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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Wang, Zherui Wang, Yan Li, Ying Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin Engineering University Harbin China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350399059

Transient noise, which often occurs in the case of the sudden state change of the water surface or underwater targets, is the main source of the radiated noise for the quiet submarines and ships. This paper proposes an auto-regressive (AR) spectral estimation method based on the α-β filtering to accurately identify and analyze the transient noise. The mathematical model of the transient noise is first constructed according to inherent characteristics of the transient noise. Then, the transient signal is truncated in the time-domain, and the AR parameters of each segment data are estimated. In order to eliminate the frequency jump between data segments, the α-β filter is introduced in this method to modify the AR parameters, as a result of obtaining the time-frequency spectral with the high accurate and resolution. Finally, simulation results show the superior advantages of this method over other methods in terms of the resolution and accuracy, which is verified by the lake experiment. © 2023 IEEE.

关键词： Spectrum analysis

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Signal space transform and multidimensional information joint processing for moving target

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Chinese Journal of Acoustics 2024年第1期43卷 23-48页

作者： SHI Yunjia PIAO Shengchun GUO Junyuan ZHANG Liu Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001 Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001 College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001

A signal space transform method for detection of line spectrum signals radiated by moving target is *** time-frequency analysis and beamforming processing,a three-dimensional(3D)time-frequency-azimuth signal space is obtained in the array coordinate *** certain target course angle,this 3D signal space is transformed to the time-frequency-relative bearing spectrum signal space in the target coordinate *** theoretical derivation,simulation,and experimental data processing are *** findings demonstrate that the method is capable of converting the target line spectrum signal 3D trajectory to a 2D trajectory on the corresponding slice in the transformed signal space and signal processing gain of multi-dimensional information joint processing is obtained easier than in 3D signal space.

关键词： system. moving transformed

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Emission of underwater acoustic orbital angular momentum and data transmission

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Chinese Journal of Acoustics 2022年第1期41卷 20-34页

作者： LU Wei SUN Hao LAN Yu Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001 Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001 College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001

Emission of underwater acoustic orbital angular momentum(OAM)is studied as well as underwater acoustic communications with OAM *** acoustic properties for circular phased array are analyzed based on point source *** optimize and design an array prototype based on cymbal *** diameter of array prototype is 180 mm,the number of array elements is 10,and the working frequency is 25 k *** emits acoustic OAM with topological charges-3 to *** favorable results provide the theoretical basis for emitting acoustic OAM with different topological *** encode on-off states of acoustic OAM with different topological charges according to ASCII code to realize underwater acoustic *** transmission results suggest that acoustic OAM with various topological charges have intrinsically orthogonal *** research will offer potential applications in underwater acoustic communications.

关键词： OAM underwater momentum

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Forward-looking Sonar Interferometric Phase Adaptive Filter

Forward-looking Sonar Interferometric Phase Adaptive Filter

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2024 OES China Ocean Acoustics, COA 2024

作者： Liu, Weilu Zhou, Tian Chen, Baowei Du, Weidong Harbin Engineering University Acoustic Science and Technology Laboratory Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin150001 China Harbin Engineering University College of Underwater Acoustic Engineering Harbin150001 China

ISBN: (纸本)9798350340297

Interferometric phase filters are the cornerstone of sonar for high-precision interferometry. This study focuses on the use of interferometric phase filter for forward-looking sonar, proposing an adaptive short-time Fourier transform filtering algorithm from the perspective of a frequency domain. The algorithm analyzes the image characteristics of forward-looking sonar and proposes a two-dimensional Chebyshev-Kaiser window. The window is determined by the signal-to-noise ratio of the interferogram so that the phase filter can dynamically adjust the filter intensity. The filter reduces interferometric phase noise, thus improving the accuracy of forward-looking sonar interferometric measurement. Simulation experiment and lake experiment show that the filter proposed in this study eliminates over 95% of residues, providing reliable data support for subsequent interferometric phase unwrapping and underwater elevation models. © 2024 IEEE.

关键词： Adaptive filters

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Unbiased Multi-Label Learning from Crowdsourced Annotations 41

Unbiased Multi-Label Learning from Crowdsourced Annotations

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41st International Conference on Machine Learning, ICML 2024

作者： Xia, Mingxuan Huang, Zenan Wu, Runze Lyu, Gengyu Zhao, Junbo Chen, Gang Wang, Haobo School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Fuxi AI Lab NetEase Inc. Hangzhou China Faculty of Information Technology Beijing University of Technology Beijing China

This work studies the novel Crowdsourced Multi-Label Learning (CMLL) problem, where each instance is related to multiple true labels but the model only receives unreliable labels from different annotators. Although a few Crowdsourced Multi-Label Inference (CMLI) methods have been developed, they require both the training and testing sets to be assigned crowdsourced labels and focus on true label inferring rather than prediction, making them less practical. In this paper, by excavating the generation process of crowdsourced labels, we establish the first unbiased risk estimator for CMLL based on the crowdsourced transition matrices. To facilitate transition matrix estimation, we upgrade our unbiased risk estimator by aggregating crowdsourced labels and transition matrices from all annotators while guaranteeing its theoretical characteristics. Integrating with the unbiased risk estimator, we further propose a decoupled autoencoder framework to exploit label correlations and boost performance. We also provide a generalization error bound to ensure the convergence of the empirical risk estimator. Experiments on various CMLL scenarios demonstrate the effectiveness of our proposed method. The source code is available at https://***/MingxuanXia/CLEAR. Copyright 2024 by the author(s)

关键词： Risk perception

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Recommender Systems with Condensed Local Differential Privacy 3rd

Recommender Systems with Condensed Local Differential Privac...

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Liu, Ao Yao, Yanqing Cheng, Xianfu State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyberspace Science and Technology Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing100191 China

ISBN: (纸本)9783030622220

Recommender systems aim at predicting users’ future behaviors by learning the users’ personal information and historical behaviors. Unfortunately, training by the user’s raw data will inevitably cause the user’s private information to leak. Moreover, exiting privacy-preserving recommendation methods are based on matrix factorization with Local Differential Privacy (LDP). Because LDP performs poorly in small or multi-dimensional data sets, these methods do not work well with sparse or multi-dimensional features. Motivated by these questions, we propose a privacy-preserving recommendation model based on Deep neural networks and Factorization Machines (DeepFM), which can provide users with recommendations under the promise of protecting users’ privacy. In our recommendation model, each user randomly perturbs the calculated gradients to satisfy Condensed Local Differential Privacy (CLDP). The recommender system collects the perturbed gradients to train a recommendation model, which guarantees the safety of users’ private information and has excellent accuracy for the recommendation. Experiments on a real-world dataset show that the recommendation accuracy of our algorithm performs better than the existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital Communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data blockchain Shar ding Associated transactions Transaction assi gnment

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An Improved Privacy-Preserving Stochastic Gradient Descent Algorithm 3rd

An Improved Privacy-Preserving Stochastic Gradient Descent A...

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Cheng, Xianfu Yao, Yanqing Liu, Ao State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyberspace Science and Technology Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing100191 China

ISBN: (纸本)9783030622220

Deep learning techniques based on neural network have made significant achievements in various fields of Artificial Intelligence. However, model training requires large-scale datasets, these datasets are crowd-sourced and model parameters will contain the encoding of private information, resulting in the risk of privacy leakage. With the trend towards sharing pre-trained models, the risk of stealing training datasets through member inference attack and model inversion attack is further heightened. To tackle this problem, we propose an improved Differential Privacy Stochastic Gradient Descent algorithm, using simulated annealing algorithm and denoising mechanism to optimize the allocation method of privacy loss and improve model accuracy. We also analyze privacy cost under random shuffle data batch processing methods in detail within the framework of Subsampled Rényi Differential Privacy. Compared with existing methods, our experiments show that we can train deep neural networks with non-convex objective function more efficiently with moderate privacy budgets. © 2020, Springer Nature Switzerland AG.

关键词： Simulated annealing

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An intelligent file transfer optimization for poor network conditions 6th

An intelligent file transfer optimization for poor network c...

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6th International Symposium on security and Privacy in Social Networks and Big data, SocialSec 2020

作者： Yan, Ming Zhang, Bo Zhao, Gang Kuang, Xiaohui Liu, Lu Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Information Center of Ministry of Human Resources and Social Security Beijing100090 China National Key Laboratory of Science and Technology on Information System Security Beijing100091 China Zhejiang Lab HangzhouZhejiang Province China

ISBN: (纸本)9789811590306

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate. © Springer Nature Singapore Pte Ltd 2020.

关键词： Transmission control protocol

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Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

Multi-frame non-coherent track-before-detect method for weak...

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2023 International Conference on Frontiers of Ocean Science and technology, ICFOST 2023

作者： Zhang, Liu Piao, Shengchun Guo, Junyuan Wang, Xiaohan National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin150001 China Key Laboratory of Marine Information Acquisition and Security Harbin Engineering University Ministry of Industry and Information Technology Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

In order to solve the problem of performance degradation of track-before-detect (TBD) methods using single frame information when received signals exhibit a large fluctuation. A TBD method utilizing multi-frame information is proposed. Specifically, the novel measurement model and likelihood ratio function based on the non-coherent accumulation of multi-frame data are established. Multi-frame integration greatly improves the signal-to-noise ratio (SNR) of measurements. Furthermore, the derived likelihood ratio function can more effectively describe the statistical characteristics of the spectrum measurements. Combining these two advantages can improve the detection performance of weak tones for passive sonar. The simulation and experimental data results both demonstrate the advantages of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Signal to noise ratio

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