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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Dimitropoulos, Nikos Milioni, Eliza Lampkowski, Marcelo Sarmas, Elissaios Kapsalis, Panagiotis Marinakis, Vangelis Doukas, Haris School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Iclei European Secretariat Freiburg im Breisgau Germany

ISBN: (纸本)9798350318067

Tackling climate change is of the utmost importance in governments' agendas. The abundance of climate related data becoming available, being published by local authorities and other sources, the digitisation of government systems under the open governance principles, and innovative data-driven services, make available resourceful and previously unseen applications that can greatly support decision-making processes. In this paper, a web-based service, to support local governments in their Sustainable Energy Climate Action Plans (SECAPs) implementation, is presented. The service incorporates open data, published from over 200 local governments, under a unified system. Data driven analytics services to classify them, compare them and recommend optimal policy actions to improve their climate change adaptation and mitigation actions are incorporated in a user-friendly application. The service aims to provide unique and tailor-made insights to local governments, monitor and follow up on their SECAPs implementation, and support their decision making processes as part of activities to tackle climate change effects they are facing. © 2023 IEEE.

关键词： Decision support systems

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Classification of energy graph-based signatures for FDI using machine learning 33

Classification of energy graph-based signatures for FDI usin...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nel, Luke Uren, Kenneth R. Van Schoor, George North-West University School of Electrical Electronic and Computer Engineering Potchefstroom South Africa North-West University Unit for Energy and Technology Systems Potchefstroom South Africa

ISBN: (纸本)9798331535162

This paper evaluates energy graph-based visualisation (EGBV) data structures for fault classification using machine learning (ML). EGBV transforms process variable (PV) data into node signature matrices (NSMs) and cost matrices. Tests with k-nearest neighbour (KNN), logistic regression, and feedforward neural networks (FNNs) show that transforming data into the NSM format achieves comparable performance to the PV format, indicating that EGBV offers similar classification characteristics. Future work could apply deep learning to the NSMs and cost matrices to leverage their structural features. © 2025 IEEE.

关键词： Contrastive Learning

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Automatic depression recognition by intelligent speech signal processing:A systematic survey

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 701-711页

作者： Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun Jiangsu Key Laboratory of Public Project Audit School of Engineering AuditNanjing Audit UniversityNanjingChina School of Information Engineering Nanjing Audit UniversityNanjingChina Key Laboratory of Modern Acoustics(MOE) School of PhysicsNanjing UniversityNanjingChina Faculty of Electrical Engineering Mathematics&Computer ScienceDelft University of TechnologyDelftThe Netherlands

Depression has become one of the most common mental illnesses in the *** better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep *** paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so ***,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and ***,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

关键词： acoustic signal processing deep learning feature extraction speech depression recognition

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Direct ink writing of gradient shear-stiffening elastomer for enhanced toughness and impact resistance

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Journal of Industrial and engineering Chemistry 2025年 149卷 593-603页

作者： Gong, Liping Zhao, Chunyu Lu, Hongda Zhang, Qingtian Chen, Zexin Yang, Junjie Yang, Jinyu Sun, Shuaishuai Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong2522 Australia CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong2522 Australia

Shear-stiffening materials have garnered significant attention due to their inherent ability to rapidly respond to changes in strain rate. However, conventional studies on these materials exhibit limited mechanical performance and insufficient structural design, hindering applications in material science and smart mechatronics. Inspired by the gradient structure of a squid beak, this study uses a direct-ink-writing (DIW) technology to develop a novel gradient shear-stiffening elastomer (GSSE). Comprehensive characterizations were conducted to optimize the printability of the shear-stiffening elastomer under various testing conditions, including rheology, compression, and tension. Additionally, the toughness and impact resistance of the elastomer were systematically investigated. It is demonstrated that the biomimetic GSSE exhibits enhanced flexibility, efficient energy dissipation, and outstanding impact resistance. Consequently, this work advances the field of shear-stiffening elastomers for diverse applications by leveraging the gradient structure design strategy to achieve superior mechanical performance under complex loading conditions. © 2025 The Author(s)

关键词： Strain rate

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector

arXiv

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arXiv 2024年

作者： Tzortzis, Alexandros Menelaos Kormpakis, Georgios Pelekis, Sotiris Michalitsi-Psarrou, Ariadni Karakolis, Evangelos Ntanos, Christos Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering Institute of Communications and Computer Systems National Technical University of Athens Greece

AI4EF (Artificial Intelligence for Energy Efficiency) is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF (Artificial Intelligence for Energy Efficiency) enables stakeholders—such as public sector representatives, energy consultants, and building owners—to model, analyse, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modelling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the platform incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. AI4EF’s compatibility with open-source identity management (Keycloak) enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an overview of AI4EF’s architecture, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI). © 2024, CC BY.

关键词： Green buildings

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Photonic Topological Spin Pump in Synthetic Frequency Dimensions

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Physical Review Letters 2024年第3期132卷 033803-033803页

作者： Joseph Suh Gyunghun Kim Hyungchul Park Shanhui Fan Namkyoo Park Sunkyu Yu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea Department of Electrical Engineering Ginzton Laboratory Stanford University Stanford California 94305 USA Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea

Reducing geometrical complexity while preserving desired wave properties is critical for proof-of-concept studies in wave physics, as evidenced by recent efforts to realize photonic synthetic dimensions, isospectrality, and hyperbolic lattices. Laughlin’s topological pump, which elucidates quantum Hall states in cylindrical geometry with a radial magnetic field and a time-varying axial magnetic flux, is a prime example of these efforts. Here we propose a two-dimensional dynamical photonic system for the topological pumping of pseudospin modes by exploiting synthetic frequency dimensions. The system provides the independent control of pseudomagnetic fields and electromotive forces achieved by the interplay between mode-dependent and mode-independent gauge fields. To address the axial open boundaries and azimuthal periodicity of the system, we define the adjusted local Chern marker with rotating azimuthal coordinates, proving the nontrivial topology of the system. We demonstrate the adiabatic pumping for crosstalk-free frequency conversion with wave front molding. Our approach allows for reproducing Laughlin’s thought experiment at room temperature with a scalable setup.

关键词： Photonics Synthetic gauge fields Topological effects in photonic systems Coupled mode theory

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2882-2896页

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science Kowloon Tsai Hong Kong Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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