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检索条件"机构=Dept. of Computer Engineering and Applied Mathematics"

共 4074 条记录，以下是11-20 订阅

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Situation Identification using Context Space Theory and Decision Tree

Situation Identification using Context Space Theory and Deci...

引用

Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2023

作者： D’aniello, Giuseppe Gaeta, Matteo Dept. of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano84084 Italy

Context Space Theory (CST) is a geometrical approach used to represent contexts and situations in situation-aware computing applications. In this theory, situations are represented in a multidimensional space, where each dimension corresponds to an interesting feature of the context. The primary advantage of CST lies in its capacity to effortlessly integrate multiple factors, creating a meaningful representation of situations that can be observed and manipulated by experts. Moreover, it empowers experts to customize the situation space to align with their knowledge and understanding of the situation. However, when applied to real-world scenarios, modeling complex situation spaces can be time-consuming and labor-intensive. This is due to the manual effort required in defining contribution functions for each context feature, as well as determining weights and thresholds to identify the situation space. To address this challenge, the paper proposes a hybrid approach that combines decision trees with the CST, thereby expediting the definition of situation spaces. Decision trees are employed to automatically identify an initial definition of the contribution functions and weights, reducing the workload on human experts. To demonstrate the efficacy of this approach, the paper showcases a case study focused on the management of the Covid-19 pandemic situation in Italy. © 2024, EasyChair. All rights reserved.

关键词： Decision trees

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Enhancing Privacy Through Time Aggregation of Load Profiles in Energy Management 8

Enhancing Privacy Through Time Aggregation of Load Profiles ...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Varenhorst, Ivo A.M. Hoogsteen, Gerwin Gerards, Marco E.T. Hurink, Johann L. dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

Demand side management (DSM) applications rely on the exchange of load profiles to effectively manage the operation of energy systems. However, sharing detailed energy profile data raises substantial privacy concerns, such as potential misuse of personal information. To mitigate these concerns, we investigate the potential of time aggregation (TA), which involves merging multiple samples of a profile into a single value representing multiple intervals. TA reduces data exchange and computational requirements in energy management and helps preserve user privacy by reducing granularity of user data. We show that, for an effective implementation of TA in energy management, it is important to make the right choice of TA method. We compare and evaluate seven different TA methods. Furthermore, we perform TA across various time frames using an optimization based DSM approach. Our findings reveal that if we aggregate load profiles from 15 minutes to 4 hours, we obtain both enhanced privacy and a 21% decrease in the required number of iterations with the investigated DSM method, albeit at the cost of a 15% decrease in objective value performance. Based on this, we conclude that depending on the application needs, TA with a carefully selected aggregation method has the potential to bring value to energy management, even when aggregating to a considerable extent. ©2024 IEEE.

关键词： Energy management

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A Comparative Analysis of Early Departure Buttons in Coordinated Control of EV Charging

A Comparative Analysis of Early Departure Buttons in Coordin...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Ozceylan, Baver Hoogsteen, Gerwin Hurink, Johann L. University of Twente Dept. of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9789531842976

The increasing adoption of electric vehicles (EVs) necessitates the efficient management of large EV parking facilities to prevent them from exceeding grid capacity and to improve the overall user experience. This paper introduces a data-driven approach for coordinated control of EV charging in an office parking facility, integrating Early Departure Buttons (EDB) into the system. These buttons provide a binary option for users to indicate an earlier departure than a predefined time. We employ the EDB data to improve departure time estimations and to address issues where EVs receive no or only very low energy. We utilize three datasets originating from different geographical locations. One dataset displays a user pattern where users leave shortly after completing their charging, unlike the other datasets which follow typical working hours. Our simulations show that the unique user pattern significantly increases fairness among users, and integrating EDBs improves fairness for the other datasets to levels similar to those of quick station turnover. © 2024 IEEE.

关键词： electric vehicle energy management smart grid user information

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Fair and Efficient Congestion Management for Low Voltage Distribution Networks 8

Fair and Efficient Congestion Management for Low Voltage Dis...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Vadavathi, Aswin Ramesh Hoogsteen, Gerwin Hurink, Johann dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed. ©2024 IEEE.

关键词： Electric power distribution

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Kernel Regression Method for Stochastic Real-time State Estimation in Power Grid 4

Kernel Regression Method for Stochastic Real-time State Esti...

引用

4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Barati, Masoud Ensaf, Mohammad Hosseini, Bamdad University of Pittsburgh Dept. Electrical and Computer Engineering Pittsburgh United States University of Washington Dept. of Applied Mathematics Seattle United States

ISBN: (纸本)9798350335408

The paper presents a physics-informed Gaussian/kernel process regression method for real-time state estimation and forecasting of the phase angle, angular speed, and mechanical wind power in a three-generator power grid system using sparse measurements. The method treats unknown terms in the swing equations, such as mechanical wind power, as random processes, which transforms these equations into stochastic differential equations. The proposed method accurately forecasts and estimates both observed and unobserved states, providing a forecast comparable to the standard data-driven GP/kernel for observed states. The method also demonstrates improved accuracy with increased observation frequency and reduced measurement errors. © 2023 IEEE.

关键词： State estimation

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Revolutionizing Residential EV Charging Through IoT-Enabled Smart Energy Metering for Sustainability 6

Revolutionizing Residential EV Charging Through IoT-Enabled ...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sivakumar, N. Charles Raja, S. Meenakshi Devi, M. Saraswathi Meena, R. Dept. of EEE Thiagarajar College of Engineering Tamilnadu Madurai India Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331522667

The rapid growth in Electric Vehicle (EV) adoption poses substantial challenges to residential energy infrastructures, especially in terms of the strain it places on local grids during charging. This paper proposes an IoT-based Smart Energy Meter System (SEM) designed to efficiently manage EV charging in residential environments. The system integrates IoT technology with advanced energy metering features, enabling real-time monitoring and control of charging operations. The system is composed of intelligent energy meters for real-time monitoring, an IoT-enabled hub for data collection and processing, and an intuitive consumers interface for customizing and managing EV charging schedules. By continuously monitoring energy consumption and charging demands, the system optimizes charging schedules to reduce peak loads, lower energy costs, and enhance grid stability. It provides residents with insights into their energy usage and charging patterns, empowering them to make informed decisions to reduce their carbon footprint. Furthermore, the system seamlessly integrates with renewable energy sources and energy storage solutions, promoting clean energy usage for EV charging. Through cloud-based analytics and predictive maintenance, it ensures reliable and uninterrupted charging services. The proposed IoT-based SEM system offers a scalable and sustainable solution for managing EV charging in residential settings, supporting electric mobility while improving energy efficiency and promoting environmental sustainability. © 2025 IEEE.

关键词： Predictive maintenance

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series Data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

引用

3rd International Conference on computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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Review of Teeth Image Segmentation 11

Review of Teeth Image Segmentation

引用

11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Grujev, Milan Ilic, Milos Milosavljevic, Aleksandar Ilic, Aleksandra Stoinev Faculty of Electronic engineering Dept. Computer Sciences Nis Serbia Toplica Academy of Applied Studies Dept. Computer Sciences Prokuplje Serbia

ISBN: (纸本)9798350386998

Tooth segmentation is a vital task in medical image analysis aimed at extracting and delineating tooth structures from computed tomography scans. This process plays a crucial role in various clinical applications, including dental diagnostics, treatment planning, and surgical interventions. Through the years, different segmentation approaches are applied on tooth scans that comes from different CT machines. Different approaches archive different segmentation results. In particular most of them provided promising results. The goal of this paper is to present current state of the art in this field. Our research mainly was focused on research results from last couple of years. In this way this paper represents good start for all researches which plan is to contribute in the field of tooth segmentation. © 2024 IEEE.

关键词： Image segmentation

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Exploiting Secure Multi-Hop Transmissions with NOMA Networks: Performance Analysis 6

Exploiting Secure Multi-Hop Transmissions with NOMA Networks...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Pramitarini, Yushintia Yoga Perdana, Ridho Hendra Shim, Kyusung An, Beongku Graduate School Hongik University Dept. of Software and Communications Engineering Korea Republic of School of Computer Engineering and Applied Mathematics Hankyong National University Korea Republic of Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350344349

In this paper, we analyze the secrecy performance of secure multi-hop transmission for the NOMA system. More specifically, the multi-hop transmission can extend the coverage of wireless transmission. However, according to the principle of the wireless medium, an eavesdropper can overhear the legitimate users' transmission, which leads to serious security issues. In order to analyze the relationship between network parameters and secrecy outage probability, we derive exact closed-form expressions for the secrecy outage probability (SOP) of cell center and cell edge users, respectively. The numerical results show that the simulation and analysis results are tightly matched. Additionally, we investigate the impact of the number of hops and the distance on the secrecy performance. © 2024 IEEE.

关键词： Transmissions

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