Article
Thermodynamics
Javier Valdes, Yunesky Masip Macia, Wolfgang Dorner, Luis Ramirez Camargo
Summary: Demand side management is a promising alternative for power systems with high shares of variable renewable energy sources. This study proposes a methodology to anonymize hourly electricity consumption profiles for industries and calculate their flexibility potential, finding significant flexibility potential in three case studies in Chile. The resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modeling exercises without confidentiality issues.
Article
Energy & Fuels
Mayank Jain, Mukta Jain, Tarek AlSkaif, Soumyabrata Dev
Summary: This paper discusses the application of clustering algorithms on electricity load demand profile datasets and the selection and applicability of cluster validation indices.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Construction & Building Technology
Mathieu Bourdeau, Philippe Basset, Solene Beauchene, David Da Silva, Thierry Guiot, David Werner, Elyes Nefzaoui
Summary: The study investigates clustering techniques on time series of daily electric load profiles in fourteen higher education buildings on the same campus, comparing different methods and analyzing the impact of data characteristics. Results indicate that using Euclidian distance under specific timeframes and time-steps provides the most consistent clustering results, revealing distinct patterns in electric load profiles of different building types.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Ramon Granell, Colin J. Axon, Maria Kolokotroni, David C. H. Wallom
Summary: Characterising the inter-seasonal energy performance of buildings is a useful tool for businesses to understand and detect anomalous patterns of energy demand. This study proposes a reduced feature set to represent daily electricity load profiles of retail stores and small supermarkets, and tests its accuracy in predicting and clustering measured patterns of demand.
ENERGY AND BUILDINGS
(2022)
Article
Computer Science, Artificial Intelligence
Ao Ma, Jingjing Li, Ke Lu, Lei Zhu, Heng Tao Shen
Summary: This article proposes a novel domain adaptation scheme named adversarial entropy optimization (AEO), which improves model discriminability and promotes representation transferability by minimizing and maximizing entropy. This approach is well aligned with the core idea of adversarial learning and achieves state-of-the-art performance across diverse domain adaptation tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Christy Green, Srinivas Garimella
Summary: An unsupervised graph signal processing-based algorithm is applied to monitor the real power signals from smart meters in low-resolution. The study presents load disaggregation to new datasets that are more representative of single-family houses in the U.S. Filtering improves accuracy of the disaggregation results and Level 2 electric vehicle chargers show promise in very low-resolution load disaggregation with high accuracy and low energy estimation error.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Biochemical Research Methods
Rasmus Froberg Brondum, Thomas Yssing Michaelsen, Martin Bogsted
Summary: This study addresses the issue of misclassification caused by unsupervised clustering of class labels by proposing regression calibration and misclassification simulation and extrapolation methods. The performance of these methods is demonstrated through simulated data from Gaussian mixture models, showing reduced bias and improved coverage of confidence intervals. Finally, the proposed methods are applied to actual data from a study on overall survival regression based on gene expression data from bone marrow samples of multiple myeloma patients.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Szymon Urban, Mikolaj Blaziak, Maksym Jura, Gracjan Iwanek, Agata Zdanowicz, Mateusz Guzik, Artur Borkowski, Piotr Gajewski, Jan Biegus, Agnieszka Siennicka, Maciej Pondel, Petr Berka, Piotr Ponikowski, Robert Zymlinski
Summary: This study used clustering techniques to analyze the natural phenotypic heterogeneity of AHF patients and identified six distinct phenotypes with different clinical characteristics and outcomes. The results are valuable for future trial design and personalized treatment.
Article
Energy & Fuels
Eric Pla, Mariana Jimenez Martinez
Summary: The Covid-19 pandemic and its restrictions have affected energy consumption patterns and load forecasting models. This paper proposes machine learning techniques to improve accuracy in day-ahead electrical load forecasting for residential households in Barcelona, Spain during lockdown. The Support Vector Machine (SVM) algorithm is used to predict electricity consumption for the following 24 hours.
Review
Thermodynamics
Elisa Guelpa, Vittorio Verda
Summary: This paper is the first survey on the use of demand side application in district heating networks, clarifying the terminology and implementation stages. Demand side management is considered as a great technique for district heating management, achieving benefits such as peak shaving, doubled load factor, and reduction of primary energy needs.
Article
Environmental Sciences
Jing Liu, Guisheng Liao, Jingwei Xu, Shengqi Zhu, Cao Zeng, Filbert H. Juwono
Summary: In this study, a novel unsupervised affinity propagation (AP) clustering radar detection algorithm is proposed to suppress clutter and detect targets for non-side-looking airborne radar. The algorithm utilizes selected power points and space-time adaptive processing (STAP) weight vector to design a matrix-transformation-based weighted input data. Two rounds of weighted AP clustering are performed using the similarity matrix, responsibility values, and availability values. A detection-discriminant criterion is then applied to judge target detection and suppress clutter. Simulations demonstrate the superiority of the proposed unsupervised algorithm in terms of detection probability and target-detection performance compared to conventional STAP, ADC, and JDL algorithms, as well as various CFAR algorithms based on SO, GO, and OS. The algorithm effectively handles the range dependence in clutter characteristics and non-independent identically distributed (non-IID) samples of non-side-looking radar.
Article
Computer Science, Interdisciplinary Applications
Jing Ke, Yiqing Shen, Yizhou Lu, Yi Guo, Dinggang Shen
Summary: In this paper, an Interaction Information Clustering (IIC) method is proposed to extract locally homogeneous features in mutually exclusive clusters. Trained in an unsupervised paradigm, the framework learns invariant information from multiple spatially adjacent regions for improved classification. Additionally, an adaptive Conditional Random Field (CRF) model is incorporated to detect spatially adjacent image patches of high morphological homogeneity in an offset-constraint free manner.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Chemistry, Analytical
Alimed Celecia, Karla Figueiredo, Carlos Rodriguez, Marley Vellasco, Edwin Maldonado, Marco Aurelio Silva, Anderson Rodrigues, Renata Nascimento, Carla Ourofino
Summary: Seismic interpretation is a crucial process in hydrocarbon exploration. Unsupervised machine learning models offer a novel approach to accurate interpretation without human bias. This study explores multiple methodologies based on unsupervised learning algorithms to interpret seismic data, evaluating two strategies and associating the results with well logs data to produce an interpretation that accurately represents the main seismic facies.
Review
Environmental Sciences
Christian Narvaez-Montoya, Jurgen Mahlknecht, Juan Antonio Torres-Martinez, Abrahan Mora, Guillaume Bertrand
Summary: Seawater intrusion is a major cause of groundwater contamination, affecting water access, food production, and ecosystems. This study conducts a systematic review and bibliometric analysis of 102 coastal hydrogeological studies to explore the techniques used for identifying seawater intrusion. Methods such as principal components analysis (PCA), hierarchical clustering analysis, K-means clustering, and self-organizing maps are explained and applied to a case study. Recommendations are made for data preprocessing, research opportunities, and publishing information to replicate and validate the studies.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Hongyun Cai, Fuzhi Zhang
Summary: The paper presents an unsupervised approach BS-SC for detecting shilling profiles, which does not require knowledge of attack size or labeling of candidate spammers. By analyzing user behaviors and utilizing behavior features extraction and behavior similarity matrix clustering, BS-SC effectively distinguishes between shilling profiles and genuine profiles. Experimental results show that BS-SC outperforms baseline unsupervised approaches, even when prior knowledge is provided.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Theofilos A. Papadopoulos, Zacharias G. Datsios, Andreas I. Chrysochos, Pantelis N. Mikropoulos, Grigoris K. Papagiannis
Summary: Accurate estimation of imperfect earth influence on conductor propagation in EM transient analysis is crucial, with consideration of frequency-dependent soil properties. The article presents an analysis of underground cable systems with FD soil properties and introduces guidelines for evaluating earth conduction effects.
IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY
(2021)
Article
Engineering, Electrical & Electronic
T. A. Papadopoulos, Z. G. Datsios, A. I. Chrysochos, P. N. Mikropoulos, G. K. Papagiannis
Summary: This paper investigates the wave propagation characteristics and transient performance of underground multiconductor cable systems in different arrangements, taking into account the impact of imperfect earth on the cable conductors and sheaths. Various earth formulations are considered, with comparisons made to approximate formulations. Resonance frequency analysis and transient simulations are conducted to evaluate the importance of frequency-dependent soil modeling and earth formulation for each cable arrangement.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Eleftherios O. Kontis, Iraklis S. Avgitidis, Theofilos A. Papadopoulos, Georgios A. Barzegkar-Ntovom, Grigoris K. Papagiannis
Summary: This paper evaluates the applicability and performance of multi-channel measurement-based identification approaches in the modal analysis of modern power systems incorporating active distribution networks. The algorithmic details and distinct characteristics of each method are briefly discussed, and the examined methods are used to identify the dominant inter-area modes contained in ringdown responses at different levels of a combined transmission-distribution network.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Angelos I. Nousdilis, Georgios C. Kryonidis, Eleftherios O. Kontis, Georgios C. Christoforidis, Grigoris K. Papagiannis
Summary: This paper discusses the challenges posed to the reliable operation of electrical systems by the integration of photovoltaics with low-voltage grids, and how distributed energy storage systems combined with PV systems can provide a promising solution. A new control strategy is proposed to improve voltage profile and maximize self-consumption, utilizing an exponential droop to mitigate net power peaks and an optimization procedure to adjust parameters accordingly. Validation of the control strategy is demonstrated through simulation and experimental results.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Energy & Fuels
Nikolaos A. Efkarpidis, Styliani A. Vomva, Georgios C. Christoforidis, Grigoris K. Papagiannis
Summary: This study analyzes the control strategies for residential energy systems consisting of photovoltaics, solar thermal collectors, heat pumps, and energy storage. The results demonstrate the importance of prioritizing thermal energy storage over battery energy storage in optimizing the system's performance. Additionally, the utilization efficiency of photovoltaic power plays a crucial role in reducing imported energy costs.
Article
Engineering, Electrical & Electronic
I. D. Pasiopoulou, E. O. Kontis, T. A. Papadopoulos, G. K. Papagiannis
Summary: This paper investigates the impact of load modeling on power system stability and provides a comprehensive review and simulation experiments. The study finds that both model structure and parameters have significant effects on the overall system stability.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Review
Energy & Fuels
Stelios C. Dimoulias, Eleftherios O. Kontis, Grigoris K. Papagiannis
Summary: This paper presents a review of available techniques for inertia estimation of synchronous devices and performs a comparative assessment of conventional measurement-based inertia-estimation techniques. The accuracy and performance of the examined methods are evaluated and recommendations for enhancing accuracy are proposed.
Article
Engineering, Electrical & Electronic
Amauri G. Martins-Britto, Theofilos A. Papadopoulos, Zacharias G. Datsios, Andreas Chrysochos, Grigoris K. Papagiannis
Summary: This article investigates the electromagnetic interference (EMI) caused by overhead transmission lines (OHLs) on aboveground pipelines. Most studies have focused on the investigation of induced currents and voltages at mains frequency, neglecting the impact of high-frequency excitation on the pipeline. This article presents a detailed analysis considering the frequency-dependent soil properties to accurately estimate the level of EMI.
IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY
(2022)
Article
Green & Sustainable Science & Technology
R. Agrawal, L. De Tommasi, P. Lyons, S. Zanoni, G. K. Papagiannis, C. Karakosta, A. Papapostolou, A. Durand, L. Martinez, G. Fragidis, M. Corbella, L. Sileni, L. Neusel, M. Repetto, I. Mariuzzo, T. Kakardakos, E. Llano Gueemes
Summary: This paper analyzes the challenges and opportunities for improving energy efficiency in small and medium enterprises (SMEs) by reviewing seven European projects. The projects aim to increase awareness of energy efficiency in SMEs and support a decision-making-oriented approach. The analysis shows that staff training, energy audits, corporate policy measures, and collaboration between SMEs in the same supply chain are key mechanisms to improve energy efficiency in these businesses.
Article
Engineering, Electrical & Electronic
Stelios C. Dimoulias, Eleftherios O. Kontis, Grigoris K. Papagiannis
Summary: A new method for on-line inertia estimation is proposed, based on the sliding window concept and using ambient responses. The method accurately identifies inertia constants of conventional synchronous generators, converter-interfaced units, and virtual power plants. It is also shown to perform well under noisy conditions and outperforms conventional methods.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Proceedings Paper
Energy & Fuels
Ioanna D. Pasiopoulou, Eleftherios O. Kontis, Ioannis A. Angelis, Theofilos A. Papadopoulos, Grigoris K. Papagiannis
Summary: This study investigates the reliability of exponential and polynomial static load models in modeling active DN under steady-state conditions. The results reveal the significant impact of DER penetration level on the accuracy and parameters of the models. The ZIP model exhibits higher accuracy compared to the exponential model.
2022 57TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC 2022): BIG DATA AND SMART GRIDS
(2022)
Proceedings Paper
Energy & Fuels
Ioanna D. Pasiopoulou, Styliani A. Vomva, Grigoris K. Papagiannis, Ggelos S. Bouhouras, Stavros P. Filippidis, Georgios C. Christoforidis
Summary: The European Union's goal to achieve climate neutrality by 2050 has increased interest in improving energy efficiency, especially among small and medium-sized enterprises (SMEs). The SMEmPower Efficiency project aims to address this issue by proposing a holistic approach and conducting energy checks and evaluations on real cases of SMEs.
2022 57TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC 2022): BIG DATA AND SMART GRIDS
(2022)
Proceedings Paper
Energy & Fuels
Stavros P. Filippidis, Nikolaos Kelepouris, Aggelos S. Bouhouras, Georgios C. Christoforidis, Styliani A. Vomva, Ioanna D. Pasiopoulou, Grigoris K. Papagiannis
Summary: This paper presents the methodology and implementation of the monitoring and targeting (M&T) and monitoring and verification (M&V) tools developed in the SMEmPower Efficiency Horizon 2020 project. These tools allow energy managers and experts of Small and Medium Enterprises (SMEs) to store and organize energy consumption and production data, identify increased energy readings, create scenarios of reduced energy consumption, and verify the efficiency of energy conservation measures.
2022 57TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC 2022): BIG DATA AND SMART GRIDS
(2022)
Proceedings Paper
Energy & Fuels
Denisa Stet, Levente Czumbil, Andrei Ceclan, Stefan Cirstea, Alexandru Muresan, Dacian Jurj, Claudia Muresan, Timea Farkas, Laura Darabant, Mihaela Cretu, Dan D. Micu, Grigoris K. Papagiannis
Summary: An Educational & Training Program developed by a consortium of eight European countries focuses on increasing SMEs' energy efficiency measures by using self-created financial tools. The aim is to enhance skills and competencies for energy professionals within SMEs.
2021 56TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC 2021): POWERING NET ZERO EMISSIONS
(2021)
Proceedings Paper
Energy & Fuels
Eleftherios O. Kontis, Theofilos A. Papadopoulos, Grigoris K. Papagiannis
Summary: This paper proposes a two-stage architecture for identifying power system inter- and intra-area modes, which is accurate when tested with simulated data from the Kundur power system.
2021 56TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC 2021): POWERING NET ZERO EMISSIONS
(2021)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
