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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Nammouchi, Amal Chaabani, Cyrine Theocharis, Andreas Kassler, Andreas Center for Digital Technology and Innovation Munich Germany Karlstad University Engineering and Physics Department Karlstad Sweden Karlstad University Computer Science Department Karlstad Sweden Deggendorf Institute of Technology Institute of Applied Computer Science Deggendorf Germany

ISBN: (纸本)9789531842976

Renewable Energy Communities (RECs), characterized by localized energy generation and consumption, are a key enabler for enhancing renewable energy utilization, cost-efficient planning and clean energy transition. However, optimizing RECs operations is challenging due to the complex interplay of different stakeholders with conflicting requirements. The complexity of managing such systems often leads to a lack of transparent and reliable decision-making, creating barriers for actors within the community. This paper explores the integration of Generative AI into Renewable Energy Communities (RECs) to enhance the transparency, explainability and accessibility of energy management systems (EMSs) that depend on solving optimization problems. We propose a novel framework, Chat-SGP, which uses generative AI to synthesize optimization modeling code to provide actionable, explainable insights for managing the REC operations. Our approach allows us to interact with the EMS through natural language queries, enhancing the system's accessibility and user-friendliness. Our evaluation shows that using GPT-4 with in-context learning performs 96.72% accuracy on average in generating correct answers. © 2024 IEEE.

关键词： Clean energy

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DRL-Driven Localization With UAV in Near-Field Communications

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IEEE Internet of Things Journal 2025年第13期12卷 22587-22598页

作者： Khan, Muhammad Fawad Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, FangJie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China University of Waterloo Department of Electrical and Computer Engineering ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

In this paper, we propose a Deep Reinforcement Learning (DRL)-based Multi-Point Localization Scheme (MLS) to efficiently localize IoT devices using a single Unmanned Aerial Vehicle (UAV) equipped with a large-scale multi-antenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multi-antenna array on the UAV captures both the Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the UAV. This approach eliminates the need for multiple hovering points required by a single-antenna UAV (SA-UAV) or the deployment of multiple SA-UAVs. To enhance localization accuracy, key hovering points for the multi-antenna UAV (MA-UAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-UAV to optimize the trade-off between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the UAV. © 2025 IEEE.

关键词： Deep reinforcement learning

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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International Journal of Information technology (Singapore) 2024年第3期16卷 1527-1538页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russian Federation Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russian Federation

The widespread dissemination of misinformation and propaganda has become a crucial issue in societal conversations. This study presents an innovative framework to counter propaganda within information warfare using a modified logistic differential equation integrated with a Physics-Informed Neural network (PINN). Employing the LIAR dataset, which reflects real-world political discourse, our model captures the intricate dynamics of propaganda. The novel approach effectively estimates underlying dynamics and unknown parameters, yielding superior accuracy (97.32%) compared to benchmark models. A parameter sensitivity analysis reveals the significant impact of the spread rate parameter, shedding light on aggressive propaganda dissemination. This research paves the way for robust methods in combating propaganda, demonstrating the effectiveness and superiority of the proposed PINN-based model over established benchmark models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Information warfare Nonlinear differential equations PINN Propaganda Random forest classifier TF-IDF

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Big data challenges and opportunities in Internet of Vehicles: a systematic review

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International Journal of Pervasive Computing and Communications 2024年第2期20卷 308-342页

作者： Hemmati, Atefeh Zarei, Mani Rahmani, Amir Masoud Department of Computer Engineering Islamic Azad University Science and Research Branch Tehran Iran Department of Computer Engineering Islamic Azad University Shahr-e-Qods Branch Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan

Purpose: Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research. Design/methodology/approach: This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis. Findings: This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues. Research limitations/implications: This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted. Originality/value: This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem. © 2024, Emerald Publishing Limited.

关键词： Big data Data analytics Intelligent transportation systems (ITS) IoV

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Resource Allocation for Throughput Maximization in Cognitive Radio network with NOMA

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computers, Materials & Continua 2022年第1期70卷 195-212页

作者： Xiaoli He Yu Song Yu Xue Muhammad Owais Weijian Yang Xinwen Cheng School of Computer Science Sichuan University of Science and EngineeringZigong643000China Department of Network Information Management Center Sichuan University of Science and EngineeringZigong643000China School of Information Engineering South West University of Science and TechnologyMianyang621010China School of Computer and Software Nanjing University of Information Science and TechnologyJiangsu210044China Alhamd Islamic University Airport Road Quetta BalochistanPakistan

Spectrum resources are the precious and limited natural *** order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G *** optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the *** the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission ***,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization ***,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to *** the same time,the convergence and time complexity of the algorithm are *** analysis and simulation results show that the algorithm can effectively improve the system *** terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.

关键词： Resource allocation non-orthogonal multiple access cognitive radio network throughput maximization Chebyshev convex

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Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective 24

Inductive Graph Alignment Prompt: Bridging the Gap between G...

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33rd ACM Web Conference, WWW 2024

作者： Yan, Yuchen Zhang, Peiyan Fang, Zheng Long, Qingqing Peking University Beijing China Hong Kong University of Science and Technology Hong Kong Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400701719

The "Graph pre-training and fine-tuning"paradigm has significantly improved Graph Neural networks(GNNs) by capturing general knowledge without manual annotations for downstream tasks. However, due to the immense gap of data and tasks between the pre-training and fine-tuning stages, the model performance is still limited. Inspired by prompt fine-tuning in Natural Language Processing(NLP), many endeavors have been made to bridge the gap in graph domain. But existing methods simply reformulate the form of fine-tuning tasks to the pre-training ones. With the premise that the pre-training graphs are compatible with the fine-tuning ones, these methods typically operate in transductive setting. In order to generalize graph pre-training to inductive scenario where the fine-tuning graphs might significantly differ from pre-training ones, we propose a novel graph prompt based method called Inductive Graph Alignment Prompt(IGAP). Firstly, we unify the mainstream graph pre-training frameworks and analyze the essence of graph pre-training from graph spectral theory. Then we identify the two sources of the data gap in inductive setting: (i) graph signal gap and (ii) graph structure gap. Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space. A theoretical analysis ensures the effectiveness of our method. At last, we conduct extensive experiments among nodes classification and graph classification tasks under the transductive, semi-inductive and inductive settings. The results demonstrate that our proposed method can successfully bridge the data gap under different settings. © 2024 ACM.

关键词： Graph neural networks

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Determination Stenosis from Coronary Stretch Images for CNN 4

Determination Stenosis from Coronary Stretch Images for CNN

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Asakawa, Tetsuya Shinoda, Hiroki Shimizu, Kazuki Nomura, Kei Aono, Masaki Toyohashi University of Technology Department of Computer Science and Engineering Aichi Japan Toyohashi Heart center Department of Radiology Aichi Japan

ISBN: (纸本)9798350395914

Coronary artery stenosis, one of the most common heart diseases, is diagnosed by a human, which is a time-consuming and labor-intensive process. Therefore, the diagnosis of coronary artery stenosis is desired to be automated, and deep learning has shown great promise in this regard. In this study, we propose two deep learning models based on coronary artery multi-planar reconstruction (MPR) images from contrast CT images: one inputs each extracted individual coronary artery image (Indiv), and the other inputs an aggregate projected view (APV). The model that used APV images exhibited a maximum F1-score of 0.779± 0.024. © 2024 IEEE.

关键词： computerized tomography

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Autonomous Vehicle Utilizing Deep Learning Methods

Autonomous Vehicle Utilizing Deep Learning Methods

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Mohanty, Sresthasa Maity, Ritu Pattanaik, Satya Ranjan Gandhi Institute for Technology Department of Computer Science Bhubaneswar India Central Tool and Training Center Department of AIML Bhubaneswar India

ISBN: (纸本)9798350369175

In today's fast-paced world, individuals are constantly seeking ways to optimize their time and safety, especially during commutes and travel. Time management, traffic congestion, and safety are critical aspects of driving that have driven researchers to develop autonomous vehicles. A critical challenge in developing autonomous vehicles is achieving reliable lateral motion control. This paper addresses this issue by leveraging the deep learning techniques, and specifically explores the benefits of the NVIDIA Udacity car simulator. Our approach involves training a convolutional neural network (CNN) model to simulate the human driving behavior by utilizing a dataset of images and corresponding steering angles recorded during manual driving sessions in the simulator. It also emphasizes the importance of preprocessing the visual data to enhance the accuracy of the steering predictions. Our model is capable of managing complex driving scenarios within a simulated environment without the risks associated with real-world testing. A Mean Squared Error of 0.0123422 and an R-squared value of 0.8587687 suggest that our model is highly effective at navigating the car across different unfamiliar tracks. © 2024 IEEE.

关键词： Traffic congestion

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Trust management in the internet of vehicles: a systematic literature review of blockchain integration

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International Journal of Information Security 2024年第4期23卷 3065-3088页

作者： Abbasi, Shirin Khaledian, Navid Rahmani, Amir Masoud Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran University of Luxembourg Esch-sur-Alzette Luxembourg Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The Internet of Vehicles (IoV) promises to revolutionize transportation in smart cities, but its interconnectedness raises critical security and privacy concerns. Limited computational power, diverse network technologies, and many sensors and vehicles challenge data integrity and trust in data exchange. Existing solutions, often dependent on specific environments and protocols, struggle to address these issues across the entire IoV ecosystem. This paper explores the potential of blockchain technology to address these challenges. We argue that blockchain’s immutability and decentralization offer a unique solution for trust management in various IoV environments. We review existing blockchain-based algorithms and models proposed for IoV integration and propose a novel taxonomy to categorize these approaches. This taxonomy will help us analyze effective parameters, implementation methods, and evaluation metrics in the reviewed literature. According to our research, the most critical evaluation parameter for blockchain-based methods is time, including system-level service-related time parameters and solution implementation time, and 38% of existing papers simulated the approach using Hyperledger. Additionally, we will identify key challenges from integrating blockchain into the IoV landscape. By providing a comprehensive review and analysis of blockchain-based trust management solutions for IoV, this paper aims to contribute to the ongoing development of secure and reliable intelligent transportation systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Deep-Learning-Based Risk Prediction with Urban Sensing Data for Consumer Driving Safety 13

Deep-Learning-Based Risk Prediction with Urban Sensing Data ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Lin, Kun-Yu Liu, Pei-Yi Hu, Chih-Lin Huang, Sheng-Zhi Chen, Yung-Hui Hui, Lin National Central University Department of Communication Engineering Taoyuan City320317 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei City251301 Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taiwan

ISBN: (纸本)9798350355079

With the provision of IoT-driven and user-provided sensing data sources in smart cities, we take advantage of deep learning techniques to analyze the spatio-temporal traffic data and predict traffic risks for driving safety. Our study continues to collect the traffic data from multiple sensing sources, and meanwhile adopts both CNN and LSTM to interpret the data collection in spatial and temporal dimensions. Thus, a novel traffic risk prediction scheme based on CNN and LSTM, named TRP-CL, is proposed to generate a traffic warning map of risks and hazard situations on a grid-scaled city map. Not only a theoretic formation but also an experimental implementation of the TRP-CL scheme are made to show the practical feasibility. © 2024 IEEE.

关键词： crowdsensing intelligent transportation system (ITS) IoT machine learning (ML) risk prediction Traffic data

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