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International Conference on Electronic measurement and Instruments, ICEMI

作者： Wenjing Mo Chuanjin Han Yuan Wang Chengdu Aircraft Industrial (Group) Co. Ltd. Chengdu China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

Flight test is an essential step before aircraft delivery. Safe flight test can not only reduce the cost of aircraft research, but also has great significance for the study of aircraft performance. In order to ensure the safety of flight test, it is necessary to implement anomaly detection for aircraft systems. Inertial navigation system, as a complex system can provide accurate position, velocity, and attitude information for aircraft during flight test. It is very important to monitor the state of inertial navigation system for ensuring the safety of flight test. However, traditional single anomaly detection models perform poorly on different test data and have poor generalization capabilities. In order to improve the generalization ability of the anomaly detection model with inertial navigation data, this paper proposes a prediction-based anomaly detection method based on an integrated one-dimensional convolutional neural network (1DCNN). Firstly, multiple training sets are divided based on the bootstrap method, and then integrated 1DCNN prediction models are built for corresponding training sets. Finally, a static threshold judgment method based on the PauTa criterion is used to detect whether any abnormality occurs. This paper verifies the effectiveness of the proposed method through experiments with real inertial navigation data. The results show that compared with the traditional 1DCNN method, the proposed method significantly improves the performance of anomaly detection and has good generalization ability on different test conditions. The TPR of anomaly data detection reaches more than 97%, which proves that the method has effective anomaly data detection for inertial data.

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Machine-learning-based detection for quantum hacking attacks on continuous-variable quantum-key-distribution systems

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Physical Review A 2023年第6期107卷 062422-062422页

作者： Chao Ding Shi Wang Yaonan Wang Zijie Wu Jingtao Sun Yiyu Mao College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha 410082 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China

Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.

关键词： Quantum cryptography

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Interpretable DRL-Based Maneuver Decision of UCAV Dogfight

Interpretable DRL-Based Maneuver Decision of UCAV Dogfight

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haoran Han Jian Cheng Maolong Lv School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China College of Aeronautics Engineering Air Force Engineering University Xi'an China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of “Dive and Chase” behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

关键词： Training Simulation Deep reinforcement learning Libraries Cybernetics

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Unsupervised Domain Adaptation Using Attention Network and New Loss Function

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 704-718页

作者： Zhao, Li-Quan Zhou, Si-Ying Teng, Zi-Ming Jiang, Zi-Cong Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan School of Electrical and Information Engineering Beihua University Jilin132013 China

An unsupervised domain adaptation method is proposed based on the step-wise adaptive feature norm method to improve transfer learning accuracy. The feature extraction network consists of channel attention and spatial attention modules, which en-hance feature extraction ability. To reduce the influence of dimension change on channel weight, a convolution layer with the same kernel size is used to replace the fully connected layer in the bottleneck layer used in the common channel attention module. In addition, a smooth loss function is also proposed. The curve of the proposed function is smoother than the L2 function that is the least square error function. The proposed function’s gradient changes more slowly than the L2 function. The proposed function is used as the activate function of classification prediction regression to reduce the risk of gradient explosion. Compared with a selection of existing methods (Resnet50, conditional domain adversarial networks method, deep adaptive network method, joint adaptation network method, domain adaptation neural network method, and stepwise adaptive feature norm method), the proposed transfer learning method has the highest accuracy than other methods on different datasets for all tasks. © 2022 Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Feature extraction

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Fault Diagnosis Method of Rolling Bearings Under Different Working Conditions Based on Federated Feature Transfer Learning

Fault Diagnosis Method of Rolling Bearings Under Different W...

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Sensing, measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Shouqiang Kang Jiawei Yang Yulin Sun Yujing Wang Qingyan Wang V.I. Mikulovich School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China Belarusian State University Minsk Belarus

A rolling bearing fault diagnosis method based on the federated feature transfer learning is proposed for the low accuracy of the diagnosis model in the presence of large differences in data distribution under different working conditions, difficulty in obtaining labeled data and non-sharing of data among different users. This method performs wavelet transformation on the time domain vibration data of rolling bearings to obtain a time-frequency diagram. The priori labeled public data and the multi-user island private data are regarded as the source domain and the target domain. The multi-representation feature extraction structure is introduced to improve the original residual network. Based on an improved residual network and multi-representation features in the source domain and the target domain, every local model and a federated global model are constructed. Through verification of bearing data, the proposed method can establish an effective fault diagnosis model with high fault diagnosis accuracy. It can integrate the knowledge of isolated island data without sharing data among multiple users.

关键词： Fault diagnosis Employee welfare Wavelet transforms Adaptation models Wavelet domain Transfer learning Rolling bearings

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RCDFNet: A 4-D Radar and Camera Dual-Level Fusion Network for 3D Object Detection

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IEEE Sensors Journal 2025年

作者： Cheng, Peifeng Yan, Hang Wang, Yukang Wang, Luping Sun Yat-sen University School of Electronics and Communication Engineering Shenzhen China Hangzhou Qianjiang Distinguished Expert Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

4-D radar, as an advanced vehicle sensor, provides denser point clouds and elevation information than millimeter-wave radar, establishing it as a valuable sensor for autonomous driving. Recently, the fusion of 4-D radar and camera has become a viable alternative to LiDAR for autonomous driving perception systems. However, existing fusion methods have not fully realized the potential of complementary advantages offered by these two modalities. To address this limitation, our study proposes RCDFNet, a dual-level fusion network for 3D object detection with 4-D radar and camera. Specifically, we first exploit the geometric information from the 4-D radar to extract semantic image features from the perspective view, yielding pseudo-camera features under the bird’s-eye view. In the first fusion level, the pseudo-camera features are integrated with the 4-D radar features using the spatial point fusion module to obtain the radar-guide fusion features. In the second fusion level, we employ the deformable attention mechanism to facilitate the interaction between camera-view-transform features and the radar-guide fusion features, to generate BEV-match fusion features. Furthermore, we propose the target-aware refinement module to learn of foreground targets’ occupancy adaptively in the bird’s-eye view, mitigating the impact of the background noise of two sensors. We validate the RCDFNet on the TJ4DRadSet and View-of-Delft (VoD) datasets. Experimental results demonstrate that RCDFNet significantly outperforms the baseline RCFusion, achieving state-of-the-art performance in 3D object detection. © 2001-2012 IEEE.

关键词： 3D object detection 4-D radar-camera fusion Autonomous driving camera deep learning radar

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A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Network 7

A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Netw...

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7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Zhang, Shujian Xu, Zhan Tian, Lu Yang, Xiaolong Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing100101 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9781665405829

Spectrum sensing can effectively improve the low utilization of spectrum resources and is one of the crucial components of cognitive radio networks. This paper proposes a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network cascaded spectrum sensing model. The model uses CNN to analyze the Short-Time Fourier transform spectrogram of the blind signal. Then the generated feature vector or feature map is passed to the LSTM according to the timestamp. Finally, it detects a signal in a specific spectrum and classifies the signal type to identify multiple signals accurately. The neural network model improves the detection probability by simultaneously acquiring the spatial and temporal characteristics of the blind signal. The experimental results show that the method in this paper can detect a variety of signals with higher detection probability within a wide range of SNR, especially under the condition of low SNR. © 2021 IEEE.

关键词： Cognitive radio

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Non-Singular Fixed-Time Robust Containment control for Autonomous Surface Vehicles

SSRN

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SSRN 2023年

作者： Ma, Junda Sun, Hefang Tan, Chong Zhao, Tianyuan Liu, Ke College of Automation Harbin University of Science and Technology Harbin150000 China Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150000 China College of Journalism and Communication Jilin University Changchun130000 China College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150000 China

For the containment control problem of autonomous surface vehicles with external disturbances, a novel non-singular fixed-time control scheme is developed, where the multi-ship system consists of real leaders and followers. In contrast with traditional containment cases studied in most of the existing literature, the real leaders are required to track the desired trajectories, and the followers need to get into the convex hull spanned by the real leaders in fixed time. The method for solving the fixed-time containment control problem is divided into the following steps: Firstly, a fixed-time disturbance observer is designed to estimate the external disturbance for the leaders and the followers, and the estimation result is used to compensate for the input of the control system;Second, combined with fixed-time stability theory and a switch function, a non-singular fixed-time trajectory tracking controller for leaders is designed, such that the position tracking error could be bounded within a fixed time;Third, to achieve formation efficiently, a non-singular fixed-time containment controller strategy incorporating with graph theory is proposed for coordinating followers, such that the fixed-time stability can be ensured. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Graph theory

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A Novel Integer Frequency Offset Estimation Method for OFDM Based on Preamble 7

A Novel Integer Frequency Offset Estimation Method for OFDM ...

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7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Liu, Xinxin Xu, Zhan Tian, Lu Yang, Xiaolong Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing100101 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9781665405829

Orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Although the existence of integer frequency offset in OFDM will not destroy the orthogonality between subcarriers, it will cause a cyclic shift of the frequency domain data after FFT transformation at the receiving end, which will affect the demodulation of data. In this paper, we propose a novel integer frequency offset estimation method based on the similarity property of adjacent subcarriers of the preamble. This method not only guarantees the original accuracy and performance of the conventional method but also achieves less resource consumption and lower computational complexity. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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Research on Voltage Sensing Characteristics of Liquid Crystal Fully Filled Pcf with Temperature Compensation

SSRN

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SSRN 2024年

作者： Li, Yang Zhang, Ren-Long Ding, Bo-Han Liu, Qiang College of Information Science and Engineering Northeastern University Shenyang110819 China School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Northeastern University at Qinhuangdao Qinhuangdao066004 China

The voltage response characteristics of nematic liquid crystal fully filled photonic crystal fiber (LC-PCF) are studied, and the temperature compensation is carried out to realize the accurate voltage measurement in temperature changing environment. In the process of fabricating the sensor probe, the capillary effect is used to fill the LC into the micro-nano pores of the PCF. Then, using the negative pressure generated by the syringe, the LC moves to the middle along the fiber length direction. The residual LC from the inner wall of the optical fiber are moved away from the fiber end by filling alcohol to avoid the carbonization of the residual LC near the end face of the fiber, due to the discharge of the splicing machine when the PCF is fused with the ordinary single-mode fiber. when the external voltage or temperature changes, the transmission spectrum of the LC-PCF can be obviously observed to shift, and then the voltage or temperature response can be analyzed. The results show that the fiber sensor possesses the sensitivity of 298 pm/V, and as the temperature is in the range of 21~27 °C, the voltage response sensitivity is basically unchanged. The temperature compensation is carried out by cascading a long period grating by which the fiber sensor has the ability to measure voltage accurately in the temperature changing environment. © 2024, The Authors. All rights reserved.

关键词： Photonic crystal fibers

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