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International Conference on Telecommunications and Signal Processing (TSP)

作者： Hassan Farran David Khoury Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Computer Science and Information and Communication Technology American University of Science and Technology Beirut Lebanon

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system.

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Application of Modified MPPT Algorithms: A Comparative Study between Different Types of Solar Cells

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Applied Solar Energy (English translation of Geliotekhnika) 2020年第5期56卷 309-323页

作者： Khodair, D. Salem, M.S. Shaker, A. El Munim, H. E. Abd Abouelatta, M. Department of Electronics and Electrical Communications Faculty of Engineering Ain Shams University Cairo11517 Egypt Department of Engineering Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Computer Engineering Computer Science and Engineering College University of Ha’il Ha’il Saudi Arabia Department of Electrical Communication and Electronics Systems Engineering Faculty of Engineering Modern Science and Arts University Cairo Egypt Department of Engineering Physics and Mathematics Faculty of Engineering Ain Shams University Cairo11517 Egypt Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo11517 Egypt

Abstract: Due to the broad needs for energy as market demands, searching on how to improve the efficiency of the PV systems is an essential concern for researchers to worry about. So, it is crucial to force the PV system to work at its peak power point in order to get the maximum available power from the photovoltaic panel. This paper presents a comprehensive comparison between four Maximum Power Point Tracking (MPPT) Algorithms;Perturb and Observe (P&O), Incremental Conductance (INC), Modified Variable Step Size Perturb and Observe (M-VSS-P&O) and Modified Variable Step Size Incremental Conductance (M-VSS-INC) by using of the DC-DC boost converter for three different kinds of solar cells. These cells are polycrystalline KC200GT cell, monocrystalline shell SQ85 cell and thin film shell ST40 cell. Simulations have been performed using MATLAB-SIMULINK for the three types of solar cells to investigate their performance under both standard test conditions (STC) and slowly varying and sudden changes in solar irradiance. The study has considered the response time, output power efficiency and steady-state-oscillations. The simulation results of the modified algorithms show an improvement in the cell performance in steady state conditions, tracking time and boost converter efficiency as well as an enhancement in the dynamic response in tracking the maximum power point (MPP) in varying climatic conditions over conventional algorithms. © 2020, Allerton Press, Inc.

关键词： Thin films

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment

Identifying Appropriate Probability Density Function for Win...

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Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Ghulam Abbas Arshad Ali Muhammad Zubair Zohaib Mushtaq Umar Farooq Irfan A. Khan Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan Intelligent Systems Lab & Automation Facility (ISLAF) IEECE University of the Punjab Lahore Pakistan Clean and Resilient Energy Systems (CARES) Lab Texas A&M University Galveston TX USA

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures-Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2 )-are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology.

关键词： Wind energy generation Electric potential Wind energy Wind speed Urban areas Sea measurements Wind power generation Probability density function Probability distribution Weibull distribution

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IoT Security Frameworks: A Comparative Review with a Focus on Privacy 2

IoT Security Frameworks: A Comparative Review with a Focus o...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Hasnawi, Abduljaleel Niu, Yitong Tawfeq, Jamal F. Ranjan Pradhan, Manas Salahat, Mohammed Ghazal, Taher M. Ministry Of Higher Education And Scientific Research Baghdad Iraq College Of Information Technology Imam Ja'afar Al-Sadiq University Department Of Communication Technology Engineering Baghdad Iraq School Of Aeronautical Engineering AnYang University Anyang China Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions. © 2024 IEEE.

关键词： Internet of things

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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How Healthcare systems engineering can Benefit from Digital Twins?

How Healthcare Systems Engineering can Benefit from Digital ...

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Annual IEEE systems Conference

作者： Nader Mohamed Jameela Al-Jaroodi Imad Jawhar Nader Kesserwan Department of Computer Science and Information Systems Pennsylvania Western University California PA USA Department of Engineering Robert Morris University Pittsburgh PA USA Faculty of Engineering Al Maaref University Beirut Lebanon

The introduction of the digital twin technology created enormous potential for several industrial sectors to improve their operations. Accordingly, they can enhance their productivity, cost-effectiveness, reliability, quality, and flexibility. One important sector that can successfully benefit from this advanced technology is healthcare systems. This paper investigates how healthcare systems engineering can benefit from utilizing digital twins. Healthcare systems engineering applies the systems view of engineering analysis and design principles to enhance healthcare services. Digital twins can enable many advantages to improve the processes and outcomes of healthcare systems. This paper discusses how digital twins can be utilized for healthcare systems engineering. It also discusses the impact of this utilization on achieving the goals of healthcare systems engineering.

关键词： Productivity Pandemics Data visualization Medical services Organizations Predictive models Data models

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Optimizing Wastewater Treatment Plant Operations: A Machine Learning Approach for Energy Consumption and Climate Dynamics Analysis 8

Optimizing Wastewater Treatment Plant Operations: A Machine ...

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8th IET Smart Cities Symposium, SCS 2024

作者： Elshabrawy, Mahmoud Rizk, Faris H. Zaki, Ahmed Mohamed Eid, Marwa M. Takieldeen, Ali E. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center Blacksburg 24060 VA United States Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra 11961 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Jadara University Research Center Jadara University Jordan Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (纸本)9781837242672

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved. © The Institution of engineering & Technology 2024.

关键词： Climate dynamics Data analysis Energy consumption Wastewater characteristics Wastewater treatment

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Full distributed P2P market and distribution network operation based on ADMM: Testing and evaluation

Full distributed P2P market and distribution network operati...

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International Conference on European Electricity Market, EEM

作者： Carlos Oliveira Micael Simões Tiago Soares Manuel A. Matos Leonardo Bitencourt Institute for Systems and Computer Engineering Technology and Science (INESC TEC) University of Porto Faculty of Engineering (FEUP) Porto Portugal Electrical Energy Department Federal University of Juiz de Fora (UFJF) MG Brazil

This work models a distributed community-based market with diverse assets (photovoltaic generators and energy storage systems), accounting for network constraints and adopting the relaxed branch flow model. The market is modeled in a single and fully distributed approach, employing the alternating direction method of multipliers (ADMM) to prevent voltage and line capacity problems in the community network and improve data privacy and reduce the communication burden. Different scenarios, based on the penalty term and the agents' number, are tested to study the efficiency of the algorithm and the convergence rate of the ADMM distributed model. The proposed method is tested on 10-bus, 22-bus, and 33-bus medium voltage radial distribution networks, where each node contains a large prosumer with one or several assets. One important conclusion is that the implemented residual balancing technique improves the efficiency of the ADMM distributed algorithm by increasing the convergence rate and reducing the computational time.

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Prediction of Consumer Purchase Intention on Green Product

Prediction of Consumer Purchase Intention on Green Product

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2021 International Seminar on Application for Technology of Information and Communication, iSemantic 2021

作者： Hariyadi, Guruh Taufan Purwanto Isnanto, R Rizal Wibowo, Adi Warsito, Budi Universitas Diponegoro Doctoral Program of Information Systems Semarang Indonesia Universitas Diponegoro Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Universitas Diponegoro Faculty of Engineerig Department of Computer Engineering Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Departement of Informatics Semarang Indonesia

ISBN: (纸本)9781665428040

Education to the public on green products has been widely carried out. Consumers who buy green products can be profiled by segmenting green consumers. In green marketing, consumer education on sustainable consumption is increasingly needed in recent years due to the problem of environmental pollution which poses a serious threat to the world population. Concern for the environment is an important topic in most countries, and individuals also study environmental issues. Individuals or groups of individuals who are 'ecologically aware' are also referred to as 'green consumers.' Such consumers are considered to have more concern for the environment. This study examined the predictions on the effect of the Green Perceived Value variables consisting of functional, conditional, and social values on the purchase intention of green consumers. It also tested the comparisons with other classification models. The models used in this research were Random Forest, Decision Tree, and Neural Network.. © 2021 IEEE.

关键词： Consumer behavior

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Deep Learning: Techniques, Taxonomy, Applications, and Future Directions

Deep Learning: Techniques, Taxonomy, Applications, and Futur...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： V Mohana Sundari R.A. Mabel Rose S.M. Sassirekha M. Nalini P. Girija Siva Subramanian R Faculty of Management SRM Institute of Science and Technology Chengalpattu Computer Science And Business Systems Panimalar Engineering College Mailam Engineering College Dept of CSE S.A.Engineering College Chennai Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept of CSE RMK College of Engg and Tech

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Deep learning has become one of the key AI techniques of the modern world and has changed many fields by its capability to learn from data, which is often very complex. The presented survey paper aims at describing fundamental techniques and taxonomy of deep learning, various applications, and potential perspectives for further research. The paper starts with presenting the concept of deep learning and its origins as well as emphasizing the important position of DL in the modern development of AI. It then goes further into deep learning workflow and frameworks and presents feedforward neural networks, CNN, RNN, generative adversarial networks, attention mechanisms, and transformers. An overview of deep learning frameworks is discussed, including supervised, unsupervised, reinforcement, transfer, semi-supervised, & self-supervised learning. The survey further continues to provide a detailed description on how DL is being widely applied in CV, NLP, speech recognition, disease diagnosis & stock price forecasting. The issues related to challenges and limitations include data dependency, interpretability, overfitting, high computational burden, and ethical concerns are presented along with ongoing work and directions for future investigations. This includes explainability of AI, incorporation of symbolic AI methods, improving the resilience and security of models, breakthroughs in hardware, as well as expansion into future domains such as quantum computing and robotics. Finally, this survey paper summarizes existing literature and predicts future research direction, emphasizing on how deep learning will revolutionalise technology and how it may foster development in various fields.

关键词： Deep learning Surveys Ethics Quantum computing Computational modeling Taxonomy Transformers Hardware Artificial intelligence Robots

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