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11th International Conference on Information Technology, Computer and electrical engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto-optimal profits for the utility and prosumers

Energy Conversion and Economics

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Energy Conversion and Economics 2022年第3期3卷 156-169页

作者： Mohan, Vivek Antonis, Anjula Mary Jisma, M. Krishnakumar, Nila Bu, Siqi Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Electrical Sustainable Energy Faculty of Electrical Engineering Applied Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Electrical Engineering The Hong Kong Polytechnic University Kowloon SAR Hong Kong

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost-effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre-tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value-at-risk (CVaR) approach;and (2) by co-optimizing the conflicting profits of the utility and prosumers using non-dominated sorting particle swarm optimization (NSPSO) to obtain a risk-adjusted Pareto-optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid-connected CIGRE low-voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare-optimized and cost-efficient. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Profitability

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Free-electron crystals for enhanced X-ray radiation

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Light(Science & Applications) 2024年第7期13卷 1299-1309页

作者： Lee Wei Wesley Wong Xihang Shi Aviv Karnieli Jeremy Lim Suraj Kumar Sergio Carbajo Ido Kaminer Liang Jie Wong School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore. Solid State Institute and Faculty of Electrical and Computer Engineering Technion–Israel Institute of TechnologyHaifa 3200003Israel. School of Electrical Engineering Fleischman Faculty of EngineeringTel Aviv UniversityTel Aviv 69978Israel Department of Applied Physics Stanford UniversityStanfordCA 94305USA. Science Mathematics and TechnologySingapore University of Technology and Design8 Somapah RoadSingapore 487372Singapore. Electrical and Computer Engineering Department UCLA420 WestwoodLos AngelesCA 90095USA. Physics and Astronomy Department UCLA475 Portola PlazaLos AngelesCA 90095USA. SLAC National Accelerator Laboratory Stanford University2575 Sand Hill RoadMenlo ParkCA 94025USA

Bremsstrahlung—the spontaneous emission of broadband radiation from free electrons that are deflected by atomic nuclei—contributes to the majority of X-rays emitted from X-ray tubes and used in applications ranging from medical imaging to semiconductor chip ***,we show that the bremsstrahlung intensity can be enhanced significantly—by more than three orders of magnitude—through shaping the electron wavefunction to periodically overlap with atoms in crystalline ***,we show how to shape the bremsstrahlung X-ray emission pattern into arbitrary angular emission profiles for purposes such as unidirectionality and ***,we find that these enhancements and shaped emission profiles cannot be attributed solely to the spatial overlap between the electron probability distribution and the atomic centers,as predicted by the paraxial and nonrecoil theory for free electron light *** work highlights an unprecedented regime of free electron light emission where electron waveshaping provides multi-dimensional control over practical radiation processes like *** results pave the way towards greater versatility in table-top X-ray sources and improved fundamental understanding of quantum electron-light interactions.

关键词： overlap attributed arbitrary

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THE INCREASING NECESSITY OF SKILLS DIVERSITY IN TEAM TEACHING 50

THE INCREASING NECESSITY OF SKILLS DIVERSITY IN TEAM TEACHIN...

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50th Annual Conference of the European Society for engineering Education, SEFI 2022

作者： Craig, T.S. Redeker, S. Department of Applied Mathematics University of Twente Enschede Netherlands Department of Electrical Engineering University of Twente Enschede Netherlands

ISBN: (纸本)9788412322262

The advantages of including technological tools, for example dynamic graphing software, into the teaching and learning of technical subjects have long been recognised. Using such tools effectively in the classroom is at least partially constrained by the teacher's knowledge and skills. Any teaching team benefits from skills diversity and always has, however, in recent years the skills needed for effective use of the tools available are becoming more numerous and more varied. Using a range of tools for assessment, rather than for in-class teaching and learning, demands an extra burden of care. Using an example from our own recent experience we illustrate the need for skills diversity in teams teaching and raise questions related to traditional assessment in mathematics. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for engineering Education, Proceedings. All rights reserved.

关键词： engineering education

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Trustworthy actionable perturbations 24

Trustworthy actionable perturbations

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Proceedings of the 41st International Conference on Machine Learning

作者： Jesse Friedbaum Sudarshan Adiga Ravi Tandon Program in Applied Mathematics University of Arizona Tucson AZ and Department of Electrical and Computer Engineering University of Arizona Tucson AZ Department of Electrical and Computer Engineering University of Arizona Tucson AZ

Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and "fool" the classifier. We propose a new framework for creating modified inputs that change the true underlying probabilities in a beneficial way which we call Trustworthy Actionable Perturbations (TAP). This includes a novel verification procedure to ensure that TAP change the true class probabilities instead of acting adversarially. Our framework also includes new cost, reward, and goal definitions that are better suited to effectuating change in the real world. We present PAC-learnability results for our verification procedure and theoretically analyze our new method for measuring reward. We also develop a methodology for creating TAP and compare our results to those achieved by previous counterfactual methods.

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Identifying the Optimal Size of a Hybrid Power System Using a Modified Genetic Algorithm 2

Identifying the Optimal Size of a Hybrid Power System Using ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Suresh, S. Muthulakshmi, M. Bhuvanesh, A. Sankarasubramanian, R.S. Rajendran, S. Jeyamurugan, J. Sri Eshwar College of Engineering Department Electronics and Communication Engineering Tamil Nadu Coimbatore India PSN Engineering College Department of Information Technology Tamil Nadu Tirunelveli India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu India PSG Institute of Technology and Applied Research Department of Mathematics Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tamil Nadu India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350327533

The need for energy is at an increasing pace and it cannot be easily fulfilled by the normal energy systems which affect the environment in various ways. To overcome this problem, a hybrid power system (HPS) is used nowadays which is a combination of energy systems based on renewable and non-renewable. This study presents a wind hybrid energy system using a modified genetic algorithm mainly aimed at minimizing the implementation cost, and emission levels, and maximizing the high power supply reliability. The optimization results are improved by integrating the sizing methods to prevent load interruptions. The fitness function of the modified GA is set as power supply probability (LPSP) loss and nonrenewable energy factor (NREF). This model is confirmed by a case study and the optimal HPS results are evaluated with the conventional techniques. With the help of the battery depth of discharge, the discharge and state charge of battery are optimized. The outcome of the suggested model proves that it achieves effective LPSP output with appropriate sizing capacities by utilizing both solar and wind power. © 2024 IEEE.

关键词： Energy Optimization Hybrid Power System Logistic Chaos Loss of Power Supply Probability Modified Genetic Algorithm Wind Energy

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Analyzing the impact of conversation structure on predicting persuasive comments online

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Journal of Ambient Intelligence and Humanized Computing 2024年第11期15卷 3719-3732页

作者： Capuano, Nicola Meyer, Marco Nota, Francesco David Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 Fisciano84084 Italy Faculty of Humanities University of Hamburg Mittelweg 177 Hamburg20148 Germany Center for Higher Defence Studies Defence Analysis and Research Institute Piazza della Rovere 83 Rome00165 Italy

The topic of persuasion in online conversations has social, political and security implications;as a consequence, the problem of predicting persuasive comments in online discussions is receiving increasing attention in the literature. Following recent advancements in graph neural networks, we analyze the impact of conversation structure in predicting persuasive comments in online discussions. We evaluate the performance of artificial intelligence models receiving as input graphs constructed on the top of online conversations sourced from the "Change My View" Reddit channel. We experiment with different graph architectures and compare the performance on graph neural networks, as structure-based models, and dense neural networks as baseline models. Experiments are conducted on two tasks: (1) persuasive comment detection, aiming to predict which comments are persuasive, and (2) influence prediction, aiming to predict which users are persuasive. The experimental results show that the role of the conversation structure in predicting persuasiveness is strongly dependent on its graph representation given as input to the graph neural network. In particular, a graph structure linking only comments belonging to the same speaker in the conversation achieves the best performance in both tasks. This structure outperforms both the baseline model, which does not consider any structural information, and structures linking different speakers’ comments with each other. Specifically, the F1 score of the best performing model is 0.58, which represents an improvement of 5.45% over the baseline model (F1 score of 0.55) and 7.41% over the model linking different speakers’ comments (F1 score of 0.54). © The Author(s) 2024.

关键词： Prediction models

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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An optimal power supervising strategy for a smart home assessed based on BPMN Framework 21

An optimal power supervising strategy for a smart home asses...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and Computer engineering, STA 2022

作者： Lanouar, Leila Rekik, Mouna Bouchaala, Olfa Krichen, Lotfi Multimédia & Web Department Tunis Tunisia Electrical Engineering Department Sfax Tunisia Department of Computer Engineering and Applied Mathematics Research Sfax Tunisia Electrical Engineering Department Sfax Tunisia

ISBN: (纸本)9781665482615

For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document implements a smart home supply strategy based on endless resources to reduce the electricity bill and confirm the energy balance. In this context, a proposed supervision algorithm operates in eight cases to reach optimal energy flow between renewable generators, home battery and grid in a smart home concept is presented. The system is evaluated using the framework 'Business Process Model and Notation' (BPMN) Camunda basing on information stored in Firebase Cloud and results are presented in order to manifest the efficiency of this control strategy. © 2022 IEEE.

关键词： Energy utilization

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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