scholarly journals A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1440 ◽  
Author(s):  
Erhu Zhang ◽  
Bo Li ◽  
Peilin Li ◽  
Yajun Chen
Keyword(s):  
Deep Learning ◽  
Classification Method ◽  
Coarse Grained ◽  
Defect Classification ◽  
Accuracy Rate ◽  
Practical Applications ◽  
Fine Grained ◽  
Deep Model ◽  
Different Types ◽  
Classification Tasks

Deep learning has been successfully applied to classification tasks in many fields due to its good performance in learning discriminative features. However, the application of deep learning to printing defect classification is very rare, and there is almost no research on the classification method for printing defects with imbalanced samples. In this paper, we present a deep convolutional neural network model to extract deep features directly from printed image defects. Furthermore, considering the asymmetry in the number of different types of defect samples—that is, the number of different kinds of defect samples is unbalanced—seven types of over-sampling methods were investigated to determine the best method. To verify the practical applications of the proposed deep model and the effectiveness of the extracted features, a large dataset of printing detect samples was built. All samples were collected from practical printing products in the factory. The dataset includes a coarse-grained dataset with four types of printing samples and a fine-grained dataset with eleven types of printing samples. The experimental results show that the proposed deep model achieves a 96.86% classification accuracy rate on the coarse-grained dataset without adopting over-sampling, which is the highest accuracy compared to the well-known deep models based on transfer learning. Moreover, by adopting the proposed deep model combined with the SVM-SMOTE over-sampling method, the accuracy rate is improved by more than 20% in the fine-grained dataset compared to the method without over-sampling.

Imbalanced sentiment classification based on sequence generative adversarial nets

2020 ◽  
Vol 39 (5) ◽  
pp. 7909-7919
Author(s):  
Chuantao Wang ◽  
Xuexin Yang ◽  
Linkai Ding
Keyword(s):  
Deep Learning ◽  
Online Reviews ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Classification Task ◽  
Algorithm Optimization ◽  
Minority Class ◽  
Sample Distribution ◽  
Practical Applications ◽  
Deep Model

The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews.

On Some Properties of the Mechanical Topology That Affect Parallel Solvers

2013 ◽  
Author(s):  
Alessandro Tasora ◽  
Dan Negrut
Keyword(s):  
A Priori ◽  
Topological Analysis ◽  
General Setting ◽  
Coarse Grained ◽  
Worst Case ◽  
Fine Grained ◽  
Parallel Solvers ◽  
Large Numbers ◽  
Graph Properties ◽  
Different Types

The efficiency of parallel solvers for large multibody systems is affected by the topology of the network of constraints. In the most general setting, that is the case of problems involving contacts between large numbers of parts, the mechanical topology cannot be predicted a priori and also changes during the simulation. Depending on the strategy for splitting the computational workload on the processing units, different types of worst case scenarios can happen. In this paper we discuss a few approaches to the parallelization of multibody solvers, ranging from the fine-grained parallism on GPU to coarse-grained parallelism in clusters, and we show how their bottlenecks are directly related to some graph properties of the mechanical topology. Drawing on the topological analysis of the constraint network and its splitting, lower bounds on the computational complexity of the solver methods are presented, and some guidelines for limiting the worst-case scenarios in parallel algorithms are put forward.

Survey and Analysis on Proxy Re-Encryption Schemes

2014 ◽  
Vol 912-914 ◽  
pp. 1538-1543
Author(s):  
Zheng Tao Jiang ◽  
Yi Peng Zhang ◽  
Chen Li ◽  
Pian Niu ◽  
Xiao Li Huang
Keyword(s):  
Efficient Solution ◽  
Practical Applications ◽  
Fine Grained ◽  
Universal Models ◽  
Different Types ◽  
Encryption Schemes

Proxy re-encryption is an efficient solution to ciphertext delegation and distribution, which also enables the sender to carry out fine-grained control on his ciphertext. This paper summarizes the progress on the proxy re-encryption schemes and their practical applications. Universal models for proxy re-encryption and its security are also induced for detailed investigation on different types of PRE schemes.

scholarly journals A Deep Learning Account of How Language Affects Thought

2021 ◽  
Author(s):  
Xiaoliang Luo ◽  
Nicholas J. Sexton ◽  
Bradley C. Love
Keyword(s):  
Deep Learning ◽  
Language Learning ◽  
Coarse Grained ◽  
Semantic Representations ◽  
Visual Stream ◽  
Fine Grained ◽  
Linguistic Label ◽  
Underlying Theory ◽  
Ventral Visual Stream ◽  
Label Use

How can words shape meaning? Shared labels highlight commonalities between concepts whereas contrasting labels make differences apparent. To address such findings, we propose a deep learning account that spans perception to decision (i.e., labelling). The model takes photographs as input, transforms them to semantic representations through computations that parallel the ventral visual stream, and finally determines the appropriate linguistic label. The underlying theory is that minimising error on two prediction tasks (predicting the meaning and label of a stimulus) requires a compromise in the network's semantic representations. Thus, differences in label use, whether across languages or levels of expertise, manifest in differences in the semantic representations that support label discrimination. We confirm these predictions in simulations involving fine-grained and coarse-grained labels. We hope these and allied efforts which model perception, semantics, and labelling at scale will advance developmental and neurocomputational accounts of concept and language learning.

scholarly journals Deep-Learning-Based Active Hyperspectral Imaging Classification Method Illuminated by the Supercontinuum Laser

2020 ◽  
Vol 10 (9) ◽  
pp. 3088 ◽  
Author(s):  
Yu Liu ◽  
Zilong Tao ◽  
Jun Zhang ◽  
Hao Hao ◽  
Yuanxi Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Classification Method ◽  
Practical Applications ◽  
Feature Representations ◽  
Wide Range ◽  
Supercontinuum Laser

Hyperspectral imaging (HSI) technology is able to provide fine spectral and spatial information of objects. It has the ability to discriminate materials and thereby has been used in a wide range of areas. However, traditional HSI strongly depends on the sunlight and hence is restricted to daytime. In this paper, a visible/near-infrared active HSI classification method illuminated by a visible/near-infrared supercontinuum laser is developed for spectra detection and objects imaging in the dark. Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. By applying the method to a selection of objects in the dark successfully, we demonstrate that with the active HSI classification method, it is possible to detect objects of interest in practical applications. Correct active HSI classification of different objects further supports the viability of the method for camouflage detection, biomedical alteration detection, cave painting mapping and so on.

Correlation between dislocation, grain boundary and interface of duplex SS in stress corrosion cracking(SCC)

1990 ◽  
Vol 48 (4) ◽  
pp. 928-929
Author(s):  
Wang Zheng-fang ◽  
Z.F. Wang
Keyword(s):  
Stainless Steel ◽  
Thermal Cycle ◽  
Secondary Phase ◽  
Corrosion Cracking ◽  
Coarse Grained ◽  
Test Environment ◽  
Fine Grained ◽  
Evaluation Test ◽  
Welding Thermal Cycle ◽  
Micro Analysis

The main purpose of this study highlights on the evaluation of chloride SCC resistance of the material,duplex stainless steel,OOCr18Ni5Mo3Si2 (18-5Mo) and its welded coarse grained zone(CGZ).18-5Mo is a dual phases (A+F) stainless steel with yield strength:512N/mm2 .The proportion of secondary Phase(A phase) accounts for 30-35% of the total with fine grained and homogeneously distributed A and F phases(Fig.1).After being welded by a specific welding thermal cycle to the material,i.e. Tmax=1350°C and t8/5=20s,microstructure may change from fine grained morphology to coarse grained morphology and from homogeneously distributed of A phase to a concentration of A phase(Fig.2).Meanwhile,the proportion of A phase reduced from 35% to 5-10°o.For this reason it is known as welded coarse grained zone(CGZ).In association with difference of microstructure between base metal and welded CGZ,so chloride SCC resistance also differ from each other.Test procedures:Constant load tensile test(CLTT) were performed for recording Esce-t curve by which corrosion cracking growth can be described, tf,fractured time,can also be recorded by the test which is taken as a electrochemical behavior and mechanical property for SCC resistance evaluation. Test environment:143°C boiling 42%MgCl2 solution is used.Besides, micro analysis were conducted with light microscopy(LM),SEM,TEM,and Auger energy spectrum(AES) so as to reveal the correlation between the data generated by the CLTT results and micro analysis.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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