• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.11-17
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• 1998

Recently, 3D GIS based on 3D geo-processing methodology and Internet environment are emerging issues in GIS fields. To design and implement 3D GIS, the strategic linkage of Java and VRML is first regarded: 3D feature format definition in the passion of conventional GIS including aspatial attributes, 3B feature indexing, 3D analytical operators such as selection, buffering, and Near, Metric operation such as distance measurement and statistical description, and 3D visualization. In 3D feature format definition, the following aspects are implemented: spatial information for 3D primitives extended from 2D primitives, multimedia data, object texture or color of VRML specification. DXF-format GIS layers with additional attributes are converted to 3D feature format and imported into this system. While, 3D analytical operators are realized in the form of 3D buffering with respect to user-defined point, line, polygon, and 3D objects, and 3D Near functions; furthermore, 'Lantern operator' is newly introduced in this 3D GIS. Because this system is implemented by Java applet, any client with Java-enable browser including VRML browser plug-in can utilize the new style of 3D GIS function in the virtual space. Conclusively, we present prototype of WWW-based 3D GIS, and this approach will be contribute to development of core modules on the stage of concept establishment and of real application model in future.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.197-205
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• 2020

Korean language has the characteristics that the pronunciation of phoneme units such as vowels and consonants are fixed and the pronunciation associated with a notation does not change, so that foreign learners can approach rather easily Korean language. However, when one pronounces words, phrases, or sentences, the pronunciation changes in a manner of a wide variation and complexity at the boundaries of syllables, and the association of notation and pronunciation does not hold any more. Consequently, it is very difficult for foreign learners to study Korean standard pronunciations. Despite these difficulties, it is believed that systematic analysis of pronunciation errors for Korean words is possible according to the advantageous observations that the relationship between Korean notations and pronunciations can be described as a set of firm rules without exceptions unlike other languages including English. In this paper, we propose a visualization framework which shows the differences between standard pronunciations and erratic ones as quantitative measures on the computer screen. Previous researches only show color representation and 3D graphics of speech properties, or an animated view of changing shapes of lips and mouth cavity. Moreover, the features used in the analysis are only point data such as the average of a speech range. In this study, we propose a method which can directly use the time-series data instead of using summary or distorted data. This was realized by using the deep learning-based technique which combines Self-organizing map, variational autoencoder model, and Markov model, and we achieved a superior performance enhancement compared to the method using the point-based data.

• Archives of Plastic Surgery
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• v.37 no.6
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• pp.721-725
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• 2010

Purpose: The survival of composite graft is dependent on three steps, (1) plasmatic imbibitions, (2) inosculation, and (3) neovascularization. Among the many trials to increase the survival rate of composite graft, prostaglandin E1 (PGE1) has beneficial effects on the microcirculatory level with vasodilating, antithrombotic, anti-inflammatory and neoangiogenic properties. Lipo-PGE1 which is lipid microspheres containing PGE1 had developed to compensate the systemic and local side effects of PGE1. This study was proposed to determine whether Lipo-PGE1 administration enhanced the survival of composite graft through neovascularization quantitatively in a rabbit ear model. Methods: Fourteen New Zealand White Rabbits each weighing 3~4 kg were divided in two groups: (1) intravenous Lipo-PGE1 injection group and (2) control group. A $2{\times}1\;cm$ sized, full-thickness rectangular composite graft was harvested in each auricle. Then, the graft was reaaproximated in situ using a 5-0 nylon suture. For the experimental group, $3{\mu}g$/kg/day of Lipo-PGE1 ($5{\mu}g$/mL) was administered intravenously through the marginal vein of the ear for 14 days. The control group was received no pharmacologic treatment. On the 14th postoperative day, composite graft of the ear was harvested and immunochemistry staining used Monoclonal mouse anti-CD 31 antibody was performed. Neoangiogenesis was quantified by counting the vessels that showed luminal structures surrounded by the brown color-stained epithelium and counted from 10 random high-power fields (400x) by independent blinded observer. Statistical analysis (Wilcoxon Signed Ranks test for nonparametric data) was performed using SPSS v12.0, with values of p<0.05 considered significant. Results: The mean number of the microvessels was $15.48{\pm}8.65$ in the experimental group and $9.82{\pm}7.25$ in the control group (p=0.028). Conclusion: The use of Lipo-PGE1 facilitated the neoangiogenesis, resulted in the improvement of the survival rate of graft. On the basis of this results, we could support wider application of Lipo-PGE1 for more effective therapeutic angiogenesis and successful survival in various cases of composite graft in the human.

• Food Engineering Progress
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• v.13 no.2
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• pp.138-146
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• 2009

The crude polysaccharide fraction from fruit body of Auricularia auricula were obtained by using hot water extraction and ethanol precipitation. As the crude polysaccharide fraction contained the brownish dark colored compounds, the adsorption study of pigments from the crude polysaccharide using activated carbon was carried out. The pigment compounds showed an absorption characteristic with $\lambda_{max}$ of 230 nm and the absorbance at 230 nm was taken as color intensity. Adsorption capacity of pigment depended on increase of the activated carbon to sample loading ratio. The adsorption capacity increased with increase of pH and temperature in the pH range of 3.0-7.0 and temperature range of 25-40$^{\circ}C$, but decreased in the temperature range of 40-70$^{\circ}C$. The optimum capacity was obtained at addition of 16.7 mg activated carbon per mL sample solution (concentration = 3 mg/mL) at pH of 7.0 and temperature of 40$^{\circ}C$. Treatment for 10 min was sufficient to achieve the 80% decolorization and 1.25 fold purification of polysaccharide. Langmuir isotherm and pseudo second-order kinetic model provided the best fitting for adsorption of the brownish dark colored compounds onto powdered active carbon. The activation energies of adsorption from the Langmuir isotherm parameter in the ranges of 25-40$^{\circ}C$ and 40-70$^{\circ}C$ was -2.54 and 4.38 kcal/g, respectively. The results of low activation energy also indicated that the adsorption process was a physical adsorption which was controlled by diffnsion.

• Journal of Technologic Dentistry
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• v.40 no.4
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• pp.217-224
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• 2018

Purpose: The aim of this study was to evaluate accuracy of three type complete mandibular denture of before and after polymerization. Methods: Mandibular edentulous model was selected as the master model. 15 study models were made by Type IV stone. Wax complete mandibular dentures were produced by the denture base and artificial teeth. Before and after curing, STL files were obtained using a blue scanner. By superimposing the digitized complete mandibular denture data(after curing) with the CAD-reference(before curing) three-dimensionally, visual fit-discrepancies were drawn by calculating the root mean square (RMS) and visualized on a color-difference map. Each calculated RMS-value was statistically analyzed by 1-way analysis of variance(ANOVA) (${\alpha}=.05$). Results: Mean(SD) RMS-values was OM group $88.98(6.10){\mu}m$, BM group $82.35(13.46){\mu}m$, BDM group $77.83(9.46){\mu}m$. The results of the 1-way ANOVA showed no statistically significant differences in the RMS values of the Three groups for the material (P > .241). Conclusion : Deformation of artificial teeth position was observed in all groups after resin polymerization. But the values, all group were within the clinically acceptable range. The values of BDM group showed the least deformation than the other two groups.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.4
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• pp.585-591
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• 2019

Objective: This study investigated carcass characteristics and meat quality of purebred Pakchong 5, crossbred pigs sired by Pakchong 5, and crossbred pigs sired by Duroc. Methods: Forty-eight pigs (average body weight of 22.25 kg) were composed of three groups as purebred Pakchong 5 (PP), Large $White{\times}Landrace$ pigs sired by Pakchong 5 (LWLRP), and Large $White{\times}Landrace$ pigs sired by Duroc (LWLRD). Each group consisted of eight gilts and eight barrows. At 109-day-raising period, pigs were slaughtered, and carcass characteristics were evaluated. Longissimus thoracis (LT) muscles from left side of carcasses were evaluated for meat quality and chemical composition. Data were analyzed using general linear model procedure, where group, sex, and their interaction were included in the model. Results: The PP had greater carcass, total lean, and ham percentages than crossbred pigs (p<0.05). LWLRP had thicker backfat and more carcass fat percentage than LWLRD (p<0.05). There were no differences (p>0.05) on cutting percentages from tender loin, loin, boston butt, and picnic shoulder among groups. The PP and LWLRP had larger loin eye area (LEA) than LWLRD (p<0.05). Gilts had more loin percentage and lower $L^*$ value than barrows (p<0.05). No meat color parameters ($L^*$, $a^*$, and $b^*$) were affected by groups (p>0.05). PP and LWLRP had larger muscle fiber diameters than LWLRD (p<0.05). However, water holding capacity, Warner-Bratzler shear force values, and chemical composition of LT were not affected by group or sex (p>0.05). Conclusion: Pakchong 5 purebred has good carcass and lean percentages. Compared to Duroc crossbred pigs, Pakchong 5 crossbreds have similar carcass and lean percentages, larger LEA, and slightly more carcass fat, with comparable meat quality and chemical composition. Pakchong 5 boars are more affordable for very small- to medium-scale pig producers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.311-320
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• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.