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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Hu, Siquan Jia, Jun Shi, Zhiguo Zhao, Jun University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Beijing Big Data Center Beijing100101 China

ISBN: (纸本)9798350383706

In the domain of few-shot learning, where the scarcity of training data poses a significant challenge, this paper introduces an innovative approach. We present a few-shot classification algorithm that utilizes the Two-Stream Frequency Domain Information Network. This algorithm delves deep into the efficacy of training different frequency components of images in the context of few-shot classification. It integrates filtered and enhanced image frequency domain information as supplementary data to be adaptively fused with the original image data, enhancing and augmenting the available information. Experimental results establish that the few-shot classification model, employing the Two-Stream Frequency Domain Information Network, outperforms leading algorithms in the field of few-shot learning. This enhancement is evidenced by an accuracy improvement of 1.06% and 0.93% on the miniImageNet and tiredImageNet datasets, respectively, when compared to the Meta-Baseline, illustrating the efficacy of this specialized approach. © 2023 IEEE.

关键词： Frequency domain analysis

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GNSS/IμUWB-Based Train Integrity Monitoring Using Fuzzy Reasoning 4

GNSS/IμUWB-Based Train Integrity Monitoring Using Fuzzy Rea...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhong, Tianyu Liu, Jiang Cai, Baigen Wang, Jian School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350359589

Integrity of a train is a significant characteristic parameter towards a railway train control system in order to guarantee the railway operational safety. The decoupling event between a locomotive and a carriage or two adjacent carriages has become a serious threat for the following train operating along the same track. Conventional train integrity monitoring solutions based on state detection with specific sensors, like the Global Navigation Satellite System (GNSS) receiver and tail wind pressure unit, may not perform effectively and safely under constrained or difficult operation environments. This paper presents a Train Integrity Monitoring System (TIMS) architecture with integration of GNSS, Inertial Measurement Unit (IMU) and Ultra-wide Band (UWB) ranging technique. To realize the effective determination of the train integrity state with multiple detection channels, the fuzzy reasoning theory is adopted for decision-making. By using the simulated Head-of-Train (HoT) and End-of-Train (EoT) platforms, both the normal and decoupling scenarios are investigated through experiments. With the practically collected sensor datasets, the different single-sensor-based methods are compared with the presented fuzzy reasoning-based solution. The comparison results illustrate the advanced performance level under the given experimental conditions, which indicate great potentials of the presented solution in novel train control systems. © 2024 IEEE.

关键词： fuzzy reasoning Global Navigation Satellite System (GNSS) railway transportation sensor integration train integrity monitoring Ultra-wide Band

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Exploring the possibilities of integration of cyber-psychology for human behaviour in a smart city

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International Journal of Information and communication technology 2024年第2期25卷 150-167页

作者： Wen, Liping Ting, Zhou Zheng, Huang Alfred Daniel, J. Antonidoss, A. Department of Psychology University of Chinese Academy of Sciences Beijing100101 China Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Medical Psychology School of Health Humanities Peking University Beijing100191 China Department of Computer Science & Engineering SNS College of Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science India

The smart city idea differs between cities and nations. In all meanings and characteristics of a smart city, public involvement is the only thing that remains common. Therefore, it is a very significant field to study human behaviour and development in smart cities. This paper presents a framework for identifying qualities necessary for people to be classified as intelligent persons and to integrate these human behavioural characteristics in cyber technology. Human behaviour in a smart city has been analysed using the machine learning algorithm and big data analytics. The integrated machine learning and big data analytics framework (iML-BD) classifies the cyber behaviour of intelligent persons in a smart city by observing the cyber activities performed by the individuals. Furthermore, this paper handles the risk factors for cyber-acquired and cyber-dependant crime violence and abuse that vulnerable internet and public access devices using blockchain technology. Blockchain is a method of storing data that takes too long to alter, modify, or manipulate. A blockchain is an electronic accounting system that is reproduced and spread through the Bitcoin protocol’s entire communication network. The case study performed on iML-BD has resulted in the highest performance in terms of prediction accuracy of 94.98%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Smart city

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Deep Reinforcement Learning Based Spectrum Prediction for Bursty Bands

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China communications 2023年第7期20卷 241-257页

作者： Tao Peng Chao Yang Peiliang Zuo Xinyue Wang Rongrong Qian Wenbo Wang Key Laboratory of Universal Wireless Communication Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Department of Electronics and Communication Engineering Beijing Institute of Electronic Science and TechnologyBeijing 100070China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

Spectrum prediction plays an important role for the secondary user(SU)to utilize the shared spectrum ***,currently utilized prediction methods are not well applied to spectrum with high burstiness,as parameters of prediction models cannot be adjusted *** paper studies the prediction problem of bursty ***,we first collect real Wi Fi transmission data in 2.4GHz Industrial,Scientific,Medical(ISM)band which is considered to have bursty *** analysis of the data indicates that the spectrum occupancy law of the data is time-variant,which suggests that the performance of commonly used single prediction model could be *** that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance,we then propose a deep-reinforcement learning based multilayer perceptron(DRL-MLP)method to address this matching *** state space of the method is composed of feature vectors,and each of the vectors contains multi-dimensional feature ***,the action space consists of several multilayer perceptrons(MLPs)that are trained on the basis of multiple classified data *** finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed *** results demonstrate that the proposed method significantly outperforms the stateof-the-art methods in terms of the prediction accuracy.

关键词： spectrum prediction burstiness ISM band IEEE 802.11 deep-reinforcement learning multilayer perceptron

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Multi-Function Radar Signal Sequence Prediction Via Trend Filtering and Similarity Comparison

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 7022-7041页

作者： Feng, Kangan Yu, Xianxiang Wang, Mou Huang, Yu Zhang, Lidong Yi, Wei Kong, Lingjiang University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518038 China Air Force Academy Beijing100085 China

Multi-function radar signal sequence prediction can provide a profound understanding of radar intentions, thereby playing a pivotal role in radar signal analysis. However, traditional methods are impeded by noise and outliers in observed sequences. This paper introduces a two-stage framework for predicting multi-function radar signal sequences by integrating robust enhanced trend filtering and signal sequence similarity comparison. In the preprocessing stage, the framework models the noise and outliers using a Gaussian mixture model, and extracts trend information through maximizing a posteriori (MAP) estimation and interpolation. In the prediction stage, it simplifies signal sequence representation by leveraging extracted trend information. The method calculates similarity between current and historical signal sequences, generating predictions of radar pulse sequences using prototypes derived from the subsequent sequences of the most similar historical segments. Distinctively, the framework streamlines data representation in prediction, reduces storage and operational demands, and operates independent of prior pulse pattern knowledge. Experimental results demonstrate substantial enhancements in predicting pulse repetition interval (PRI) of multi-function radar signal sequences compared to existing methods. © 1965-2011 IEEE.

关键词： Signal filtering and prediction

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Towards Integrated communication and Localization in Emergency UAV Systems: A Joint Trajectory and Resource Allocation Design

Towards Integrated Communication and Localization in Emergen...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Tian, Zeyu Wang, Li Xu, Lianming Chang, Zheng Fei, Aiguo Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

ISBN: (纸本)9798350351255

In this paper, we present a communication and localization co-design (CLCD) scheme tailored for unmanned aerial vehicle (UAV) assisted emergency networks, with the goal of enhancing rescue operation efficiency and network resource utilization. Specifically, we delve into the mechanism of mutual benefit between communication and localization in UAV wireless networks, establishing a utility function that combines communication rate and localization error. Building on this, we develop a beamforming scheme to facilitate high data rate communication, incorporating angle of arrival (AOA) localization information for guidance. A deep reinforcement learning (DRL)-based communication and localization coordinated optimization (CLCO) algorithm is further proposed to optimize the UAV trajectory and the transmit power in real-time, guaranteeing reliable communication and localization services. Extensive simulation results validate our approach, showcasing up to a 40% improvement in joint utility compared to baseline schemes. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhanced Data Mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： Data aggregation

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Enhancing Cross-Community Diffusion Through Swarm Intelligence 11

Enhancing Cross-Community Diffusion Through Swarm Intelligen...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Wu, Xiao-Kun Zhao, Tian-Fang Chen, Wei-Neng Renmin University of China School of Journalism and Communication Beijing China Jinan University School of Journalism and Communication Guangzhou China South China University of Technology School of Computer Science and Technology Guangzhou China

ISBN: (纸本)9798350385670

The proliferation of information online reveals a discernible pattern: a social information barrier named 'information cocoons.' Within these cocoons, individuals are inclined to engage exclusively with and focus on information sources aligning with their pre-existing views, predisposing them to neglect or dismiss contrary information. Aiming to address this issue, this paper extends the existing Information Maximization problem by incorporating network community information. It formulates a cross-community communication maximization problem and proposes effective solutions, encompassing the following key elements. Firstly, a cross-community communication effect evaluation index is introduced based on diversity entropy, to address the challenge of unclear criteria for evaluating cross-community communication effects. This index is designed to assess the diversity of information in cross-community communication. Then, employing linear weighting, the multi-objective communication optimization problem related to cross-community communication is transformed into a single-objective optimization problem. Various heuristic and meta-heuristic methods are employed to solve such problems. Through simulation experiments, the efficiency and solution quality of different methods are evaluated. This empirical approach serves to validate the effectiveness of the proposed methods in diverse scenarios. © 2024 IEEE.

关键词： Swarm intelligence

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Activation Distance and Capacity Analysis for Ambient Backscatter communications with Sensitivity Constraint and Beamforming

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China communications 2024年第11期21卷 257-266页

作者： Mu Yunping Fan Dian Wang Gongpu Xu Yongjun Kuang Lei Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China China Academy of Information and Communications Technology Beijing 100191China School of Communication and Information Engineering Chongqing University of Posts and TelemcommunicationsChongqing 400065China Shanghai Key Laboratory of Multidimensional Information Processing Engineering Center of SHMEC for Space Information and GNSSSchool of Information Science and TechnologyEast China Normal UniversityShanghai 200241China

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication *** letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system ***,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna ***,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is ***,simulation results are provided to corroborate our proposed studies.

关键词： activation distance ambient backscatter communications beamforming channel capacity power allocation sensitivity

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Ship Detection and Recognition Based on Improved YOLOv7

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computers, Materials & Continua 2023年第7期76卷 489-498页

作者： Wei Wu Xiulai Li Zhuhua Hu Xiaozhang Liu School of Information and Communication Engineering Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China

In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of *** improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the *** paper also introduces a novel multi-scale feature fusion module,which comprises Path Aggregation Network(PAN)modules,enabling the efficient capture of ship features across different ***,data preprocessing is enhanced through the application of data augmentation techniques,including random rotation,scaling,and cropping,which serve to bolster data diversity and *** distribution of positive and negative samples in the dataset is balanced using random sampling,ensuring a more accurate representation of real-world *** experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness,highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.

关键词： Ship position prediction target detection YOLOv7 data augmentation techniques

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