A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection

The quality control is an essential step in fabric industries. Detectdefects in the early stages can reduce costs and increase the qualityof the products. Currently, this task is mainly done by humans,whose judgment can be affected by fatigue. Computer vision-basedtechniques can automatically detect defects, reducing the need forhuman intervention. In this context, this work proposes an imageblock-processing approach, where we compare the Segmentation-Based Fractal Texture Analysis, Gray Level Co-Occurrence Matrix,and Local Binary Pattern in the feature extraction step. Aimingto show the efficiency of this approach for the problem, these resultswere compared with the same algorithms without the blockprocessingapproach. A Support Vector Machine optimized by Grid-Search Algorithm was used to classify the fabrics. The databaseused, which is available online, is composed of 479 images fromsamples with defects and without it. The results show that thisblock processing approach can improve the classification results,achieving 100% in this work.

Based on Parameter Optimization of SVM to Establish an Auxiliary Diagnosis Model of Benign and Malignant Pulmonary Nodules (Preprint)

BACKGROUND Solitary pulmonary nodule (SPN) is a common disease in clinic but it is difficult to diagnose[1]. Since most patients have no symptoms when nodules are found, doctors' judgment of nodules is mainly based on their clinical experience, which is highly subjective.Therefore, it is necessary to establish an accurate and objective method for the diagnosis of benign and malignant pulmonary nodules. OBJECTIVE The SVM parameters were optimized by the intelligent algorithm, and the auxiliary diagnosis model of benign and malignant solitary pulmonary nodules combining CT images and serological indicators was constructed, and its test efficiency was evaluated. METHODS CT images and serum indexes of 1030 patients (515 cases of lung cancer and 515 cases of benign pulmonary nodules) diagnosed in our hospital between July 2015 and December 2018 were collected. The CT images of pulmonary nodules were characterized by artificial dimension reduction for feature extraction and assignment,At the same time, the serological indexes were tested; Logistic regression analysis was used to screen CT features and serum indexes of lung cancer; Grid, PSO and GS were used to find the optimal parameters C and g of SVM, and an auxiliary diagnosis model of benign and malignant solitary pulmonary nodules was constructed. RESULTS A total of 9 quantitative image features were extracted from the lung lesion regions segmented from the CT images to describe the phenotypic features of the tumor and their values were successfully assigned. 8 related serological indicators were detected, totaling 17 indicators.The main features of lung cancer including nodule site, edge condition, burr sign, foliation sign, cyfra21-1, scc-ag, CA153 and CA125 were obtained through Logistic regression analysis.Based on the above 8 screening indexes and 17 overall indexes, SVM modeling was carried out after optimization by three intelligent algorithms. The prediction results of the three algorithms in the SVM model with 8 indexes included were as follows: the prediction accuracy of the SVM model under optimization by grid search algorithm was 100%.The accuracy of SVM model was 99.5146% under gga and PSO, and 98.544% under default parameters.The three algorithms were consistent in the prediction results of the SVM model with 17 indexes, and the accuracy reached 100%, while the model accuracy under the default parameters was 88.350%. CONCLUSIONS The accuracy of SVM model can be improved by searching the optimal parameters of SVM with intelligent algorithm.8 relevant indexes screened by the logistic system are selected, and the prediction of the SVM model under optimization by the grid search algorithm can select the least inclusion indexes and guarantee the accuracy, which is the best choice.

Fatigue Crack Length Estimation and Prediction using Trans-fitting with Support Vector Regression

This paper explains the proposed method of Team SeouLG in detail for crack length estimation and prediction based on wave signals. The proposed method consists of two parts; (1) crack length estimation using support vector regression (SVR), (2) crack length prediction using new trans-fitting method. For the estimation of the crack length, we define the features based on the filtered wave signals and construct the model using SVR method. The hyper-parameters of the SVR model are selected based on grid search algorithm. The prediction of the crack length is based on the previous crack length, which is estimated based on the wave signals. In this part, we apply a new proposed trans-fitting method. The trans-fitting method updates the selected candidate function to fit the trend of the crack propagation from the training dataset. By translocating the selected candidate functions to the estimated crack length, we can predict the crack length of the target cycles. The proposed method is validated with the new given specimens. The results show that the proposed method can estimate and predict the crack length to lead Team SeouLG to the first place in 2019 PHM Conference Data Challenge.

PENGGUNAAN SUPPORT VECTOR REGRESSION DALAM PEMODELAN INDEKS SAHAM SYARIAH INDONESIA DENGAN ALGORITME GRID SEARCH

Indonesia as the largest Muslim population country in the world is a very potential market for sharia stocks. Sharia stocks performance can be seen from the Indonesia Sharia Stock Index (ISSI). Stock index modeling is conducted to determine the factors that affect the stock index or to predict the value of the stock index. Modeling using regression analysis is based on assumptions that do not always match with the characteristics of stock data that fluctuate. Support Vector Regression (SVR) method is a non-parametric approach based on machine learning. The problem often encountered in the analysis using SVR is to determine the optimal parameters to produce the best model. The determination of the optimal parameters can be solved by using the grid search algorithm. The purpose of this research is to make ISSI model using SVR with grid search algorithm with independent variable BI Rate, money supply, and exchange rate (USD / IDR). The best SVR model was obtained using weekly data with a total of 343 periods as well as a linear kernel with parameters ε = 0.03 and C = 2. The evaluation of the best model SVR is RMSE of 2.289 and correlation value of 0.873.

Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.

Research on GRU-based credibility prediction of Web services

The service system is based on the SOA architecture, and its component services are usually deployed by third-party service providers in an open network environment. This openness also brings confusion to service system while extending functions. Unavailability of a single service may result in the unavailability of the entire service system. This paper uses Web service credibility as a standard to measure whether Web service is available. Web service credibility is calculated by 12 factors that affect quality of Web service. According to time series of Web service credibility in the past, credibility at next time period can be predicted. This paper proposes a Gated Recurrent Unit (GRU) algorithm which uses grid search algorithm and adaptive moment estimation (Adam) to solve above problem. In this algorithm, grid search algorithm is used to get the best hyper-parameters of network and Adam is used to correct the gradient in the gradient descent. Finally, based on a large number of real Web services, the GRU prediction algorithm is verified by experiments. Experimental results show that the GRU algorithm has higher prediction accuracy than other methods in Web service credibility prediction.