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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Abdullah, Murad Mow, Wai Ho The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong University of Science and Technology Internet of Things Thrust Area Hong Kong

ISBN: (纸本)9781665475549

Polarization-adjusted convolutional (PAC) codes of short blocklengths over the binary-input additive white Gaussian noise channel have excellent performance. To further improve the performance of the PAC codes, we adopt the asymptotic frame error rate (AFER) optimality criterion, i.e., the primary and secondary criteria are to maximize the minimum Hamming distance and to minimize the error coefficient of the code, *** this paper, we conduct a new optimization search using a graphic processing unit that jointly optimizes the rate profile and the convolutional transform of a PAC code. Our search has discovered many short record-breaking binary linear block codes (representable as PAC codes) which achieve smaller error coefficients at the same best-known minimum distance compared to the best records in the literature. In particular, at a frame error rate of 10-9, an optimized [64, 32] PAC code with a minimum distance of 12 outperforms the best-known counterpart by 0.40 dB and is 1.1 dB away from the Polyanskiy-Poor-Verdú bound. © 2023 IEEE.

关键词： Simulated annealing

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An Intrusion Detection Scheme Based on Sequential Tri-Way Decisions of Multiple Granularity for Intelligent Connected Vehicles

An Intrusion Detection Scheme Based on Sequential Tri-Way De...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Yongzhong Wang, Qi Li, Yuheng Zhang, Shipeng School of Automotive Engineering Nantong Institute of Technology Nantong226010 China Internet of Things and Big Data Major of Macao Polytechnic University Macao China School of Computer Science Jiangsu University of Science and Technology Zhenjiang212003 China

ISBN: (纸本)9789819639762

With the rapid developing technology of the internet of things, intelligent connected vehicles are facing more and more serious cybersecurity threats and challenges. With the developing of cybersecurity technology for intelligent connected vehicles it has posed a comprehensive challenge to the existing cybersecurity technologies. Intrusion detection is an important research field of cybersecurity. An intrusion detection scheme based on sequential tri-way decisions of multiple granularities (S3WD-MG) is proposed for Intelligent Connected Vehicles. The S3WD-MG method obtains feature sets of multi-granularities through the auto-encoder of deep learning. This method combines multiple granularities and sequential tri-way decisions to make decisions by the most appropriate for network behavior through by the decision threshold. The experiment results on NSL-KDD show that S3WD-MG method does have a good performance in intrusion detection. Compared with other models, our proposed S3WD-MG method has a higher detection’s rate and the robustness of algorithm is stronger. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Network intrusion

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Ensemble Label Correction for Hyperspectral Image Classification

Ensemble Label Correction for Hyperspectral Image Classifica...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Youqiang Liu, Jiaxi Sun, Jin Shi, Hao Ge, Zixian Cao, Guo School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9798350303759

The presence of noisy labels in the training sets poses a challenge to achieving high-precision classification of hyperspectral images (HSIs). To address this issue, this paper proposes an ensemble label correction (ELC) method for HSI classification that incorporates the concept of ensemble learning into deep neural networks. Our ELC model utilizes ensembles to gradually correct determined noisy labels during training epochs. At the end of training, most wrong labels will be corrected, and the model's robustness will also be increased. In our experiments, we use convolutional neural networks to implement our ELC model. Our experiments on two real HSI datasets illustrate that the proposed approach demonstrates promising results even in the presence of label noise. © 2023 IEEE.

关键词： Deep neural networks

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Impact of Solar Intensity and PV Module Temperature on Power Quality and Transformer Life in Grid-Connected Solar Photovoltaic Power Plants 10th

Impact of Solar Intensity and PV Module Temperature on Power...

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10th International Symposium on Hydrogen Energy, Renewable Energy and Materials, HEREM 2024

作者： Rajput, Saurabh Kumar Deo, Arjun Kulshrestha, Deepansh Nandanwar, Sonali R. Kolhee, M.L. Centre for Internet of Things Madhav Institute of Technology and Science Gwalior India Energy Institute Bangalore A Centre of Rajiv Gandhi Institute of Petroleum Technology Bengaluru India Department of Electrical Engineering Madhav Institute of Technology and Science Gwalior India Department of Electrical and Electronics Engineering IES College of Technology Bhopal India Faculty of Engineering and Science University of Agder Kristiansand Norway

ISBN: (纸本)9789819632718

Integration this paper explores the seasonal changes in photovoltaic array performance, power quality, and transformer loading in summer. The temperature difference (Tc-Ta) increases from 3 ℃ to 24.9 ℃ during 6:00 AM to 12:00 PM, resulting in PV efficiency dropping from 16.53% to 14.05%. The solar intensity peaks at 749 W/m2, but the total harmonic distortion has decreased from 6.16% to 1.68%, indicating better quality of power. The power factor (PF) reduces from 0.91 to 0.63 with an increase in PV output. Transformer loading rises by 5.8% due to thermal losses, 2.69% due to THDi, and 10.10% due to low PF. The overloading factor was 1.20 and LoL factor was 6, and hence the transformer was under much stress due to higher temperatures and low PF. This study highlights the necessity for strategies to optimize PV performance and manage transformer stress during summer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photovoltaics

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Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition

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IEEE Transactions on Image Processing 2023年 32卷 2719-2733页

作者： Liu, Wenxuan Zhong, Xian Zhou, Zhuo Jiang, Kui Wang, Zheng Lin, Chia-Wen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan430070 China Peking University School of Electronics Engineering and Computer Science Beijing100091 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University School of Computer Science Wuhan430072 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://***/51cloud/DRDN. © 1992-2012 IEEE.

关键词： Economic and social effects

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New Search for Optimized PAC Codes Under SCL Decoding

New Search for Optimized PAC Codes Under SCL Decoding

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Abdullah, Murad Mow, Wai Ho Department of Electronic and Computer Engineering Hong Kong the Hong Kong University of Science and Technology Internet of Things Thrust Area Information Hub Hong Kong

ISBN: (纸本)9798350370218

In this paper, we propose some new constraints on the rate profile based on the reliability of the bit-channels to optimize the polarization-adjusted convolutional (PAC) codes under successive cancellation list (SCL) decoding. We apply simulated annealing to jointly optimize the rate profile and the convolution impulse response of the PAC codes to minimize the asymptotic frame error rate (AFER) in such a constrained search space. Simulation results show that our [64, 32], [128, 32], and [256, 128] optimized PAC codes outperform their corresponding Q-Iearning-based optimized PAC codes. Specifically, our [64, 32] optimized PAC code under SCL decoding with a list size of 8 outperforms the cyclic redundancy check (CRC) aided 5G polar code under SCL decoding with a list size of 32 by about 0.5 dB at the FER of 10-4• Our [256, 128] optimized PAC code outperforms the Q-Iearning-based optimized PAC by about 0.2 dB at the FER of 10-3 under SCL decoding with a list size of 32. © 2023 IEEE.

关键词： asymptotic frame error rate Polarization-adjusted convolutional codes simulated annealing successive cancellation list

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Prevention of Security Against Attacks in Order to Balance the Syatem Using Optimisation

Prevention of Security Against Attacks in Order to Balance t...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kannagi, A. Kumar, Santosh Vishwakarma, Priya Department of Computer Bangalore India Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

The need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain the required performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of softwaredefined networks as a means to implement these concepts. Then we provide some examples from the literature on selfaware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and suggestions for further work are listed. © 2023 IEEE.

关键词： active networks cognitive psychology decentralized decision making energy consumption network performance overlay networks and cloud task management systems Quality of Experience (QoE) Quality of Service (QoS) reinforcement learning Self-aware networks self-measurement self-optimization software-defined networks (SDN)

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Extensions of Local-Mean K-Nearest Neighbor Classifier with Various Distance Metrics 5

Extensions of Local-Mean K-Nearest Neighbor Classifier with ...

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5th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Lei, Xin Yang, Sichang Zhou, Longsheng School of Computer and Information Technology Xinyang Normal University Xinyang Henan China School of Internet of Things Engineering Jiangnan University Wuxi Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9798350333442

K-Nearest Neighbor (KNN) classification algorithm is one of the simplest methods in data mining classification technology. The LMKNN algorithm based on this method uses the local average vector of each category to classify the data. However, because distance measurement only uses Euclidean distance algorithm, it is not suitable for some cases. Therefore, this paper proposes a local mean k-nearest neighbor classification algorithm based on different distance algorithms. And compare the classification capabilities of the LMKNN model based on four distance measurement algorithms through experiments. The results show that the LMKNN algorithm based on Minkowski and City block distance metric schemes has more advantages than the LMKNN algorithm based on Euclidean distance metric schemes. © 2023 IEEE.

关键词： Data mining

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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Telemetry Simulation & Analysis 1

Telemetry Simulation & Analysis

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Gupta, Pratyush Gupta, Harshal Ushasukhanya, S. Vijayaragavan, E. SRM Institute of Science and Technology Computer Science with Specialisation in Internet of Things Chennai India SRM Institute of Science and Technology Networking and Communications Chennai India SRM Institute of Science and Technology Mechanical Engineering Chennai India

ISBN: (纸本)9798350336009

The purpose of this project is to analyze telemetry data for a comprehensive analysis of driving conditions, range, electric motor performance, and other characteristics linked to telemetry packets based on information transmitted by the car Telematics Control Unit (TCU). The strategy is intended to assist us in eliminating the requirement for sending test vehicles out to verify every tiny modification made by the software team and its impact on the core functionality of the vehicle systems. In the automobile industry, telemetry provides very useful information about a driver's performance and vehicle by collecting telemetry data from sensors within the vehicle TCU. This is done for various reasons varying from, insurance rating and predictive maintenance to staff compliance monitoring. Telemetry analysis has taken over the complete control of the automobile sector. These conclusions are driving the automobile industry toward some unbelievable and never-before results. The automobile industry is on a roll and Telemetry Analysis is its wheels. Telemetry Analysis has helped the automobile industry achieve things that were beyond our imaginations from analyzing the trends to understanding the supply chain management, from taking care of its customers to turning our wildest dream of connected cars into a reality, Vehicle Telemetry Analysis is well and truly driving the automobile industry crazy. © 2023 IEEE.

关键词： Telematics

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