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2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Wiwechpaisankul, Weeratep Rattananatthawon, Ongorn Sirisumrannukul, Somporn Sabpayakom, Noppatee. Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand College of Industrial Technology of Power Electronics Engineering Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

Transmission system expansion planning (TSEP) is the process of identifying time, location, and the number of transmission facilities will be needed in the future. TSEP is an optimization problem with the objective to minimize the investment cost to ensure supply-demand adequacy, subject to constraints on the power balance and the apparent power capability limit of transmission lines. This paper presents transmission system expansion planning based on an optimal power flow and a genetic algorithm (GA). The OPF problem was solved by the PowerWorld simulator, and the GA was coded in Python and developed on the built-in SimAuto function in the PowerWorld simulator. The simulation results from a case study of a modified Garver's 6-bus power system show that the developed methodology can determine the optimal sites and capacities of transmission lines to be added to the system. © 2022 IEEE.

关键词： Genetic algorithms

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Enhancing Breast Cancer Diagnosis with Multi-Resolution Vision Transformers and Robust Decision-Making

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IEEE Access 2025年 13卷 89704-89722页

作者： Sabry, Margo Balaha, Hossam Magdy Ali, Khadiga M. Soliman, Tayseer Hassan A. Gondim, Dibson Ghazal, Mohammed Alghamdi, Norah Saleh El-Baz, Ayman Assiut University Information Systems Department Asyut71511 Egypt University of Louisville J. B. Speed School of Engineering Bioengineering Department LouisvilleKY40292 United States Mansoura University Faculty of Medicine Pathology Department Mansoura35511 Egypt University of Louisville Laboratory Medicine Department of Pathology LouisvilleKY40292 United States Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh11564 Saudi Arabia

This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. Performance was evaluated via metrics such as precision, recall, the F1 score, accuracy, and specificity across 50 trials with perturbed conditions. The framework achieved a top accuracy of 97.08%, with precision and recall above 94%. The suggested stack configuration outperformed individual models and other stacking configurations, demonstrating balanced performance with minimal variability. Statistical analysis highlighted the reliability and consistency of the framework under perturbed conditions. The multi-resolution ViT-based framework significantly improves BC classification by integrating multiscale analysis and ensemble decision-making. Its high accuracy and robustness make it a valuable tool for reducing interobserver variability in digital pathology workflows. © 2013 IEEE.

关键词： Image segmentation

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Machine Learning Techniques in Additive Manufacturing: A Review

Machine Learning Techniques in Additive Manufacturing: A Rev...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Ahmed Alsalem Mohamed Zohdy Department of Industrial and Systems Engineering Oakland University Rochester MI USA Department of Electrical and Computer Engineering Oakland University Rochester MI USA

Additive manufacturing (AM) is a 3D printing process widely used in industries such as automotive, healthcare, aerospace, and consumer goods. It creates different 3D objects by adding different layers over one another. AM manufacturing processes are capable enough to produce complex parts, consisting of intricate designs and geometries, characterized by well-established grain structure and properties without expensive tooling in reduced lead time and at low cost. It is a multi-physical process, including various parameters that impact the final product's quality. Moreover, Machine Learning (ML) techniques are suitable approaches aimed at understanding and predicting the complex processes of AM. In this study, an overview of AM and the challenges in the AM is presented, as well as the ML approaches to address the challenges in AM processes are also discussed. The recently introduced ML techniques in AM processes are evaluated explicitly. To be more precise, limited training data, material choice, and standardization regarding data pose major challenges that need to be addressed when applying ML techniques to AM methods.

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation Network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

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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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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 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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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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