• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2527-2532
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• 2012

Objectives: The aim of the present study was to explore mechanisms underlying the effects of down-regulating ${\beta}$-catenin expression on esophageal carcinoma (EC) cells. Methods: Cell cycle distribution and apoptosis were determined using flow cytometry and annexin V apoptosis assay, respectively. Transmission electron microscopy (TEM) was used to examine changes in ultrastructure, while expression of cyclin D1 protein and mRNA was detected by western blot and real-time PCR. Proliferating cell nuclear antigen (PCNA) and extracellular signal-regulated kinase (ERK) 1-2 were evaluated by Western blot analysis. PCNA labeling index (LI) was determined by immunocytochemistry. Results: Compared with pGen-3-con transfected and Eca-109 cells, the percentage of G0/G1-phase pGen-3-CTNNB1 transfected cells was obviously increased (P<0.05), with no significant difference among the three groups with regard to apoptosis (P>0.05). pGen-3-CTNNB1 transfected cells exhibited obvious decrease in cyclin D1 mRNA and protein expression (P<0.05) and the ultrastructure of Eca-109 cells underwent a significant change after being transfected with pGen-3-CTNNB1, suggesting that down-regulating ${\beta}$-catenin expression can promote the differentiation and maturation. The expression of PCNA and the ERKI/2 phosphorylation state were also down-regulated in pGen-3-CTNNB1 transfected cells (P<0.05). At the same time, the PCNA labeling index was decreased accordingly (P<0.05). Conclusion: Inhibition of EC Eca-109 cellproliferation by down-regulating ${\beta}$-catenin expression could improve cell ultrastructure by mediating blockade in G0/G1 through inhibiting cyclin D1, PCNA and the MAPK pathway (p-ERK1/2).

• Toxicological Research
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• v.34 no.1
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• pp.1-6
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• 2018

The purpose of this study was to detect cell death in the liver of mice treated with thioacetamide (TAA) using fluorescence bioimaging and compare this outcome with that using conventional histopathological examination. At 6 weeks of age, 24 mice were randomly divided into three groups: group 1 (G1), control group; group 2 (G2), fluorescence probe control group; group 3 (G3), TAA-treated group. G3 mice were treated with TAA. Twenty-two hours after TAA treatment, G2 and G3 mice were treated with Annexin-Vivo 750. Fluorescence in vivo bioimaging was performed by fluorescence molecular tomography at two hours after Annexin-Vivo 750 treatment, and fluorescence ex vivo bioimaging of the liver was performed. Liver damage was validated by histopathological examination. In vivo bioimaging showed that the fluorescence intensity was increased in the right upper part of G3 mice compared with that in G2 mice, whereas G1 mice showed no signal. Additionally ex vivo bioimaging showed that the fluorescence intensity was significantly increased in the livers of G3 mice compared with those in G1 or G2 mice (p < 0.05). Histopathological examination of the liver showed no cell death in G1 and G2 mice. However, in G3 mice, there was destruction of hepatocytes and increased cell death. Terminal deoxynucleotidyl transferase dUTP nick end labeling staining confirmed many cell death features in the liver of G3 mice, whereas no pathological findings were observed in the liver of G1 and G2 mice. Taken together, fluorescence bioimaging in this study showed the detection of cell death and made it possible to quantify the level of cell death in male mice. The outcome was correlated with conventional biomedical examination. As it was difficult to differentiate histological location by fluorescent bioimaging, it is necessary to develop specific fluorescent dyes for monitoring hepatic disease progression and to exploit new bioimaging techniques without dye-labeling.

• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.691-697
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• 2014

Optical measurements of the morphological and biochemical imaging of phytoplankton are presented. Employing quantitative phase imaging techniques, 3-D refractive index maps and high-resolution 2-D quantitative phase images of individual live phytoplankton are simultaneously obtained without exogenous labeling agents. In addition, biochemical information of individual phytoplankton including volume, mass, and density of individual phytoplankton are also quantitatively obtained from the measured refractive index distributions. We expect the present method to become a powerful tool for the study of phytoplankton.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.727-729
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• 1999

We have developed a realtime phonetic typewriter implemented on IBM PC with sound card based on Windows 95. In this system, analyzing of speech signal, learning of neural network, labeling of output neurons and visualizing of recognition results are performed on realtime. The developing environment for speech processing is established by adding various functions, such as editing, saving, loading of speech data and 3-D or gray level displaying of spectrogram. Recognition experimental using Korean phone had a 71.42% for 13 basic consonant and 90.01% for 7 basic vowel accuracy.

• Exercise Science
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• v.21 no.3
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• pp.289-298
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• 2012

In the present study, we investigated the effect of treadmill exercise on apoptotic neuronal cell death in the retinas of streptozotocin-induced diabetic rats. Twenty-eight male Sprague-Dawley rats were used for this study. The animals were divided into four groups(n = 7 in each group):(1) control group, (2) exercise group, (3) diabetes-induced group, (4) diabetes-induced and exercise group. Diabetes mellitus(DM) was induced by intraperitoneal injection of streptozotocin. The rats in the exercise groups were forced to run on the treadmill for 30 minutes once a day, five times per a week, during 12 weeks. In this study, a terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling(TUNEL) assay and western blot for the expressions of caspase-3, cytochrome c, Bax, and Bcl-2 in the retinas were conducted for the detection of apoptotic retinal cell death. The present results showed that the number of TUNEL-positive cells was increased in the retinas of the diabetic rats, whereas treadmill exercise suppressed this number. The expressions of pro-apoptotic factors caspase-3, cytochrome c, and Bax were enhanced and the expressions of anti-apoptotic factor Bcl-2 was decreased in the retinas of the diabetic rats. In contrast, treadmill exercise suppressed the expressions of caspase-3, cytochrome c, and Bax and increased the expression of Bcl-2. The present study demonstrated that treadmill exercise suppressed diabetes-induced apoptotic neuronal cell death in the retinas. Based on the present results, treadmill exercise may be effective therapeutic strategy for the alleviating complications of diabetes patients.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.542-551
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• 2000

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.