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7th International Conference on Information Science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies between Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. The study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. Through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. The core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. The practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. The model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. The paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. This research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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Cost-Effective Optimization of Sizing and Charging Profiles for PHEV Parking Lots in Smart Microgrids Using Harmony Search Algorithm

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IEEE Access 2025年 13卷 79053-79069页

作者： Kamarposhti, Mehrdad Ahmadi Shokouhandeh, Hassan Ahmed, Emad M. Zaki, Zaki A. Colak, Ilhami Barhoumi, El Manaa Eguchi, Kei Islamic Azad University Department of Electrical Engineering Jo.C. Jouybar Iran Department of Electrical Engineering Tehran*** Iran Jouf University College of Engineering Department of Electrical Engineering Al-Jouf Sakaka72388 Saudi Arabia Istinye University Faculty of Engineering and Natural Science Department of Electrical and Electronics Engineering Istanbul34010 Turkey Dhofar University College of Engineering Department of Electrical and Computer Engineering Salalah Oman Fukuoka Institute of Technology Department of Information Electronics Fukuoka811-0295 Japan

This paper presents an optimization approach for managing the charging and discharging of electric vehicles (EVs) in parking lots using the Harmony Search (HS) and Differential Evolution (DE) algorithms. The study is conducted on a standard IEEE 33-bus grid considering three EV penetration levels: 11.3%, 35%, and 45%. The objective is to minimize operational costs while improving grid performance. Simulation results indicate that increasing EV penetration slightly raises overall expenses due to the higher cost of vehicle charging compared to the revenue from discharging. However, EV participation significantly reduces ohmic losses and improves the grid load profile. The proposed HS algorithm outperforms the DE algorithm by achieving lower microgrid costs and better convergence efficiency. Specifically, HS reduces energy losses by up to 40%, demonstrating its effectiveness in optimizing energy management for microgrids with high EV integration. © 2013 IEEE.

关键词： Electric buses

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Improving Power Response of Single-Controllable VSG-Based Active Distribution Network

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

Controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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Psychological Impact of Internet Blackouts: A Case Study With Machine Learning-Based Stress Analysis

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IEEE Access 2025年 13卷 83505-83527页

作者： Islam, Mohammad Ariful Islam, Tahidul Hossain, Gahangir Mofazzal Hossain, M. The University of Melbourne Department of Computing and Information Systems MelbourneVIC3010 Australia Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka1000 Bangladesh University of North Texas Department of Data Science DentonTX76205 United States Southeast University Department of Electrical and Electronic Engineering Dhaka1208 Bangladesh

Access to the internet has become a vital part of modern life, especially for communication and essential services. However, during politically sensitive times, internet blackouts can disrupt daily routines, leading to significant psychological impacts. This study explores the mental health effects of the internet shutdown imposed during the Bangladesh Quota Movement in July 2024, when the government cut off access to control information flow. The blackout hindered communication, financial transactions, and critical services, amplifying stress, feelings of isolation, and emotional distress. A survey of 2,085 participants was conducted to assess the behavioral, emotional, and psychological consequences, particularly in academic, work, and social settings. Stress levels among respondents varied from minimal to extreme, reflecting widespread mental distress. To classify these stress levels, machine learning models;Decision Tree (DT), Random Forest (RF), Bernoulli Naive Bayes (BNB), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) were applied. The SVM model outperformed others, achieving high precision (99.33%), recall (99.00%), F1-score (99.33%), and accuracy (99.49%). This study underscores the urgent need for mental health support during such crises, particularly in low- and middle-income countries like Bangladesh, where mental health care is often neglected. These findings are aligned with Sustainable Development Goal (SDG 3), "Good Health and Well-Being," stressing the importance of mental health interventions in fostering resilience, well-being, and social stability during crises. © 2013 IEEE.

关键词： Support vector machines

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DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4194-4205页

作者： Jafari, Bahareh Hasna, Mazen Pishro-Nik, Hossein Zorba, Nizar Khattab, Tamer Saeedi, Hamid Qatar University Department of Electrical Engineering Doha Qatar University of Massachusetts Department of Electrical and Computer Engineering AmherstMA01003 United States University of Doha for Science and Technology College of Engineering and Technology Doha Qatar

Coverage holes pose critical challenges to reliability of wireless networks and their quality of service (QoS) and therefore should be avoided in the coverage design. In this paper, we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations, and we propose specific UAV path planning. A point is said to be in a coverage hole if the coverage probability for that point is below a certain threshold, e.g., 90%. This definition is more suitable for applications such as surveillance or sensor networks. In this paper, we target applications such as wireless communications for which QoS requirement allow only for short time disconnections, i.e., minimal outage time. As such, in addition to avoiding coverage holes, we should also make the outage time as small as possible. By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. We show that for a typical point on the cell, there is a trade-off between minimizing the maximum outage time length and consumed mechanical energy. Our observations indicate that such a trade-off is more pronounced for spiral trajectories compared to oval trajectories, but both of them are useful depending on the QoS and energy constraints imposed by the system. © 2020 IEEE.

关键词： Energy utilization

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Optimization of Wireless Power Transfer Waveforms and In-Vivo Receivers for Implantable Medical Devices 14

Optimization of Wireless Power Transfer Waveforms and In-Viv...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Liu, Hanbing Zan, Xin University of Maryland Department of Electrical and Computer Engineering College Park United States

ISBN: (纸本)9798331516116

Providing high power levels of several watts, achieving high-efficiency in-vivo receivers, and reaching implantable size simultaneously within the specific absorption rate (SAR) and heat dissipation limits are challenging for wireless power transfer (WPT) in implantable medical devices (IMDs). To overcome these challenges, waveforms and receivers together with operating frequencies should be optimized. In this paper, we propose a solution that optimizes WPT waveforms with different peak-to-average power ratios for different loads, controlled by duty cycles, quality factors, and phase shifts between the transmitter and receiver. With a diameter of 65 mm, three-turn, four-layer PCB coils operating at 6.78 MHz, and a coupling coefficient of 0.255, the optimized system can achieve a maximum power of 5.9 W at a load of 20 Ω, and 6.4 W at a load of 25 Ω under SAR and heat dissipation limits, reaching the end-to-end efficiency of 76.4% and 77.4%, respectively. Compared to conventional operating conditions, the optimized system increases the power level by 19.6% and 7.0% at load resistances of 20 Ω and 25 Ω, respectively. © 2025 IEEE.

关键词： Wireless power transfer

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Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery

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IEEE Transactions on Biomedical engineering 2025年第6期72卷 1886-1896页

作者： Xu, Jialang Wang, Jiacheng Yu, Lequan Stoyanov, Danail Jin, Yueming Mazomenos, Evangelos B. University College London UCL Hawkes Institute The Department of Medical Physics and Biomedical Engineering London United Kingdom Xiamen University Department of Computer Science School of Informatics Xiamen China The University of Hong Kong Department of Statistics and Actuarial Science Hong Kong National University of Singapore Department of Biomedical Engineering The Department of Electrical and Computer Engineering Singapore

Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait priors for SIS, incorporating global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE), to boost SIS performance in each site. GPD represents the first attempt at head- wise assignment for multi-headed self-attention personalization. To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer- wise aggregation solutions via hypernetworks for each site's personalized parameters. The mutual shape information of instruments is maintained and shared via SGE, which enhances the cross-style shape consistency on the image level and computes the shape-similarity contribution of each site on the prediction level for updating the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51% Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. © 1964-2012 IEEE.

关键词： Robotic surgery

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Detecting Concept Shifts Under Different Levels of Self-awareness on Emotion Labeling 27th

Detecting Concept Shifts Under Different Levels of Self-awa...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Choi, HyoSeon Choi, Dahoon Kaongoen, Netiwit Kim, Byung Hyung Department of Electrical and Computer Engineering Incheon Korea Republic of Department of Computer Engineering Incheon Korea Republic of Department of Artificial Intelligence Inha University Incheon Korea Republic of School of Computing KAIST Daejeon Korea Republic of

ISBN: (纸本)9783031782008

Generalizing deep learning for all requires individual self-assessment. However, the quality of ground-truth labels depends on the annotators’ self-awareness. Real-world datasets inevitably experience the Concept Shift problem. Recent advances in Out-of-distribution (OOD) detection have received much attention due to its ability to alleviate distribution shift problems by distinguishing between anomalous and in-distribution(ID) data samples. Existing approaches underlie pre-trained ID models learned with class-balanced data. However, this assumption makes the methods incapable when the ID models are trained with inter- and intra-class variance depending on user characteristics, such as gender, culture, and genetics. We present an OOD detection framework. Our system builds a generalized ID model by extracting high-quality data from high-dimensional neural activities considering individuals’ cognitive and perceptional ability to evaluate self-assessments. The proposed system detects and removes abnormal pairs of data and labels to enhance model performance by considering the maximum softmax probability approach. Experimental results on public EEG datasets in emotion recognition demonstrate the superiority of our method despite the non-stationary nature of EEG signals. The codes are available at https://***/affctivai/coglier. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Emotion Recognition

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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