scholarly journals Novel MOA Fault Detection Technology Based on Small Sample Infrared Image

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1748
Author(s):  
Baoquan Wei ◽  
Yong Zuo ◽  
Yande Liu ◽  
Wei Luo ◽  
Kaiyun Wen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Recognition Rate ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Small Sample ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Networks ◽  
Extended Model ◽  
Detection Technology

This paper proposes a novel metal oxide arrester (MOA) fault detection technology based on a small sample infrared image. The research is carried out from the detection process and data enhancement. A lightweight MOA identification and location algorithm is designed at the edge, which can not only reduce the amount of data uploaded, but also reduce the search space of cloud algorithm. In order to improve the accuracy and generalization ability of the defect detection model under the condition of small samples, a multi-model fusion detection algorithm is proposed. Different features of the image are extracted by multiple convolutional neural networks, and then multiple classifiers are trained. Finally, the weighted voting strategy is used for fault diagnosis. In addition, the extended model of fault samples is constructed by transfer learning and deep convolutional generative adversarial networks (DCGAN) to solve the problem of unbalanced training data sets. The experimental results show that the proposed method can realize the accurate location of arrester under the condition of small samples, and after the data expansion, the recognition rate of arrester anomalies can be improved from 83% to 85%, showing high effectiveness and reliability.

scholarly journals Characterization of the Effectiveness of Reporting Lists of Small Feature Sets Relative to the Accuracy of the Prior Biological Knowledge

2010 ◽  
Vol 9 ◽  
pp. CIN.S4020 ◽  
Author(s):  
Chen Zhao ◽  
Michael L. Bittner ◽  
Robert S. Chapkin ◽  
Edward R. Dougherty
Keyword(s):  
Small Sample ◽  
Training Data ◽  
Small Samples ◽  
Classification Error ◽  
Biological Knowledge ◽  
Feature Sets ◽  
Good Feature ◽  
Prior Biological Knowledge ◽  
Selection Algorithms ◽  
Power Curves

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based error estimators typically perform poorly on small samples, exhibiting optimistic bias or high variance. One way around the problem is limit the number of features being considered, restrict features sets to sizes such that all feature sets can be examined by exhaustive search, and report a list of the best performing feature sets. If the list is short, then it greatly restricts the possible feature sets to be considered as candidates; however, one can expect the lowest error estimates obtained to be optimistically biased so that there may not be a close-to-optimal feature set on the list. This paper provides a power analysis of this methodology; in particular, it examines the kind of results one should expect to obtain relative to the length of the list and the number of discriminating features among those considered. Two measures are employed. The first is the probability that there is at least one feature set on the list whose true classification error is within some given tolerance of the best feature set and the second is the expected number of feature sets on the list whose true errors are within the given tolerance of the best feature set. These values are plotted as functions of the list length to generate power curves. The results show that, if the number of discriminating features is not too small—that is, the prior biological knowledge is not too poor—then one should expect, with high probability, to find good feature sets. Availability: companion website at http://gsp.tamu.edu/Publications/supplementary/zhao09a/

scholarly journals TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1724
Author(s):  
Zilu Ying ◽  
Chen Xuan ◽  
Yikui Zhai ◽  
Bing Sun ◽  
Jingwen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Network Architecture ◽  
Learning Strategy ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Speckle Noise ◽  
Small Sample ◽  
Operating Conditions ◽  
Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

scholarly journals An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

2021 ◽  
Vol 13 (19) ◽  
pp. 3864
Author(s):  
Changjie Cao ◽  
Zongyong Cui ◽  
Zongjie Cao ◽  
Liying Wang ◽  
Jianyu Yang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Training Set ◽  
Stationary Target ◽  
Sample Set ◽  
Filtering Approach

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

scholarly journals Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mengmeng Huang ◽  
Fang Liu ◽  
Xianfa Meng
Keyword(s):  
Target Recognition ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Training Data ◽  
Small Scale ◽  
Data Set ◽  
Shape Information ◽  
Target Shape ◽  
The One

Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set.

scholarly journals A High-Dimensional and Small-Sample Submersible Fault Detection Method Based on Feature Selection and Data Augmentation

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 204
Author(s):  
Penghui Zhao ◽  
Qinghe Zheng ◽  
Zhongjun Ding ◽  
Yi Zhang ◽  
Hongjun Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fault Detection ◽  
Data Augmentation ◽  
Detection Method ◽  
Small Sample ◽  
Sensor Data ◽  
Generative Adversarial Networks ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Data Generation

The fault detection of manned submersibles plays a very important role in protecting the safety of submersible equipment and personnel. However, the diving sensor data is scarce and high-dimensional, so this paper proposes a submersible fault detection method, which is made up of feature selection module based on hierarchical clustering and Autoencoder (AE), the improved Deep Convolutional Generative Adversarial Networks (DCGAN)-based data augmentation module and fault detection module using Convolutional Neural Network (CNN) with LeNet-5 structure. First, feature selection is developed to select the features that have a strong correlation with failure event. Second, data augmentation model is conducted to generate sufficient data for training the CNN model, including rough data generation and data refiners. Finally, a fault detection framework with LeNet-5 is trained and fine-tuned by synthetic data, and tested using real data. Experiment results based on sensor data from submersible hydraulic system demonstrate that our proposed method can successfully detect the fault samples. The detection accuracy of proposed method can reach 97% and our method significantly outperforms other classic detection algorithms.

scholarly journals Small Sample Issues for Microarray-Based Classification

2001 ◽  
Vol 2 (1) ◽  
pp. 28-34 ◽  
Author(s):  
Edward R. Dougherty
Keyword(s):  
Microarray Data ◽  
Small Sample ◽  
Training Data ◽  
Small Samples ◽  
Classification Rules ◽  
Classifier Design ◽  
Sample Classification ◽  
Sample Data ◽  
The Impact ◽  
Biological Differences

In order to study the molecular biological differences between normal and diseased tissues, it is desirable to perform classification among diseases and stages of disease using microarray-based gene-expression values. Owing to the limited number of microarrays typically used in these studies, serious issues arise with respect to the design, performance and analysis of classifiers based on microarray data. This paper reviews some fundamental issues facing small-sample classification: classification rules, constrained classifiers, error estimation and feature selection. It discusses both unconstrained and constrained classifier design from sample data, and the contributions to classifier error from constrained optimization and lack of optimality owing to design from sample data. The difficulty with estimating classifier error when confined to small samples is addressed, particularly estimating the error from training data. The impact of small samples on the ability to include more than a few variables as classifier features is explained.

Research of Electronic Nose Pattern Recognition Algorithm Based on SVM

2012 ◽  
Vol 220-223 ◽  
pp. 2244-2247 ◽  
Author(s):  
Wei Liang ◽  
Li Na Zhang ◽  
Xiao Wei Li ◽  
Yan Di Zuo
Keyword(s):  
Pattern Recognition ◽  
Electronic Nose ◽  
Recognition Rate ◽  
Sensor Arrays ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Small Sample ◽  
Small Samples ◽  
Support Vector ◽  
Pattern Recognition Algorithm

In order to improve the recognition rate of the electronic nose system for small samples, an electronic nose pattern recognition algorithm based on support vector machine (SVM) is proposed in this paper. Identification experiments for three kinds of wine with similar odor were carried out. The sensor arrays are optimized by means of principal component analysis (PCA) method first. Then, make comparing experiment using different algorithms for different number of training samples of wine. The related results show that PCA-SVM based pattern recognition algorithms has high recognition accuracy, stronger classification capability, and has potential advantages in small sample classification and recognition experiments.

scholarly journals A Simple Outdoor Environment Obstacle Detection Method Based on Information Fusion of Depth and Infrared

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yaguang Zhu ◽  
Baomin Yi ◽  
Tong Guo
Keyword(s):  
Edge Detection ◽  
Information Fusion ◽  
Recognition Rate ◽  
Infrared Image ◽  
Depth Map ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Intelligent Vehicles ◽  
Outdoor Environment ◽  
Depth Information

In allusion to the existing low recognition rate and robustness problem in obstacle detection; a simple but effective obstacle detection algorithm of information fusion in the depth and infrared is put forward. The scenario is segmented by the mean-shift algorithm and the pixel gradient of foreground is calculated. After pretreatment of edge detection and morphological operation, the depth information and infrared information are fused. The characteristics of depth map and infrared image in edge detection are used for the raised method, the false rate of detection is reduced, and detection precision is improved. Since the depth map and infrared image are not affected by natural sunlight, the influence on obstacle recognition due to the factors such as light intensity and shadow is effectively reduced and the robustness of the algorithm is also improved. Experiments indicate that the detection algorithm of information fusion can accurately identify the small obstacle in the view and the accuracy of obstacle recognition will not be affected by light. Hence, this method has great significance for mobile robot or intelligent vehicles on obstacle detection in outdoor environment.

scholarly journals Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Mobeen Ahmad ◽  
Usman Cheema ◽  
Muhammad Abdullah ◽  
Seungbin Moon ◽  
Dongil Han
Keyword(s):  
Facial Recognition ◽  
Recognition Rate ◽  
Synthetic Data ◽  
Personal Identification ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Face Images ◽  
Recognition Algorithms ◽  
Facial Images

Applications for facial recognition have eased the process of personal identification. However, there are increasing concerns about the performance of these systems against the challenges of presentation attacks, spoofing, and disguises. One of the reasons for the lack of a robustness of facial recognition algorithms in these challenges is the limited amount of suitable training data. This lack of training data can be addressed by creating a database with the subjects having several disguises, but this is an expensive process. Another approach is to use generative adversarial networks to synthesize facial images with the required disguise add-ons. In this paper, we present a synthetic disguised face database for the training and evaluation of robust facial recognition algorithms. Furthermore, we present a methodology for generating synthetic facial images for the desired disguise add-ons. Cycle-consistency loss is used to generate facial images with disguises, e.g., fake beards, makeup, and glasses, from normal face images. Additionally, an automated filtering scheme is presented for automated data filtering from the synthesized faces. Finally, facial recognition experiments are performed on the proposed synthetic data to show the efficacy of the proposed methodology and the presented database. Training on the proposed database achieves an improvement in the rank-1 recognition rate (68.3%), over a model trained on the original nondisguised face images.

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