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Predicting Smart Grid Stability with Multi-Directional LSTM

Predicting Smart Grid Stability with Multi-Directional LSTM

引用

2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Vishnoi, Manish Srivastav, Anamika Thiruvenkadam, T. Vivekananda Global University Department of Civil Engineering Jaipur India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

The expression relates to the national electric arrangement, used to deliver electricity via the electrical plants to the customers. It is made up of converters, distributors, monitoring stops and lines of communication. Large electricity generation plants that produce hundreds of thousands of watts of electricity and redistribute it across many demographic areas now make up the electrical system. Efficient management of the electricity delivered to many customer domain names including households, smart cities, companies, and organizations, is vital. To meet the changing needs for electricity, an electricity network with advanced technology is being implemented. Its CPS, which stands for concept, which integrates data centres with mechanical structures, is what the idea of smart grids is based on. The Computer Intelligence (also known as module represents the IT component in the smart energy system scenario integrated with CPS, while the energy consumption units represent the physical components. This study proposes a unique Broad Long and Short-Term Memory tackle to forecast the smart energy system reliability. The outcomes are compared with different well-liked artificial intelligence techniques, including neural networks with recurrent neurons (an RNN), gates recurrent units (the Government Relations Unit), and conventional the LSTM algorithm The research results demonstrate how the strategy works better than any other Learning methods. © 2023 ieee.

关键词： Artificial intelligence techniques Conventional LSTM cyber-Physical systems (CPS) Electricity distribution Energy system reliability and Machine learning methodologies Gated recurrent units (GRU) Multidirectional LSTM Recurrent neural networks (RNN) Smart grids

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Research on Neural Network Prediction of Optimal Exhaust Pressure of Heat Pump System 5

Research on Neural Network Prediction of Optimal Exhaust Pre...

引用

5th international conference on intelligent control, Measurement and Signal Processing, ICMSP 2023

作者： Zhou, Weiqing Li, Zhixin Yang, Dongfang Zhang, Jilong School of Energy and Power Engineering Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9798350336030

The widespread application of heat pump technology presents significant growth opportunities for the heat pump industry. As the demand for high temperature heat pumps in industry rises, compound high temperature heat pumps and transcritical heat pump systems are gaining increasing attention. Due to their critical operating characteristics, heat pump systems currently possess the optimal exhaust pressure in terms of energy efficiency. This paper uses experimental data as the basis for the calculation and fitting of the optimal pressure, and employs an artificial neuron network scheme to analyze the correlation between the influence of the system state parameters and the optimal exhaust pressure, in order to better solve the problem of maintaining the system in a high energy-efficient operating condition under variable operating conditions. The impact of genetic and particle swarm algorithms on the outcomes is also incorporated to the artificial neuron network. The experimental outcomes demonstrate that the particle swarm optimised neural network has superior generalisation properties. © 2023 ieee.

关键词： Heat pump systems

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Experimental Analysis of Plant Disease Identification Methodology Based on Improved Classification and Deep Learning Principle

Experimental Analysis of Plant Disease Identification Method...

引用

2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Shibu, S. Priyadharshini, S. Madhini, M. Radha, K. Giri, Pallavi Samhan, Ahmad A. Ali Panimalar Engineering College Department of Ece Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India S.A. Engineering College Department of Ece Chennai India Dr. Mahalingam College of Engineering and Technology Department of Cse Pollachi Coimbatore India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Deoartment of Software Engineering Zarqa Jordan Universityof Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The agriculture industry is essential for providing excellent food and significantly contributes to the growth of economies and people. Plant diseases can result in substantial reductions in food output and diminish species diversity. The prompt identification of plant diseases by precise or automated detection methods can improve food production quality and save economic losses. In recent years, deep learning has significantly enhanced the identification accuracy of image categorization and object detection systems. In this paper, a novel method termed Learning Enabled Disease Detection Logic (LDDL) is introduced for the efficient identification of plant diseases, which is cross-validated against the traditional Convolutional Neural Network (CNN) to assess the efficacy of the proposed scheme. The report delineates the specific crop illnesses addressed, the models utilized, the data sources referenced, and the overall efficacy as measured by the performance criteria applied in each study for plant disease diagnosis. The findings demonstrate that the proposed model, LDDL, achieves superior accuracy compared to prevalent illness detection methods and identifies the primary parameters influencing the efficacy of deep learning-based tools. The research offers significant insights for researchers, practitioners, and industry professionals in plant disease detection by addressing challenges and limitations, presenting a thorough understanding of the current research landscape, elucidating the advantages and drawbacks of various methods, and suggesting potential solutions to facilitate their implementation. © 2024 ieee.

关键词： Convolutional neural networks

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Boosting intelligent Diagnostic Process in Internet Hospital: A Conversational-AI-Enhanced Framework

Boosting Intelligent Diagnostic Process in Internet Hospital...

引用

2023 ieee international conference on systems, Man, and cybernetics, SMC 2023

作者： Wang, KeXin Yao, Shengyue Wu, Ziyi Wang, Fei-Yue Lin, Yilun Chen, Yan Urban Computing Lab Shanghai Artificial Intelligence Laboratory Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China School of Health Policy and Management Nanjing Medical University Nanjing China Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Gusu School Nanjing Medical University Suzhou China Jiangsu Provincial Institute of Health Nanjing China

ISBN: (纸本)9798350337020

The development of Internet Hospital attracts growing attention worldwide to improve medical service quality and efficiency. However, the existing Internet Hospital failed to fully allocate the patients' consultation demands and improve the level of satisfaction, which is mainly caused by an overlong online waiting time during the diagnostic process. The emergence of Large-Language-Model (LLM) technology provides an opportunity to improve the existing synchronous and sequential online diagnostic process towards an asynchronous and concurrent process. With the consideration of applying LLM technology in developing the Internet Hospital, a conversational-AI-enhanced intelligent diagnostic process framework is proposed in this paper. By hierarchically decomposing the online diagnostic service into three layers, namely the AI doctor, the rotating doctor, and the expert doctor, the diagnostic process is capable of providing instant treatments with a lower misdiagnosis rate, meanwhile relieving the workload of human doctors. In addition, a case study of the Internet Hospital operated by Jiangsu Provincial Hospital is conducted, which reveals the importance of boosting the diagnostic progress by the proposed framework. Further, the proposed framework is examined by a numerical experiment based on statistical data. The experiment results indicate that both the patient waiting time and the misdiagnosis rate can be significantly reduced, which suggests a great potential for applying the proposed framework in practice. © 2023 ieee.

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SentinelScan: Advanced Network Scanner and Packet Detection Suite 8

SentinelScan: Advanced Network Scanner and Packet Detection ...

引用

8th international conference on Inventive systems and control, ICISC 202

作者： Seshapriyan, T. Dinesh, S.M. Godwin Ponsam, J. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798350386578

In today's interconnected digital landscape, safeguarding networked systems and preserving data integrity needs more attention. With the evolution of cybersecurity threats, the emergence of SentinelScan signifies a proactive response to these challenges. SentinelScan employs advanced packet manipulation techniques and realtime packet sniffing capabilities to offer a comprehensive defence against a wide spectrum of cyber threats. It integrates pivotal concepts in network security, including cryptographic algorithms, authentication mechanisms, secure communication protocols, and adherence to security standards. SentinelScan's efficacy depends on the use of Scapy, a robust packet manipulation tool for enabling realtime packet sniffing. Customized Python scripts enable Scapy to intercept and analyze network packets, providing valuable insights into traffic patterns, protocol utilization, and potential security vulnerabilities. The proposed model comprises of various layers of the networking stack, including application layer protocols (client-server architecture, web, and email protocols) and transport layer protocols (UDP and TCP), detailing the connection establishment, reliable data transfer, and congestion control mechanisms. The proposed research methodology emphasizes precision, dependability, and reproducibility, ensuring rigorous environment configuration and documentation. These measures enhance the robustness of the findings, making SentinelScan an efficient tool for safeguarding digital assets. © 2024 ieee.

关键词： Data transfer

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OIDM: An Observability-based intelligent Distributed Edge Sensing Method for Industrial cyber-Physical systems

OIDM: An Observability-based Intelligent Distributed Edge Se...

引用

ieee conference on Decision and control

作者： Shigeng Wang Tiankai Jin Yehan Ma Cailian Chen Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system’s ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness.

关键词： Job shop scheduling Accuracy Processor scheduling Linear approximation cyber-physical systems Aerospace electronics Probabilistic logic Sensor systems Observability intelligent sensors

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Design and Experimental of Human Tracking control System Based on Infrared Sensor

Design and Experimental of Human Tracking Control System Bas...

引用

ieee Information technology, Networking, Electronic and automation control conference

作者： Cui Xiaoming Geng Yulong Kou Zhiwei Cao Xinyuan Qiao Yanjun College of Electric Power Inner Mongolia University of Technology Hohhot China Engineering Research Center of Large Energy Storage Technology Ministry of Education Hohhot China Inner Mongolia Key Laboratory of Electromechanical Control Hohhot China Northern Branch Beijing Energy International Holding Co. Ltd Hohhot China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

With the development of intelligent monitoring and automation technology, human tracking control systems are increasingly widely used in fields such as security monitoring, smart homes, and robot navigation. This paper proposes a design of a human tracking control system based on pyroelectric infrared (PIR) sensors and has conducted experimental verification. The system utilizes the high sensitivity of PIR sensors to infrared radiation, and achieves accurate tracking of human movement through signal processing and algorithm optimization. The experimental results show that the proposed system can effectively track human forms in different environments, and has high practicality and reliability.

关键词： Tracking Signal processing algorithms Signal processing control systems Robot sensing systems Infrared sensors Sensor systems Security Monitoring intelligent sensors

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Distributed Enclosing control of Second-order Multi-Agent systems with An Unknown Dynamic Target 6

Distributed Enclosing Control of Second-order Multi-Agent Sy...

引用

6th international conference on intelligent Autonomous systems, ICoIAS 2023

作者： Chen, Wenting Dou, Liya Li, Dazi School of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9798350371253

In this article, we study the enclosing control problem for a group of agents of second-order dynamics with a moving target of unknown velocity. In particular, only one pathfinder has access to the relative position information of the target, and the target velocity is modeled as a dynamic signal generated by an exogenous linear system. Using the local relative position measurements, an observer of the target position is developed for each agent, based on which an estimator is designed for each agent to estimate the dynamic target velocity. Furthermore, based on the estimates of the target position and target velocity, a distributed control law is proposed for each agent consisting of distance regulation respect to the target and angle repulsion to its neighbors in conjunction with compensation of the target velocity. It is theoretically shown that the multi-agent system can globally asymptotically track the dynamic target with a circular formation of desired radius and arbitrary interval pattern. Finally, the proposed control law is shown to be effective by a simulation example. © 2023 ieee.

关键词： Multi agent systems

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Thin Film Spectral Measurement System Developed Based on Micro Spectrometer

Thin Film Spectral Measurement System Developed Based on Mic...

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2023 international conference on Advanced Robotics and intelligent systems, ARIS 2023

作者： Tsai, Ciao-Ming Weng, Ting-Wen Kong, Wei-Yi Yang, Chang-Ting Ko, Cheng-Hao National Tsing Hua University Department of Power Mechanical Engineering Hsinchu Taiwan National Taiwan University of Science and Technology Department of Graduate Institute of Automation and Control Taipei Taiwan Industrial Technology Research Institute Research Fellow Department of Material and Chemical Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350302714

Thin film technology has wide applications in several fields such as semiconductor manufacturing, biochemical sensors, solar cells and more. However, the instruments used to detect the properties of thin films remain bulky and expensive. In this study, we use a micro spectrometer fabricated using MEMS (Micro-Electro-Mechanical systems) technology to design a system for thin film spectral measurements. The performance of this measurement system is validated using a quantum dot film. We also measured the interference spectrum of the quantum dot film in a sealed gas chamber environment. A reaction between the film and water vapor was observed, where the adsorption of water vapor by the film resulted in a shift of the interference spectral peak towards a longer wavelength. Regression analysis revealed a strong correlation (R2>0.95) between the wavelength shift of the peak and the relative humidity within the gas chamber. © 2023 ieee.

关键词： Thin films

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial automation (ROBOTHIA), ieee international conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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