scholarly journals Radar Emitter Identification under Transfer Learning and Online Learning

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 15 ◽  
Author(s):  
Yuntian Feng ◽  
Yanjie Cheng ◽  
Guoliang Wang ◽  
Xiong Xu ◽  
Hui Han ◽  
...  
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Model Simulation ◽  
Training Data ◽  
Identification Accuracy ◽  
Support Vector ◽  
Target Domain ◽  
Identification Methods ◽  
Identification Method

At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identification methods usually include an offline training stage and online identifying stage, which cannot achieve the real-time identification of the radar emitter. Aimed at the above problems, this paper proposes a radar emitter identification method based on transfer learning and online learning. First, for the case where the target domain contains only a small number of labeled samples, the TrAdaBoost method is used as the basic learning framework to train a support vector machine, which can obtain useful knowledge from the source domain to aid in the identification of the target domain. Then, for the case where the target domain does not contain labeled samples, the Expectation-Maximization algorithm is used to filter the unlabeled samples in the target domain to generate the available training data. Finally, to make the identification quickly and accurately, we propose a radar emitter identification method, based on online learning to ensure real-time updating of the model. Simulation experiments show that the proposed method, based on transfer learning and online learning, has higher identification accuracy and good timeliness.

scholarly journals An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 271 ◽  
Author(s):  
Yuntian Feng ◽  
Guoliang Wang ◽  
Zhipeng Liu ◽  
Runming Feng ◽  
Xiang Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Training Data ◽  
Identification Accuracy ◽  
Support Vector ◽  
Learning Method ◽  
Target Domain ◽  
Identification Method ◽  
Svm Model ◽  
Testing Data ◽  
Task Simulation

Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.

TrCSVM: a novel approach for the classification of melanoma skin cancer using transfer learning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Support Vector Machine ◽  
Skin Lesion ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Training Data ◽  
Support Vector ◽  
Second Phase ◽  
Target Domain ◽  
Content Type

PurposeThe study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.Design/methodology/approachIn this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.FindingsThe experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.Originality/valueExperiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

scholarly journals In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2589 ◽  
Author(s):  
Yongxiang Li ◽  
Wei Zhao ◽  
Qiushi Li ◽  
Tongcai Wang ◽  
Gong Wang
Keyword(s):  
Product Quality ◽  
Real Time ◽  
Case Studies ◽  
Complex Geometry ◽  
Identification Accuracy ◽  
In Situ Monitoring ◽  
Support Vector ◽  
Fused Filament Fabrication ◽  
Vibration Sensors

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.

scholarly journals Real-Time Behaviour Planning and Highway Situation Analysis Concept with Scenario Classification and Risk Estimation for Autonomous Vehicles

Designs ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 4 ◽  
Author(s):  
Bence Dávid ◽  
Gergő Láncz ◽  
Gergely Hunyady
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Risk Estimation ◽  
Training Data ◽  
Traffic Density ◽  
Support Vector ◽  
Situation Analysis ◽  
Probabilistic Networks ◽  
Time Operation

The development of autonomous vehicles is one of the most active research areas in the automotive industry. The objective of this study is to present a concept for analysing a vehicle’s current situation and a decision-making algorithm which determines an optimal and safe series of manoeuvres to be executed. Our work focuses on a machine learning-based approach by using neural networks for risk estimation, comparing different classification algorithms for traffic density estimation and using probabilistic and decision networks for behaviour planning. A situation analysis is carried out by a traffic density classifier module and a risk estimation algorithm, which predicts risks in a discrete manoeuvre space. For real-time operation, we applied a neural network approach, which approximates the results of the algorithm we used as a ground truth, and a labelling solution for the network’s training data. For the classification of the current traffic density, we used a support vector machine. The situation analysis provides input for the decision making. For this task, we applied probabilistic networks.

scholarly journals Deep Transfer Learning and Time-Frequency Characteristics-Based Identification Method for Structural Seismic Response

2021 ◽  
Vol 7 ◽  
Author(s):  
Wenjie Liao ◽  
Xingyu Chen ◽  
Xinzheng Lu ◽  
Yuli Huang ◽  
Yuan Tian
Keyword(s):  
Seismic Response ◽  
Transfer Learning ◽  
Wavelet Transforms ◽  
Frequency Characteristics ◽  
Identification Accuracy ◽  
Common Interest ◽  
Time Frequency ◽  
Identification Method ◽  
Share Data ◽  
The Cost

The cost of dedicated sensors has hampered the collection of the high-quality seismic response data required for real-time health monitoring and damage assessment. The emergence of crowdsensing technology, where a large number of mobile devices collectively share data and extract information of common interest, may help remove such obstacles and mitigate the seismic hazard. The present study proposes a crowdsensing-oriented vibration acquisition and identification method based on time–frequency characteristics and deep transfer learning. It can distinguish the responses during an earthquake event from vibration under serviceability conditions. The core classification process is performed using a combination of wavelet transforms and deep transfer networks. The latter were pre-trained using finite element models calibrated with the monitored seismic responses of the structures. The validation study confirmed the superior identification accuracy of the proposed method.

scholarly journals GLACIER IDENTIFICATION FROM LANDSAT8 OLI IMAGERY USING DEEP U-NET

2020 ◽  
Vol V-3-2020 ◽  
pp. 381-386
Author(s):  
Q. He ◽  
Z. Zhang ◽  
G. Ma ◽  
J. Wu
Keyword(s):  
Regional Climate ◽  
Spectral Characteristics ◽  
Semantic Segmentation ◽  
Identification Accuracy ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Identification Methods ◽  
Identification Method ◽  
Data Source

Abstract. Glacier is one of the clearest signal of climate change, and its changes have important effects on regional climate and water resources. Glacier identification is the basic of glacial changes research. Traditional remote sensing glacier identification methods usually perform simple bands calculation based on the spectral characteristics of glacier. The identification results are greatly affected by threshold segmentation. In addition, there is a misclassification of water body and glacier. As a simple and efficient semantic segmentation network, U-Net has been widely used in many fields of image processing. This paper performs an improved semantic segmentation network Deep U-Net for glacier identification using Landsat 8 OLI image as the data source, and compares it with the traditional NDSI glacier identification method. The identification results are validated by the glacier label data produced by visual interpretation. The results indicate that the proposed method achieves an identification accuracy of 97.27%, which is higher than the NDSI glacier identification method. It can effectively exclude the interference of water bodies on glacier identification, and has a higher degree of automation.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

scholarly journals Real-Time Parameter Estimation of a Nonlinear Vessel Steering Model Using a Support Vector Machine

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Haitong Xu ◽  
M. A. Hinostroza ◽  
Vahid Hassani ◽  
C. Guedes Soares
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Parameter ◽  
Least Square ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Parameters ◽  
Ship Model ◽  
Free Running ◽  
Marine Vessels

The least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, maneuvering tests are carried out based on a scaled free-running ship model. The parameters are estimated using standard LS-SVM and compared with the theoretical solutions. Then, an online version, a sequential least-square support vector machine, is derived and used to estimate the parameters of vessel steering in real-time. The results are compared with the values estimated by standard LS-SVM with batched training data. By comparison, a sequential least-square support vector machine can dynamically estimate the parameters successfully, and it can be used for designing a dynamic model-based controller of marine vessels.

Parallel SMO for Traffic Flow Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 843-848 ◽  
Author(s):  
Fan Wang ◽  
Guo Zhen Tan ◽  
Chao Deng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Good Method ◽  
Transportation Systems ◽  
Training Data ◽  
Support Vector ◽  
Training Time ◽  
The Real ◽  
Traffic Flow Forecasting

Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems and advanced traveler information systems. Since Support Vector Machine (SVM)have better generalization performance and can guarantee global minima for given training data, it is believed that SVR is an effective method in traffic flow forecasting. But with the sharp increment of traffic data, traditional serial SVM can not meet the real-time requirements of traffic flow forecasting. Parallel processing has been proved to be a good method to reduce training time. In this paper, we adopt a parallel sequential minimal optimization (Parallel SMO) method to train SVM in multiple processors. Our experimental and analytical results demonstrate this model can reduce training time, enhance speed-up ratio and efficiency and better satisfy the real-time demands of traffic flow forecasting.

Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning

2021 ◽  
Author(s):  
Justin Larocque-Villiers ◽  
Patrick Dumond
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Training Data ◽  
Fault Classification ◽  
Target Domain ◽  
Bearing Faults

Abstract Through the intelligent classification of bearing faults, predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required to develop algorithms capable of generalized intelligent fault detection across domains and discrepancies. In this study, current benchmarks on source–target domain discrepancy challenges are reviewed using the Case Western Reserve University (CWRU) and the Paderborn University (PbU) datasets. A convolutional neural network (CNN) architecture and data augmentation technique more suitable for generalization tasks is proposed and tested against existing benchmarks on the Pb U dataset by training on artificial faults and testing on real faults. The proposed method improves fault classification by 13.35%, with less than half the standard deviation of the compared benchmark. Transfer learning is then used to leverage the larger PbU dataset in order to make predictions on the CWRU dataset under a challenging source-target domain discrepancy in which there is minimal training data to adequately represent unseen bearing faults. The transfer learning-based CNN is found to be capable of generalizing across two open-source datasets, resulting in an improvement in accuracy from 53.1% to 68.3%.

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