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An Adversarial Defense Algorithm Based on Triplet Network and Voting Decision

An Adversarial Defense Algorithm Based on Triplet Network an...

引用

2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Zheng, Haonan Zhao, Jiajia Tan, Wei School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In the field of artificial intelligence, neural network is one of the key technologies used for image classification and recognition. However, recent work has demonstrated that deep neural networks are easily attacked by adversarial examples to make misjudgments. Adversarial examples are almost indistin-guishable from normal examples and yet cannot be classified correctly by neural networks. The existence of adversarial examples is a major obstacle to the practical application and deployment of neural networks, so the research on adversarial defense algorithms is an important topic in the field of AI security. This paper proposes an adversarial example defense algorithm based on a triplet network and voting decision mechanism. Firstly, two neural networks with different structures are trained based on normal dataset. Secondly, the first network is fine-tuned using the adversarial examples generated by these two networks, resulting in a third neural network. Then, these three neural networks are used as sub-networks in parallel to construct a triplet network. Through adversarial training and differences in structures, the transferability of adversarial examples among the three sub-networks is weakened. Finally, the final classification result is obtained by majority voting, based on the parallel output results of the three sub-networks. Through the complementarity between these three sub-networks, the defense against adversarial examples is realized. The experimental results demonstrate the effectiveness of this algorithm. © 2022 IEEE.

关键词： Deep neural networks

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A Comparative Study on Adversarial Attacks for Visible and Infrared Images

A Comparative Study on Adversarial Attacks for Visible and I...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Guo, Haiwang Tan, Wei Zhao, Jiajia School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China Agent Cooperation Laboratory Science and Technology on Complex System Control and Intelligent Beijing100074 China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In recent years, the booming development of artificial intelligence has driven the development and progress of society. With its excellent performance, deep learning has been widely used in many fields, including some security-sensitive fields such as unmanned systems, auto-driving and so on. Research has found that deep neural networks are vulnerable to adversarial samples, which can lead to misclassification of neural networks and can cause serious safety incidents. Hence, the study of adversarial samples has received increasing attention, and researchers have conducted studies from different perspectives, and relevant research results have emerged. However, most of these studies have focused on visible images, and few have studied infrared images, which play an important role in military and other important fields. In this paper, a comparative study on adversarial attacks for visible and infrared images is presented. A number of adversarial attack experiments are conducted on ResNet18 and VGG16 models for visible and infrared images. By comparing the experimental results, the characteristics of infrared adversarial samples are derived relative to those with visible adversarial samples. Then, a gradient attack method from a frequency domain perspective is proposed. The adversarial perturbation noise is added to the low frequency component of images. It is verified experimentally that the proposed method improves the transferability of infrared adversarial samples compared to some baseline methods. © 2022 IEEE.

关键词： Deep neural networks

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Research on scattering characteristics of antenna array 4

Research on scattering characteristics of antenna array

引用

4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Chen, Ao Zong, Xianzheng Nie, Zaiping Liu, Fan Wang, Youcheng Jiang, Peng School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781733509619

A new RCS reduction method for the antenna array is proposed. Previous works have shown that the radiation pattern function of an antenna array is equal to the array element radiation pattern function multiplying the radiation array factor. Similarly, scattering factor is used to express an important parameter for analyzing the scattering characteristics of antenna arrays. The expression equation of the scattering factor of several conventional arrays can be derived directly, and then the maximum scattering direction of the antenna array can be obtained. In order to prove the validation of the scattering factor, several arrays are simulated by integral equation method, and finally proposed the condition of non-scattering-grating-lobe. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Antenna arrays

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A Method for Marine Ship Formation Intention Recognition based on BiConvLSTM-Attention 3

A Method for Marine Ship Formation Intention Recognition bas...

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3rd International Conference on Electronic Information Engineering and Computer science, EIECS 2023

作者： Xi, Yunqiao Zhang, Junping Liu, Jian Wang, Linlin Xu, Hongfeng Chen, Linxiu Jiang, Jixiang Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Institute of Electrical and Mechanical Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350316674

To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recognition. This paper proposes a bidirectional convolutional long short term memory-attention network for marine formation target intention recognition. The network takes the multi-source trajectory data of the marine ship formation target as the input, extracts and uses the target change rule and time domain characteristics of the Marine formation data, and trains the model to have the ability to independently learn the weight of information in different time periods. The simulation results show that the method has good performance and can meet the needs of practical application. © 2023 IEEE.

关键词： intention recognition long short-term marinship formation temporal features

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Channel Estimation Based on Lorentzian Adaptive Algorithm in a-stable Noise Environment 9

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： Cherednichenko, Aleksey Huang, Xinqi Wang, Yanyan Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781728198057

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel ina-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons. © 2020 IEEE.

关键词： Channel estimation

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An Estimation Method for Scramjet Inlet Mach Number and Mass Flow Rate Based on Deep Learning

An Estimation Method for Scramjet Inlet Mach Number and Mass...

引用

International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Kong, Chen Liu, Hao Xu, Cheng Chang, Juntao School of Energy Science and Engineering Harbin Institute of Technology Harbin China Xi’an Aerospace Propulsion Institute Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811694912

Accurate estimation of scramjet inlet parameters (including Mach number and air mass flow rate) is essential for hypersonic flight control. These critical scramjet inlet parameters could be obtained by estimating the air data parameters through the inertial navigation system, but they have large errors. The flush air data sensing system is mainly used for post-flight analysis. This paper proposes an estimation method for scramjet inlet parameters based on deep learning. Accurate estimations of air data are not needed. Instead, the measurements of the transducers on the inlet wall are directly used as the input of the artificial neural network, and then the scramjet inlet parameter (Mach number or air mass flow rate) is output. The results show that the estimation accuracy of the scramjet inlet parameters has been greatly improved. This work provides a new idea for the estimation of the scramjet inlet parameters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mass transfer

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Optimal Terrain Following Trajectory Regeneration Using Linear Gauss Pseudo-Spectral Method

Optimal Terrain Following Trajectory Regeneration Using Line...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Feng, Yijun Chen, Wanchun Yang, Liang Wei, Donghui Beihang University School of Astronautics Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

A numerical method for two-dimensional terrain following optimal trajectory regeneration is proposed in this paper, which corrects the planned optimal trajectory to avoid collision with previously unknown obstacles. The neighboring extremal problem is established near the optimal terrain following trajectory, which can be regarded as a two-point boundary-value problem (TPBVP) and solved using Linear Gauss Pseudo-Spectral (LGP) method. Compared with the classical backward sweep method for solving TPBVPs, LGP method is much faster in obtaining approximate results. Numerical simulations are provided to validate the effectiveness and demonstrate the features of the problem. © 2019 IEEE.

关键词： Numerical methods

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

引用

5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 IEEE.

关键词： Electroencephalography

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Monocular visual based obstacle distance estimation method for ultra-low altitude flight

Monocular visual based obstacle distance estimation method f...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Cheng, Jin Wei, Donghui Chen, Wanchun Gao, Wang Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Astronautics Beijing China

ISBN: (纸本)9781728137926

This paper focuses on the problem of ultra-low-altitude flight obstacle distance estimation for monocular vision-assisted aircraft. First, the basic error model of distance estimation using sequence images is derived and the phenomenon of "epipole dead zone" is revealed. Then the distance estimation algorithm based on pairs of coplanar points is proposed as well as its error model. This algorithm is proved to be a solution of the "epipole dead zone" problem theoretically. Finally, numerical simulations show that the proposed distance estimation algorithm can effectively improve the accuracy of distance estimation in the "epipole dead zone". © 2019 IEEE.

关键词： Vision

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Decoupling Design Based on Active Disturbance Rejection control Approach for Near Space Vehicle Autopilot

Decoupling Design Based on Active Disturbance Rejection Cont...

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Chinese intelligent systems Conference, CISC 2019

作者： Li, Hui Yin, Zhide Li, Fengyi Liu, Hongyu Beijing Electro-Mechanical Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Institute of Control and Electronic Technology Beijing100038 China

ISBN: (纸本)9789813296855

A decoupled control approach based on linear active disturbance rejection control idea is proposed. The plant model, in which the coupling dynamics is as a part of disturbance, is established, and some virtual control variables are defined. Thus the original coupling plant is divided into some single-input single-output systems and the virtual control variables are willing to be determined by linear active disturbance rejection control method, and the real control variables are easily solved from virtual control variables according to their relationship. The approach is employed to a hypersonic near space vehicle lateral autopilot. The numerical simulations show that the proposed approach has good decoupling effectiveness and guarantees angle command signal tracking accuracy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Disturbance rejection

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