• Mood & Emotion
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• v.16 no.3
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• pp.140-151
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• 2018

Objectives : The purpose of this study was to examine effects of adjunctive aripiprazole versus bupropion, on depressive symptoms of female depression. Methods : Sixty six female patients with major depressive disorders were enrolled from a six-week, randomized prospective open-label multi-center study. Participants were randomized to receive aripiprazole (2.5-10 mg/day) or bupropion (150-300 mg/day). Montgomery Asberg Depression Rating Scale, 17-item Hamilton Depression Rating scale (HAM-D17), Iowa Fatigue Scale, Drug-Induced Extrapyramidal Symptoms Scale, Psychotropic-Related Sexual Dysfunction Questionnaire scores, and Clinical Global Impression-Severity (CGI-S) were obtained at baseline and after one, two, four, and six weeks. Changes on individual items of HAM-D17 were assessed as well as on composite scales (anxiety, insomnia and drive), and on four core subscales that capture core depression symptoms. Results : Overall, both treatments improved depressive symptoms, without causing serious adverse events. There were significant differences in the HAM-D17 total score (p=0.046) and CGI-S (p=0.004), between aripiprazole and bupropion augmentation, favoring aripiprazole over bupropion. Aripiprazole revealed significantly greater effect size in depressed mood (p=0.006), retardation (p=0.005), anxiety psychic (p=0.032), and general somatic symptom (p=0.01). Conclusion : While both treatments were effective, results of this study suggested that aripiprazole may be preferable, in treating general and core symptoms of female depression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.531-534
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• 2014

DLNA is an established industry standard which supports contents sharing among smart devices in home wired- and wireless-network environment and is well known in Korea as Allshare or Smartshare. The DLNA standard is implemented as built-in services in most of Android smart phones and tablets. In addition to the handheld devices, DLNA service can also be employed in speakers, printers, and so on. However, users have reported many interoperability issues between DLNA devices. Developers typically identify causes by analyzing the packet exchange information between devices. However, this approach costs them to put additional effort to filter relevant packets, to reconstruct packet exchange history and the protocol flow. Consequently, it ends up with increased development time. In this paper, we demonstrate a technique to automatically analyze and visualize the packet exchange history. We modified a router firmware to capture and store packets exchanged between DLNA devices, and then analyze and visualize the stored packet exchange history for developers. We believe that visualized packet exchange history can help developers to test the interoperability between DLNA devices with less effort, and ultimately to improve the productivity of developers.

• Journal of Korean Society on Water Environment
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• v.35 no.2
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• pp.145-151
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• 2019

The purpose of this study was to investigate treatment characteristics of diatomite filtration, that would allow water recovery from biologically-treated effluent for reuse. Diatomite, Celpure 100, and acid clay were used as filter-aids, with a support filter manufactured from polyethylene (PE), and polypropylene (PP). This pre-coating process using diatomite filter-aids, is used in the filtration range of pressure filters, and has consistently provided high-quality separation. The results showed that variations in average removal efficiency of SS, and T-P from biologically treated effluent by the diatomite-coated PE filter, were approximately 82.2 ~ 88.9 % and 4.8 ~ 21.1 %, respectively. T-P treatment efficiency of the PP filter pre-coated with diatomite and $Celpure^{(R)}100$ at $57.64g/m^2$, was approximately $24{\pm}10%$ and $40{\pm}15%$ on average, respectively. Particle size distribution of secondary effluent varied from 0.05 to $200{\mu}m$, and $d_{50}$ value was $20.76{\mu}m$. The size distribution of particles in the diatomite filtrate ranged from 1.26 to $101.1{\mu}m$ when pre-coated with diatomite filter-aid, at a content of $57.64g/m^2$. Diatomite filter aids, i.e., the particles that form the pre-coating layer, capture very fine particles as well as macromolecules, owing to their complex structure with numerous fine microscopic pores, and surface properties. The filtration process using diatomite and $Celpure^{(R)}100$ as filter aids, has been successfully applied, to recover water from sewage for reuse. The disadvantage of the process, is that the particle size of the filter-aid is spent, because of pressurization.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.62-69
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• 2011

Six commonly used non-linear growth functions were fitted to individual tree height-dbh data of eight major tree species measured by the $5^{th}$ National Forest Inventory in Chungcheong province. A total of 2,681 trees were collected from permanent sample plots across Chungcheong province. The available data for each species were randomly splitted into two sets: the majority (90%) was used to estimate model parameters and the remaining data (10%) were reserved to validate the models. The performance of the models was compared and evaluated by $R^2$, RMSE, mean difference (MD), absolute mean difference (AMD) and mean difference(MD) for diameter classes. The combined data (100%) were used for final model fitting. The results showed that these six sigmoidal models were able to capture the height-diameter relationships and fit the data equally well, but produced different asymptote estimates. Sigmoidal growth models such as Chapman-Richards, Weibull functions provided the most satisfactory height predictions. The effect of model performance on stem volume estimation was also investigated. Tree volumes of different species were computed by the Forest Resources Evaluation and Prediction Program using observed range of diameter and the predicted tree total height from the six models. For trees with diameter less than 30 cm, the six height-dbh models produced very similar results for all species, while more differentiation among the models was observed for large-sized trees.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.318-329
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• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.99-113
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• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.141-156
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• 2023

Due to the unprecedented worldwide pandemic of the new Covid-19 infection, business trends of companies have changed significantly. Therefore, it is strongly required to monitor the rapid changes of innovation trends to design and plan future businesses. Since the pandemic, many studies have attempted to analyze business changes, but they are limited to specific industries and are insufficient in terms of data objectivity. In response, this study aims to analyze business trends after Covid-19 using Crunchbase, a global startup data. The data is collected and preprocessed every two years from 2018 to 2021 to compare the business trends. To capture the major trends, a network analysis is conducted for the industry groups and industry information based on the co-occurrence. To analyze the minor trends, LDA-based topic modelling and word2vec-based clustering is used. As a result, e-commerce, education, delivery, game and entertainment industries are promising based on their technological advances, showing extension and diversification of industry boundaries as well as digitalization and servitization of business contents. This study is expected to help venture capitalists and entrepreneurs to understand the rapid changes under the impact of Covid-19 and to make right decisions for the future.

• The Journal of Korean Academy of Prosthodontics
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• v.62 no.2
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• pp.157-164
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• 2024

A variety of digital technologies are being used throughout the entire implant treatment process of diagnosis, surgery, impression, design, and fabrication of prostheses. In this case, using a digital surgical guide, sinus floor elevation was performed without complications, and the implants were placed in the planned position. After the healing period for osseointegration, CAD-CAM (Computer-aided design-Computer-aided manufacturing) customized abutments and provisional prostheses were delivered. While using the provisional prosthesis, occlusal change was observed. To transfer the intermaxillary relationship and abutment position that reflect occlusal change and axial displacement, double scanning and abutment-level digital impressions were taken. Abutment superimposition was used to capture the subgingival margin without gingival retraction. Then, the definitive prosthesis was designed and fabricated with digital system. We report a case applying digital system, to achieve the predictable result as well as the efficient treatment process from implant surgery to fabricating prosthesis in the posterior area.

• Journal of Korean Society of Forest Science
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• v.107 no.4
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• pp.351-360
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• 2018

This study was conducted to analyze the responses of chlorophyll contents and growth of Pinus densiflora and Quercus variabilis seedlings on distance from the well and $CO_2$ flux after the artificial $CO_2$ release. From June 1 to 30, 2016, $CO_2$ gas was injected at the rate of $6L\;min^{-1}$ at the study site in Eumseong. Chlorophyll content was analyzed in the middle of July, 2016, and root collar diameter (RCD), height (H), and biomass were measured in May and December, 2016 after planting 2-year-old P. densiflora and 1-year-old Q. variabilis seedlings in May, 2015. The chlorophyll content of P. densiflora seedlings did not show a significant correlation with $CO_2$ flux, whereas the chlorophyll content of Q. variabilis seedlings showed a significant negative correlation with increasing $CO_2$ flux (P<0.05). The RCD and H growth rates of both species showed the significant difference in the distance from the well of the $CO_2$ anthropogenic release treatment. In particular, the RCD and H growth rate of P. densiflora seedlings and the RCD growth rate of Q. variabilis seedlings increased significantly as the seedlings were closer to the well, but the H growth rate of Q. variabilis seedlings decreased significantly. In addition, as the $CO_2$ concentration in the ground increases, ${\Delta}R/S$ ratio increases in both species, suggesting that the high $CO_2$ concentration in the soil promotes carbon distribution relative to the root part. The results of this study can be used as data necessary to monitor the $CO_2$ leakage and physiological and growth responses of both species to leakage of stored $CO_2$ in the future.