• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.91-101
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• 2011

This research aims to suggest web-based 3D face modeling system for hairline modification surgery. As public interests in beauty regarding face escalate with era of wide persoanl mobile smart iCT devices, need for medical information system is urgent and increasing demand. This research attempted to build 3D facing modeling library deploying conventional technology and proprietary software available. Implications from the our experiment found that problems and requirement for developing new web based standard. We suggest new system from our experiment and literature review regarding relevant technologies. Main features of our suggested systems is based on studies regarding hair loss treatment such as medical science, beauty studies and information technology. This system processes input images of 2D frontal and profile pictures of face into 3D face modeling with mesh-data. The mesh data is compatible with web standard technology including SVG and Canvas Tag supported natively by HTML5.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.225-229
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• 1998

The purpose of the current study was comparative analysis of autonomic and electrocortical responses to passive and active touch of the tektites with different subjective emotional preference. Perspective goal of the project is development of a template for classification of tactile stimuli according to subjective comfort and associated physiological manifestations. The study was carried out on 36 female college students. Physiological signals were acquired by Grass and B10PAC 100 systems with AcqKnowledge III software. Frontal, parietal and occipital EEG (relative power spectrum /percents/ of EEG bands - delta, theta, slow and fast alpha, low and fast beta), and autonomic variables, namely heart rate (HR), respiratory sinus arrhythmia (RSA), pulse transit time (PTT), respiration rate (RSP) and skin conductance parameters (SCL, amplitude, rise time and number of SCRs) were analyzed for rest baseline and stimulation conditions. Analysis of the overall pattern of reaction indicated that autonomic response to tactile stimulation was manifested in a form of moderate HR acceleration, RSP increase, RSA decrease (lowered vagal tone), decreased n and increased electrodermal activity (increased SCL, several SCRs) that reflects general sympathetic activation. Parietal EEG effects (on contra-lateral side to stimulated hand) were featured by short-term alpha-blocking, slightly reduced theta and significantly increased delta and enhanced fast beta activity with few variations across stimuli. The main finding of the study was that most and least preferred textures exhibited significant differences in autonomic (HR, RSP, PTT, SCR, and at less extent in RSA and SCL) and electrocortical responses (delta, slow and fast alpha, fast beta relative power). These differences were recorded both in passive and active stimulation modes, thus demonstrating reproducibility of distinction between most and least emotionally preferred tactile stimuli, suggesting influence of psychological factors, such as emotional property of stimulus, on physiological outcome. Nevertheless, development of sufficiently sensitive .and reliable template for classification of emotional responses to tactile stimulation based on physiological response pattern may require more extensive empirical database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1690-1710
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• 2014

In this paper, we propose the rapid implementation of a 3-dimensional (3D) facial reconstruction from a single frontal face image and introduce a design for its application on a mobile device. The proposed system can effectively reconstruct human faces in 3D using an approach robust to lighting conditions, and a fast method based on a Canonical Correlation Analysis (CCA) algorithm to estimate the depth. The reconstruction system is built by first creating 3D facial mapping from a personal identity vector of a face image. This mapping is then applied to real-world images captured with a built-in camera on a mobile device to form the corresponding 3D depth information. Finally, the facial texture from the face image is extracted and added to the reconstruction results. Experiments with an Android phone show that the implementation of this system as an Android application performs well. The advantage of the proposed method is an easy 3D reconstruction of almost all facial images captured in the real world with a fast computation. This has been clearly demonstrated in the Android application, which requires only a short time to reconstruct the 3D depth map.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.96-106
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• 1997

The clinical validity of a korean EEG and EP mapping system(Neuronics) was evaluated with schizophrenic patients(n=20), normal controls(n=19), and 10 patients with central nervous system disease(8 patients with cerebrovascular accident, 1 patient with brain mass, and 1 patient with periodic paralysis). In the normal control group, the pattern of resting computerized EEG with eyes closed showed normal parieto-occipital dominance of alpha wave. Compared with normal controls, schizophrenic patients had more delta activity in the frontal region, and less alpha activity especially in the parieto-occipital region. In most cases patients with cortical organic lesions(n=5) revealed increased delta and theta activity and decreased alpha activity on the lesion areas. These findings were compatible with their MRI and clinical findings. However in the cases of subcortical lesions(n=5) EEG showed various findings which suggest diverse influences of subcortical abnormalities on cortical activities. The P300 of schizophrenic group was smaller and more delayed than those of normal controls. These results are generally compatible with the previous studies using other EEG and EP mapping systems consequenty and suggest that the this EEG and EP mapping system(Neuronics) has clinical validity.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of Genetic Medicine
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• v.18 no.2
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• pp.142-146
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• 2021

Alagille syndrome (AGS) is a rare autosomal dominant inherited disorder, with major clinical manifestations of bile duct paucity, cholestasis, cardiovascular anomaly, ophthalmic abnormalities, butterfly vertebrae, and dysmorphic facial appearance. It is caused by heterozygous mutations in JAG1 or NOTCH of the Notch signaling pathway presenting with variable phenotypic penetrance and involving multiple organ systems. The following case report describes a unique case of a 16-year-old female with AGS who presented with the primary complaint of renovascular hypertension. She had a medical history of ventricular septal defect and polycystic ovary syndrome. The patient had a dysmorphic facial appearance including frontal bossing, bulbous tip of the nose, a pointed chin with prognathism, and deeply set eyes with mild hypertelorism. Stenoocclusive changes of both renal arteries, celiac artery, lower part of the abdominal aorta, and left intracranial artery, along with absence of the left internal carotid artery were found on examination. Whole exome sequencing was performed and revealed a pathologic mutation of JAG1, leading to the diagnosis of AGS. Reverse phenotyping detected butterfly vertebrae and normal structure and function of the liver and gallbladder. While the representative symptom of AGS in most scenarios is a hepatic problem, in this case, the presenting clinical features were the vascular anomalies. Clinical manifestations of AGS are diverse, and this case demonstrates that renovascular hypertension might be in some cases a presenting symptom of AGS.

• Ocean Science Journal
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• v.40 no.1
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• pp.25-44
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• 2005

Long-term, continuous, and real-time ocean monitoring has been undertaken in order to evaluate various oceanographic phenomena and processes in the East/Japan Sea. Recent technical advances combined with our concerted efforts have allowed us to establish a real-time monitoring system and to accumulate considerable knowledge on what has been taking place in water properties, current systems, and circulation in the East Sea. We have obtained information on volume transport across the Korea Strait through cable voltage measurements and continuous temperature and salinity profile data from ARGO floats placed throughout entire East Sea since 1997. These ARGO float data have been utilized to estimate deep current, inertial kinetic energy, and changes in water mass, especially in the northern East Sea. We have also developed the East Sea Real-time Ocean Buoy (ESROB) in coastal regions and made continual improvements till it has evolved into the most up-to-date and effective monitoring system as a result of remarkable technical progress in data communication systems. Atmospheric and oceanic measurements by ESROB have contributed to the recognition of coastal wind variability, current fluctuations, and internal waves near and off the eastern coast of Korea. Long-tenn current meter moorings have been in operation since 1996 between Ulleungdo and Dokdo to monitor the interbasin deep water exchanges between the Japanese and Ulleung Basins. In addition, remotely sensed satellite data could facilitate the investigation of atmospheric and oceanic surface conditions such as sea surface temperature (SST), sea surface height, near-surface winds, oceanic color, surface roughness, and so on. These satellite data revealed surface frontal structures with a fairly good spatial resolution, seasonal cycle of SST, atmospheric wind forcing, geostrophic current anomalies, and biogeochemical processes associated with physical forcing and processes. Since the East Sea has been recognized as a natural laboratory for global oceanic changes and a clue to abrupt climate change, we aim at constructing a 4-D continuous real-time monitoring system, over a decade at least, using the most advanced techniques to understand a variety of oceanic processes in the East Sea.

• Herbal Formula Science
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• v.25 no.4
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• pp.483-497
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• 2017

Background : Behavioral stress has been suggested as one of the significant factors that is able to disrupt physiological systems and cause depression as well as changes in various body systems. The stressful events can alter cognition, learning, memory and emotional responses, resulting in mental disorders such as depression and anxiety. Results : We used a restraint stress model to evaluate the alteration of behavior and stress-related blood parameter. The animals were randomly divided into two groups of five animals each group. Furthermore, we assessed the change of body weight to evaluate the locomotor activity as well as status of emotional and anxiety in mice. After 7 days of restraint stress, the body weight had significantly decreased in the restraint stress group compared with the control group. We also observed stress-associated behavioral alterations, as there was a significant decrease in open field and forced swim test, whereas the immobilization time was significantly increased in the stress group compared to the control group. We observed the morphological changes of neuronal death and microglia by immunohistochemistry and western blot. In our study restraint stress did not cause change in neuronal cell density in the frontal cortex and CA1 hippocampus region, but there was a trend for an increased COX-2 and iNOS protein expression and microglia (CD11b) in brain, which is restraint stress. Conclusion : Our study, there were significant alterations observed in the behavioral studies. We found that mice undergoing restraint stress changed behavior, confirming the increased expression of inflammatory factors in the brain.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.90-95
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• 2014

The researchs using brain-computer interface, the new interface system which connect human to macine, have been maded to implement the user-assistance devices for control of wheelchairs or input the characters. In recent researches, there are several trials to implement the speech recognitions system based on the brain wave and attempt to silent communication. In this paper, we studied how to extract features of vowel based on international phonetic alphabet (IPA), as a foundation step for implementing of speech recognition system based on electroencephalogram (EEG). We conducted the 2 step experiments with three healthy male subjects, and first step was speaking imagery with single vowel and second step was imagery with successive two vowels. We selected 32 channels, which include frontal lobe related to thinking and temporal lobe related to speech function, among acquired 64 channels. Eigen value of the signal was used for feature vector and support vector machine (SVM) was used for classification. As a result of first step, we should use over than 10th order of feature vector to analyze the EEG signal of speech and if we used 11th order feature vector, the highest average classification rate was 95.63 % in classification between /a/ and /o/, the lowest average classification rate was 86.85 % with /a/ and /u/. In the second step of the experiments, we studied the difference of speech imaginary signals between single and successive two vowels.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.