• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.4
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• pp.413-433
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• 1994

Jinhae Bay once was a productive area of fisheries. It is, however, now notorious for its red tides; and oxygen deficient water-masses extensively develop at present in summer. Therefore the shellfish production of the bay has been decreasing and mass mortality often occurs. Under these circumstances, the three-dimensional numerical hydrodynamic and the material cycle models, which were developed by the Institute for Resources and Environment of Japan, were applied to analyze the processes affecting the oxygen depletion and also to evaluate the environment capacity for the reception of pollutant loads without dissolved oxygen depletion. In field surveys, oxygen deficient water-masses were formed with concentrations of below 2.0mg/l at the bottom layer in Masan Bay and the western part of Jinhae Bay during the summer. Current directions, computed by the $M_2$ constituent, were mainly toward the western part of Jinhae Bay during flood flows and in opposite directions during ebb flows. Tidal currents velocities during the ebb tide were stronger than that of the flood tide. The comparision between the simulated and observed tidal ellipses showed fairly good agreement. The residual currents, which were obtained by averaging the simulated tidal currents over 1 tidal cycle, showed the presence of counterclockwise eddies in the central part of Jinhae Bay. Density driven currents were generated southward at surface and northward at the bottom in Masan Bay and Jindong Bay, where the fresh water of rivers entered. The material cycle model was calibrated with the data surveyed in the field of the study area from June to July, 1992. The calibrated results are in fairly good agreement with measured values within relative error of $28\%$. The simulated dissolved oxygen distributions of bottom layer were relatively high with the concentration of $6.0{\sim}8.0mg/l$ at the boundaries, but an oxygen deficient water-masses were formed within the concentration of 2.0mg/l at the inner part of Masan Bay and the western part of Jinhae Bay. The results of sensitivity analyses showed that sediment oxygen demand(SOD) was one of the most important influence on the formation of oxygen depletion. Therefore, to control the oxygen deficient water-masses and to conserve the coastal environment, it is an effective method to reduce the SOD by improving the polluted sediment. As the results of simulations, in Masan Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $50\%$ reduction in input COD loads from Masan basin and $70\%$ reduction in SOD was conducted. In the western part of Jinhae Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $95\%$ reduction in SOD and $90\%$ reduction in culturing ground fecal loads was conducted.

• The Journal of Korean Academy of Prosthodontics
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• v.45 no.4
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• pp.506-521
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• 2007

Statement of problem: The cumulative success rate of wide implant is still controversial. Some previous reports have shown high success rate, and some other reports shown high failure rate. Purpose: The aim of this study was to analyze, and compare the biomechanics in wide implant system embeded in different width of crestal bone under different occlusal forces by finite element approach. Material and methods: Three-dimensional finite element models were created based on tracing of CT image of second premolar section of mandible with one implant embedded. One standard model (6mm-crestal bone width, 4.0mm implant diameter central position) was created. Varied crestal dimension(4, 6, 8 mm), different diameter of implants(3.3, 4.0, 5.5, 6.0mm), and buccal position implant models were generated. A 100-N vertical(L1) and 30 degree oblique load from lingual(L2) and buccal(L3) direction were applied to the occlusal surface of the crown. The analysis was performed for each load by means of the ANSYS V.9.0 program. Conclusion: 1. In all cases, maximum equivalent stress that applied $30^{\circ}$ oblique load around the alveolar bone crest was larger than that of the vertical load. Especially the equivalent stress that loaded obliquely in buccal side was larger. 2. In study of implant fixture diameter, stress around alveolar bone was decreased with the increase of implant diameter. In the vertical load, as the diameter of implant increased the equivalent stress decreased, but equivalent stress increased in case of the wide implant that have a little cortical bone in the buccal side. In the lateral oblique loading condition, the diameter of implant increased the equivalent stress decreased, but in the buccal oblique load, there was not significant difference between the 5.5mm and 6.0mm as the wide diameter implant. 3. In study of alveolar bone width, equivalent stress was decreased with the increase of alveolar bone width. In the vertical and oblique loading condition, the width of alveolar bone increased 6.0mm the equivalent stress decreased. But in the oblique loading condition, there was not a difference equivalent stress at more than 6.0mm of alveolar bone width. 4. In study of insertion position of implant fixture, even though the insertion position of implant fixture move there was not a difference equivalent stress, but in the case of little cortical bone in the buccal side, value of the equivalent stress was most unfavorable. 5. In all cases, it showed high stress around the top of fixture that contact cortical bone, but there was not a portion on the bottom of fixture that concentrate highly stress and play the role of stress dispersion. These results demonstrated that obtaining the more contact from the bucco-lingual cortical bone by installing wide diameter implant plays an important role in biomechanics.

• Journal of the Korean earth science society
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• v.29 no.1
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• pp.1-12
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• 2008

A geophysical mapping was performed for Hwasan caldera which is located in Euisung Sub-basin of the southeastern part of the Korean Peninsula. In order to overcome the limitation of the previous studies, remote sensing technic was used and dense potential data were obtained and analyzed. First, we analyzed geological lineament for target area using geological map, digital elevation model (DEM) data and satellite imagery. The results were greatly consistent with the previous studies, and showed that N-S and NW-SE direction are the most dominant one in target area. Second, based on the lineament analysis, highly dense gravity data were acquired in Euisung Sub-basin and an integrated interpretation considering air-born magnetic data was made to investigate the regional structure of the target area. The results of power spectrum analysis for the acquired potential data revealed that the subsurface of Euisung Sub-basin have two density discontinuities at about 1 km and 3-5 km depth. A 1 km depth discontinuity is thought as the depth of pyroclastic sedimentary rocks or igneous rocks which were intruded at the ring vent of Hwasan caldera, while a 3-5 km depth discontinuity seems to be associated with the depth of the basin basement. In addition, three-dimensional gravity inversion for the total area of Euisung Sub-basin was carried out, and the inversion results indicated two followings; 1) Cretaceous Palgongsan granite and Bulguksa intrusion rocks, which are located in southeastern part and northeastern part of Euisung Sub-basin, show two major low density anomalies, 2) pyroclastic rocks around Hwasan caldera also have lower density when compared with those of neighborhood regions and are extended to 1.5 km depth. However, a poor vertical resolution of potential survey makes it difficult to accurately delineate the detailed structure caldera which has a vertically developed characteristic in general. To overcome this limitation, integrated analysis was carried out using the magnetotelluric data on the corresponding area with potential data and we could obtain more reasonable geologic structure.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.10 no.1
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• pp.100-112
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• 2005

Soil temperature was measured from the surface to 40 cm depth at three stations with different heights in tidal flat of Gomso Bay, west coast of Korea, for one month in every season 2004 to examine the thermal structure and the variation. Mean temperature in surface layer was higher in summer and lower in winter than in lower layer, reflecting the seasonal variation of vertically propagating structure of temperature by heating and cooling from the tidal flat surface. Standard deviation of temperature decreased from the surface to lower layer. Periodic variations of solar radiation energy and tide mainly caused short term variation of soil temperature, which was also intermittently influenced by precipitation and wind. Time series analysis showed the power spectral energy peaks at the periods of 24, 12 and 8 hours, and the strongest peak appeared at 24 hour period. These peaks can be interpreted as temperature waves forced by variations of solar radiation, diurnal tide and interaction of both variations, respectively. EOF analysis showed that the first and the second modes resolved 96% of variation of vertical temperature structure. The first mode was interpreted as the heating antl cooling from tidal flat surface and the second mode as the effect of phase lag produced by temperature wave propagation in the soil. The phase of heat transfer by 24 hour period wave, analyzed by cross spectrum, showed that mean phase difference of the temperature wave increased almost linearly with the soil depth. The time lags by the phase difference from surface to 10, 20 and 40cm were 3.2,6.5 and 9.8 hours, respectively. Vertical thermal diffusivity of temperature wave of 24 hour period was estimated using one dimensional thermal diffusion model. Average diffusivity over the soil depths and seasons resulted in $0.70{\times}10^{-6}m^2/s$ at the middle station and $0.57{\times}10^{-6}m^2/s$ at the lowest station. The depth-averaged diffusivity was large in spring and small in summer and the seasonal mean diffusivity vertically increased from 2 cm to 10 cm and decreased from 10 cm to 40 cm. Thermal propagation speeds were estimated by $8.75{\times}10^{-4}cm/s,\;3.8{\times}10{-4}cm/s,\;and\;1.7{\times}10^{-4}cm/s$ from 2 cm to 10 cm, 20 cm and 40 cm, respectively, indicating the speed reduction with depth increasing from the surface.

• Journal of the Korean Geotechnical Society
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• v.21 no.2
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• pp.113-120
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• 2005

An understanding of soil-structure interaction is the key to rational and economical design for laterally loaded drilled shafts. It is very difficult to formulate the ultimate lateral capacity into a general equation because of the inherent soil nonlincarity, nonhomogeneity, and complexity enhanced by the three dimensional and asymmetric nature of the problem though extensive research works on the behavior of deep foundations subjected to lateral loads have been conducted for several decades. This study reviews the four most well known methods (i.e., Reese, Broms, Hansen, and Davidson) among many design methods according to the specific site conditions, the drilled shaft geometric characteristics (D/B ratios), and the loading conditions. And the hyperbolic lateral capacities (H$_h$) interpreted by the hyperbolic transformation of the load-displacement curves obtained from model tests carried out as a part of this research have been compared with the ultimate lateral capacities (Hu) predicted by the four methods. The H$_u$ / H$_h$ ratios from Reese's and Hansen's methods are 0.966 and 1.015, respectively, which shows both the two methods yield results very close to the test results. Whereas the H$_u$ predicted by Davidson's method is larger than H$_h$ by about $30\%$, the C.0.V. of the predicted lateral capacities by Davidson is the smallest among the four. Broms' method, the simplest among the few methods, gives H$_u$ / H$_h$ : 0.896, which estimates the ultimate lateral capacity smaller than the others because some other resisting sources against lateral loading are neglected in this method. But it results in one of the most reliable methods with the smallest S.D. in predicting the ultimate lateral capacity. Conclusively, none of the four can be superior to the others in a sense of the accuracy of predicting the ultimate lateral capacity. Also, regardless of how sophisticated or complicated the calculating procedures are, the reliability in the lateral capacity predictions seems to be a different issue.

• The Korean Journal of Pesticide Science
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• v.8 no.3
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• pp.151-161
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• 2004

Three dimensional quantitative structure-activity relationships (3D-QSAR) studies for the protox inhibition activities against root and shoot of rice plant (Orysa sativa L.) and barnyardgrass (Echinochloa crus-galli) by a series of new A=3,4,5,6-tetrahydrophthalimino, B=3-chloro-4,5,6,7-tetrahydro-2H-indazolyl and C=3,4-dimethylmaleimino group, and R-group substituted on the phenyl ring in 1-(5-methyl-3-phenylisoxazolin-5-yl)methoxy-2chloro-4-fluorobenzene derivatives were performed using comparative molecular field analyses (CoMFA) methodology with Gasteiger-Huckel charge. Four CoMFA models for the protox inhibition activities against root and shoot of the two plants were generated using 46 molecules as training set and the predictive ability of the each models was evaluated against a test set of 8 molecules. And the statistical results of these models with combination (SIH) of standard field, indicator field and H-bond field showed the best predictability of the protox inhibition activities based on the cross-validated value $r^2_{cv.}$ $(q^2=0.635\sim0.924)$, conventional coefficient $(r^2_{ncv.}=0.928\sim0.977)$ and PRESS value $(0.091\sim0.156)$, respectively. The activities exhibited a strong correlation with steric $(74.3\sim87.4%)$, electrostatic $(10.10\sim18.5%)$ and hydrophobic $(1.10\sim8.30%)$ factors of the molecules. The steric feature of molecule may be an important factor for the activities. We founded that an novel selective and higher protox inhibitors between the two plants may be designed by modification of X-subsitutents for barnyardgrass based upon the results obtained from CoMFA analyses.

• Journal of dental hygiene science
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• v.17 no.2
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• pp.134-141
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• 2017

This study examined 191 pregnant women before delivery in an obstetrics and gynecology clinic in North Gyeongsang Province from May to September 2016 by using a questionnaire after obtaining informed consent for voluntary participation in the study. The study was performed to investigate the association of depression with sociodemographic characteristics, pregnancy-related characteristics, social support, sleep quality and Oral Health Impact Profile (OHIP) in pregnant women. The prevalence of depression among the pregnant women was 25.1% in the healthy group and 74.9% in the depression group. The depression level was significantly higher in women in the depression group who were unsatisfied with their marriage life, had no occupation, had lower social support, had poor sleep quality and had higher OHIP scores. The results of the logistic regression analysis indicated that, the risk ratio for more severe depression was significantly higher in the group with no experience of miscarriage and induced childbirth than in the group with childbirth experience. Conversely, the risk ratio for more severe depression was significantly lower in the group with high social support than in the group with low social support. Depression in the respondents significantly positively correlated with sleep quality and OHIP score but significantly negatively correlated with social support. The multiple regression analysis revealed that the depression level was significantly higher by 22.3% among pregnant women with lower marital satisfaction, no childbirth experience, lower social support and higher OHIP scores. In summary, depression was related to marital satisfaction, childbirth experience, social support, and OHIP score, among others, in pregnant women in this study. Therefore, further investigation is warranted to construct programs and measures that will help build positive thinking by designing and verifying a three-dimensional study model by taking into consideration various variables to reduce the incidence of depression in pregnant women.