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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Zhang, Xiaotong Wang, Kun Li, Zhenjiang Zhang, Jin City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Southern University of Science and Technology Res. Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350362015

This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation. © 2024 IEEE.

关键词： Millimeter waves

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Analysis and optimization of clinical pathways using timed continuous Petri nets. 28

Analysis and optimization of clinical pathways using timed c...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Arzola, César Mahulea, Cristian Júlvez, Jorge Universidad de Zaragoza Dept. of Computer Science and Systems Engineering Spain Universidad de Zaragoza Aragón Institute for Engineering Research Spain

ISBN: (纸本)9798350339918

This paper presents a novel approach to analyze and optimize healthcare systems based on clinical pathways, using timed continuous Petri nets (TCPNs) under infinite server semantics. TCPNs allow for an efficient continuous-time analysis of the patient flow and resource utilization dynamics of these types of systems. We demonstrate the feasibility and effectiveness of our method through a case study of a hip fracture pathway at the "Lozano Blesa"University Clinical Hospital, in Zaragoza Spain. Additionally, we propose a method to optimize the behavior of the modeled system by taking a control theory approach, which enables the system to achieve maximum throughput more efficiently. Finally, we provide simulation results to demonstrate the effectiveness of the proposed controller in practical settings. © 2023 IEEE.

关键词： Nonlinear analysis

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Exploring the Influence of Digital Media on Secondary School Students' Daily Lives 7

Exploring the Influence of Digital Media on Secondary School...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Gogh, Elod Kovari, Attila Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The focus of this study is to investigate how digital media use affects the daily activities, emotional well-being, and general lifestyle of high school students in relation to education amidst concerns about a 'new terrain or venue' for learning in an era moving full swing towards digitization. The study, conducted with students from grades 9-11, seeks to identify overall trends and associations involving digital media interactions: considering both byproducts of using new technology as an educational tool- in addition discursive aspects allowing for different forms of distraction. In summary, the results showed that digital media use is common and often regarded as educationally beneficial or a positive form of relaxation;however, daily activities are it affected by substantial adverse impacts on everyday life and time management. Suggestions focus on supporting healthy digital media habits, including content-wise engagement and time management tools. © 2024 IEEE.

关键词： Daily Activities Digital Media Digital Media Impact

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Network-Agnostic Voltage-to-Power Sensitivities of a Real-Time Reduced Active Distribution Network

Network-Agnostic Voltage-to-Power Sensitivities of a Real-Ti...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350327748

The advent of distribution-level phasor measurement units (D-PMUs) has enhanced the observability of active distribution networks (ADNs) and improved their energy management and control. Recently, various ADN control strategies that require detailed network parameter information have been proposed based on the voltage-to-power sensitivities of the ADN nodes. This paper proposes a new realistic approach to determining nodal voltage-to-power sensitivities of a synchrophasor measurement-based reduced ADN model. The real-time synchrophasor data dynamically updates the parameters of the reduced equivalent model to follow the time-varying operating conditions of the ADN accurately. The voltage-to-power sensitivities of the retained observable nodes are derived from the power flow Jacobian matrix using reduced equivalent model parameters. Thus, the proposed approach is agnostic about detailed line impedance data of the complex ADN. Further, the computational complexity is significantly decreased with the reduced number of nodes in the equivalent ADN model. The proposed approach has been tested on modified IEEE-33 bus benchmark system with high penetration of photovoltaic inverters using a DIgSILENT PowerFactory MATLAB co-simulation platform. © 2023 IEEE.

关键词： Electric power distribution

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Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics

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Remote Sensing in Earth systems sciences 2025年第1期8卷 200-212页

作者： Krishnamoorthy, R. Tanaka, Kazuaki Begum, M. Amina Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of ECE Sri Venkateswaraa College of Technology Tamilnadu Chennai India

The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly through satellite imagery, has revolutionized the ability to collect vast amounts of environmental data over large spatial and temporal scales. However, the complexity of these datasets, which often include intricate spatial patterns and long-term temporal trends, requires advanced machine learning models capable of handling both dimensions effectively. In this study, we present a Hybrid GIS-Transformer Network for analyzing oceanographic and coastal dynamics using remote sensing data. The proposed model integrates the spatial feature extraction capabilities of the GIS network with the temporal analysis strengths of the transformer model. This approach enables a comprehensive understanding of both spatial and temporal patterns in high-resolution satellite imagery, allowing for more accurate detection and prediction of environmental changes. Our methodology was tested across multiple tasks, including coastal erosion detection, sea surface temperature (SST) prediction, wave height prediction, and ocean currents detection. The Hybrid GIS-Transformer model consistently outperformed existing models such as CNN, ResNet50, MobileNet, EfficientNet, and LSTM. For coastal erosion detection, the hybrid model achieved an accuracy of 96.12%, while for SST prediction, the accuracy was 95.43%. In wave height prediction, the model achieved an accuracy of 93.02%, and for ocean currents detection, an accuracy of 91.88% was recorded. These results demonstrate the model’s superiority in capturing both fine spatial details and long-term temporal dynamics compared with individual models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Coastal dynamics Deep learning Environmental monitoring Hybrid GIS-Transformer Machine learning Oceanographic analysis Remote sensing Sea surface temperature prediction Spatial feature extraction Temporal analysis Wave height prediction

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Cyber Attack Detection with Encrypted Network Connection Analysis 12th

Cyber Attack Detection with Encrypted Network Connection Ana...

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12th International Symposium on Intelligent Manufacturing and Service systems, IMSS 2023

作者： Gonen, Serkan Karacayilmaz, Gokce Artuner, Harun Bariskan, Mehmet Ali Yilmaz, Ercan Nurcan Software Engineering Department Istanbul Gelisim University Istanbul Turkey Hacettepe University Institute of Science Ankara Turkey Computer Engineering Department Hacattepe University Ankara Turkey Computer Engineering Department Istanbul Gelisim University Istanbul Turkey Electrical – Electronic Engineering Department Gazi University Ankara Turkey

ISBN: (纸本)9789819960613

The evolution of science and technology has led to increasingly complex cyber security threats, with advanced evasion techniques and encrypted communication channels making attacks harder to detect. While encryption has improved privacy and confidentiality for users, it has also provided a new avenue for attackers to exploit. Traditional intrusion detection systems, which transitioned from signature-based to behavior-based approaches, have struggled to keep up with these challenges. To address this issue, researchers have turned to continuous system monitoring and network traffic packet analysis. However, this method can be resource-intensive and time-consuming, particularly when analyzing encrypted packets. In this study, the JA3 fingerprint infrastructure was examined as a potential solution for quickly detecting attacks conducted over encrypted sessions while minimizing system downtime and damage. The results demonstrated that the JA3 infrastructure effectively detected attacks carried out via encrypted channels. Although Windows 10 and Kali 2020.4 operating systems were used as the victim and attacker systems respectively, the methodology can be applied to other operating systems and network hardware by following the outlined steps. This research is expected to make a significant contribution to the field of encryption-based attack prevention. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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An with effective ensemble classifier for network intrusion detection system

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Peer-to-Peer Networking and Applications 2025年第2期18卷

作者： Nandhini, U. Santhosh Kumar, S.V.N. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

The increasing frequency of network attacks poses a significant challenge in maintaining network security. In response to security vulnerabilities, the employment of Intrusion Detection systems (IDS) has become crucial. An IDS is a software application, which monitors network traffic to monitor and identify various malicious actions in the network. Network Intrusion Detection systems (NIDS) specifically examine network traffic to identify and monitor the various malicious activities identified by the network nodes. However, many existing IDS solutions encounter difficulties in achieving optimal feature selection, aiming for high classification accuracy while minimizing false alarm rates. In the proposed system, the Linear Discriminant Analysis (LDA) technique is employed to reduce the dataset dimensionality. Further, an Enhanced Whale Optimization Algorithm (EWOA) is utilized for efficient feature selection for global optimization tasks. To perform classification, an ensemble classifier is proposed which employs Random Forest (RF) and XGBoost. The main objective of the proposed approach is to improve classification accuracy while minimizing the false alarm rate by combining the benefits of RF and XGBoost. The proposed work is implemented by using the python jupyter framework. The experimental results show that the proposed system has an overall classification accuracy of 96.08%, a false alarm rate of 0.01%, and an overall detection rate of 94.03%, by using the NSL-KDD dataset when it is compared with their counterparts. © The Author(s).

关键词： Application programs

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An Efficient Data Collection using Whale Optimization Algorithm (WOA) in Wireless Sensor Network 2

An Efficient Data Collection using Whale Optimization Algori...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Pasupathi, Udhayakumar Gopi Saminathan, A. Manikandan, A. Subhashini, M. Gowrishankar, C. Poornappriya, T.S. Department of Computer Science Tamilnadu India Npr College of Engineering and Technology Dindigul India Srm Institute of Science and Technology Chennai India Department of Computer Science Trichy India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tiruchirappalli621215 India Aspire Systems Pvt Ltd Karnataka 560037 India

ISBN: (纸本)9798350388916

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the Whale Optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated. © 2024 IEEE.

关键词： Decision making

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DDA: Distillation-Driven Acceleration of the Reverse Diffusion Process for Stochastic Multi-Ship Trajectory Prediction

DDA: Distillation-Driven Acceleration of the Reverse Diffusi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Kun Han, Qilong Yao, Jingzheng Wu, Changmao Zhang, Yuntao College of Computer Science and Technology Harbin Engineering University Harbin China Yantai Research Institute Harbin Engineering University Yantai China Institute of Software Chinese Academy of Sciences Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350368741

Modeling stochastic multi-ship trajectories is vital for maritime safety and interaction efficiency. Recent researches show that diffusion models excel in trajectory prediction, surpassing GANs and VAEs in generation quality, diversity and stability. However, their slow sampling speed remains a major limitation, as producing high-quality trajectories typically requires hundreds of denoising steps. We introduce DDA, a novel method that accelerates multi-ship trajectory generation by distilling the reverse diffusion process, progressively reducing sampling steps by half while minimizing quality loss. We use CVAE-based encoder to map multimodal inputs into state embeddings in the latent space, and use distillation diffusion in the latent space to more quickly and better represent multi-ship trajectories. The diffusion model uses Transformer-based core, and we incorporate SO(2) invariance and equivariance to enhance model representation. Validation on real-world AIS datasets shows that the student model retains high-quality trajectory generation while sampling speed is approximately 30 times faster. © 2025 IEEE.

关键词： Accelerating Sampling AIS Diffusion Model Progressive Distillation VAE

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Location Enhancement and Multi-template Fusion for Fast Long-Term Tracking

Location Enhancement and Multi-template Fusion for Fast Long...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Shixin Chen Department of Computer Science Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Owing to the challenge of target occlusion leading to tracking failure during the target tracking process, achieving efficient and robust tracking of targets under occlusion scenarios has become a focal point of research. In this paper, we addresses the issue of tracking failure caused by the reduction or disappearance of target appearance information in occlusion scenarios. It proposes a study on target tracking under occlusion scenes based on deep learning methodologies. A foundation tracker is constructed that leverages location enhancement and multi-template fusion. Built upon this, graph attention mechanisms and target loss detection mechanisms are introduced. In cases where the target is lost due to occlusion, a re-detection strategy is employed to resume tracking once the target reappears. Experiments conducted on datasets demonstrate that the proposed algorithm can effectively determine whether a target is undergoing occlusion and significantly enhances tracking performance under such conditions.

关键词： Deep learning Target tracking Correlation Automation Attention mechanisms Neural networks Filtering algorithms Artificial intelligence

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