• Advances in concrete construction
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• v.14 no.1
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• pp.15-34
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• 2022

The use of waste rubber in concrete can reduce natural aggregate consumption and improve some technical properties of concrete. Although there are several equations for estimating the mechanical properties of concrete containing waste rubber, limited numbers of machine learning-based models have been proposed to predict the mechanical properties of rubbercrete. In this study, an extensive database of the mechanical properties of rubbercrete was gathered from a comprehensive survey of the literature. To model the mechanical properties of rubbercrete, M5P tree and linear gene expression programming (LGEP) methods as two machine learning techniques were employed to achieve reliable mathematical equations. Two procedures of input variable selection were considered in this study. The crucial component ratios of rubbercrete and concrete age were assumed as the input variables in the first procedure. In contrast, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber were considered the second procedure of the input variables. The results show that the models obtained by LGEP are more accurate than those achieved by the M5P model tree and existing traditional equations. Besides, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber are better predictors of the mechanical properties of rubbercrete compared to the first procedure of input variable selection.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1435-1444
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• 2006

We present an effective and real-time face detection method based on Principal Component Analysis(PCA) and Support Vector Machines(SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training data set in a significant way, so that it showed 90.1 % detection rates with a small quantity of training data. it can process 8 frames per second for $320{\times}240$ pixel images. This is an acceptable processing time for a real-time system.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.248-256
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• 2020

In this study, predictive models based on machine learning for evaluating the extent of hazardous zone of explosive gases are developed. They are able to provide important guidelines for installing the explosion proof apparatus. 1,200 research data sets including 12 combustible gases and their extents of hazardous zone are generated to train predictive models. The extent of hazardous zone is set to an output variable and 12 variables affecting an output are set as input variables. Multiple linear regression, principal component regression, and artificial neural network are employed to train predictive models. Mean absolute percentage errors of multiple linear regression, principal component regression, and artificial neural network are 44.2%, 49.3%, and 5.7% and root mean square errors are 1.389m, 1.602m, and 0.203 m respectively. Therefore, it can be concluded that the artificial neural network shows the best performance. This model can be easily used to evaluate the extent of hazardous zone for explosive gases.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.808-816
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• 2004

This paper proposes the method of implementation of practical online face verification system based on multiple feature combination and a similarity space. The main issue in face verification is to deal with the variability in appearance. It seems difficult to solve this issue by using a single feature. Therefore, combination of mutually complementary features is necessary to cope with various changes in appearance. From this point of view, we describe the feature extraction approaches based on multiple principal component analysis and edge distribution. These features are projected on a new intra-person/extra-person similarity space that consists of several simple similarity measures, and are finally evaluated by a support vector machine. From the experiments on a realistic and large database, an equal error rate of 0.029 is achieved, which is a sufficiently practical level for many real- world applications.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2000.11a
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• pp.89-92
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• 2000

Non-aqueous tape casting of LTCC with PVB binder has been studied in a continuous tape casting machine. The aim of the study was to evaluate this type of system in the different Binder/Plasticizer and solid content compositions. Each four slurry compositions were used in the experiments with varied binder/plasticizer and solid content. All the slurries gave good quality tapes with smooth surfaces without blisters or pinholes. The highest mechanical properties was obtained for the B/P=3.0, powder content 70vol% composition from 52 ${\mu}{\textrm}{m}$ green tapes, a tensile strength of 4.6 MPa and a linear extension of 29.5%

• Journal of the Korean Society for Precision Engineering
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• v.5 no.1
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• pp.84-93
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• 1988

The on-line detection of the chip flow is one of the most important technologies in com- pletly automatic operation of machine tool, such as FMS and Unmanned Factories. This problem has been studied by many researchers, however, it is not solved as yet. For the recognition of chip flow in this study, the dynamic cutting force components due to the chip breaking were measured by dynamometer of piezo-electric type, and the frequency components of cutting force were also analyzed. From the measured results, the effect of cutting conditions and tool geometry on the dynamic cutting force component and chip formation were investigated in addition to the relationships between frequency of chip breaking (fB) and side serrated crack (fC) of chip. As a result, the following conclusions were obtaianed. 1) The chip formations have a large effect on the dynamic cutting force components. When chip breaking takes place, the dynamic cutting force component greatly increases, and the peridoic components appear, which correspond to maximum peak- frequency. 2) The crater wear of tool has a good effect on the chip control causing the chiup to be formed as upward-curl shape. In this case, the dymamic cutting force component greatly increases also 3) fB and fC of chip are closely corelated, and fC of chips has a large effect on the change of the situation of chip flow and dynamic cutting force component. 4) Under wide cutting conditions, the limit value (1.0 kgf) of dynamic cutting force component exists between the broken and continuous chips. Accordingly, this value is suitable for recognition of chip flow in on-line control of the cutting process.