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Digital Twin for Advanced automation of Future Smart Grid 1

Digital Twin for Advanced Automation of Future Smart Grid

引用

1st international conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Khan, Sohaib Ali Rehman, Hafiz Zia Ur Waqar, Asad Khan, Zeashan Hameed Hussain, Muntazir Masud, Usman Karachi Pakistan Air University Department of Mechatronics & Biomedical Engineering Islamabad Pakistan Bahria University Department of Electrical Engineering Islamabad Pakistan National University of Science & Technology Dept of Avionics Engineering CAE Islamabad Pakistan Air University Department of Electrical & Computer Engineering Islamabad Pakistan Faculty of Electrical & Electronics Engineering UET Taxila Pakistan University of Kassel Germany

ISBN: (纸本)9781665472753

This paper presents a framework for the implementation of a digital twin (DT) in electrical grid management. automation in the electrical energy network has resulted in the transformation into Smart grid, which is utilized for the generation, transmission, and distribution of electrical power as well as interconnecting microgrids with dynamic scheduling and trading options. The evolution of the digital twin offers added advantages including real-time condition monitoring based maintenance of assets based on data analytics, energy forecasting, and prediction for appropriate decision making by investors. Thus, fault diagnosis and detection can be easily handled in the advanced automated future grid. These features have enhanced reliability and offer optimized energy management by incorporating a virtual DT domain. In this paper, some major benefits of establishing a digital twin for the smart-grid is highlighted followed by the case study on monitoring a single component of the Smart grid that is evaluated for the remaining useful life (RUL) of the equipment by using artificial intelligence (AI) algorithm. This approach of preventive maintenance based on DT can be effectively utilized for all the key components in the smart grid-connected via a sensor network for data sampling to reduce downtime and improve the reliability of the overall system. © 2023 IEEE.

关键词： Fault detection

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Flight Technology Assessment Based on Machine Learning and SMOTE Algorithm 5

Flight Technology Assessment Based on Machine Learning and S...

引用

5th international conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Lei, Anran Zhou, Shicheng Cao, Xinyi Ma, Wenya Hubei Normal University School of Electrical Engineering and Automation Huangshi China Hubei Normal University School of Computer and Information Engineering Huangshi China Hubei Normal University School of Mathematics and Statistics Huangshi China Hubei Normal University College of Foreign Studies Huangshi China

ISBN: (纸本)9798350359930

This article first uses the PCA algorithm to reduce the dimensionality of expert-based flight parameter-based flight technical assessment report data. Then, the reduced data is respectively introduced into eight machine learning algorithms, including Logistic Regression, Decision Tree, etc. for pilot qualification assessment and prediction. Next, the SMOTEE algorithm is used to optimize the above eight models respectively. Finally, this article a dopts the PCA-SMOTE-Random Forest model with the best prediction effect to evaluate and predict the flight quality. Through confusion matrix diagrams and ROC curve diagrams, the accuracy of the algorithm's prediction is nearly 100%. Finally, this article discusses a flight technology evaluation method based on flight parameter. © 2023 IEEE.

关键词： Forecasting

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Overcoming Occlusion for Robust Facial Expression Recognition using Adaptive Dual-Attention Net 6

Overcoming Occlusion for Robust Facial Expression Recognitio...

引用

6th international conference on Information Technologies and Electrical engineering, ICITEE 2023

作者： Ge, Changqiang Peng, Guofeng Zhu, Wenbin Fan, Zhenhong Zhu, Xiaochun Hu, Bin School of Automation Nanjing Institute of Technology China School of Electronic and Optical Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400708299

Due to obstructions in the natural environment, practical emotion recognition applications often experience reduced recognition accuracy. In order to address this technological challenge, this paper proposes an emotion recognition approach embedding adaptive dual-channel attention, specifically designed for recognizing facial expressions in obscured faces. This method employs an adaptive dual-channel attention mechanism to focus on the uncovered facial feature areas, enhancing the algorithm's capability to concentrate on features. Additionally, it utilizes a dual-stream residual multi-scale network to augment perception of features at various scales, thus improving recognition accuracy. Experimental evaluations conducted on the AffectNet dataset and SFEW 2.0 dataset demonstrate that this approach yields superior recognition accuracy compared to existing methods. This research provides an effective avenue for enhancing user experience in applications such as human-computer interaction. © 2023 ACM.

关键词： Emotion Recognition

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Smart Contract Generation through NLP and Blockchain for Legal Documents 2

Smart Contract Generation through NLP and Blockchain for Leg...

引用

2nd international conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ahmed, Sayyed Usman Danish, Abutalha Ahmad, Nesar Ahmad, Tameem Department of Computer Engineering Z. H. College of Engineering & Technology Aligarh Muslim University UP Aligarh202002 India

The rise in legislation and the need for task automation systems has resulted in an amplified requirement for software development. This is aimed at improving the accuracy and efficiency of reading and interpreting laws in legal activities, allowing for enhanced precision and swiftness. Currently, there is a need for analysts to interpret written legislation and then encode it into computer programs, which often leads to errors. As cryptocurrencies gain popularity, there is growing interest in utilizing Blockchain technology in the legal field. Specifically, smart contracts can be used to integrate business rules into laws and automate blockchain management. However, the process of writing fast and high-quality code can be improved by leveraging artificial intelligence based techniques such as natural language processing (NLP) to help practitioners. Despite reviewing the current state-of-the-art, there is a lack of existing work that combines smart contracts and NLP in the context of legislation analysis. This study work generates intelligent code from legislation analysis, utilizing NLP and Blockchain for this purpose. In this research work a pilot prototype for smart contract generation is developed and initial code samples are presented. This work demonstrates a promising results with 96% accuracy in Name Entity Recognition (NER) and also highlights the importance of smart contract generation through NLP. © 2024 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Formal Modeling and Verification of Industrial Robotic Arm - A Case Study 23

Formal Modeling and Verification of Industrial Robotic Arm -...

引用

23rd IEEE international conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Zia, Ilmaan Basit Ur Rahim, Muhammad Abdul Liu, Tairan He, Zhangying California State University Long Beach Department of Computer Science and Engineering CA United States California State University Long Beach Department of Mechanical and Aerospace Engineering CA United States

ISBN: (纸本)9798350359398

Robotic arm systems are revolutionizing industries by automating operations and extending capabilities beyond human limitations, prioritizing accuracy and precision. These systems redefine boundaries across various sectors, including healthcare, mobile automation, and space exploration. Ensuring the safety of the robotic arm and its surrounding environment is paramount during its operations. This paper presents a detailed investigation of formal modeling and verification in the context of an industrial robotic arm, specifically a multi-joint mechanism equipped with advanced capabilities such as object manipulation, obstacle detection, and avoidance. The study uses the model-checking tool to capture the complex behavior of the system precisely. In-depth simulation and verification techniques are used for the formal model, focusing on its crucial properties, including safety, liveness, fairness, and deadlock-freeness. © 2023 IEEE.

关键词： Accident prevention

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Design and Simulation of Pipeline Structure of Filling Liquid Flow Standard Facility 5

Design and Simulation of Pipeline Structure of Filling Liqui...

引用

5th international conference on Mechatronics and computer Technology engineering, MCTE 2022

作者： Hou, Xue-Qing Zhang, Wei Li, Bin School of Mechatronics Engineering and Automation Shanghai University China Shanghai Institute of Quality Inspection and Technical Research China

ISBN: (纸本)9781510661073

In order to solve the problems of small flow and intermittent flow in the filling liquid flow standard facility, a facility pipeline structure which can maintain the pressure stability of the verification pipeline in the process of flow calibration and filling repeatability verification is designed in this paper. At the same time, in order to prove the feasibility of maintaining the pressure stability of the calibration pipeline, the hydrodynamics numerical simulation of the pipeline structure of the facility is carried out. © 2022 SPIE.

关键词： Filling

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Generation of Indian Sign Language Letters, Numbers, and Words

Generation of Indian Sign Language Letters, Numbers, and Wor...

引用

2024 international conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Yadav, Ajeet Kumar Kumar, Nishant Rathna, G.N. Indian Institute of Science Electrical Communication Engineering Banglore Bengaluru India Indian Institute of Science Computer Science and Automation Banglore Bengaluru India Indian Institute of Science Electrical Engineering Banglore Bengaluru India

ISBN: (纸本)9798350360660

Sign language, which contains hand movements, facial expressions and bodily gestures, is a significant medium for communicating with hard-of-hearing people. A well-trained sign language community communicates easily, but those who don't know sign language face significant challenges. Recognition and generation are basic communication methods between hearing and hard-of-hearing individuals. Despite progress in recognition, sign language generation still needs to be explored. The Progressive Growing of Generative Adversarial Network (ProGAN) excels at producing high-quality images, while the Self-Attention Generative Adversarial Network (SAGAN) generates feature-rich images at medium resolutions. Balancing resolution and detail is crucial for sign language image generation. We are developing a Generative Adversarial Network (GAN) variant that combines both models to generate feature-rich, high-resolution, and class-conditional sign language images. Our modified Attention-based model generates high-quality images of Indian Sign Language letters, numbers, and words, outperforming the traditional ProGAN in Inception Score (IS) and Frechet Inception Distance (FID), with improvements of 3.2 and 30.12, respectively. Additionally, we are publishing a large dataset incorporating high-quality images of Indian Sign Language alphabets, numbers, and 129 words. © 2024 IEEE.

关键词： Generative adversarial networks

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Protein-Folding Inspired Programmable Control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

引用

2024 IEEE international conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Feng Gu, Yuping Wang, Zebin Yuan, Shuai Yang, Yongliang School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110106 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781665481090

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem. © 2024 IEEE.

关键词： Robot programming

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AI-ML Trained Object Recognition System Development using Google Teachable Machine with the Help of Data Sciences 15

AI-ML Trained Object Recognition System Development using Go...

引用

15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Prasad, Rajneesh Pavithra, G. Manjunath, T.C. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore560111 India

ISBN: (纸本)9798331300579

Object recognition is a crucial task in computer vision and artificial intelligence, as it plays a vital role in many applications such as autonomous vehicles, surveillance systems, and *** this paper, we present an overview of AI Trained object recognition using Google Teachable *** Teachable Machine is a web-based platform that allows users to train machine learning models without any coding or programming *** explore the steps involved in training an object recognition model using Google Teachable Machine and evaluate the performance of the model on a real-world *** results show that Google Teachable Machine is a powerful and user-friendly tool for training object recognition models with high *** research focuses on the development of an AI-ML trained object recognition system using Google Teachable Machine, enhanced by data science *** objective is to create a robust and efficient system capable of accurately identifying and classifying various objects in *** the user-friendly interface of Google Teachable Machine, the system is trained with diverse datasets, incorporating advanced machine learning algorithms and data preprocessing *** approach ensures high accuracy and reliability in object recognition *** integration of data science principles allows for thorough analysis and optimization of the training data, improving the system's performance and *** resulting system demonstrates significant potential for applications in areas such as security, automation, and augmented reality, showcasing the synergy between AI, machine learning, and data sciences in solving complex recognition problems. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Fault-Tolerant FPGA-Based System for Mitigating SEUs in Configuration and User Bits

Fault-Tolerant FPGA-Based System for Mitigating SEUs in Conf...

引用

2024 international conference on Microelectronics, ICM 2024

作者： Amer, O.T. Alkady, G.I. Mohamed, F.Y. Mahmoud, A.W. Amer, H.H. Daoud, R.M. Cairo University Communication & Computer Eng. Dep. Faculty of Engineering Cairo Egypt American University in Cairo Electronics & Communications Engineering Department Sead Group Cairo Egypt

ISBN: (纸本)9798350379396

This paper addresses two issues. First, in the context of factory automation, a low-cost fault-tolerant sensor system is proposed along with its FPGA-based voter. Second, the voter itself is designed to recover from failures affecting its own circuitry. The sensor system consists of one high-quality sensor used for measurement along with two functionally identical, but lower cost sensors used for monitoring purposes. A biased voter circuit is designed and implemented on the same Field Programmable Gate Array (FPGA) used to house the controller. It is shown that the voter can detect and recover from Single Event Upsets (SEUs) which often occur in the harsh factory environments. Both FPGA configuration and user bits are protected. It was shown that the proposed design requires less resources and power than some other designs presented in the literature. Focusing next on recovering from SEUs in both user and configuration FPGA bits, another use case is investigated, namely an I2C master;it is shown that the proposed design again requires less resources than some other commonly used techniques. Both proposed designs were implemented on the Xilinx Zynq-7000 (xc7z020clg400-1). © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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