• Journal of Korea Multimedia Society
• /
• v.21 no.6
• /
• pp.659-666
• /
• 2018

The classification of the tablets recovered according to prescription changes is usually carried out manually by a number of pharmacists at the hospitals. Relatively high-wage pharmacists carry out the reclassification of the tablets, which results in a large loss of time and labor, and if the tablets are incorrectly classified, this can lead to medical accidents. In order to overcome these problems, a new automatic tablet classifying machine has been introduced. In the conventional automatic tablet classifying machine, tablets having various shapes, sizes, and colors are transferred to a classifying machine through the line feeder. Problems such as breakaway of the tablets from the line feeder, pilling of the tablets in the line feeder, and appearance contamination of the tablets occur. In this paper, we propose a system that automatically classifies the shape, size, and color of tablets through individual supply method by vacuum adsorption and image processing.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3217-3238
• /
• 2018

In this paper, a depth image up-sampling method is put forward by using pixel classifying and jointed bilateral filtering. By analyzing the edge maps originated from the high-resolution color image and low-resolution depth map respectively, pixels in up-sampled depth maps can be classified into four categories: edge points, edge-neighbor points, texture points and smooth points. First, joint bilateral up-sampling (JBU) method is used to generate an initial up-sampling depth image. Then, for each pixel category, different refinement methods are employed to modify the initial up-sampling depth image. Experimental results show that the proposed algorithm can reduce the blurring artifact with lower bad pixel rate (BPR).

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.2
• /
• pp.249-253
• /
• 2004

When we decide the software quality on the basis of the software measurement, the transitive property which is a requirement for an equivalence relation is not always satisfied. Therefore, we propose a scheme for classifying the software quality that employs a tolerance relation instead of an equivalence relation. Given the experimental data set, the proposed scheme generates the tolerant classes for elements in the experiment data set, and generates the tolerant ranges for classifying the software quality by clustering the means of the tolerance classes. Through the experiment, we showed that the proposed scheme could product very useful and valid results. That is, it has no problems that we use as the criteria for classifying the software quality the tolerant ranges generated by the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2558-2573
• /
• 2019

We present a deep CNN-based approach for classifying artistic media from artwork images. We aim to classify most frequently used artistic media including oilpaint brush, watercolor brush, pencil and pastel, etc. For this purpose, we extend VGGNet, one of the most widely used CNN structure, by substituting its last layer with a fully convolutional layer, which reveals class activation map (CAM), the region of classification. We build two artwork image datasets: YMSet that collects more than 4K artwork images for four most frequently used artistic media from various internet websites and WikiSet that collects almost 9K artwork images for ten most frequently used media from WikiArt. We execute a human baseline experiment to compare the classification performance. Through our experiments, we conclude that our classifier is superior in classifying artistic media to human.

• Journal of Information Processing Systems
• /
• v.18 no.2
• /
• pp.268-281
• /
• 2022

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.

• Journal of the Korean Mathematical Society
• /
• v.47 no.2
• /
• pp.299-309
• /
• 2010

The Weyl group of a compact connected Lie group is a reflection group. If such Lie groups are locally isomorphic, the representations of the Weyl groups are rationally equivalent. They need not however be equivalent as integral representations. Turning to the invariant theory, the rational cohomology of a classifying space is a ring of invariants, which is a polynomial ring. In the modular case, we will ask if rings of invariants are polynomial algebras, and if each of them can be realized as the mod p cohomology of a space, particularly for dihedral groups.

• Journal of Biomedical Engineering Research
• /
• v.10 no.3
• /
• pp.343-350
• /
• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.3
• /
• pp.179-183
• /
• 2006

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

• Journal of Industrial Technology
• /
• v.19
• /
• pp.403-408
• /
• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.41 no.3
• /
• pp.211-219
• /
• 2017

In this study, we performed numerical simulations on a circulating air classifier using a commercial computational fluid dynamics program. The variations in the grade efficiency, the cut-size and the cut-sharpness were calculated and discussed. By controlling the rotating speed of the main fan, the cut-size could be rapidly increased. However the linearity of the cut-size variation with respect to the main fan speed was not sufficient for application to contaminated soil classification processes. On the other hand, by varying the rotating speed of the classifying fan, the cut-size gradually decreased and could be precisely adjusted. Using both the main fan and the classifying fan, we could achieve larger cut-sharpness values and better classifying performances.