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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island

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Global Energy Interconnection 2025年第2期8卷 225-239页

作者： Aripriharta Adiguna, Satria Afandi, Arif N. Bagaskoro, Muhammad Cahyo Department of Electrical Engineering and Informatics Faculty of Engineering State University of Malang Malang65145 Indonesia Indonesia

This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo, utilizing the region's significant solar energy potential and high irradiation levels. This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to maximize energy output, reduce levelised energy costs, and maximally reduce carbon emissions. Results show Tarakan as the most optimal location, generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO2e, while Samarinda is the least optimal location. Economically, electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value (NPV) with a payback period (PP) of 9–12 years, while a tariff of IDR 1,500/kWh is considered unfavorable. The findings demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project's financial viability, with a positive NPV and reasonable PP. Implementing renewable energy systems in Kalimantan Island can improve energy efficiency and significantly reduce carbon emissions, supporting environmental sustainability goals. © 2025 Global Energy Interconnection Group Co. Ltd.

关键词： Zero-carbon

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ALCS-PP: Adaptive Latency Control Scheme for Packet Pacing in Self-Clocked Congestion Control

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IEEE Access 2025年 13卷 77080-77123页

作者： Dhia Zubaydi, Haider Samir Jagmagji, Ahmed Molnár, Sandor Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Artificial Intelligence Budapest1111 Hungary

Effective congestion control is crucial for maintaining network stability, especially in dynamic environments. Self-Clocked Rate Adaptation for Multimedia (SCReAM) is a hybrid loss and delay-based algorithm for real-time multimedia transmission. It encounters challenges with static pacing and low latency under fluctuating round-trip time (RTT) and jitter, resulting in ineffective bandwidth utilization. To address these issues, this paper proposes an Adaptive Latency Control Scheme for Packet Pacing (ALCS-PP), a novel congestion control scheme featuring dynamic rate adjustment and adaptive packet pacing. The evaluation process is divided into three phases. The first phase involves initial performance evaluation using default parameters to establish a baseline comparison. The next phase explores parameter-specific experiments, where individual parameters such as target latency, jitter thresholds, and rate scaling factors are tuned to assess their impact on performance. The last phase investigates multi-parameter combinations to optimize the overall system configuration under various network conditions. The proposed scheme effectively reduced network queue delay (NQD) by up to 21.91% and smoothed RTT (sRTT) by 13.44%, ensuring better responsiveness in real-time multimedia transmission. These improvements are achieved through adaptive tuning using dynamic soft clamping and gradual corrections, minimizing performance oscillations. Additionally, the scheme maintains metrics such as network throughput (NThput), congestion window (CWND), and bytes in flight (BiF) close to SCReAM’s baseline values, ensuring stable performance. The experimental evaluation highlights the algorithm’s capability to balance delay reduction and network stability, making it suitable for delay-sensitive applications such as 5G, Internet of Things (IoT), and real-time multimedia streaming. © 2013 IEEE.

关键词： Adaptive control systems

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A Self-adapting Pixel Antenna - Substrate Lens System for Infrared Frequencies

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica electrical engineering and Computer Science 2025年第1期69卷 14-25页

作者： Shubbar, Mustafa Rakos, Balázs Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

In this work, we propose a concentrator lens - antenna arrangement, potentially suitable for infrared sensing and energy harvesting rectenna systems. The structure consists of a pixel antenna, self-adapting for the direction of the incident infrared radiation, and a concentrator lens, both optimized for the mid-infrared spectrum. The silicon substrate lens is situated above the pixel antenna, enabling the concentration of the incident light on the antenna;silicon was chosen due to its transparency in the IR spectrum. We examine how various parameters of the lens, in conjunction with the different states of the pixel antenna, affects the performance of the system. The simulations show that the gain of the arrangement increases considerably in correlation with the radius of the lens. The results suggest that the energy conversion efficiency of infrared rectenna systems can be enhanced by several orders of magnitude with the utilization of the proposed arrangement. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Rectennas

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Selection and evaluation of a set of attributes appropriate for detection of antisocial behaviour in online media

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Multimedia Tools and Applications 2025年 1-39页

作者： Machová, Kristína Balara, Viliam Mach, Marian Kožík, Štefan Faculty of Electrical Engineering and Informatics Department of Cybernetics and Artificial Intelligence Technical University of Košice Letná 9 Košice042 00 Slovakia

Nowadays the world of modern technologies brings new ways of communication and interaction between people. Online communication becomes faster and more convenient, but it also enables an exchange of more dangerous information. Antisocial behaviour in online web discussions becomes one of the most serious problems. This paper is focused on the analysis and identification of the most typical attributes of antisocial behaviour in the online space. Our research attempts to distinguish the most characteristic features of suspicious contributors to identify attributes that can define antisocial behaviour in the best way. The main objective is to evaluate the success of these attributes in the automatic detection of the suspicious contributors—trolls using classification methods of machine learning such as naïve Bayes, decision trees, random forest, logistic regression, and support vector machine. The methods were selected from the point of view of evaluation of suitability of individual attributes and evaluation of selected sets of attributes. The results of test of models learned using mentioned methods are discussed from this point of view and lead to a selection of a small set of attributes from all considered attributes. © The Author(s) 2025.

关键词： Adversarial machine learning

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Observer-based Implementation of Discrete Gabor Transform

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica electrical engineering and Computer Science 2025年第1期69卷 79-88页

作者： Ország, Bence Sujbert, László Department of Artificial Intelligence and Systems Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

The most commonly used technique in time-frequency analysis is the short-time Fourier transform. It can be used to determine the spectral components as they change over time by computing the Fourier transform of a windowed segment of the signal. A fundamental constraint of this method is that the frequency resolution of the representation in the time-frequency domain will be linear by design. The frequency adaptive nonstationary discrete Gabor transform offers an alternative that does not have this limitation. A Luenberger observer is capable of the implementation of the short-time Fourier transform, and its numerical advantages are already established based on the work of Hostetter and Péceli. Here, we introduce the family of discrete Gabor transforms and its properties along with a constructive method to define such transforms. Furthermore, we show that they are realizable by Luenberger observers which are capable of the error-free reconstruction of the observed signal in finite steps. The dead-beat property is derived for the state variables as well which estimate the transform of the windowed signal. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Frequency domain analysis

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Perspectives of semantic modeling in categories

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Journal of King Saud University - Computer and Information Sciences 2025年第3期37卷 1-28页

作者： Steingartner, William Faculty of Electrical Engineering and Informatics Technical University of Košice Letná 1/9 Košice 04200 Slovakia

This paper explores the domain of semantic modeling, emphasizing the thoughtful influence of category theory on imperative programming. We aim to present a formal semantics model by seamlessly integrating categorical concepts. A core focus of this study is clarifying relationships between objects representing the memory states as denotation functions. Extending our exploration, we examine categorical modeling within the context of structural operational semantics. An important aspect is the purposeful use of coalgebras to express program behavior, especially concerning memory configurations. Our methodology involves modeling denotation within categories, considering input/output values and state dynamics. This synthesis leads to a comprehensive and robust semantic framework. Notably, our research concludes with the development of a category tailored for memory configurations, effectively mirroring program transitions. Execution dynamics finds representation through a customized polynomial endofunctor, accommodating a range of transition types. Importantly, our study contributes mathematical rigor to the domain of program semantics, offering practical applications across various IT challenges, including recursive calculations, function relationships, and component program systems. Significantly, this study underscores the enduring relevance of category theory in the dynamic landscape of semantic modeling, offering insights into potential practical applications within programming and related fields. © The Author(s) 2025.

关键词： Categorical modeling Category theory Denotational semantics Imperative programming Semantic modeling System engineering

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VASIR: Open-Source Android Application for Visual Analog Scale Intelligibility Rating of Dysarthric Speech

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IEEE Access 2025年 13卷 36492-36501页

作者： Kryze, Petr Illner, Vojtech Kouba, Tomas Novotny, Michal Simek, Michal Svihlik, Jan Tykalova, Tereza Rusz, Jan Czech Technical University in Prague Faculty of Electrical Engineering Department of Circuit Theory Prague166 27 Czech Republic University of Chemistry and Technology Faculty of Chemical Engineering Department of Mathematics Informatics and Cybernetics Prague166 28 Czech Republic

We present an open-source application for mobile devices called VASIR that provides a reliable and sensitive tool for accessible intelligibility rating using visual analog scales. VASIR is validated by examining the intelligibility in 23 Parkinson’s disease (PD) patients treated with subthalamic nucleus deep brain stimulation (DBS) in on and off stimulation states, and 23 healthy controls. Two rater groups of naive and expert listeners performed intelligibility ratings using the provided application. An excellent intraclass correlation was found in both the naive (ICC = 0.97) and expert (ICC = 0.95) groups. Both listener groups were able to confidently differentiate between the controls and DBS groups (p © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://***/licenses/by/4.0/

关键词： Neurodegenerative diseases

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A Siamese-based Approach to Improve Parkinson’s Disease Detection and Severity Prediction from Speech Using X-Vector Embedding

Infocommunications Journal

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Infocommunications Journal 2025年第1期17卷 76-81页

作者： Jenei, Attila Zoltán Ágoston, Réka Valálik, István Department of Telecommunications and Artificial Intelligence Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Neurosurgery St. John’s Hospital Budapest Hungary

—Parkinson’s disease is incurable and is considered one of the most common neurological diseases. It is a progressive disease, which highlights the importance of early detection. Machine learning-based diagnostic support is desirable since the diagnosis is based on history, visual inspection, and drug tests. Speech is presumed to be one of the promising biomarkers that can predict the state of the disease. Combining speech data with deep learning feature extraction in Siamese-based architecture may improve the detection compared with direct regression with acoustic and prosodic features. Read text-based speech samples were acquired from 98 patients with Parkinson’s disease and 107 healthy participants. Feature vectors were extracted with pre- trained x-vector embedding and were used directly with a support vector regressor in a nested cross-validation setup (baseline approach). Furthermore, pairs were allocated, and difference vectors were calculated. These difference vectors were then used to train support vector regressor models in nested cross-validation (Siamese-based approach). Severity predictions and classification were performed with the outcomes. The Siamese-based setup outperformed the baseline approach both in regression and classification metrics. The relative improvement in root mean square error is 14.4%, and the Pearson correlation is 12.5% at best. After the classification, the relative improvement is 6.0% in sensitivity, 3.0% in specificity, and 4.5% in accuracy. Furthermore, comparing the test sample to not only one but multiple others decreases the average standard deviation of the predicted severity by 16.5% in relative value. Changing only the architecture of the traditional examination setup to a Siamese-based approach may increase the performance of the models. © 2025 Scientific Association for Infocommunications. All rights reserved.

关键词： Support vector regression

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Ambient Intelligence to Detect Misuse of Electricity Consumption Based on IoT Using Blockchain Technology

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IEEE Access 2025年 13卷 80371-80394页

作者： Hadi, Soiful Syafei, Wahyul Amien Wibowo, Adi Information System School of Postgraduate Diponegoro University Semarang Indonesia Department of Electrical Engineering Diponegoro University Semarang Indonesia Departement of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Electricity abuse and energy inefficiencies are still open issues in smart grid systems, demanding high-performance anomaly detection mechanisms. In this paper, we propose an IoT-enabled electricity monitoring system that combines machine learning (LightGBM) and blockchain (Polygon network) for real-time anomaly detection, secure data storage, and transparent energy tracking. IoT smart meters are utilized to monitor real-time electricity usage data, whereas LightGBM classifies anomalies efficiently with high precision. The key innovation is the use of blockchain for decentralized anomaly logging with tamper-proof records and enhanced trustworthiness. Unlike centralized approaches, Polygon blockchain immutably stores electricity data, giving verifiable anomaly logs. Using an interactive IoT dashboard and real-time notifications, users can monitor consumption patterns and respond to anomalies efficiently. The proposed system achieves 96.77% accuracy, an AUC-ROC of 0.99, and an F1-score of 96.69%, outperforming CNN-LSTM and CNN-XGBoost in both accuracy and use of computational resources. Despite these advantages, blockchain transaction costs (0.001996 POL per transaction) and IoT-wallet integration complexity pose challenges. Future work will explore cost-reducing blockchain optimizations such as meta-transactions and relayers, along with model enhancements for IoT scalability. This approach provides a low-cost, scalable, and secure solution for smart energy management, enhancing grid security and sustainability. © 2013 IEEE.

关键词： Smart contract

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Advanced Automated Consolidometer for Enhanced Soil Consolidation Testing 1

Advanced Automated Consolidometer for Enhanced Soil Consolid...

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1st engineering and Science International Conference 2024, ESIC 2024

作者： Suli, Adinda Sabrina Fadhl'Abbas, Muhammad Nawir, Hasbullah Mahayana, Dimitri School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Bandung Indonesia

Geotechnical analysis of clay soils traditionally relies on incremental load consolidation tests to determine key parameters for estimating soil settlement, which often require extensive testing times of up to 288 hours. This study presents an advanced automated consolidometer system aimed at enhancing both the accuracy and efficiency of soil consolidation testing. The system integrates microcontroller technology and sensors for real-time measurements of axial stress, displacement, and pore water pressure. Key components include a load cell for axial pressure measurement, a dial indicator for monitoring displacement, and a pressure transmitter for pore water pressure assessment, all managed by a stepper motor controlled through an Arduino microcontroller. Data acquisition and processing are facilitated using LabVIEW, enabling seamless and efficient data management. The system utilizes On-Off (Bang-Bang) control logic for precise load application during soil consolidation tests. This straightforward control strategy ensures consistent performance by switching the motor on and off as needed, maintaining loads within specified thresholds. Additionally, Linear Quadratic Regulator (LQR) analysis is employed to comprehensively evaluate the system's stability, transient response, and control dynamics, offering a theoretical framework for optimal control performance. The modified CRS (Constant Rate of Strain) method integrated into the system reduces testing time significantly to just 8.5 hours, achieving a 97% reduction compared to the traditional incremental load method, while maintaining high accuracy in test results. By leveraging both practical and analytical control strategies, the automated consolidometer achieves remarkable efficiency and precision, making it highly applicable for modern geotechnical engineering needs. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Soil testing

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