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

作者： Xia, Yinfeng Wang, Yanyan Li, Yingsong Xue, Wei Ye, Qiubo Harbin Engineering University College of Information and Communication Engineering Harbin China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China Jimei University School of Information Engineering Xiamen361021 China

ISBN: (纸本)9781728198057

In this paper, we will propose a super wide bandwidth low frequency antenna with a compact size for HF, VHF and UHF applications. The developed low frequency antenna consists of coplanar guide feeding structure, sleeve-like structure with lumped elements, reactance loading in the feeding. The antenna is investigated and evaluated in the HFSS, and the simulation results shows that the proposed antenna has a very wide fractional bandwidth of 191.9% from 10 MHz to 481 MHz for supporting the HF, VHF, UHF and P-band detection system applications in various platform like aircraft, ship and vehicles. © 2020 IEEE.

关键词： Bandwidth

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Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

arXiv

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arXiv 2022年

作者： Liu, Tianying Zhang, Lu Wang, Yang Guan, Jihong Fu, Yanwei Zhao, Jiajia Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai201804 China The Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200438 China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Shanghai200433 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Distributed square-root cubature Kalman filter for underwater cyber-physical systems with multiple fading measurements

Distributed square-root cubature Kalman filter for underwate...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Ye Chen Pengpeng Ye Yuan Liang Lin Meng Artificial Intelligence Research Institute of Industrial Technology Nanjing Institute of Technology Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory BeijingChina School of Automotive and Rail Transit Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9781665426220

In this article, we focus on the problem of the distributed nonlinear state estimation for a class of underwater cyber-physical systems with fading measurements. In this article, we propose a modified square-root cubature Kalman filter to tackle with the nonlinear system model. And the diffusion strategy is introduced in this paper to improve the system's estimation performance. To ease the internode communication cost, we propose an event-triggered mechanism to avoid the unnecessary information transmission. The corresponding esti-mation algorithm is also derived in this article. The simulation results show that the proposed algorithm can estimate the target's state accurately with less communication resources.

关键词： Fading channels Costs Simulation Information processing Cyber-physical systems Filtering algorithms Social intelligence

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Research on Performance Evaluation Method of UAV Multi-Source Data Fusion Based on Credibility

Research on Performance Evaluation Method of UAV Multi-Sourc...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Hua-pin Geng Jia-hui Tong Beijing Electromechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Based on the analysis of aircraft multi-source test data and the characteristics of common data fusion methods, this paper puts forward a multi-source data fusion performance evaluation process model based on credibility, and studies the key points of test data pre-processing and compatibility test Methods, credibility analysis methods and multi-source test data fusion methods based on credibility, focusing on the credibility analysis method of multi-source data based on evidence distance and the credibility analysis method based on consistency analysis, from the perspective of reliability, objectively measure the weight of multi-source data in fusion, and carry out typical Validation to test the effectiveness of the multi-source test data fusion performance evaluation method based on credibility.

关键词： Quality function deployment Xenon Rail to rail outputs

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An Approximated Domination Relationship based on Binary Classifiers for Evolutionary Multiobjective Optimization

An Approximated Domination Relationship based on Binary Clas...

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Congress on Evolutionary Computation

作者： Hao Hao Aimin Zhou Hu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China

Preselection is an important strategy to improve evolutionary algorithms’ performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a pre-selection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.

关键词： Filtering Computational modeling Sociology Training data Evolutionary computation Approximation algorithms Linear programming

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Depth Estimation for Small Obstacles Based on Monocular Vision

Depth Estimation for Small Obstacles Based on Monocular Visi...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Wang Gao Changqing Wang Lei Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.

关键词： intelligent agents Estimation control systems Training Deep learning Computer vision complex systems

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A Multi-branch Parallel Simulation Method based on Symbiotic Potential

A Multi-branch Parallel Simulation Method based on Symbiotic...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhao, Lu Zhen-Biao, Tu Jia-Ning, Wei Yu-Tong, Zhu Tong-Lin, Liu Qing-Hua, Liu Beijing Electro-Mechanical Engineering Insititute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

A multi-branch parallel simulation method and an architecture of application system are proposed in this paper based on symbiotic potential, and method of construction and calibration of parallel battlefield based on real data, as well as generation of simulation branch based on situation interpretation etc. are proposed. Fast building and adjustment of situation of real time simulation are realized based on simulation to meet the requirements of consistency and parallel operation between virtual simulation and real systems. Multibranch operation management, multi-branch highperformance operation support and other methods are proposed to support the dynamic generation of massive parallel simulation copies based on real-time intelligence and ultra-realtime and large-sample operation to meet the requirements of advanced evaluation of real-time situation. At the same time, a set of simulation application verification frameworks for typical task situation is presented to support the construction of multibranch simulation operation environment, and provide technical verification means for real-time situation prediction and evaluation. © 2019 IEEE.

关键词： Real time systems

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Estimate depth information from monocular infrared images based on deep learning 7

Estimate depth information from monocular infrared images ba...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Li, Xu Ding, Meng Wei, Donghui Wu, Xiaozhou Cao, Yunfeng Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing China Beijing Electro-.Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China

ISBN: (纸本)9781728170862

In the field of automatic driving, vision-based driving assistance system(V-DAS) is a research direction. Aiming at the demand of V-DAS for low visibility environment perception, we propose a depth estimation method based on monocular infrared images. Our method uses an end-to-end self-supervised deep learning framework, and uses the reprojection relationship between monocular video frames to construct the loss function, which makes it easy to collect dataset. In order to mitigate the influence of infrared noise, we adopt the minimum reprojection loss method. To ensure the neural network can learn infrared features better, we use transfer learning from RGB image dataset first. Qualitative experiments on FLIR datasets show that the proposed method can obtain pixel-lev el dense depth from monocular infrared images. Experiments on real roads show that the proposed method can effectively perceive the depth information of the target in the night, and the absolute error is 13.2% within 15m. This can meet the requirements of collision avoidance in most emergency situations during automatic driving. © 2020 IEEE.

关键词： Deep learning

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

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Aleksey Cherednichenko Xinqi Huang Yanyan Wang Yingsong Li Harbin Engineering University Harbin China Intelligent Agent Cooperation Laboratory Science and Technology on Complex System Control Beijing China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198064

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel inα-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.

关键词： Computer simulation Adaptive algorithms Channel estimation Adaptive arrays

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Failure mode and effects analysis based on intuitionistic fuzzy sets and evidential correlation coefficient

Failure mode and effects analysis based on intuitionistic fu...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Hang Huang, Chan Lu, Mingsheng Dong, Xiaofei School of Software Northwestern Polytechnical University Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Xi'an Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Agent Cooperation Laboratory Science and Technology on Complex System Control and Intelligent Beijing China

ISBN: (纸本)9781728137926

As an effective implement, failure mode and effects analysis (FMEA) is widely applied in the security of system for practical application. Nowadays, many methods determine the order of fault mode by a crisp risk priority number (RPN). However, these methods exist several shortcomings, for instance, the correlation of the assessments given by team members are not fully considered. In this article, a new method for risk assessment and sequence for failure modes in FMEA is proposed on account of the D-S evidence theory and the evidential correlation coefficient. By using the proposed approach, the weights of team members for each failure mode and risk factor is obtained. Then the weighted assessments are used to perform the aggregation process by Intuitionistic fuzzy weighted averaging (IFWA) operator. A classic application regarding risk assessment is used to verify the effectiveness of the proposed method. © 2019 IEEE.

关键词： Failure modes

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