scholarly journals Recognition of the Typical Distress in Concrete Pavement Based on GPR and 1D-CNN

2021 ◽  
Vol 13 (12) ◽  
pp. 2375
Author(s):  
Juncai Xu ◽  
Jingkui Zhang ◽  
Weigang Sun
Keyword(s):  
Field Testing ◽  
Concrete Pavement ◽  
Training Dataset ◽  
Practical Applications ◽  
Pavement Distress ◽  
Testing Dataset ◽  
Signal Features ◽  
Actual Field ◽  
Deep Learning Network ◽  
Ground Penetrating

Ground-penetrating radar (GPR) signal recognition depends much on manual feature extraction. However, the complexity of radar detection signals leads to conventional intelligent algorithms lacking sufficient flexibility in concrete pavement detection. Focused on these problems, we proposed an adaptive one-dimensional convolution neural network (1D-CNN) algorithm for interpreting GPR data. Firstly, the training dataset and testing dataset were constructed from the detection signals on pavement samples of different types of distress; secondly, the raw signals are were directly inputted into the 1D-CNN model, and the raw signal features of the radar wave are extracted using the adaptive deep learning network; finally, the output used the Soft-Max classifier to provide the classification result of the concrete pavement distress. Through simulation experiments and actual field testing, the results show that the proposed method has high accuracy and excellent generalization performance compared to the conventional method. It also has practical applications.

scholarly journals Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification

2021 ◽  
Vol 15 ◽  
Author(s):  
Aimei Dong ◽  
Zhigang Li ◽  
Qiuyu Zheng
Keyword(s):  
Matrix Factorization ◽  
Feature Space ◽  
Signal Classification ◽  
Training Dataset ◽  
Eeg Signal ◽  
Practical Applications ◽  
Testing Dataset ◽  
Eeg Signal Classification ◽  
Original Feature ◽  
Non Negative Matrix Factorization

EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method.

scholarly journals Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN

2020 ◽  
Vol 15 (1) ◽  
pp. 588-596 ◽  
Author(s):  
Jie Meng ◽  
Linyan Xue ◽  
Ying Chang ◽  
Jianguang Zhang ◽  
Shilong Chang ◽  
...  
Keyword(s):  
Deep Neural Networks ◽  
Alimentary Tract ◽  
Multiple Scale ◽  
Colorectal Polyps ◽  
Training Dataset ◽  
Cad System ◽  
Multicenter Trials ◽  
Testing Dataset ◽  
Adenoma Detection ◽  
Aided Diagnosis

AbstractColorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required.

scholarly journals Unknown SAR Target Identification Method Based on Feature Extraction Network and KLD–RPA Joint Discrimination

2021 ◽  
Vol 13 (15) ◽  
pp. 2901
Author(s):  
Zhiqiang Zeng ◽  
Jinping Sun ◽  
Congan Xu ◽  
Haiyang Wang
Keyword(s):  
Feature Extraction ◽  
Position Angle ◽  
Automatic Target Recognition ◽  
Identification Accuracy ◽  
Training Dataset ◽  
Target Feature ◽  
Practical Applications ◽  
Feature Vectors ◽  
Identification Method ◽  
Leibler Divergence

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.

Application of Symplectic Partitioned Runge-Kutta Method and Total-Field Scattered-Field Formulation to Simulation of GPR Data

2014 ◽  
Vol 926-930 ◽  
pp. 2777-2780
Author(s):  
Hong Yuan Fang ◽  
Jian Li ◽  
Jia Li
Keyword(s):  
Scattered Field ◽  
Measured Signal ◽  
Total Field ◽  
Time Step ◽  
Absorbing Boundary ◽  
Runge Kutta Method ◽  
Actual Field ◽  
Ground Penetrating ◽  
Radar Wave ◽  
Difference Time

The second-order Lobatto IIIA-IIIB symplectic partitioned RungeKutta (SPRK) method, combining with the first-order Mur absorbing boundary condition, is developed for the simulation of ground penetrating radar wave propagation in layered pavement structure. For 2-dimetional case, a significant advantage of this method is that only two functions need to be calculated at each time step. The total-field/scattered-field technique is used for plane wave excitation. Numerical examples are presented to verify the accuracy and efficiency of the proposed algorithm. The results illustrate that the reflected signal calculated by the SPRK method is in good agreement with that obtained using the finite difference time domain (FDTD) scheme, but the CPU time consumed by proposed algorithm is reduce about 20% of the FDTD scheme. In addition, an actual field test is conducted to evaluate the further performance of the SPRK method. It is found that the simulated waveform fits well with the measured signal in many aspects, especially in the peak amplitude and time delay.

Predicting endoscopic remission in Crohn’s disease by the modified multiplier SES-CD (MM-SES-CD)

Gut ◽  
2021 ◽  
pp. gutjnl-2020-323799
Author(s):  
Neeraj Narula ◽  
Emily C L Wong ◽  
Jean-Frederic Colombel ◽  
William J Sandborn ◽  
John Kenneth Marshall ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Assessment Tool ◽  
Training Dataset ◽  
Mucosal Inflammation ◽  
Youden Index ◽  
Active Therapy ◽  
Testing Dataset ◽  
Endoscopic Remission

Background and aimsThe Simple Endoscopic Score for Crohn’s disease (SES-CD) is the primary tool for measurement of mucosal inflammation in clinical trials but lacks prognostic potential. We set to develop and validate a modified multiplier of the SES-CD (MM-SES-CD), which takes into consideration each individual parameter’s prognostic value for achieving endoscopic remission (ER) while on active therapy.MethodsIn this posthoc analysis of three CD clinical trial programmes (n=350 patients, baseline SES-CD ≥ 3 with confirmed ulceration), data were pooled and randomly split into a 70% training and 30% testing cohort. The MM-SES-CD was designed using weights for individual parameters as determined by logistic regression modelling, with 1-year ER (SES-CD < 3) being the dependent variable. A cut point score for low and high probability of ER was determined by using the maximum Youden Index and validated in the testing cohort.ResultsBaseline ulcer size, extent of ulceration and presence of non-passable strictures had the strongest association with 1-year ER as compared with affected surface area, with differential weighting of individual parameters across disease segments being observed during logistic regression. The MM-SES-CD was generated using this weighted regression model and demonstrated strong discrimination for ER in the training dataset (area under the receiver operator curve (AUC) 0.83, 95% CI 0.78 to 0.94) and in the testing dataset (AUC 0.82, 95% CI 0.77 to 0.92). In comparison to the MM-SES-CD scoring model, the original SES-CD score lacks accuracy (AUC 0.60, 95% CI 0.55 to 0.65) for predicting the achievement of ER.ConclusionsWe developed and internally validated the MM-SES-CD as an endoscopic severity assessment tool to predict one-year ER in patients with CD on active therapy.

scholarly journals A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video)

2021 ◽  
Vol 11 ◽  
Author(s):  
Dehua Tang ◽  
Jie Zhou ◽  
Lei Wang ◽  
Muhan Ni ◽  
Min Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Training Dataset ◽  
Deep Convolutional Neural Networks ◽  
Testing Dataset ◽  
Kappa Value ◽  
The Mean ◽  
Gastric Cancer Patients ◽  
Novel Model

Background and AimsPrediction of intramucosal gastric cancer (GC) is a big challenge. It is not clear whether artificial intelligence could assist endoscopists in the diagnosis.MethodsA deep convolutional neural networks (DCNN) model was developed via retrospectively collected 3407 endoscopic images from 666 gastric cancer patients from two Endoscopy Centers (training dataset). The DCNN model’s performance was tested with 228 images from 62 independent patients (testing dataset). The endoscopists evaluated the image and video testing dataset with or without the DCNN model’s assistance, respectively. Endoscopists’ diagnostic performance was compared with or without the DCNN model’s assistance and investigated the effects of assistance using correlations and linear regression analyses.ResultsThe DCNN model discriminated intramucosal GC from advanced GC with an AUC of 0.942 (95% CI, 0.915–0.970), a sensitivity of 90.5% (95% CI, 84.1%–95.4%), and a specificity of 85.3% (95% CI, 77.1%–90.9%) in the testing dataset. The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model’s assistance (accuracy: 84.6% vs. 85.5%, sensitivity: 85.7% vs. 87.4%, specificity: 83.3% vs. 83.0%). The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model’s assistance (0.430–0.629 vs. 0.660–0.861). The diagnostic duration reduced considerably with the assistance of the DCNN model from 4.35s to 3.01s. The correlation between the perseverance of effort and diagnostic accuracy of endoscopists was diminished using the DCNN model (r: 0.470 vs. 0.076).ConclusionsAn AI-assisted system was established and found useful for novice endoscopists to achieve comparable diagnostic performance with experts.

scholarly journals Emotion Detection using Social Media and Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4491-4494
Author(s):  
Mr. Bhavar Shivam S.
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Training Dataset ◽  
Emotion Detection ◽  
Pos Tagging ◽  
Testing Dataset ◽  
Svm Algorithm ◽  
The Right ◽  
Negative Sentiment

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.

A Two-Layer Learning Architecture for Multi-Class Protein Folds Classification

2013 ◽  
pp. 786-797
Author(s):  
Ruofei Wang ◽  
Xieping Gao
Keyword(s):  
Query Protein ◽  
Training Dataset ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Protein Folds ◽  
Testing Dataset ◽  
Ensemble Strategy ◽  
Component Classifier ◽  
Independent Testing Dataset

Classification of protein folds plays a very important role in the protein structure discovery process, especially when traditional sequence alignment methods fail to yield convincing structural homologies. In this chapter, we have developed a two-layer learning architecture, named TLLA, for multi-class protein folds classification. In the first layer, OET-KNN (Optimized Evidence-Theoretic K Nearest Neighbors) is used as the component classifier to find the most probable K-folds of the query protein. In the second layer, we use support vector machine (SVM) to build the multi-class classifier just on the K-folds, generated in the first layer, rather than on all the 27 folds. For multi-feature combination, ensemble strategy based on voting is selected to give the final classification result. The standard percentage accuracy of our method at ~63% is achieved on the independent testing dataset, where most of the proteins have <25% sequence identity with those in the training dataset. The experimental evaluation based on a widely used benchmark dataset has shown that our approach outperforms the competing methods, implying our approach might become a useful vehicle in the literature.

Experience with Field Testings of SHE on Research Catchments

1984 ◽  
Vol 15 (4-5) ◽  
pp. 283-294 ◽  
Author(s):  
Børge Storm ◽  
K. Høgh Jensen
Keyword(s):  
New Zealand ◽  
Soil Water ◽  
Flow Model ◽  
Complete System ◽  
Field Testing ◽  
Practical Applications ◽  
Extensive Field ◽  
Soil Water Flow ◽  
Hydrologic System ◽  
The Individual

The development of the European Hydrologic System (SHE) has now reached such a stage, that it is ready for practical applications. Extensive field testings and associated developments have been carried out in recent years. The testings have included the complete system as well as the individual components. Particular emphasis has been given to the development and testing of the soil water flow model. The paper demonstrates results from a field testing of SHE on the Wye Catchment in Britain, as well as examples of applications on small experimental catchments in Germany and New Zealand.

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