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2025 International Conference on computer, electrical and Communication engineering, ICCECE 2025

作者： Lalitha, T. Szymański, Jerzy Ryszard Zurek-Mortka, Marta Sathiyanarayanan, Mithileysh Srilekha, K.R. Presidency University School of Information Science Karnataka Bengaluru India Casimir Pulaski University of Radom Faculty of Transport Electrical Engineering and Computer Sciences Department of Electrical Drives and Industrial Electronics Poland Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pulaskiego Radom Poland MIT Square London United Kingdom Vit University Tamilnadu Vellore India

ISBN: (纸本)9798331513894

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal's death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation Network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis. © 2025 IEEE.

关键词： Low emission

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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

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Ain Shams engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment 8

Analyzing the Behavioral Aspects of Implementing Green IT in...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Syamsuar, Dedy Triando, Fero Ulfa, Maria Antoni, Darius Mailangkay, Adele Witarsyah, Deden School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Bina Darma Postgradute Department Palembang Indonesia Universitas Bina Darma Information System Study Program Computer Science Faculty Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350326093

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy. © 2023 IEEE.

关键词： Decision making

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An Application of Pontryagin Neural Networks to Solve Optimal Quantum Control Problems

An Application of Pontryagin Neural Networks to Solve Optima...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Nahid Binandeh Dehaghani A. Pedro Aguiar Electrical and Computer Engineering Department FEUP - Faculty of Engineering Research Center for Systems and Technologies (SYSTEC) University of Porto Porto Portugal

Reliable high-fidelity quantum state transformation has always been considered as an inseparable part of quantum information processing. In this regard, Pontryagin Minimum (or maximum) Principle (PMP) has proved to play an important role to achieve the maximum fidelity in an optimum time or energy. Motivated by this, in this work, we formulate a control constrained optimal control problem where we aim to minimize time and also energy subjected to a quantum system satisfying the bilinear Schrödinger equation. We derive the first order optimality conditions through the application of PMP resulting in a boundary value problem. Next, in order to obtain efficient numerical results, we exploit a particular family of physics-informed neural networks that are specifically designed to tackle the indirect method based on the PMP. We show that this method can significantly speed up the process by first obtaining a set of relations which finally let us compute the optimal control strategy to determine the time- and energy-optimal protocol driving a general initial state to a target state by a quantum Hamiltonian with bounded control. We make use of the so-called “qutip” package in python, and the newly developed “tfc” python package.

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Development of IoT Solution Prototype for Reliable and Sustainable Transport System in Uncertain Epidemiological Conditions 26

Development of IoT Solution Prototype for Reliable and Susta...

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26th International Scientific Conference Transport Means 2022

作者： Patlins, A. Bekeris, M. Riga Technical University Faculty of Electrical and Environmental Engineering Institute of Industrial Electronics and Electrical Engineering Kipsalas street 6A RigaLV-1048 Latvia Riga Technical University Faculty of Computer Science and Information Technology Kipsalas street 6A RigaLV-1048 Latvia

In the frame of current research authors has developed a prototype of the Internet of Things, which reflects the obtained measurement data, in real time. The resulting data provides a convenient opportunity to analyze the quality of air in public transport vehicles - humidity, level of CO2 and temperature. Developed also notifications component, records can be filtered from the selected database if the defined CO2, temperature or humidity measures are exceeded. Using developed prototype, experiments (measurements) were performed in public transport company's "Rīgas Satiksme" vehicles such as: trolley bus, tram and bus. The goal was to determine if air is being ventilated in public transport in a sufficient level, as well as to observe the temperature and humidity differences during the routes. Results of research are positive and authors can make assumption about the possibility to use such kind of IoT prototypes to make public transport system more reliable and sustainable also in uncertain epidemiological conditions. © 2022 Kaunas University of Technology. All rights reserved.

关键词： Carbon dioxide

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An Advanced Defense Mechanism for Detecting False Data Injection Attacks in Cyber-Physical systems through Feature Selection Based-Machine Learning Algorithms

An Advanced Defense Mechanism for Detecting False Data Injec...

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Advances in electrical and Communication Technologies (ICAECOT), International Conference on

作者： Younes Gasmi Noureddine Bali Atif Karim Soufiane Belagoune Electrical and Industrial Systems Laboratory (LSEI) Faculty of Electrical Engineering University of Science and Technology of Houari Boumediene (USTHB) BP 32 ElAlia Bab Ezzouar Algiers Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

False Data Injection Attacks (FDIAs) are among the most common cyber threats targeting measurement data and sensor readings in power systems. These attacks pose a significant risk to the integrity of power system data, as they can evade conventional Bad Data Detection (BDD) mechanisms. This evasion can lead to incorrect decisions by power system operators and control centers during Power System State Estimation (PSSE), potentially resulting in serious damage to the system topology and economic losses. While previous approaches have primarily focused on enhancing BDD algorithms, there remains a significant gap in effectively detecting stealthy FDIA attacks. This paper presents an approach that leverages Machine Learning (ML) algorithms, including supervised models such as Decision Trees (DT) and K-Nearest Neighbours (KNN), alongside ensemble classifiers like Random Forest (RF), XGBoost (XGB), and AdaBoost (ADABO). A feature selection technique is applied post-training of the XGBoost model to identify the most relevant features, thereby improving the performance of the classifiers. The proposed method has been rigorously tested on the IEEE-14 bus system, demonstrating significant improvements in detection accuracy and computational efficiency. The results are discussed in detail across various metrics and time-based evaluations, highlighting the robustness of our approach.

关键词： Measurement Training Machine learning algorithms Accuracy Biological system modeling Feature extraction Power systems Classification algorithms Topology State estimation

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Quantum State Transfer Optimization: Balancing Fidelity and Energy Consumption using Pontryagin Maximum Principle

Quantum State Transfer Optimization: Balancing Fidelity and ...

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International Conference on systems and Control (ICSC)

作者： Nahid Binandeh Dehaghani A. Pedro Aguiar Electrical and Computer Engineering Department Research Center for Systems and Technologies (SYSTEC) ARISE Faculty of Engineering University of Porto Porto Portugal

We address a control-constrained optimal control problem pertaining to the transformation of quantum states. Our objective is to steer a quantum system from an initial state to a desired target state while adhering to the principles of the Liouville-von Neumann equation. To achieve this, we introduce a cost functional that balances the dual goals of fidelity maximization and energy consumption minimization. We derive optimality conditions in the form of the Pontryagin Maximum Principle (PMP) for the matrix-valued dynamics associated with this problem. Subsequently, we present a time discretized computational scheme designed to solve the optimal control problem. This computational scheme is rooted in an indirect method grounded in the PMP, showcasing its versatility and efficacy. To illustrate the practicality and applicability of our methodology, we employ it to address the case of a spin ½ particle subjected to interaction with a magnetic field. Our findings shed light on the potential of this approach to tackle complex quantum control scenarios and contribute to the broader field of quantum state transformations.

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An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

An observer comparison study to evaluate a machine learning ...

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Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

作者： Abdihamzehkolaei, Alireza Mirniaharikandehei, Seyedehnafiseh Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. School of Electrical and Computer Engineering University of Oklahoma NormanOK United States School of Systems Engineering and Informatics Universidad Nacional de San Agustín de Arequipa Arequipa Peru Medicine Faculty Universidad Nacional de San Agustín de Arequipa Arequipa Peru

ISBN: (数字)9781510660403

ISBN: (纸本)9781510660397

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the abstract

关键词： COVID-19

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IoT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time 3rd

IoT Enabled Driver Compatible Cost-Effective System for Drow...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Sarkar, Argha Kundu, Mayuri Pareek, Prakash Gupta, Nishu Reis, Manuel J. Cabral S. School of Computer Science and Engineering REVA University Karnataka Bangalore560064 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

The present work researches driver drowsiness, which constitutes a huge problem, and can turn into fatal incidents potentially involving the losing of lives, being it while driving in a highway (car, bus, truck, etc.), in the railway, or any other transportation mean (ship, airplane, etc.). Recent technology has been involved in the study of the fatigue behaviour. The purpose of this work is to identify the in-driver’s drowsiness, contributing to get rid of the accidents, mainly during the night, and to ensure better safety in train and on the highways. A camera is used to capture images from the driver, and face detection is executed in real-time to find out the exact position of driver’s face. Here, the blinking of the driver’s eye is under consideration. The fundamental concept lies in the closing of the driver’s eye for a specific duration of time. If the closing time exceeds that certain duration, drowsiness is detected and an alarm is sounded for awareness. Python and OpenCV (using Haarcascade library) are the fundamental programming platforms for sensing the facial attributes. In this work, a cost-effective system for the driver’s drowsiness detection is aimed. Further, the result also indicates the reduction of response and processing time, which is ideal for real time detection. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Python

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Control of Squirrel Cage Induction Motor using Conventional Controllers and fuzzy logic 19

Control of Squirrel Cage Induction Motor using Conventional ...

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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Lachekhab, Fadhila Zamoum, Razika Boushaki Bougheloum, Dhya Eddine Benyahia, Sofiane Acheli, Dalila Kouzou, Abdellah University of Mhamed Bougara Systems and Telecommunications Engineering Laboratory Faculty of Hydocarbons and Chemistry Boumerdes Algeria Iinstitute of Electrical and Electronic Engeneering University of Boumerdes Systems and Telecommunications Engineering Laboratory Boumerdes Algeria Institute of Electrical and Electronic Engeneering University of Boumerdes Boumerdes Algeria University of Mhamed Bougara Systems and Telecommunications Engineering Laboratory Faculty of Technology Boumerdes Algeria University of Djelfa Applied Automation and Industrial Diagnostics Laboratory Laadi Faculty of Science and Technology Djelfa Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The major goal of this work is to develop a control mechanism for the squirrel cage induction motor that will deliver the best dynamic response. This will be accomplished through the use of vector control techniques and the integration of fuzzy logic with traditional controllers. MATLAB/SIMULINK was used to compare controllers (PID, Fuzzy, and Fuzzy -PID) to the standard PI controller at various load situations, analyzing performance such as rising time (tr), overshoot (Mp), settling time (ts), and steady state error. © 2022 IEEE.

关键词： Fuzzy logic

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