• The Mathematical Education
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• v.60 no.2
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• pp.209-228
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• 2021

The purpose of this study is developed the Item Feature Analysis (IFA) frameworks for curriculum-based assessments, focusing on Math competency and representation in secondary schools and implemented the IFA in National Assessment of Educational Achievement. To conduct the study, previous studies were analyzed, and feasibility studies were conducted twice. As a result of the study, we structured the IFA framework based on the 2015 revised mathematics curriculum in Korea and developed a method to analyze the characteristics of the math items. The results of structuring the framework for math included two categories: math competency in the content aspects, and representation type in the formal aspects. Specifically, 12 features of math competency and 8 features of representation type were identified, and an item feature analysis framework composed of these features was developed. The math competency was developed based on the subject competency of 2015 national curriculum. Math assessments in high schools, which have been changed to the competency-based assessments, had more frequency of the feature of math competency compared to middle schools. In this study, implemented the IFA in National Assessment of Educational Achievement and explored the way of ensuring the validity. These have been proved as critical applications for ensuring the validity of curriculum-based student assessment as well as building a tool for assessment.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.188-199
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• 2013

This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.353-356
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• 2003

As the wireless mobile telecommunication system has been developed with astonishment, its offering service has also widely been expanded including various data service. Currently, the wireless mobile telecommunication network presents voice service that covers for the most part of the whole service areas. For this reason, the availability of the switching capacity in the mobile switching center(MSC) is manipulated by the required volume of voice service. However, considering the increase of data service, it is desirable for the current switching method to be modified for more efficiency. In this Paper, we analyze the data traffic caused by providing data service in the wireless mobile telecommunication network. For this, we are firstly going to review the result of the analysis in the feature of the data traffic. Secondly, based on the review, we are also going to perform analyzing the other feature of the data traffic normally generated in the wireless mobile telecommunication network. We expect that this paper would be utilized as an elementary source for the feature of the SMS data .traffic and it will be an honour for ourselves to work on it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.804-807
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• 2002

Edge of image have a many information about input image. There is a many applications to using a edge detection and uses by variable special effect. Edge detection is a field of image analysis, image segmentation using a pixel make the one field for decision of image construction. In this paper, image segmentation through many ways of edge detection for image segmentation. First of all, it analyze feature of image and extract by feature of each image, to adopt way of edge detection to selective. It realize edge detection efficiently, consider to feature of language through using a java image segmentation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1393-1404
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• 1995

For the factory automation(FA) of production or assembly lines, computer vision techniques have been widely used for the recognition and position-control of objects. In this application, it is very important to analyze characteristic features of each object and to find an efficient matching algorithm using the selected features. If the object has regular or homogeneous patterns, the problem is relatively simple. However, If the object is shifted or rotated, and if the depth of the input visual system is not fixed, the problem becomes very complicated. Also, in order to understand and recognize objects with concentric noise patterns, it is more effective to use feature-information represented in polar coordinates than in cartesian coordinates. In this paper, an algorithm for the recognition of objects with concentric circular noise-patterns is proposed. And position-conrtol information is calculated with the matching result. First, a filtering algorithm for eliminating concentric noise patterns is proposed to obtain concentric-feature patterns. Then a shift, rotation and scale invariant alogrithm is proposed for the recognition and position-control of objects uusing invariant feature information. Experimental results indicate the effectiveness of the proposed alogrithm.

• International Journal of Contents
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• v.13 no.4
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• pp.70-79
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• 2017

Images are an important element in patents and many experts use images to analyze a patent or to check differences between patents. However, there is little research on image analysis for patents partly because image processing is an advanced technology and typically patent images consist of visual parts as well as of text and numbers. This study suggests two methods for using image processing; the Scale Invariant Feature Transform(SIFT) algorithm and Optical Character Recognition(OCR). The first method which works with SIFT uses image feature points. Through feature matching, it can be applied to calculate the similarity between documents containing these images. And in the second method, OCR is used to extract text from the images. By using numbers which are extracted from an image, it is possible to extract the corresponding related text within the text passages. Subsequently, document similarity can be calculated based on the extracted text. Through comparing the suggested methods and an existing method based only on text for calculating the similarity, the feasibility is achieved. Additionally, the correlation between both the similarity measures is low which shows that they capture different aspects of the patent content.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.269-272
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• 2000

It is need to develop and apply a human pulse diagnosis system providing a quantitative and automatic analysis in the the oriental medicine. In order to analyze quantitatively the characteristic of pulsation, each of points had to be recognized accurately notifying the existence and the position of feature point in the wave form. And getting the period of human pulse. Thus, in this paper, it is proposed the preprocessing method of human pulse and the detection method of period by Wavelet Transformation. The human pulse is seprated from each band through Wavelet Transformation and feature points can be recognized through over the fact, and then the parameter of proposed Mac-Jin parameter is measured. Commonly, Human pulse signal has often various noises which are baseline drift, high frequency noise and so on. So it is significant to remove that noises. Thus, in this paper, the one period of human pulse is deciede and the feature points are detected after doing the preprocessing by wavelet transformation. As a result, it could be confirmed that this method is effective as a real program for the auto-diagnosis of human pulse.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.1-10
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• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.59-66
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• 2022

This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.281-286
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• 2003

Since National Geographic Information System was started, paper maps have been made with computer aided editing of digital map, instead of etching map-size negative film. Automated paper mapping system's necessity is growing more and more, because digital map has changed into Ver.2.0 which include attributes of feature. Therefore, in this study we try to analyze correlation of the digital map feature code and the 1/5,000 topographic map specifications which is necessary for paper mapping automatization using digital map Ver.2.0, and try to develop fundamental modules which will play a core role in automated paper mapping system.