• Korean Journal of Applied Biomechanics
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• v.32 no.4
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• pp.147-152
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• 2022

Objective: The purpose of this study was to analyze the effects of club head and golf ball kinematics and body alignment according to the swing plane during golf driver swing. Method: Sixteen college golfers participated in this study. Kinematic data of the club head and golf ball were collected using golf swing analysis system (Trackman Ver. 3e). The body alignment variables were collected using 8 motion capture system. An Independent samples t-test was used for comparison between the Out-to-In group and In-to-Out group, and the statistical significance level was set at .05. Results: For the club head related variables, club path and club face angle showed higher values in Out-to-In swing plane than In-to-Out swing plane. For the kinematic variables of the golf ball, the total distance showed a higher value in the In-to-Out swing plane than that of the Out-to-In swing plane. For the body alignment, the In-to-Out swing plane showed higher values than the Out-to-In swing plane for the pelvis rotation angle and trunk rotation angle. Conclusion: This study suggest that it would be more effective to use the In-to-Out swing plane for increasing the total distance during the golf driver swing.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1720-1728
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• 2017

Active shape model is widely used in the field of image processing especially on arbitrary meaningful shape extraction from single gray level image. Cootes et. al. showed efficient detection of variable shape from image by using covariance and mean shape from learning. There are two stages of learning and testing. Hahn applied enhanced shape alignment method rather than using Cootes's rotation and scale scheme. Hahn did not modified the profile itself. In this paper, the method using directional one dimensional profile is proposed to enhance Cootes's one dimensional profile and the shape alignment algorithm of Hahn is combined. The performance of the proposed method was superior to Cootes's and Hahn's. Average landmark estimation error for each image was 27.72 pixels and 39.46 for Cootes's and 33.73 for Hahn's each.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.292-293
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• 2008

In this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images are hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.120.2-120.2
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• 2015

We propose a novel direct writing technique with a femtosecond laser enabling selective modification of not only the morphology of conducting polymer thin films but also the orientation and alignment of the polymer crystal. Surface relief gratings resulting from photoexpansion on P3HT:PCBM and PEDOT:PSS thin films were fabricated by femtosecond laser direct writing. The photoexpansion was induced at laser fluence below the ablation threshold of the thin film. The morphology (size and shape) of photoexpansion could be quantitatively controlled by laser writing parameters such as focused beam size, writing speed, and laser fluence. GIWAX results showed that face-on P3HT crystals were largely increased in the photoexpansion in comparison with pristine region of the thin film. In addition, the face-on P3HTs in the photoexpansion were aligned with their orientation along the polarization of the laser. The micro-RAMAN spectra confirmed that neither chemical composition change nor the polymer chain breaking was observable after femtosecond laser irradiation. We believe that this laser direct writing technique opens a new door to the fabrication of more efficient OPVs via non-contact, toxic-free approach.

• Carbon letters
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• v.15 no.2
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• pp.142-145
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• 2014

Isotropic synthetic graphite scrap and phenolic resin were mixed, and the mixed powder was formed at 300 MPa to produce a green body. New bulk graphite was produced by carbonizing the green body at $700^{\circ}C$, and the bulk graphite thus produced was impregnated with resin and re-carbonized at $700^{\circ}C$. The bulk density of the bulk graphite was $1.29g/cm^3$, and the porosity of the open pores was 29.8%. After one impregnation, the density increased to $1.44g/cm^3$ while the porosity decreased to 25.2%. Differences in the pore distribution before and after impregnation were easily confirmed by observing the microstructure. In addition, by using an X-ray diffractometer, the degrees-of-alignment (Da) were obtained for one side perpendicular to the direction of compression molding of the bulk graphite (the "top-face"), and one side parallel to the direction of compression molding (the "side-face"). The anisotropy ratio calculated from the Da-values obtained was 1.13, which indicates comparatively good isotropy.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.816-824
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• 2009

In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1044-1049
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• 2006

얼굴 추적은 Vision base HCI의 핵심인 얼굴인식, 표정인식 그리고 Gesture recognition등의 다른 여러 기술을 지원하는 중요한 기술이다. 이런 얼굴 추적기술에는 영상(Image)의 Color또는 Contour등의 불변하는 특징들을 사용 하거나 템플릿(template)또는 형태(appearance)를 사용하는 방법 등이 있는데 이런 방법들은 조명환경이나 주위 배경등의 외부 환경에 민감하게 반응함으로 해서 다양한 환경에 사용할 수 없을 뿐더러 얼굴영상만을 정확하게 추출하기도 쉽지 않은 실정이다. 이에 본 논문에서는 deformable한 model을 사용하여 model과 유사한 shape과 appearance를 찾아 내는 AAM(Active Appearance Model)을 사용하는 얼굴 추적 시스템을 제안하고자 한다. 제안된 시스템에는 기존의 Combined AAM이 아닌 Independent AAM을 사용하였고 또한 Fitting Algorithm에 Inverse Compositional Image Alignment를 사용하여 Fitting 속도를 향상 시켰다. AAM Model을 만들기 위한 Train set은 150장의 4가지 형태에 얼굴을 담고 있는 Gray-scale 영상을 사용 하였다. Shape Model은 각 영상마다 직접 표기한 47개의 Vertex를 Trianglize함으로서 생성되는 71개의 Triangles을 하나의 Mesh로 구성하여 생성 하였고, Appearance Model은 Shape 안쪽의 모든 픽셀을 사용해서 생성하였다. 시스템의 성능 평가는 Fitting후 Shape 좌표의 정확도를 측정 함으로서 평가 하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.44-51
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• 2009

This paper addresses a face verification method using the nonlinear composite filter. This face verification process can be simple and speedy because it does not require any reprocessing such as face detection, alignment or cropping. The optimum nonlinear composite filter is derived by minimizing the output energy due to additive noise and an input scene while maintaining the outputs of training images constant. The filter is equipped with the discrimination capability and the robustness to additive noise by minimizing the outputs of the input scene and the noise, respectively. We build the nonlinear composite filter with two training images and compare the filter with the conventional synthetic discriminant function (SDF) filter. The receiver operating characteristics (ROC) curves are presented as a metric for the performance evaluation. According to the experimental results the optimum nonlinear composite filter is shown to be a robust scheme for face verification in low resolution and noise environments.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.