• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.462-475
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• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

• ETRI Journal
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• v.43 no.5
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• pp.812-824
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• 2021

Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue. As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable. To solve this problem, we propose a structural similarity measurement method suitable for the characteristics of social networks. The proposed method excludes redundant users and combines the attribute information to measure the privacy status of users. Using this approach, users can intuitively recognize their privacy status on OSNs. Experiments using real data show that our method can effectively and accurately help users improve their privacy disclosures.

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.521-529
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• 2009

In this paper, we present a vision-based human primitive motion recognition method. It models the reference motion patterns, recognizes a user's motion, and measures the similarity between the reference action and the user's one. In order to recognize a motion, we provide a pattern modeling method based on the Hidden Markov Models. In addition, we provide a similarity measurement method between the reference motion and the user's one using the editing distance algorithm. Experimental results show that the recognition rate of ours is above 93%. Our method can be used in the motion recognizable games, the motion recognizable postures, and the rehabilitation training systems.

• Korean Journal of Applied Biomechanics
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• v.31 no.3
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• pp.199-204
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• 2021

Objective: The purpose of this study was to present a new idea of methodology to evaluate Poomsae performance using inertial measurement unit (IMU) sensors in terms of signal processing techniques. Method: Ten collegian Taekwondo athletes, consisting of five Poomsae elite athletes (age: 21.4 ± 0.9 years, height: 168.4 ± 11.3 cm, weight: 65.0 ± 10.6 kg, experience: 12 ± 0.7 years) and five breaking demonstration athletes (age: 21.0 ± 0.0 years, height: 168.4 ± 4.7 cm, weight: 63.8 ± 8.2 kg, experience: 13.0 ± 2.1 years), voluntarily participated in this study. They performed three different black belt Poomsae such as Goryeo, Geumgang, and Taebaek Poomsae repeatedly twice. Repeated measured motion data on the wrist and ankle were calculated by the methods of cosine similarity and Euclidean distance. Results: The Poomsse athletes showed superior performance in terms of temporal consistency at Goryeo and Taebaek Poomsae, cosine similarity at Geumgang and Taebaek Poomsae, and Euclidian distance at Geumgang Poomsae. Conclusion: IMU sensor would be a useful tool for monitoring and evaluating within-subject temporal variability of Taekwondo Poomsae motions. As well it distinguished spatiotemporal characteristics among three different Poomsae.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.536-539
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• 2007

JSMS(Java source code Similarity Measurement System)는 자바 소스 코드의 유사도를 측정하고 이와 관련한 소스코드의 정보를 시각적으로 표시하는 시스템이다. 기존의 표절 검사 시스템은 소스코드의 구조적 특징을 반영하지 못해 유사도 결과의 신뢰성이 낮고 대부분 편리성과 가독성이 좋지 않아 사용하기 불편하였다. 본 논문에서 제안하는 JSMS는 이러한 단점을 보완하기 위해 함수 선형화를 사용하여 소스코드의 구조적 특징을 반영하였다. 또한 쉽고 간단한 조작으로 편리성을 제공하며, 관련 정보와 유사 구간을 시각적으로 표시하여 가독성을 높였다. 향후 다양한 언어 지원과 폭넓은 시각적 정보 제공을 보완하여 사용자의 학습 자료로 사용할 수 있으며, 소스코드 표절의 객관적 기준이 되는 도구로 활용 가능하다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.122-130
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• 2009

The paper presents an algorithm which uses code vectors to represent comer geometry information for searching the similar images from a database. The comers have been extracted by finding the intersections of the detected lines found using Hough transform. Taking the comer as the center coordinate, the angles of the intersecting lines are determined and are represented using code vectors. A code book has been used to code each comer geometry information and indexes to the code book are generated. For similarity measurement, the histogram of the code book indexes is used. This result in a significant small size feature matrix compared to the algorithms using color features. Experimental results show that use of code vectors is computationally efficient in similarity measurement and the comers being noise invariant produce good results in noisy environments.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.62-68
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• 2013

This paper proposes a method for identification of harmful video images based on the degree of harmfulness in the video content. To extract harmful candidate frames from the video effectively, we used a video color extraction method applying a projection map. The procedure for identifying the harmful video has five steps, first, extract the I-frames from the video and map them onto projection map. Next, calculate the similarity and select the potentially harmful, then identify the harmful images by comparing the similarity measurement value. The method estimates similarity between the extracted frames and normative images using the critical value of the projection map. Based on our experimental test, we propose how the harmful candidate frames are extracted and compared with normative images. The various experimental data proved that the image identification method based on the 2-dimensional projection map is superior to using the color histogram technique in harmful image detection performance.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.521-526
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• 2012

The performance of arrow from a manufacturing process depends on arrow's trajectory(archer's paradox) and intensity of an impact points. Especially, when conducting a shooting experiment over and over in the same experiment condition, the intensity of impact point is an objective standard to judge the performance of the arrow. However, the analysis method for the impact point is not enough, a previous research of the arrow's performance has been focused on a skill to optimize a manufacturing variables(feathers of an arrow, barb of an arrow, arrow's shaft, weight, external diameter, spine). In this paper, We propose measurement system of arrow's impact point with Mamdani fuzzy inference system and similarity of polygon for automation of impact point's measurement. Measuring the impact point data of the arrow moving with a high speed(approximately 275km/h) by using line laser and photo diode array, then the measured data are mapped to arrow's impact point with fuzzy inference and similarity of polygon.