• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.844-851
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• 2019

In this paper, we propose a loss function in CNN that introduces inter-class amplitudes to increase inter-class loss and reduce intra-class loss to increase of face recognition performance. This loss function increases the distance between the classes and decreases the distance in the class, thereby improving the performance of the face recognition finally. It is confirmed that the accuracy of face recognition for visible light image of proposed loss function is 99.62%, which is better than other loss functions. We also applied it to face recognition of visible and near-infrared complex images to obtain satisfactory results of 99.76%.

• 한국산학기술학회논문지
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• 제21권5호
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• pp.7-13
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• 2020

본 논문에서는 사람의 비정상적인 호흡과 정상적인 호흡 신호를 획득한 후, 이 신호들을 분석하고, 특히 비정상 호흡신호를 감지하는 방법과 정상 및 비정상 신호를 분류하는 방법을 제시한다. 본 연구에서 사람의 호흡신호는 BIOPAC 장비를 활용하여 획득하며, 사람의 호흡 상태를 정량적인 수치 정보를 활용하여 판단한다. 궁극적으로 본 논문에서는 일반 환경에서 사람의 호흡상태를 신호로 획득하여 분석하고, 호흡상태를 모니터링 할 수 있는 시스템을 개발하고자 하며, 무호흡 상태를 감지 할 수 있는 방법을 제안한다. 획득되는 호흡신호를 활용하여 정량적인 정보를 바탕으로 호흡신호를 상태에 따라 분류한다. 접촉식 의료장비를 활용하여 호흡신호를 획득하고 호흡상태를 분류하기 전에 잡음제거 알고리즘을 적용한다. 기존의 사비츠키-골레이 필터와 중간값 필터의 장점만을 활용하여 혼합필터를 사용하여 신호를 분석하기에 적절한 상태가 되도록 한다. 서로 다른 호흡 상태, 즉 서로 다른 클래스간 거리는 최대로 하고, 동일한 호흡상태, 즉 같은 클래스 간의 거리는 최소로 하기 위해 신호 획득후 신호의 특징값들 간의 상호상관 계수를 계산한다. 제안하는 알고리즘은 실제 호흡 환경에 적용할 수 있을 정도로 직관적이고, 제안하는 방법을 증명하기 위한 실험 결과들을 함께 제시한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• 한국전문물리치료학회지
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• 제21권3호
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• pp.73-79
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• 2014

The purposes of the current study were to (1) estimate the inter-rater agreement for visual assessment of scapular downward rotation (SDR), (2) develop the scapular downward rotation index (SDRI) as a method to measure SDR objectively and quantitatively, and (3) analyze the intra- and inter-rater reliability of the SDRI. Twenty subjects with scapular downward rotation syndrome (SDRS) were recruited for this study. The visual assessment and the measurement for the SDRI were conducted by two examiners in two sessions each. The SDRI [$(a-b){\div}a{\times}100$] is calculated with the measurement of two linear distances: One is a perpendicular distance from the root of the scapular spine to the thoracic mid-line (a), and the other is a perpendicular distance from the inferior angle of the scapula to the thoracic mid-line (b). Cohen's kappa coefficient was calculated to estimate the inter-rater agreement for visual assessment. Intra-class correlation coefficients (ICCs) with a 95% confidence interval (CI), the standard error of measurement, and minimal detectable differences were calculated to assess intra- and inter-rater reliability of SDR measurement using the SDRI. The results indicated that the kappa coefficient of inter-rater agreement for visual assessment was fair (${\kappa}=.21$). The intra-rater reliability of SDR measurement using the SDRI was excellent for examiner 1 (ICC=.92, 95% CI=.78~.97) and good for examiner 2 (ICC=.82, 95% CI=.55~.93). The inter-rater reliability was moderate (ICC=.73, 95% CI=.32~.89). These findings showed that SDR measurement using the SDRI for subjects with SDRS may be considered reliable and better than the visual assessment.

• Earthquakes and Structures
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• 제11권3호
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• pp.505-516
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• 2016

This paper evaluates the scaling of ground motions recorded from nine intermediate-depth earthquakes produced in the Vrancea seismic zone in Romania. The considered ground motion database consists of 363 horizontal recordings obtained on soil classes B and C (according to Eurocode 8). An analysis of the inter- and intra-event spectral accelerations is performed in order to gain information regarding the magnitude and distance scaling of the Vrancea ground motions. The analyses reveal a significant influence of the earthquake magnitude and focal depth on the distance scaling and different magnitude and distance scaling for the two soil classes. A linear magnitude and distance scaling is inferred from the results for the range of magnitudes $5.2{\leq}M_W{\leq}7.1$. The results obtained are checked through stochastic simulations and the influence of the stress drop and kappa values on the ground motion levels is assessed. In addition, five ground motion models which were tested in other studies using recordings from Vrancea earthquakes are analyzed in order to evaluate their corresponding host stress drop and kappa. The results show generally a direct connection between the host kappa values and the host stress drop values. Moreover, all the ground motion models depict magnitude dependent host kappa and stress drop levels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.40-49
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• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.153-162
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• 2022

Relevance. Innovative activities in education should be aimed at ensuring the comprehensive development of the individual and professional development of students. The main idea of modular technology is that the student should learn by himself, and the teacher manages his learning activities. The advantage of modular technology is the ability of the teacher to design the study of the material in the most interesting and accessible forms for this part of the study group and at the same time achieve the best learning results. Innovative Moodle technology. it is gaining popularity every day, significantly expanding the space of teaching and learning, allowing students to study inter-faculty university programs in depth. The purpose of this study is to assess the quality of implementation of the e-learning system Moodle. The study was conducted at the South Ukrainian National Pedagogical University named after K. D. Ushinsky in order to identify barriers to the effective implementation of innovative distance learning technologies Moodle and introduce a new model that will have a positive impact on the development of e-learning. Methodology. The paper used a combination of theoretical and empirical research methods. These include: scientific analysis of sources on this issue, which allowed us to formulate the initial provisions of the study; analysis of the results of students 'educational activities; pedagogical experiment; questionnaires; monitoring of students' activities in practical classes. Results. This article evaluates the implementation of the principles of distance learning in the process of teaching and learning at the University in terms of quality. The experiment involved 1,250 students studying at the South Ukrainian National Pedagogical University named after K. D. Ushinsky. The survey helped to identify the main barriers to the effective implementation of modern distance learning technologies in the educational process of the University: the lack of readiness of teachers and parents, the lack of necessary skills in applying computer systems of online learning, the inability to interact with the teaching staff and teachers, the lack of a sufficient number of academic consultants online. In addition, internal problems are investigated: limited resources, unevenly distributed marketing advantages, inappropriate administrative structure, and lack of innovative physical capabilities. The article allows us to solve these problems by gradually implementing a distance learning model that is suitable for any university, regardless of its specialization. The Moodle-based e-learning system proposed in this paper was designed to eliminate the identified barriers. Models for implementing distance learning in the learning process were built according to the CAPDM methodology, which helps universities and other educational service providers develop and manage world-class online distance learning programs. Prospects for further research focus on evaluating students' knowledge and abilities over the next six months after the introduction of the proposed Moodle-based program.

• 응용통계연구
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• 제29권5호
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• pp.835-847
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• 2016

공간통계 방법 중 지역에 대한 어떤 집합체 자료나 평균자료들을 분석하는데 일반적으로 공간적 자기회귀(conditionally autoregressive) 모형을 사용한다. 공간적 자기회귀 모형에 정의되는 공간적 이웃 소지역들은 중점의 거리나 근접성으로 정의된다. Kyung과 Ghosh (2010)는 방향에 따라서 이웃간 자기상관성의 크기가 다른 공간적 확장 모형을 제시하였다. 제안된 방향적 조건부 자기회귀(directional conditionally autoregressive) 모형은 고유 이방성을 모형화하여 기존의 CAR과정을 일반화한다. 제시한 방향적 조건부 자기회귀모형의 최대우도 추정량의 특성에 대해 설명하였고, 스코틀랜드 그레이터 글래스고우의 로그변환된 부동산 가격에 적용하여 조건부 자기회귀모형과 비교하였다.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1133-1146
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• 2016

공간통계 방법 중 지역에 대한 어떤 집합체 자료나 평균자료들을 분석하는데 일반적으로 공간적 자기회귀 (conditionally autoregressive) 모형을 사용한다. 공간적 자기회귀 모형에 정의되는 공간적 이웃 소지역들은 중점의 거리나 근접성으로 정의된다. Kyung과 Ghosh (2009)는 방향에 따라서 이웃간 자기상관성의 크기가 다른 확장된 공간 모형을 제시하였다. 제안된 방향적 조건부 자기회귀 (directional conditionally autoregressive) 모형은 고유 이방성을 모형화하여 기존의 CAR과정을 일반화한다. 제시한 방향적 조건부 자기회귀모형의 모수추정으로 마르코프 체인 몬테 카를로 방법을 기반으로 한 베이즈 추정법을 제시한다. 제시한 모형을 스코틀랜드 그레이터 글래스고우의 로그변환된 부동산 가격에 적용하여 조건부 자기회귀모형과 비교하였다.