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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Takeru Miyazaki Shunsuke Araki Satoshi Uehara Faculity of Management and Information Sciences Kyushu Inst. of Information Sciences Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan Faculty of Environmental Engineering The Univ. of Kitakyushu Fukuoka Japan

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes.

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State Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年第4期40卷 3057-3073页

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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Photonic Crystal Design for Bloch Surface Wave Sensing 5

Photonic Crystal Design for Bloch Surface Wave Sensing

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5th International Conference on Applications of Optics and Photonics, AOP 2022

作者： Dias, Bernardo De Almeida, José M. M. M. Coelho, Luís C. C. INESC TEC-Institute for Systems and Computer Engineering Technology and Science Faculty of Sciences University of Porto Porto4169-007 Portugal Department of Physics and Astronomy Faculty of Sciences University of Porto Porto4169-007 Portugal Department of Physics School of Science and Technology University of Trás-os-Montes e Alto Douro Vila Real5001-801 Portugal

Bloch Surface Waves (BSW) consist of electromagnetic modes generated at the interface between a photonic crystal and an isotropic dielectric. This type of surface mode displays sharp resonances and high sensitivity to external refractive index variations, and thus appears to be an ideal candidate for usage in optical sensors. Nevertheless, design and optimization of photonic crystals is not a trivial task and constitutes an ongoing field of research. The sensitivity of BSW in both refractometric and adsorption sensing is calculated analytically using first-order perturbation theory for TE modes, allowing the understanding of how several physical parameters of the photonic crystal influence the sensitivity. Preliminary experimental results are presented, which aim to use the analytical calculations to allow for both refractometric and adsorption sensing in a single photonic crystal structure. © Published under licence by IOP Publishing Ltd.

关键词： Photonic crystals

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Generating Muslim Name using Character-Level Language Model in Deep Learning

Generating Muslim Name using Character-Level Language Model ...

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Smart Computing and Application (ICSCA), International Conference on

作者： Syed Atif Ali Shah Wael M.S. Yafooz Abdel-Hamid M. Emara Hashim Sajid Department of CS Air University Islamabad Pakistan Department of CS Al-Madinah International University Malaysia Department of Computer Science College of Computer Science and Engineering Taibah University Department of Computers and Systems Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of CS Abasyn University Pakistan

this paper proposes novel deep-learning models that can generate Muslim names. A recurrent neural network (RNN) approach is used as the machine learning model. Generate new names, using the character-level language model. This technique has learned the different name patterns and generates new names. In order to achieve this objective, thousands of Muslim names have been collected from popular domains and then trimmed for the data set. After processing the whole dataset, 1000 pure and most common names are selected for a dataset. The deployed system is using some important functions provided by neural network libraries. Here, the argument also reveals that if a huge data set is used to employ this deep learning technique, more accurate results are expected. These outcomes will be better when compared with the traditional machine learning algorithms. The efficacy of the proposed model is proved by the similarity of artificial names with real names.

关键词： Training Deep learning Vocabulary Recurrent neural networks Machine learning algorithms Computational modeling Symbols

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Deep Learning-Based Intrusion Detection Technique for IoT Security

Deep Learning-Based Intrusion Detection Technique for IoT Se...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Tariq Ahamed Ahanger Abdullah Aljumah Imdad Ullah Department of Management Information Systems CoBA Prince Sattam Bin Abdulariz University Al-Kharj Saudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Faculty of Engineering School of Computer Science The University of Sydney Sydney NSW Australia

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Numerous connected machines compose the Internet of Things (IoT) like sensors and actuators, via wired or wireless networks. The number of IoT apps, including Vehicular Ad-hoc Networks (VANETs), Healthcare, Smart communities, and Wearable have recently risen significantly. Data Protection and Security is becoming more critical as the number of IoT-linked devices grows. In this paper, we suggest a new deep learning-based intrusion detection system (DL-IDS) to identify security breaches in IoT environments to address the difficulties of protecting IoT applications. There are several IDSs developed by researchers, optimal learning and management of data set functions are neglected, which are essential problems that impact the accuracy of tracking threats. To achieve optimum detection recognition, the presented framework incorporates the SMO technique and the Deep stacked polynomial network. SMO chooses the optimal features in the data, and SDPN categorizes it as regular or vulnerable. The presented model is analyzed for performance assessment in comparison to the state-of-the-art decision-making models. In terms of accuracy, recall, precision, and F -score, the detailed review suggests that presented technique acquires enhanced performance and is immensely efficient.

关键词： Analytical models Wireless sensor networks Accuracy Smart cities Reviews Wireless networks Decision making Intrusion detection Data collection Internet of Things

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Stabilizing Control Design by Integrable Differential Passive Outputs*

Stabilizing Control Design by Integrable Differential Passiv...

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IEEE Conference on Decision and Control

作者： Morteza Nazari Monfared Yu Kawano Michele Cucuzzella Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Graduate School of Advance Science and Engineering Hiroshima University Higashi-hiroshima Japan Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen AG the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Differential passivity of a nonlinear system has been introduced as passivity of its variational system. Applying standard passivity-based control techniques to differentially passive systems leads to controllers for the corresponding variational systems. However, it is challenging to construct controllers for the original nonlinear systems if the differential passive outputs are non-exact differential one-forms. In this letter, our objective is to provide a systematic procedure to address this issue when differential passive outputs are integrable, i.e., when non-exact differential passive outputs can be made exact by multiplying them by suitable integrating factors. In particular, under suitable detectability assumptions, we propose one static and two dynamic state feedback stabilizing controllers, where each dynamic controller has a form of input-and output-shaping, respectively. We illustrate their effectiveness by stabilization of counter-current heat exchangers.

关键词： Heating systems State feedback Systematics Control design Color Nonlinear systems Standards

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ChatGPT: Applications, Opportunities, and Threats

ChatGPT: Applications, Opportunities, and Threats

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Aram Bahrini Mohammadsadra Khamoshifar Hossein Abbasimehr Robert J. Riggs Maryam Esmaeili Rastin Mastali Majdabadkohne Morteza Pasehvar Department of Systems and Information Engineering University of Virginia Charlottesville VA USA School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Industrial Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Medicine Faculty of Medical Sciences The American University Managua Nicaragua Department of Industrial Engineering Faculty of Engineering Islamic Azad University Tehran Iran

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.

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AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design

AI-based Flexible Online Laboratory Learning System for Post...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Elmesalawy, Mahmoud M. Atia, Ayman Yousef, Ahmed Mohamed Fahmy El-Haleem, Ahmed M. Abd Anany, Mohamed G. Elmosilhy, Noha A. Salama, Abdullah I. Hamdy, Alaa Zoghby, Helmy M. El Din, Eman Serag El Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligent Computer Science Department Cairo Egypt Giza Egypt Fayoum University Faculty of Specific Education Education Technology Department Fayoum Egypt Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Helwan University Faculty of Engineering Electrical Power and Machines Engineering Department Cairo Egypt Network Planning Department Cairo Egypt

ISBN: (纸本)9781665412438

The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.

关键词： COVID-19

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A Comparison of Various Deep Learning Methods for Household Load Forecasting

A Comparison of Various Deep Learning Methods for Household ...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Karthikeyan Deivamani Farshid Norouzi Aditya Shekhar Pavol Bauer Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Department of DC Systems Energy Conversion and Storage Delft University of Technology Delft The Netherlands

Forecasting energy consumption is vital for smart grid operations to manage demand, plan loads, and optimize grid operations. This work aims at reviewing and experimentally evaluating six univariate deep learning architectures to forecast load for a single household using a real-world dataset. Multi-layer perceptron (MLP), Convolutional neural network (CNN) and recurrent neural networks (Simple RNN, Long Short Term Memory (LSTM)) were the neural network methods that were analysed along with robust LSTM architectures like Bi- directional LSTM and CNN-LSTM Hybrid. All the models were tuned using Bayesian optimization and evaluated using root mean squared error (RMSE) as the metric. In addition to neural network models, Seasonal ARIMA (SARIMA) a statistical model is also presented to observe the performance. As a result, Bi-directional LSTM was observed to have achieved the best performance with the smallest value of RMSE; however, it was also observed that differences in performances between other neural network models were quite low, especially between the RNN architectures. Additionally, although machine learning methods performed better than SARIMA the former model was more complex and computationally intensive.

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