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arXiv 2025年

作者： Sepanj, M. Hadi Ghojogh, Benyamin Fieguth, Paul Vision and Image Processing Group Systems Design Engineering University of Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo ON Canada

Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear correlations, they often neglect the intricate relations between samples and the nonlinear dependencies inherent in complex data. In this paper, we introduce Correlation-Dependence Self-Supervised Learning (CDSSL), a novel framework that unifies and extends existing SSL paradigms by integrating both linear correlations and nonlinear dependencies, encapsulating sample-wise and feature-wise interactions. Our approach incorporates the Hilbert-Schmidt Independence Criterion (HSIC) to robustly capture nonlinear dependencies within a Reproducing Kernel Hilbert Space, enriching representation learning. Experimental evaluations on diverse benchmarks demonstrate the efficacy of CDSSL in improving representation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Bio-Inspired Intelligence-Based Multiagent Navigation with Safety-Aware Considerations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2946-2961页

作者： Lei, Tingjun Luo, Chaomin Yang, Simon X. Carruth, Daniel W. Bi, Zhuming Mississippi State University Department of Electrical and Computer Engineering MS39762 United States Laboratory School of Engineering GuelphONN1G 2W1 Canada Mississippi State University Center for Advanced Vehicular Systems MS39762 United States Purdue University Fort Wayne Department of Civil and Mechanical Engineering Fort WayneIN46805 United States

Multiple autonomous vehicles (MAVs) enhance efficiency and task execution compared to a single vehicle. Real-world applications necessitate MAVs to safely navigate in dynamic formation along planned trajectories, while sensing, mapping, and avoiding obstacles. Addressing the need for trajectory adaptation amidst real-world scenarios, a safety-aware bio-inspired framework is proposed in this article. Our approach employs a chaotic gravitational search algorithm (CGSA) for global trajectory generation in a predefined formation. A quadtree-driven variable resolution (QVR) algorithm using monocular cameras provides occupancy grid maps (OGMs) at different resolutions. A formation control with target tracking minimizes a potential function for MAVs to follow the CGSA trajectory. Additionally, a bio-inspired neural network (BNN) local navigator coupled with dynamic moving windows (DMW) advances obstacle avoidance and refines safe trajectories using QVR and OGMs. Simulation and comparative studies validate the framework's robustness and effectiveness for MAVs. © 2020 IEEE.

关键词： Trajectories

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Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT 15

Evaluating Non-Expert Stakeholder Interaction with Artificia...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tsihrintzis, George A. Sarmas, Elissaios Marinakis, Vangelis Panagoulias, Dimitrios Tsihrintzi, Evangelia-Aikaterini Virvou, Maria University of Piraeus Department of Informatics Piraeus Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunicatios Athens Greece

ISBN: (纸本)9798350368833

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement. © 2024 IEEE.

关键词： Decision making

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Water Trees in Solid Insulating Materials

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Journal of engineering Science and Technology Review 2021年第5期14卷 216-226页

作者： Stavroulakis, Pavlos E. Danikas, Michael G. Democritus University of Thrace Department of Electrical and Computer Engineering School of Engineering Power Systems Laboratory Xanthi67100 Greece

The process of degradation in cable insulation is a major problem in cable engineering. This happens because high and medium voltage cables are often exposed to water and moisture. The consequence of water on the cable insulation system results in changes in properties and treeing. This paper studies the phenomenon of water trees in solid cable insulation and how their presence affects an installation which is powered by degraded cable via Simulink software. Alternating electric field and humidity are the main factors which lead to appearance of water trees and their effect is studied. In addition, two basic methods are analyzed which are used in the detection of water trees, namely the method of dissipation factor tanδ measurement, for which universal acceptable criteria have been established in the detection of water trees, and the method of DC leakage current measurement, for which some service providers have established criteria for assessing the condition of cables and have been universally accepted. The largest part of this paper shows the simulations which were performed in a single-phase system and a three-phase system using SIMULINK software which aims to study the differences in the behavior of a system with healthy cables and a system with degraded cables in the event of a fault, such as short circuit, lightning overvoltage, and in the event of interruption of the load supply from the network. © 2021 School of Science, IHU. All rights reserved.

关键词： Forestry

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Wavelet Convolutional Neural Network for Low-Resolution Brain MRI Images 22

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Oh, Soo Min Li, Yifan Chong, Jo Woon Massachusetts Institute of Technology Wireless Information and Network Sciences Laboratory CambridgeMA02139 United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States Texas A&m University Department of Statistics College StationTX77843 United States Texas Tech University Department of Electrical and Computer Engineering LubbockTX79409 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331520526

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training. © 2025 IEEE.

关键词： Convolutional neural networks

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Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

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Journal of Modern Power systems and Clean Energy 2022年第5期10卷 1184-1193页

作者： Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang IEEE State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.999078China Institute of Physical Internet and the School of Intelligent Systems Scienceand Engineering Jinan UniversityZhuhai 519070China

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

关键词： Wind power prediction wind generation time series analysis logistic function based classification

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Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

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Neural Computing and Applications 2025年第16期37卷 9295-9308页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

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Soft Artificial Synapse Electronics

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Research 2024年第4期2025卷 101-118页

作者： Md.Rayid Hasan Mojumder Seongchan Kim Cunjiang Yu Department of Electrical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Materials Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Materials Research Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA

Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow

关键词： dielectric utilizing gained

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Chip-scale spontaneous quasi-phase matched second harmonic generation in a micro-racetrack resonator

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Science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 66-74页

作者： Tingge Yuan Jiangwei Wu Yi'an Liu Xiongshuo Yan Haowei Jiang Hao Li Zhaokang Liang Qiang Lin Yuping Chen Xianfeng Chen State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and Astronomy.Shanghai Jiao Tong UniversityShanghai 200240China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal UniversityJinan 250358China Department of Electrical and Computer Engineering University of RochesterNew York 14627USA

In this paper,we demonstrate efficient spontaneous quasi-phase matched(SQPM)second harmonic generation(SHG)in a microracetrack resonator on X-cut thin film lithium *** approach does not involve poling,but exploits the anisotropy of the crystals to allow the phase-matching condition to be fulfilled spontaneously as the TE-polarized light circulates in a specifically designed racetrack *** experiment,normalized on-chip conversion efficiencies of 1.01×10-4/W and 0.43×10-4/W are achieved by 37th-order and 111th-order SQPM,*** configurable SQPM will benefit the application of nonlinear frequency conversion and quantum source generation in chip-scale integrated photonics compatible with standard CMOS fabrication processes.

关键词： lithium niobate on insulator quasi-phase matching second harmonic generation

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Operational strategies for a photovoltaic-thermal system integrated with an electric air-to-water heat pump 3

Operational strategies for a photovoltaic-thermal system int...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Russo, Giuseppe Olympios, Andreas V. Pandey, Chandan Buonomano, Annamaria Markides, Christos N. University of Naples Federico II Department of Industrial Engineering Naples80125 Italy Laboratory Sargent Centre for Process Systems Engineering Department of Chemical Engineering London United Kingdom University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (纸本)9798350375923

In this study, operational strategies are investigated for an air-based photovoltaic-thermal (PV-T) system integrated with a domestic air-to-water heat pump. The PV-T system is used to simultaneously generate electricity and heat ambient air, which serves as the heat source for the heat pump. By utilising a variable-speed fan to regulate airflow, the PV-T system outlet temperature is adjusted, and the focus is on how different outlet temperatures impact the PVT system electrical and thermal efficiency, heat pump performance, and overall electricity requirements for heating. The performance of the integrated system is assessed for a case study of a household application in north Italy. The optimal PV-T outlet temperature is found to be between 30 - 35 °C, resulting in only 5% less electricity production than an equivalent PV system with equal nominal electricity capacity, while reducing overall electricity required for heating by over 15% compared to PV-integrated heat pumps. © 2024 IEEE.

关键词： Photovoltaics

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