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22nd IEEE International Conference on communication technology, ICCT 2022

作者： Zhang, Lan Ma, Liang Tian, Feng Liang, Tianyi Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China The 36th Research Institution of China Electronics Technology Group Corporation Jiaxing341000 China Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9781665470674

In order to effectively cope with the external malicious jamming from jammers and avoid mutual interference caused by competitive channels among users, an intelligent antijamming decision-making method based on deep reinforcement learning for multi-user communication is proposed in this paper. In multiple jamming modes, the base station perceives the current spectrum information of the multi-user and the jammer and makes it as the input of the deep reinforcement learning strategy neural network, then selects the joint action according to the dynamic greedy algorithm, which will help the users intelligently select the communication frequency band via the feedback from the base station. The simulation results show that the proposed method effectively overcome the communication jamming induced by external jammers and internal users. Moreover, the normalized throughput of each user in the proposed algorithm is much higher than the random frequency hopping and fixed ϵ deep reinforcement learning algorithm with faster convergence speed. © 2022 IEEE.

关键词： Reinforcement learning

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Fast Informer-Based Time Series Detection

Fast Informer-Based Time Series Detection

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Weiguo Shen Tianhao Xia Dongwei Xu Qi Xuan Yun Lin Wei Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China Zhejiang University of Technology College of Information Engineering Institute of Cyberspace Security Binjiang Institute of Artificial Intelligence HangZhou China Zhejiang University of Technology Institute of Cyberspace Security Binjiang Institute of Artificial Intelligence HangZhou China College of Information and Communication Engineering Harbin Engineering University Binjiang Institute of Artificial Intelligence Harbin China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

Time series data are pivotal in modern technology and industrial applications,but they are frequently impacted by anomalies due to system failures,cyberattacks,or unexpected events. Therefore,swift and accurate anomaly detection in time series is crucial. This paper introduces ‘TransIDS’,an intrusion detection method that focuses on the efficient extraction and detection of time series features. TransIDS uses the Informer model to capture temporal features within data frames more effectively,improving detection performance and reducing runtime. We also fine-tune the Informer model and introduce an automatic anomaly threshold selection method,optimizing it for anomaly detection tasks. We validate TransIDS through experiments on various datasets and compare it with three existing methods. The results show that TransIDS outperforms the compared models in detection performance.

关键词： Runtime Accuracy Antenna theory Time series analysis Intrusion detection Predictive models Feature extraction Transformers Data models Anomaly detection

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Precipitation Retrieval of FY-3D MWHTS and MWRI Data Based on Light Gradient Boosting Model

Precipitation Retrieval of FY-3D MWHTS and MWRI Data Based o...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yanfang Lv Lanjie Zhang Yifan Zhang Biao Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

As an essential component of the earth system, precipitation plays a crucial role in the regional and global water cycle. This paper uses observations from the Fengyun-3D microwave humidity and temperature sounder and microwave radiation imager, as well as Integrated Multi-satellite Retrievals for GPM (IMERG), to establish a precipitation retrieval model based on the light gradient boosting model (LGBM). Using IMERG as the comparison data, the performance of precipitation identification with the LGBM-based precipitation retrieval model is as follows: accuracy is 0.9302, probability of detection is 0.7683, false alarm rate is 0.3529, and critical success index is 0.5414. The performance of precipitation estimation is also evaluated, with a correlation coefficient of 0.7536, a mean absolute error of 0.9119, and a mean squared error of 3.9506. In summary, this study achieves good performance in precipitation identification and estimation by the LGBM model.

关键词： Earth Correlation coefficient Precipitation Estimation Humidity Boosting Data models

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer Based on LightGBM

Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Xuchen Dai Biao Zhang Yifan Zhang Lanjie Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) passive microwave radiometer under the severe weather such as typhoon is evaluated. Based on the observation data of HY-2C, this study explores the influence of brightness temperature (TB) observed by microwave radiometer on sea surface wind speed retrieval under typhoon conditions. In the case of typhoon precipitation, the attenuation of the atmosphere to the high frequency channel is stronger, which makes the sea surface information observed by the microwave radiometer very weak. Therefore, we mainly select the TB of horizontal polarization and vertical polarization observation of 6.9GHz and 10.7GHz channels to carry out the study of typhoon wind speed retrieval. This paper constructs a matching data set based on the TB observed by HY-2C passive microwave radiometer, HY-2C L2B wind product and H*wind reanalysis wind field data in October and November 2020. The maximum differences of spatial and temporal were set as 25km and 30minutes, respectively. Then, the typhoon wind speed retrieval model based on LightGBM is established. We use the data of October 2020 to train the model and the data of November 2020 to test model performance. Comparing with the H*wind reanalysis wind field data, the bias and root-mean-square error of retrieved wind speed are 0.15m/s and 1.26m/s, respectively. In addition, the correlation coefficient is 0.90, indicating that the retrieved wind speed has a good consistency with H*wind data.

关键词： Microwave measurement Sea surface Tropical cyclones Wind speed Microwave theory and techniques Data models Brightness temperature

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Enhancing privacy protection in intelligent and connected vehicles: a styleGAN3-based image desensitization dataset and framework 4

Enhancing privacy protection in intelligent and connected ve...

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Wang, Jingyan Wang, Liyong Ji, Haojie Fang, Junzhe Guo, Teng Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University No.12 Xiaoying East Road Haidian District Beijing100192 China Zaozhuang Network Security Center No. 2621 Guangming Avenue Xuecheng District Shandong Province 277020 China

ISBN: (纸本)9781510686106

Intelligent and connected vehicles (ICVs) collect vast video footage, inadvertently capturing privacy-sensitive data. To address this, we introduce the IDD-ICV dataset, featuring diverse in-vehicle camera footage. To protect privacy, we propose a styleGAN3 based framework that integrates MHA (Multi-Head Attention) for detecting and desensitizing facial data in datasets. This approach ensures compliance with data security standards and enhances ICVs' privacy protection capabilities during the life cycle of data. Our study contributes to the development of secure and trustworthy autonomous transportation systems by demonstrating the effectiveness of our framework in safeguarding privacy-sensitive information. © 2024 SPIE.

关键词： Differential privacy

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第03期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Research on the Wirtinger Flow algorithm based on quadratic distribution initial value 1

Research on the Wirtinger Flow algorithm based on quadratic ...

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1st Advanced Imaging and information Processing Conference, AIIP 2023

作者： Xie, Zhenfei Yu, Xuelian Wang, Zhengxian Li, Heng Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China Shenzhen Engineering Laboratory of Aerospace Detection and Imaging College of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781510671904

Phase retrieval algorithms, such as the Wirtinger Flow (WF) algorithm, are widely used in various fields. As a non-convex optimization algorithm for phase retrieval, WF is commonly employed in the reconstruction of holograms in holographic image projection. These types of algorithms typically involve two stages: an initialization stage and an iterative optimization stage. In the initialization stage, an initial value is provided, and a spectral method is used to calculate an approximate solution as the initial guess. The iterative optimization stage then utilizes the Wirtinger gradient to iteratively compute and converge the initial guess to a nearby real solution, thereby obtaining the global optimal solution. However, due to the random nature of the initial values, the computed results often exhibit significant instability. To address this issue, this paper proposes an approach based on a quadratic distribution for improving the stability of the results. In the initialization stage, the initial value is set as the quadratic distribution initial value. Then, the spectral method is applied again to calculate the initial guess. Since the quadratic distribution initial value is artificially assigned, it enhances the stability of the computed results. To validate this method, the paper applies the quadratic distribution initial value to both the initialization stage of the WF algorithm and the Truncated Amplitude Flow (TAF) algorithm. A comparison is made between the results obtained using random initial value and those obtained using the quadratic distribution initial values. The results demonstrate that compared to random initial values, the quadratic distribution initial values can achieve faster and equally accurate computation results with higher stability. Finally, this method is applied to simulation experiments of in-line digital holography, and the reconstruction results from the experiments further confirm the effectiveness of our approach. © 2023 SPIE.

关键词： Convex optimization

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Viewpoint Rotation Prediction based Resource-Efficient Holographic Type communication in EON enabled 6G RAN

Viewpoint Rotation Prediction based Resource-Efficient Holog...

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International Conference on Optical communications and Networks (ICOCN)

作者： Xin Wang Chengyuan Zhang Yafei Wang Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry and Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University (BISTU) Beijing China

A LSTM based viewpoint rotation prediction enabled resource-efficient holographic type communication is investigated in EON enabled 6G RAN. The CensNet enhanced PPO is used for feature extraction based DU-CU deploymen... 详细信息

ISBN: (数字)9798350367652

ISBN: (纸本)9798350367669

关键词： 6G mobile communication Simulation Benchmark testing Prediction algorithms Feature extraction Optical fiber communication Long short term memory

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Pruning CNN based on Combinational Filter Deletion 50

Pruning CNN based on Combinational Filter Deletion

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Huang, Wenli Wang, Xiujie Zhao, Zhihong Su, Li Sui, Shuai Wang, Jinjun School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Science and Technology on Communication Networks Laboratory Shijiazhuang China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Liaoning University of Technology Jinzhou China Institute of Artificial Intelligence and Robotic Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665464543

Network pruning is a figurative model compression technique designed to lighten and accelerate neural network models. Most existing pruning methods prioritize the selection of filters by their importance or apply regularization based on the properties of individual filters, neglecting the internal connections within combinations of multiple filters. This work introduces a pruning method termed Combinational Filter Deletion (CFD), which incorporates a straightforward yet effective evaluation metric based on the diversity of filter combination distributions to reveal the characteristics inherent to multiple filter interactions. CFD enables the exploration of an expanded search space, offering a greater array of choices and leveraging the intrinsic information of conventional layers. Moreover, this method is both general and non-exclusive, capable of enhancing the efficacy of other single-filter-based pruning techniques. © 2024 IEEE.

关键词： Wiener filtering

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