• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.43-49
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• 2005

A few studies have shown that the function of fusiform face area is selectively involved in the perception of faces including a race difference. We investigated the neural substrates of the face-selective region called fusiform face area in the ventral occipital-temporal cortex and same-race memory superiority in the fusiform face area by the event-related fMRI. In our fMRI study, subjects (Oriental-Korean) performed the implicit distinction of the race while they consciously made familiar-judgments, regardless of whether they considered a face as Oriental-Korean or European-American. For race distinction as an implicit task, the fusiform face areas (FFA) and the right parahippocampal gyrus had a greater response to the presentation of Oriental-Korean faces than for the European-American faces, but in the conscious race distinction between Oriental-Korean and European-American faces, there was no significant difference observed in the FFA. These results suggest that different activation in the fusiform regions and right parahippocampal gyrus resulting from superiority of same-race memory could have implicitly taken place by the physiological processes of face recognition.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.41 no.4
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• pp.325-331
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• 2015

Skin is exposed to sunlight or artificial indoor light on a daily. The reached solar light on the earth surface consist of 50% visible light and 45% infrared (IR) except for ultra violet (UV). The negative effects of UV including UVB and UVA have been steadily investigated within the last decades. However, little is known about the effects of visible or IR light. In this study, we irradiated human dermal fibroblasts using light emitting diode (LED) to investigate the optimal parameter for enhancing cell growth and collagen synthesis. We found that red of 630 nm and green of 520 nm enhance the cell proliferation, but irradiation with purple and blue light exerts toxic effects. To examine the response of irradiation time and light intensity on the fibroblasts, cells were exposed to red or green light with intensities from 0.05 to $0.75mW/cm^2$. Procollagen secretion was increased of 1.4 fold by 10 min irradiation, while 30 min treatment decreased the collagen synthesis of dermal fibroblasts. Treatment with red of $0.3mW/cm^2$ and green of 0.15 and $0.3mW/cm^2$ resulted in enhancement of collagen mRNA. Lastly, we investigated the combinatorial effect of red and green light on dermal fibroblasts. The sequential irradiation of red and green light is an efficient way for the purpose of the increase in the number of fibroblasts than single light treatment. On the other hand, the exposure of red light alone was more effective method for enhancing of collagen secretion. Our study showed that specific light parameters accelerated cell proliferation, gene expression and collagen secretion on human dermal fibroblasts. In conclusion, we demonstrate that light exposure with specific parameter has beneficial effects on the function of dermal fibroblasts, and suggests the possibility of its cosmetically and clinical application.

• Economic and Environmental Geology
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• v.57 no.5
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• pp.577-592
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• 2024

This study presents the synthesis and characterization of MgAl-layered double hydroxide (LDH)/rice husk hydrochar (RHH) nanocomposites (MgAl-LDH/RHHs) via an in situ one-pot hydrothermal route at 150 ℃, utilizing Mg:Al molar ratio of 2:1 for arsenic remediation. The formation of MgAl-LDH/RHHs and their physicochemical properties were evaluated under varying hydrothermal aging times systematically. Prolonging the aging period to 12 hrs significantly enhanced the crystallinity and crystal size of the LDHs, resulting in a 3D hierarchical structure with the highest specific surface area (27.98 m²/g) formed on the hydrochar surface. The hexagonal crystal structure (d₀₀₃ = 0.8246 nm) was characterized by a rhombohedral unit cell with lattice parameters a = 0.3049 nm and c = 2.4738 nm, and a high positive charge density of 4.284 e/nm². These properties were found to be favorable for the sorption of arsenic oxyanions. Batch adsorption experiments were conducted to assess the potential of MgAl-LDH/RHHs-12h for the remediation of arsenic-contaminated soils. The original soil sample (CY) was mechanically sieved into fine-grained (CYF, < 75 ㎛) and coarse-grained (CYC, 75 ㎛-2 mm) fractions. When these soil samples were reacted with deionized water, arsenate was identified as the dissolved arsenic species, with concentrations of 2.85 mg/L for CY, 4.02 mg/L for CYF, and 2.55 mg/L for CYC, respectively. Kinetic sorption experiments, conducted at pH 5.0 and 8.0 in the presence and absence of 0.1 M NaCl as a background electrolyte, revealed that arsenic sorption onto MgAl-LDH/RHHs-12h was inhibited at pH 8 in the presence of NaCl. These findings suggest that effective arsenic sorption requires low pH conditions with minimal background electrolytes in soils.

• The Korean Journal of Nuclear Medicine
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• v.38 no.5
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• pp.338-343
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• 2004

Purpose: The purpose of this study was to develop a small animal PET using dual layer phoswich detector to minimize parallax error that degrades spatial resolution at the outer part of field-of-view (FOV). Materials and Methods: A simulation tool GATE (Geant4 Application for Tomographic Emission) was used to derive optimal parameters of small PET, and PET was developed employing the parameters. Lutetium Oxyorthosilicate (LSO) and Lutetium-Yttrium Aluminate-Perovskite(LuYAP) was used to construct dual layer phoswitch crystal. $8{\times}8$ arrays of LSO and LuYAP pixels, $2mm{\times}2mm{\times}8mm$ in size, were coupled to a 64-channel position sensitive photomultiplier tube. The system consisted of 16 detector modules arranged to one ring configuration (ring inner diameter 10 cm, FOV of 8 cm). The data from phoswich detector modules were fed into an ADC board in the data acquisition and preprocessing PC via sockets, decoder block, FPGA board, and bus board. These were linked to the master PC that stored the events data on hard disk. Results: In a preliminary test of the system, reconstructed images were obtained by using a pair of detectors and sensitivity and spatial resolution were measured. Spatial resolution was 2.3 mm FWHM and sensitivity was 10.9 $cps/{\mu}Ci$ at the center of FOV. Conclusion: The radioactivity distribution patterns were accurately represented in sinograms and images obtained by PET with a pair of detectors. These preliminary results indicate that it is promising to develop a high performance small animal PET.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.431-438
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• 2020

In this paper, the method to extract lip contour by multiple threshold is proposed. Spyridonos et. el. proposed a method to extract lip contour. First step is get Q image from transform of RGB into YIQ. Second step is to find lip corner points by change point detection and split Q image into upper and lower part by corner points. The candidate lip contour can be obtained by apply threshold to Q image. From the candidate contour, feature variance is calculated and the contour with maximum variance is adopted as final contour. The feature variance 'D' is based on the absolute difference near the contour points. The conventional method has 3 problems. The first one is related to lip corner point. Calculation of variance depends on much skin pixels and therefore the accuracy decreases and have effect on the split for Q image. Second, there is no analysis for color systems except YIQ. YIQ is a good however, other color systems such as HVS, CIELUV, YCrCb would be considered. Final problem is related to selection of optimal contour. In selection process, they used maximum of average feature variance for the pixels near the contour points. The maximum of variance causes reduction of extracted contour compared to ground contours. To solve the first problem, the proposed method excludes some of skin pixels and got 30% performance increase. For the second problem, HSV, CIELUV, YCrCb coordinate systems are tested and found there is no relation between the conventional method and dependency to color systems. For the final problem, maximum of total sum for the feature variance is adopted rather than the maximum of average feature variance and got 46% performance increase. By combine all the solutions, the proposed method gives 2 times in accuracy and stability than conventional method.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.165-179
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• 2005

The object of this study is a development of a stochastic user equilibrium transit assignment algorithm for multiple user classes considering stochastic characteristics and heterogeneous attributes of passengers. The existing transit assignment algorithms have limits to attain realistic results because they assume a characteristic of passengers to be equal. Although one group with transit information and the other group without it have different trip patterns, the past studies could not explain the differences. For overcoming the problems, we use following methods. First, we apply a stochastic transit assignment model to obtain the difference of the perceived travel cost between passengers and apply a multiple user class assignment model to obtain the heterogeneous qualify of groups to get realistic results. Second, we assume that person trips have influence on the travel cost function in the development of model. Third, we use a C-logit model for solving IIA(independence of irrelevant alternatives) problems. According to repetition assigned trips and equivalent path cost have difference by each group and each path. The result comes close to stochastic user equilibrium and converging speed is very fast. The algorithm of this study is expected to make good use of evaluation tools in the transit policies by applying heterogeneous attributes and OD data.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.4
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• pp.394-400
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• 2016

The b-type natriuretic peptide (BNP) values and increase on functional disorder in the ventricle, and are used as an index to diagnose heart failure and predict the prognosis. BNP values is known to be relevant to dyssystole in congestive heart failure. This study aimed to identify correlation between the BNP values and the items that indicate the diastolic function in echocardiography. The research divided 188 patients who went through the BNP test and echocardiography in the hospital into the groups with the BNP values; <100, 100-300, 301-600, 601-900, and >901 pg/mL. As the BNP values increase, there was relevance with the echocardiography items of ejection fraction, size of left atrium, E velocity, A velocity, Deceleration time, E/A ratio, E', A', S' and E/E'. In comparison on the groups divided based on the BNP values, E/E' had the highest relevance. The research also categorized 67 patients who diagnosed with heart failure. In comparison on the groups of the heart failure patients, the BNP values of the three groups of Grade I: $623.0{\pm}459.7pg/mL$, Grade II: $1013.2{\pm}1155.1pg/mL$ and Grade III: $1693.4{\pm}1544.0pg/mL$, respectively (p<0.01). As the grade was higher, there was a higher relevance with the echocardiography items of ejection fraction, size of left atrium, E velocity, A velocity, Deceleration time, E/A ratio, E', A', S' and E/E' (p<0.001). Higher BNP values had a higher relevance with the items that indicate the diastolic function in echocardiography and the BNP values of the Restrictive physiology group were the highest in echocardiography. So the BNP values was thought to be valuable to predict diastolic function of heart.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.199-210
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• 2002

In this paper, we carried out the study on speech recognition using the KM-Net topology design algorithm based on decision tree state-clustering to improve the performance of acoustic models in speech recognition. The Korean has many allophonic and grammatical rules compared to other languages, so we investigate the allophonic variations, which defined the Korean phonetics, and construct the phoneme question set for phonetic decision tree. The basic idea of the HM-Net topology design algorithm is that it has the basic structure of SSS (Successive State Splitting) algorithm and split again the states of the context-dependent acoustic models pre-constructed. That is, it have generated. the phonetic decision tree using the phoneme question sets each the state of models, and have iteratively trained the state sequence of the context-dependent acoustic models using the PDT-SSS (Phonetic Decision Tree-based SSS) algorithm. To verify the effectiveness of the above algorithm we carried out the speech recognition experiments for 452 words of center for Korean language Engineering (KLE452) and 200 sentences of air flight reservation task (YNU200). Experimental results show that the recognition accuracy has progressively improved according to the number of states variations after perform the splitting of states in the phoneme, word and continuous speech recognition experiments respectively. Through the experiments, we have got the average 71.5%, 99.2% of the phoneme, word recognition accuracy when the state number is 2,000, respectively and the average 91.6% of the continuous speech recognition accuracy when the state number is 800. Also we haute carried out the word recognition experiments using the HTK (HMM Too1kit) which is performed the state tying, compared to share the parameters of the HM-Net topology design algorithm. In word recognition experiments, the HM-Net topology design algorithm has an average of 4.0% higher recognition accuracy than the context-dependent acoustic models generated by the HTK implying the effectiveness of it.