelligence and Machine Learning Vishnu Institute of Technology (Affiliated to JNTUKKakinada) Bhimavaram India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Dysmenorrhea, commonly known as period pain, significantly affects the quality of life for adolescent girls and post-marriage women, yet there are limited tools available for accurately predicting its onset and severity. The issue addressed by this research is the lack of effective prediction methods for dysmenorrhea. The outcome of the study is the development of a machine learning-based predictive model that utilizes predictive analytics and Generative Adversarial Networks (GANs) for period pain prediction. The model was trained using neural networks and enhanced with data augmentation to improve dataset diversity and robustness. By analyzing comprehensive data that includes physiological, psychological, and lifestyle factors, the model achieved superior accuracy in predicting the onset and severity of dysmenorrhea, compared to traditional methods. This research demonstrates the potential of machine learning to offer personalized healthcare interventions and early detection for dysmenorrhea. It also highlights the challenges faced by post-marriage women with dysmenorrhea, advocating for targeted healthcare solutions across life stages. The study showcases the power of advanced predictive analytics to transform the management of menstrual health and improve adolescent health outcomes.

关键词： Analytical models Pain Medical services Transforms Predictive models Generative adversarial networks Data augmentation Data models Robustness Predictive analytics

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Understanding the Aftermath of COVID-19: A Data-Driven Exploration of Health Effects

Understanding the Aftermath of COVID-19: A Data-Driven Explo...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Arikumar K. Selvaraj P. Naveen Akash B. Venkata Swamy Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The COVID-19 pandemic has ravaged public health in a manner that is unprecedented: millions of infections and fatalities worldwide. Now that the immediate crisis seems to recede, there is growing concern over long-term health consequences faced by survivors, commonly referred to as “long COVID.” This study aims to systematically explore the multifaceted health effects experienced by individuals who have recovered from COVID-19, focusing on physical, mental, and cognitive health issues that have emerged in the aftermath of the infection. Employing a data-driven approach, the collected extensive survey data from a diverse cohort of COVID-19 survivors to assess their post-recovery health status. The survey included questions targeting various health dimensions, such as fatigue, pain, anxiety, depression, and cognitive function. To analyze the data, utilized several machine learning algorithms to identify significant health complications and to determine the interrelationships among various health factors.

关键词： COVID-19 Surveys Machine learning algorithms Pandemics Pain Pulmonary diseases Mental health Physiology Stakeholders Public healthcare

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Real-Time Distribution Network Opeartion optimization Using Mixed Safe Multi-Agent Deep Reinforcement learning Method

Real-Time Distribution Network Opeartion Optimization Using ...

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Energy, Power and Electrical Technology (ICEPET), International conference on

作者： Bo Xu Yunrui Zeng Zeqi Wang Tianyu Jiang Longquanyi Power Supply Bureau Chengdu Power Electric Bureau Chengdu China Customer Service Center State Grid Tianfu Power Supply Company Chengdu China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

The fluctuation in distributed generation and load significantly impacts the safety of system operation. This paper introduces a real-time distribution network operation optimization method based on multi-agent deep reinforcement learning to quickly determine the operation scheme for diverse controllable devices, such as switches, energy storage systems, and reactive compensators. To adapt to the mixed action settings within physical constraints, a novel mixed safe multi-agent soft Actor-Critic algorithm is introduced to train the policy. This approach entails constructing discrete neural networks and continuous neural networks, with two safety layers implemented to rectify unsafe discrete and continuous actions. a numerical analysis is conducted on the IEEE 33-bus system to confirm the efficacy of the proposed methods.

关键词： Performance evaluation Numerical analysis Systems operation Neural networks Static VAr compensators optimization methods Distribution networks

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Advancing Fake News Detection: From Traditional Machine learning to Transformer-Based Approaches

Advancing Fake News Detection: From Traditional Machine Lear...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： I V Abhinand K P Ajal Krishna Ajay Krishna J Jayadev K Reji Rahmath T Divya Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannurm A. P. J. Abdul Kalam Technological University Thiruvananthapuram kerala India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems.

关键词： Reviews Biological system modeling Morphology Syntactics Transformers Stability analysis Robustness Multilingual Internet of Things Fake news

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Research and Application of intelligent Cluster Injection Technology for Shale Gas Foam Drainage and Gas Recovery

Research and Application of Intelligent Cluster Injection Te...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International conference on

作者： Yixin Jiang Haohong Gao Nan Li Chi Wu Haoyu Wang Zhigang Wang Mai Chen Dong Wei Petrochina Zhejiang Oilfield Zhejiang China Research Institute of Petroleum Exploration & Development Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Shale gas resources show extremely attractive development prospects, and as a significant growth driver in the natural gas sector, they have enormous potential, signalling an important expansion of future energy supply. As the main deliquification technology measure of shale gas production stability, bubble drainage gas recovery technology has some prominent problems such as low intelligence, low efficiency and high cost. In this paper, guided by intelligent operation, online intelligent optimization and wireless RTU management and control technology are developed, with the goal of reducing costs and increasing efficiency, a controllable multi-channel distributor and an innovative skid-mounted design mode are developed, and an intelligent bubble drainage cluster filling equipment suitable for shale gas platform wells is developed, which realizes the intelligent foam drainage and gas recovery integrating self-collection, self-analysis, self-control for the first time. Now, the equipment has been used in 788 wells in Zhejiang oilfield for more than 280 days without manual foam deliquification operations in the platform. As a result, the operation cost is reduced by 60%, and the decline rate of gas field is reduced by 8%. This measure has achieved remarkable results in improving efficiency and reducing costs, laying a solid technical foundation for the optimization and upgrading of gas field operation and intelligent transformation, and significantly promoting the process of improving quality and efficiency of gas field production.

关键词： Wireless communication Cloud computing Costs Liquids Process control Production Predictive models Mathematical models optimization Thermal stability

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