• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.5
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• pp.475-482
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• 2010

In this paper, an efficient two-level parallel domain decomposition algorithm is suggested to solve large-DOF structural problems. Each subdomain is composed of the coarse problem and local problem. In the coarse problem, displacements at coarse nodes are computed by the iterative method that does not need to assemble a stiffness matrix for the whole coarse problem. Then displacements at local nodes are computed by Multi-Frontal Sparse Solver. A parallel version of PCG(Preconditioned Conjugate Gradient Method) is developed to solve the coarse problem iteratively, which minimizes the data communication amount between processors to increase the possible problem DOF size while maintaining the computational efficiency. The test results show that the suggested algorithm provides scalability on computing performance and an efficient approach to solve large-DOF structural problems.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.113-115
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• 2005

This thesis presents a method for 3D modeling of facial expression from frontal and mirror-reflected multi-image. Since the proposed system uses only one camera, two mirrors, and simple mirror's property, it is robust, accurate and inexpensive. In addition, we can avoid the problem of synchronization between data among different cameras. Mirrors located near one's cheeks can reflect the side views of markers on one's face. To optimize our system, we must select feature points of face intimately associated with human's emotions. Therefore we refer to the FDP (Facial Definition Parameters) and FAP (Facial Animation Parameters) defined by MPEG-4 SNHC (Synlhetic/Natural Hybrid Coding). We put colorful dot markers on selected feature points of face to detect movement of facial deformation when subject makes variety expressions. Before computing the 3D coordinates of extracted facial feature points, we properly grouped these points according to relative part. This makes our matching process automatically. We experiment on about twenty koreans the subject of our experiment in their late twenties and early thirties. Finally, we verify the performance of the proposed method tv simulating an animation of 3D facial expression.

• Korean Journal of Cognitive Science
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• v.18 no.4
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• pp.445-455
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• 2007

We present a new biometric identification system based on multi-view features of teeth using principal components analysis(PCA). The multi-view features of teeth consist of the frontal view, the left side view and the right side view. In this paper, we try to stan the foundations of a dental biometrics for secure access in real life environment. We took the pictures of the three views teeth in the experimental environment designed specially and 42 principal components as the features for individual identification were developed. The classification for individual identification based on the nearest neighbor(NN) algorithm is created with the distance between the multi-view teeth and the multi-view teeth rotated. The identification performance after rotating two degree of test data is 95.2% on the left side view teeth and 91.3% on the right side view teeth as the average values.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.561-562
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• 2008

The numerical analysis of sound radiation by vibrating structure is a well known and mature technology used in many industries. Accurate methods based on the boundary or finite element method have been successfully developed over the last two decades and are now available in standard CAE tools. These methods are however known to require significant computational resources which, furthermore, very quickly increase with the frequency of interest. The low speed of most current methods is a main obstacle for a systematic use of acoustic CAE in industrial design processes. In this paper we are going to present a set of innovative techniques that significantly speed-up the calculation of acoustic radiation indicators (acoustic pressure, velocity, intensity and power; contribution vectors). The modeling is based on the well known combination of finite elements and infinite elements but also combines the following ingredients to obtain a very high performance: o a multi-frontal massively parallel sparse direct solver; o a multi-frequency solver based on the Krylov method; o the use of pellicular acoustic modes as a vector basis for representing acoustic excitations; o the numerical evaluation of Green functions related to the specific geometry of the problem under investigation. All these ingredients are embedded in the ACTRAN/AR CAE tool which provides unprecedented performance for acoustic radiation analysis. The method will be demonstrated on several applications taken from various industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4903-4929
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• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

• Archives of Plastic Surgery
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• v.40 no.5
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• pp.564-569
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• 2013

Background Scalp defects may be caused by various etiological factors, and they represent a significant surgical and aesthetic concern. Various surgical techniques can be applied for reconstructive work such as primary closure, skin grafting, pedicled or free flaps. In this article, the authors share their clinical experience with scalp operations using the technique of local flaps and discuss the application of this method from the perspective of not only the size of the defect, but also in relation to the anatomical area, quality of surrounding tissue, and patient's condition. Methods During the period from December 2007 to December 2012, 13 patients with various scalp defects, aged 11 to 86 years, underwent reconstruction with local pedicle flaps. The indications were based on the patients' condition (age, sex, quality of surrounding tissue, and comorbidities) and wound parameters. Depending on the size of the defects, they were classified into three groups as follows: large, 20 to 50 $cm^2$; very large, 50 to 100 $cm^2$; extremely large, 100 $cm^2$. The location was defined as peripheral (frontal, temporal, occipital), central, or combined (more than one area). We performed reconstruction with 11 single transposition flaps and 1 bipedicle with a skin graft on the donor area, and 2 advancement flaps in 1 patient. Results In all of the patients, complete tissue coverage was achieved. The recovery was relatively quick, without hematoma, seroma, or infections. The flaps survived entirely. Conclusions Local flaps are widely used in scalp reconstruction since they provide healthy, stable, hair-bearing tissue and require a short healing time for the patients.

• 한국전산유체공학회:학술대회논문집
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• 2000.10a
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• pp.1-8
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• 2000

In this work, Internet Supercomputing methodology is introduced and the concept is materialized for large-scale finite element analysis. The primary resources of Internet Supercomputing are numerous idling PCs connected by Internet with no regards to their locations. Therefore, it becomes one of the most affordable ways to achieve supercomputing power unlimitedly if the appropriate parallel algorithm and the operating program are developed for this slow network environment. Under the above concept, virtual supercomputing system InterSup I is constructed and tested. To establish the InterSup I system, 64 CPU nodes, which are located in several places and connected by Internet, are conscripted, and parallel finite element software is developed for linear static analysis of structures based on the parallel multi-frontal algorithm. By the established InterSup I system, analysis of finite element structural model having around five million DOFs are solved to check the affordability and effectiveness of Internet Supercomputing.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.859-862
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• 2004

전두엽에서 뇌파를 측정했을 때 안전도에 의해 뇌파는 크게 왜곡되게 되는데, 제안한 뇌파 적응필터를 이용하여 측정된 뇌파에서 안전도를 제거하게 된다. 제안한 필터는 전두엽에서 다채널 뇌파를 처리할 수 있는 구조이며 적응필터 기반의 FIR 필터구조로 이루어져 기존의 다채널 적응필터 구조보다 계산량을 크게 줄였고, 짧은 연산 시간으로 실시간 DSP 보드 수행시 더 많은 채널을 수행할 수 있게 되었다. 또한 일반적인 적응필터와는 달리 기준신호 없이 신호처리가 가능한 적응 신호선 보정기 구조이므로 한 채널에 대해서 하나의 입력 신호로 원하는 신호를 얻을 수 있다. 실험을 통하여 제안한 FIR 필터가 뇌파 측정시 안전도를 효과적으로 제거함을 확인하였다.

• Korean Journal of Acupuncture
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• v.30 no.3
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• pp.161-170
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• 2013

Objectives : Our study aimed to investigate the sustained effects of sham (SHAM) and verum acupuncture (ACUP) into the post-stimulus resting state. Methods : In contrast to previous studies, in order to define the changes in resting state induced by acupuncture, changes were evaluated with a multi-method approach by using regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF). Twelve healthy participants received SHAM and ACUP stimulation right GB34 (Yanglingquan) and the neural changes between post- and pre-stimulation were detected. Results : The following results were found; in both ReHo and ALFF, the significant foci of; left and right middle frontal gyrus, left medial frontal gyrus, left superior frontal gyrus, and right posterior cingulate cortex, areas that are known as a default mode network, showed increased connectivity. In addition, in ReHo, but not in ALFF, brain activation changes in the insula, anterior cingulate cortex, and the thalamus, which are associated with acupuncture pain modulation, were found. Conclusions : In this study, results obtained by using ReHo and ALFF, showed that acupuncture can modulate the post-stimulus resting state and that ReHo, but not ALFF, can also detect the neural changes that were induced by the acupuncture stimulations. Although more future studies with ReHo and ALFF will be needed before any firm conclusions can be drawn, our study shows that particularly ReHo could be an interesting method for future clinical neuroimaging studies on acupuncture.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.