• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.388-393
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• 2019

As a fiiting method to data with outliers, RANSAC(RANdom SAmple Consensus) based algorithm is widely used in fitting of line, circle, ellipse, etc. CUDA is currently most widely used GPU with massive parallel processing capability. This paper proposes an efficient CUDA implementation of multiple planes fitting using RANSAC with 3d points data, of which one set of 3d points is used for one plane fitting. The performance of the proposed algorithm is demonstrated compared with CPU implementation using both artificially generated data and real 3d heights data of a PCB. The speed-up of the algorithm over CPU seems to be higher in data with lower inlier ratio, more planes to fit, and more points per plane fitting. This method can be easily applied to a wide variety of other fitting applications.

• Information and Communications Magazine
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• v.31 no.12
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• pp.38-43
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• 2014

효과적인 칼로리 소비는 칼로리 균형의 측면에서 매우 중요하다. 활동으로 인해 소비되는 열량은 사람의 일일 총 소비 열량 중 15~30%를 차지하며 사람들은 오직 이 부분만을 조절할 수 있다. 트레드밀 위에서의 활동 강도가 증가 할수록 소비되는 칼로리의 증가 경향은 $y=27.33e^{0.21x}$의 지수함수 형태의 곡선에 피팅됨을 확인할 수 있었다. 또한 인공신경망을 활용한 소모 열량 추정 알고리즘은 전체적 상관관계가 R=0.94로 좋은 결과를 보여주었다.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.451-458
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• 2013

In this paper, we have developed an intellectual sizing and fitting system for bike user. In order to prevent the disease, we should adjust the bike's frame and select several components in the body size. Then it propose an extractions algorithm of avatar imagination with the cromakey method and realize the sizing and fitting.

• Smart Media Journal
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• v.3 no.2
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• pp.9-13
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• 2014

The Levenberg-Marquardt (LM) algorithm is the most widely used fitting algorithm. It outperforms simple gradient descent and other conjugate gradient methods in a wide variety of problems. Based on the work of paper[1], we propose a modified Levenberg-Marquardt algorithm for better performance of mobile system. The LM parameter at the $k_{th}$ iteration is chosen ${\mu}=A{\bullet}{\parallel}f(x){\parallel}{\bullet}I$ where f is the residual function, and J is the Jacobi of f. In this paper, we show this method is more efficient than traditional method under the situation that the system iteration is limited.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.159-165
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• 2017

In order to solve problems in automatic quality inspection of tablet capsules, computation-efficient image processing technique, appropriate threshold setting, edge detection and segmentation methods are required. And since existing automatic system for quality inspection of tablet capsules is of very high cost, it needs to be reduced through the realization of low-price hardware system. This study suggests a technique that uses low-cost camera module to obtain image and inspects dents on tablet capsules and sorting them by applying TLS curve fitting technique and edge-based image segmentation. In order to assess the performance, the major classifications algorithm of PCA, ICA and SVM are used to evaluate training time, test time and accuracy for capsule image area and curve fitting edge data sets.

• Journal of the Korea Computer Graphics Society
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• v.28 no.4
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• pp.1-11
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• 2022

The Skinned Multi-Person Linear Model (SMPL) is the most widely used parametric 3D Human Model optimized and learned from CAESAR, a 3D human scanned database created with measurements from 3,800 people living in United States in the 1990s. We point out the lack of racial diversity of body types in SMPL and propose K-SMPL that better represents Korean 3D body shapes. To this end, we develop a fitting algorithm to estimate 2,773 Korean 3D body shapes from Korean body measurement data. By conducting principle component analysis to the estimated Korean body shapes, we construct K-SMPL model that can generate various Korean body shape in 3D. K-SMPL model allows to improve the fitting accuracy over SMPL with respect to the Korean body measurement data. K-SMPL model can be widely used for avatar generation and human shape fitting for Korean.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.13-19
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• 2023

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.751-753
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• 2013

안드로이드 플랫폼은 타 모바일 플랫폼보다 보안에 있어서 더 많은 취약점을 안고 있다. 따라서 현재 발생하고 있는 대부분의 모바일 악성코드는 안드로이드 플랫폼에서 발생하고 있다. 현재 악성코드 탐지 기법 중 기계학습을 도입한 방법은 변종 악성코드의 대처에 유연하다. 하지만 기계학습기법은 불필요한 Feature를 학습데이터로 사용할 경우, 오버피팅이 발생하여 전체적인 성능을 저하시킬 수 있다. 본 논문에서는 안드로이드 플랫폼에서 발생하는 리소스를 모니터링하여 Feature vector를 생성하고, Feature-selection 알고리즘을 통하여 Feature의 수에 따라 기계학습 Classifier를 통한 악성코드 탐지의 성능지표를 보인다. 이를 통하여, 기계학습을 통한 악성코드 탐지에서 Feature-selection의 필요성과 중요성을 설명한다.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.241-246
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• 2020

In this paper, we propose a neural network model to converge the marine electric propulsion system and deep learning algorithm to predict the DC/DC converter output current in the electric propulsion regeneration system and to predict the battery charge during regeneration. In order to experiment with the proposed neural network, the input voltage and current of the PCM were measured and the data set was secured on the prototype PCM board. In addition, in order to improve the learning results in the insufficient data set, the scale of the data set was increased through data fitting and its learning was executed further. After learning, the difference between the data prediction result of the neural network model and the actual measurement data was compared. The proposed neural network model effectively showed the prediction of battery charge according to changes in input voltage and current. In addition, by predicting the characteristic change of the analog circuit constituting the DC/DC converter through a neural network, it is determined that the characteristics of the analog circuit should be considered when designing the regeneration system.