scholarly journals A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4919
Author(s):  
Peng Man ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Network Model ◽  
Pulse Width ◽  
Metric Learning ◽  
Identification Algorithm ◽  
Individual Sample ◽  
Electronic Warfare ◽  
Specific Emitter Identification ◽  
Working Parameters ◽  
And Training ◽  
Working Parameter

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples.

A Study on Deburring Process of Micro Channel Using Ep and MAP Hybrid Process

2013 ◽  
Vol 741 ◽  
pp. 39-44
Author(s):  
Chang Geun Park ◽  
Byung Hun Son ◽  
Jae Seob Kwak
Keyword(s):  
Performance Criteria ◽  
Hybrid Process ◽  
Magnetic Flux Density ◽  
Micro Channel ◽  
Polishing Process ◽  
Experiment Method ◽  
Low Efficiency ◽  
Magnetic Abrasive Polishing ◽  
Working Parameters ◽  
Working Parameter

Magnetic abrasive polishing is one of the most promising finishing methods applicable to complex surfaces. Nevertheless this process has a low efficiency when applied to very hardened materials. For this reason, EP-MAP hybrid process was developed. EP-MAP process is expected to machine complex and hardened materials.In this study, deburring process using EP-MAP hybrid process was proposed. EP-MAP hybrid deburring process is applied to micro channel, and thereby it can obtain both deburring process and polishing process. To evaluate the performance criteria of the EP-MAP hybrid deburring process, EP-MAP hybrid deburring process on the micro channel was performed. Through investigating the effect of working parameters, namely magnetic flux density, electric potential, working gap and feed rate, error of height and surface roughness according to working parameter is analyzed using design of experiment method.

scholarly journals Rare bioparticle detection via deep metric learning

RSC Advances ◽  
2021 ◽  
Vol 11 (29) ◽  
pp. 17603-17610
Author(s):  
Shaobo Luo ◽  
Yuzhi Shi ◽  
Lip Ket Chin ◽  
Yi Zhang ◽  
Bihan Wen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Networks ◽  
Metric Learning ◽  
Practical Applications ◽  
Deep Metric Learning

Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.

scholarly journals KDAS-ReID: Architecture Search for Person Re-Identification via Distilled Knowledge with Dynamic Temperature

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 137
Author(s):  
Zhou Lei ◽  
Kangkang Yang ◽  
Kai Jiang ◽  
Shengbo Chen
Keyword(s):  
State Of The Art ◽  
Identification Algorithm ◽  
Student Model ◽  
Deep Convolutional Neural Networks ◽  
Fast Speed ◽  
Training Stage ◽  
Knowledge Distillation ◽  
And Training ◽  
Better Than ◽  
Teacher Model

Person re-Identification(Re-ID) based on deep convolutional neural networks (CNNs) achieves remarkable success with its fast speed. However, prevailing Re-ID models are usually built upon backbones that manually design for classification. In order to automatically design an effective Re-ID architecture, we propose a pedestrian re-identification algorithm based on knowledge distillation, called KDAS-ReID. When the knowledge of the teacher model is transferred to the student model, the importance of knowledge in the teacher model will gradually decrease with the improvement of the performance of the student model. Therefore, instead of applying the distillation loss function directly, we consider using dynamic temperatures during the search stage and training stage. Specifically, we start searching and training at a high temperature and gradually reduce the temperature to 1 so that the student model can better learn from the teacher model through soft targets. Extensive experiments demonstrate that KDAS-ReID performs not only better than other state-of-the-art Re-ID models on three benchmarks, but also better than the teacher model based on the ResNet-50 backbone.

Research on a New Convolutional Neural Network Model Combined With Random Edges Adding

2021 ◽  
Vol 12 (1) ◽  
pp. 67-76
Author(s):  
Jin Zhang ◽  
Sen Tian ◽  
XuanYu Shu ◽  
Sheng Chen ◽  
LingYu Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Small World ◽  
Cross Layer ◽  
Data Set ◽  
Speed Up ◽  
Model Recognition ◽  
And Training

It is always a hot and difficult point to improve the accuracy of the convolutional neural network model and speed up its convergence. Based on the idea of the small world network, a random edge adding algorithm is proposed to improve the performance of the convolutional neural network model. This algorithm takes the convolutional neural network model as a benchmark and randomizes backwards and cross layer connections with probability p to form a new convolutional neural network model. The proposed idea can optimize the cross-layer connectivity by changing the topological structure of the convolutional neural network and provide a new idea for the improvement of the model. The simulation results based on Fashion-MINST and cifar10 data set show that the model recognition accuracy and training convergence speed are greatly improved by random edge adding reconstructed models with a probability of p = 0.1.

A Robust ToA and Pulse Width Estimator for Electronic Warfare Applications

2020 ◽  
Author(s):  
Aline Silva ◽  
Rafael Figueirêdo ◽  
Luiz Segadilha ◽  
Sergio Neves ◽  
Jean Marc-Lopez
Keyword(s):  
Pulse Width ◽  
Electronic Warfare

scholarly journals Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jing Wang ◽  
Chunjiao Dong ◽  
Chunfu Shao ◽  
Shichen Huang ◽  
Shuang Wang
Keyword(s):  
Network Model ◽  
Evaluation System ◽  
Large Scale ◽  
Urban Traffic ◽  
Identification Algorithm ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Key Nodes

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

scholarly journals Design and characterization of a 6–18 GHz GaN on SiC high-power amplifier MMIC for electronic warfare

2019 ◽  
Vol 11 (7) ◽  
pp. 625-634
Author(s):  
Eduardo Oreja Gigorro ◽  
Emilio Delgado Pascual ◽  
Juan José Sánchez Martínez ◽  
María Luz Gil Heras ◽  
Virginia Bueno Fernández ◽  
...  
Keyword(s):  
Power Amplifier ◽  
High Power ◽  
Duty Cycle ◽  
Pulse Width ◽  
Pulse Mode ◽  
Gate Length ◽  
Electronic Warfare ◽  
Power Added Efficiency ◽  
High Power Amplifier

AbstractA 6–18 GHz high-power amplifier (HPA) design in GaN on SiC technology is presented. This power amplifier consists of a two-stage corporate amplifier with two and four transistors, respectively. It has been fabricated on UMS using their 0.25 µm gate length process, GH25. A study of the suitable attachment method and measurement on wafer and on jig are detailed. This HPA exhibits an averaged output power of 39.2 dBm with a mean gain of 11 dB in saturation and a 24.5% maximum power added efficiency in pulse mode operation with a duty cycle of 10% with a 25 µs pulse width.

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