• Journal for History of Mathematics
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• v.18 no.1
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• pp.33-48
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• 2005

The Rule of False Position is known as an arithmetical solution of algebraical equations. On the other hand, the Excess-Deficit Rule is an algorithm for calculating about excessive or deficient quantitative relations, which is found in the ancient eastern mathematical books, including the nine chapters on the mathematical arts. It is usually said that the origin of the Rule of False Position is the Excess-Deficit Rule in ancient Chinese mathematics. In relation to these facts, we pose two questions: - As many authors explain, the excess-deficit rule is a solution of simultaneous linear equations？ - Which relation is there between the two rules explicitly？ To answer these Questions, we consider the Rule of Single/Double False Position and research the Excess-Deficit Rule in some ancient mathematical books of Chosun Dynasty that was heavily affected by Chinese mathematics. And we pursue their historical traces in Egypt, Arab and Europe. As a result, we can make sure of the status of the Excess-Deficit Rule differing from the Rectangular Arrays(the solution of simultaneous linear equations) and identify the relation of the two rules: the application of the Excess-Deficit Rule including supposition in ancient Chinese mathematics corresponds to the Rule of Double False Position in western mathematics. In addition, we try to appreciate didactical value of the Rule of False Position which is apt to be considered as a historical by-product.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.742-762
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• 2015

In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN's entangled data distribution due to high levels of appearance variations in unconstrained environments, DML's goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN's performance on faces with large variances, such as pose and expression.

• Journal of Korean Neurosurgical Society
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• v.40 no.6
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• pp.445-449
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• 2006

Objective : The accurate anatomy of the facial nerve is essential for successful surgical outcome. The purpose of the present study is to know such information on the facial nerve from a series of specimens. Methods : This study is based on cadaveric dissection of 41 Korean fetus and stillborn infant and describes anatomical variations of the peripheral branches of the facial nerve that pose a importance in a number of neruosurgical procedure. Results : The branching patterns were classified into six types according to modified Davis classification : the frequencies of occurrence were : type I, 4.9%; type II, 24.4%; type III, 34.1%; type IV, 19.5%; type V, 12.2%; and type VI, 4.9%. Types II, III and IV together accounted for almost 80% of the specimens. Conclusion : Compared to previous adult and western stillborn fetus cadaveric studies, there was no significant difference in the percentage of the types between the subjects in the present study, similar pattern and anatomic distribution.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.198-199
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• 2014

In construction management the estimation procedure of construction expanses follows a series of submission phases: production of drawings, the assessment report, and the expanse report. In South Korea, it is a widely known issue that the expanse report only includes the net expanses at each construction phase and part, which makes it difficult to trace detailed basis from the records. This issue with inefficient record management should pose a number of problems, which result from discontinuation of construction record, unproductiveness for reproduction of records at each construction and submission phases for construction management, and failure to perform fair management among the contracting parties. Thus, the amendment in which the assessment report and the quantity estimation report reflect common codes to share throughout types of construction, space, and parts should be applied into practices so as to model production of acceptable reports and record.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.674-679
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• 2006

In this paper a gesture recognition system using 3D feature data is described. The system relies on a novel 3D sensor that generates a dense range mage of the scene. The main novelty of the proposed system, with respect to other 3D gesture recognition techniques, is the capability for robust recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly employing 3D hand features. Moreover, the proposed approach does not rely on colour information, and guarantees robust segmentation of the hand under various illumination conditions, and content of the scene. Several novel 3D image analysis algorithms are presented covering the complete processing chain: 3D image acquisition, arm segmentation, hand -forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is tested in an application scenario involving the recognition of sign-language postures.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.8
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• pp.1075-1094
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• 2013

This study analyzed the lower body type of women aged 20 to 30 to understand their respective characteristics. The research method was restricted to the use of direct measurements data and 3D measurements data of the Sixth Size Korea. Factor analysis, cluster analysis, ANOVA, Duncan's test, discriminant analysis, t-test, and ${\chi}^2$-test were performed for the statistical analysis of the data using SPSS Win 20.0 program. The results of this study are as follows. Lower body type based on 3D measurements were classified into 3 types (obese lower body, long lower body, and small lower body). Lower body type based on direct measurements were classified into 3 types (obese lower body, thick and long lower body, and small lower body). Lower body type based on the direct measurement of sitting pose were classified into 3 types (obese lower body, long and thin lower body, and short lower body). The age differences in the lower body types could be analyzed by an evaluation of the 3D simulation of the lower body.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.17-24
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• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.951-952
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• 2023

소위 '인생네컷'이라 불리는 셀프사진관은 MZ 세대의 새로운 놀이 문화로 떠오르며 사용자 수가 나날이 증가하고 있다. 그러나 짧은 시간 내에 다양한 포즈를 취해야 하는 셀프사진관 특성상 촬영이 낯선 사람에게는 여전히 진입장벽이 존재한다. 더불어 매번 비슷한 포즈와 사진 결과물에 기존 사용자는 점차 흥미를 잃어가는 문제점도 발생하고 있다. 이에 본 연구에서는 셀프사진관 사용자의 관계를 분류하는 모델을 개발하여 관계에 따른 적합하고 다양한 포즈를 추천하는 프레임워크를 제안한다. 사용자의 관계를 'couple', 'family', 'female_friend', 'female_solo', 'male_friend', 'male_solo' 총 6 개로 구분하였고 실제 현장과 유사하도록 단색 배경의 이미지를 우선으로 학습 데이터를 수집하여 모델의 성능을 높였다. 모델 학습 단계에서는 모델의 성능을 높이기 위해 여러 CNN 기반의 모델을 전이학습하여 각각의 정확도를 비교하였다. 결과적으로 195 장의 test_set 에서 accuracy 0.91 의 성능 평가를 얻었다. 본 연구는 객체 인식보다 객체 간의 관계를 학습시켜 관계성을 추론하고자 하는 것을 목적으로, 연구 결과가 희박한 관계 분류에 대한 주제를 직접 연구하여 추후의 방향성이나 방법론과 같은 초석을 제안할 수 있다. 또한 관계 분류 모델을 CCTV 에 활용하여 미아 방지 혹은 추적과 구조 등에 활용하여 국가 치안을 한층 높이는 데 기대할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.