• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.34 no.3
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• pp.341-349
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• 2008

Purpose: The purpose of this study was to evaluate the $Osstem^{(R)}$ implants (US II/SS II implants) through the retrospective study for the clinical success rate during the installation of the $Osstem^{(R)}$ implants (US II/SS II implants) by using of the procedures of maxillary sinus floor elevation. Materials and methods: The current study was researched in the 6 medical institutions: Chonnam National University, Chosun University, Pusan DaeDong Hospital, Bundang Seoul National University Hospital, Ap-Seon Clinic, and All Clinic. Based on the total number of 116 patients whose treatment was the installation of the US II/SS II implants with the procedures of the maxillary sinus floor elevation, they were conferred on the dental records of the patients under the joint consultation of the 6 medical institutions. On the dental recording charts, there were included in as the following; the name of the institutions, gender, age, with or without smoking or drinking, with or without the generalized diseases, the height of the alveolar bone on the operational sites, elapsed edentulous state period, the state of the opposed or adjacent teeth, the methods of the maxillary sinus floor elevation, secondary time period for surgery, the lengths, types, and diameters of implants, with or without bone transplantation or the types of bone, postoperative current bone height, current adjacent soft tissue state of the implants, with or without the success of the installations of the implants. We have done our survey with the clinical and radiolographical examinations and dental questionaries. The success and survival rate of the implants was evaluated. Results 1. Total number of the patients with the installation of the US II implants were 62. The 252 numbers of US II implants were installed on the 89 maxillary sinuses. The patient's mean age was 54.1 years old and there were 36 men and 27 women. 2. Total number of patients with the installation of SS II implant were 57. The 165 numbers of SS II implants were planted on the 80 maxillary sinuses. Their mean age was 48.7 years old and there were 37 men and 20 women. 3. The follow-up period was 30.7 months(21-49 mon) on average. The vertical bone loss of installed implants after the procedures of the maxillary sinus elevation was 1.1 mm on average in SS II and 1.3 mm on average in US II. There existed no statistical significance on each group. The mean enlarged bone height after the maxillary sinus floor elevation was 8.2 mm. 4. For the procedures of the maxillary sinus elevation, the Lateral approach technique occupied 87.1%, which was the most used one. In addition, the most frequently used transplanted bone was autogenous bone only which was 72.7% during the maxillary sinus floor elevation. 5. The complication of maxillary sinus floor elevation were perforation of sinus membrane, disesthesia on doner site, exposure of cover screw and exposure of maxillar bone. 6. The survival rate of US II and SS II after maxillary sinus floor elevation was 99.2% and 95.8%, respectively. And the success rate of US II and SS II after maxillary sinus floor elevation was 97.6% and 89.7%, respectively. Conclusion : On the evaluation of the analysis of our study, both US II and SS II implants showed the excellent clinical results by use of the procedures of maxillary sinus floor elevation.

• The Journal of Korean Academic Society of Nursing Education
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• v.4 no.2
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• pp.249-263
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• 1998

The competence of newly graduated nurses is based on various clinical expriences gained when they were students. Therefore, instructors of nursing students, professors in nursing schools or directors in hospitals must play a critical role in assisting them to obtain various knowledge and experienced nursing skills. The purpose of this study was to investigate nursing care activities and nursing care hours practiced by nursing students in a general hospital. The subject students were total 214 nursing students, 2nd graders(sophomores) and 3rd graders(juniors) from 5 Junior Nursing Colleges in Seoul and they practiced at S general hospital to gain clinical experience. The data were collected for 4 days. The tools for this study were the direct nursing care activity list consisted of 15 nursing areas and the indirect nursing care activity list consisted of 9 nursing areas. The subject students were supposed to record their own score. The results of this study are ; 1. The nursing care hours per nursing student 1) The average total nursing care hours a day per each nursing student are 362.65 mins(6.04hr), the direct nursing care hours per each nursing stuent are 202.09 mins(direct nursing care rate 56.0%) and it is higher than the indirect nursing care hours, 159.75mins(indirect nursing care rate 44.0%). The direct nursing care rate of each student by a team approach in the evening shift(56.3%) is higher than that in the day shift(55.8%). 2) The hours of checking vital signs are the longest(47.35mins) among the direct nursing care activites and next is in order of counseling 8l emotional support, nurse rounds, and accompaning a patient during examination. The hours of reporting are the longest(32.39mins) among the indirect nursing care activites, and next is the activities related to education such as reviewing chart, looking up references, etc. 3) The freqency of checking vital signs practiced by nursing student is the highest(the average of 55.7 times) among the direct nursing care activities and next is in order of nurse rounds, assistance of feeding, and counseling & emotional support. The required time for nursing students to accompany their patient during examination is the longest(20.7mins) and next are in order of restriction on patient' activity, orientated by a head nurse, skin care, sitz bath, bathing & hair shampoo, and assisting with patients' exercise. 2. The nursing care hours per grader 1) The average hours of total nursing care per a nursing student are 369.2mins(6.2hrs) to 2nd graders, 355.9mins(5.9 hrs) to third graders. The direct nursing care rate per each nursing student to 3rd graders(59.3%) was higher than that to End graders(52.8%). 2) For 2nd graders, the highly marked nursing activities are teaching associated with direct nursing care activities such as drawing up papers, looking up references, reviewing charts, and being orientated by staff nurses. For 3rd graders, measurments, observations, and nurse rounds in indirect nursing care activities are taken highly 3) The most frequent practice of the nursing care activites is checking vital signs : 65 times to 3rd graders and 46.5 times to 2nd graders. Our suggestions based on the results of this study are : 1. It is recommanded to repeat the same designed study in a variety of clinical fields for further study. 2. It is recommanded to collect data not by self-record method but by observated method. 3. It is needed for nursing instructors in Nursing Schools and in hospitals to develop the guidelines and check-list of clinical practice courses.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.9 no.2
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• pp.237-246
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• 1998

Objectives and Methods：This study investigated clinical characteristics, treatment modality, outcome of 57 children and adolescent inpatients(male 53, female 4) who were diagnosed as pervasive developmental disorder(PDD) by DSM-Ⅳ criteria recent five years. Results：1) The mean age at admission was $96{\pm}28.2$ months, and the mean age at which they first visited treatment facility was $52{\pm}26.6$ months. The mean hospitalization period was $43.7{\pm}31.3$ days. 2) Diagnosis：Twenty-seven(47.4%) of subjects met DSM-Ⅳ criteria for PDD NOS. Fifteen (26.3%) met for autistic disorder, nine(15.8%) met for Asperger's syndrome, and two(3.5%) met for childhood disintegrative disorder. 3) Comorbid diagnosis：The most common comorbid dignosis was attention deficit hyperactivity disorder(23.8%). 4) IQ test：IQ test for twenty-eight subjects was possible. The Average of the subjects was $70{\pm}27.5$. Fifteen(53.6%) of the subjects were approximate or under 70. 5) Neurology Abnormality：EEG findings of eleven(21.2%) subjects were abnormal, brain CT or MRI findings of eight subjects(21.6%) were abnormal. 6) Family Hx：Depressive disorder were found in Eight mothers(14%). Familial loading was found in twenty families(35.1%), and familial loading of PDD was found in three(5.3%). Conclusion：The most important thing for the management of PDD is early detection and early treatment. To do so, multidisciplinary team approach should be emphasized.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.3
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• pp.95-105
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• 2013

This study on circular identifying purposes was performed of Youngbeokji space located in Seongnagwon(Scenic Sites No.35). Through the data acquisition of 3D high precision, such as the surrounding terrain of the Youngbeokji. The results of this study is summarized like the following. First, the purpose of the stone structures and structure within the Youngbeokji search is an important clue to find that earlier era will be a prototype. 3D scan method of enforcement is searching the whole structure, including the surrounding terrain and having the easy way. Second, the measurement results are as follows. Department of bedrock surveyed from South to North was measured by 7,665mm. From East to West was measured at 7,326mm. The size of the stone structures, $1,665mm{\times}1,721mm$ in the form of a square. Its interior has a diameter of 1, 664mm of hemispherical form. In the lower portion of the rock masses in the South to the North, has fallen out of the $1,006mm{\times}328mm$ scale traces were discovered. Third, the Youngbeokji recorded in the internal terrain Multiresolution approach. After working with the scanner and scan using the scan data, broadband, to merge. Polygon Data conversion to process was conducted and mash as fine scan data are converted to process data. High resolution photos obtained through the creation of 3D terrain data overlap and the final result. Fourthly, as a result of this action, stone structure West of the waterway back outgoing times oil was confirmed. Bangjiwondo is estimated to be seokji of structure hydroponic facility confirmed will artificially carved in the bedrock. As a result of this and the previous situation of the 1960s could compare data was created. This study provides 3D precision ordnance through the acquisition of the data. Excavations at the circle was able to preserve in perpetuity as digital data. In the future, this data is welcome to take a wide variety of professionals. This is the purpose of this is to establish foundations and conservation management measures will be used. In addition, The new ease of how future research and 3D scan unveiled in the garden has been used in the study expect.

• Journal of the Korean GEO-environmental Society
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• v.15 no.1
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• pp.63-68
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• 2014

In this study, it was found that determined the photocatalytic degradation of antibiotics (lincomycin, LM) with various catalyst composite of titanium dioxide ($TiO_2$), hydroxyapatite (HAP) and germanium (Ge) under UV-A irradiation. At first, various type of complex catalysts were investigated to compare the enhanced photocatalytic potential. It was observed that in order to obtain the removal efficiencies were $TiO_2/HAP/Ge$ > $TiO_2/Ge$ > $TiO_2/HAP$. The composition of $TiO_2/HAP/Ge$ using a statistical approach based on mixture analysis design, one of response surface method was investigated. The independent variables of $TiO_2$ ($X_1$), HAP ($X_2$) and Ge ($X_3$) which consisted of 6 condition in each variables was set up to determine the effects on LM ($Y_1$) and TOC ($Y_2$) degradation. Regression analysis on analysis of variance (ANOVA) showed significant p-value (p < 0.05) and high coefficients for determination value ($R^2$ of $Y_1=99.28%$ and $R^2$ of $Y_2=98.91%$). Contour plot and response curve showed that the effects of $TiO_2/HAP/Ge$ composition for LM degradation under UV-A irradiation. And the estimated optimal composition for TOC removal ($Y_2$) were $X_1=0.6913$, $X_2=0.2313$ and $X_3=0.0756$ by coded value. By comparison with actual applications, the experimental results were found to be in good agreement with the model's predictions, with mean results for LM and TOC removal of 99.2% and 49.3%, respectively.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.13 no.1
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• pp.117-128
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• 2002

Objectives：For the purpose of obtaining the more vivid present status and prevention program of teenage pregnancy, this survey was done by Obstetricians, as study subject, who manage the pregnant teenager in real clinical situation. Methods：Structured survey form about teenage pregnancy was sent to 2,800 obstetricians. That form contained frequency, characteristics, decision making processes, and psychiatric aspects of the teenage pregnancy. 349 obstetricians replied that survey form and we analysed these datas. Results：(1) The trend of teenage pregnancy was mildly increased. (2) The most common cases were unwanted pregnancy by continuing sexual relationship with boyfriends rather than by forced, accidental sexual relationship with multiple partners. (3) The most common reason of labor was loss the time of artificial abotion. (4) Problems of pregnant girls' were conduct behaviors and poor informations about contraception rather than sexual abuse or mental retardation. (5) Most obstetricians percepted the necessity of psychiatric consultation, however psychiatric consultation was rare due to parents refusal and abscense of available psychiatric facility. (6) For the prevention of teenage pregnancy, the most important thing was practical education about contraception. Conclusions：Based on the result of this study, further study using structured interview schedule with pregnant girl is needed for the detecting risk factor of teenage pregnancy and effective systematic approach to pregnant girl.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.139-160
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• 2017

Technology commercialization creates effective economic value by linking the company's R & D processes and outputs to the market. This technology commercialization is important in that a company can retain and maintain a sustained competitive advantage. In order for a specific technology to be commercialized, it goes through the stage of technical planning, technology research and development, and commercialization. This process involves a lot of time and money. Therefore, the duration and cost of technology commercialization are important decision information for determining the market entry strategy. In addition, it is more important information for a technology investor to rationally evaluate the technology value. In this way, it is very important to scientifically estimate the duration and cost of the technology commercialization. However, research on technology commercialization is insufficient and related methodology are lacking. In this study, we propose an evaluation model that can estimate the duration and cost of R & D technology commercialization for small and medium-sized enterprises. To accomplish this, this study collected the public data of the National Science & Technology Information Service (NTIS) and the survey data provided by the Small and Medium Business Administration. Also this study will develop the estimation model of commercialization duration and cost of R&D performance on using these data based on the market approach, one of the technology valuation methods. Specifically, this study defined the process of commercialization as consisting of development planning, development progress, and commercialization. We collected the data from the NTIS database and the survey of SMEs technical statistics of the Small and Medium Business Administration. We derived the key variables such as stage-wise R&D costs and duration, the factors of the technology itself, the factors of the technology development, and the environmental factors. At first, given data, we estimates the costs and duration in each technology readiness level (basic research, applied research, development research, prototype production, commercialization), for each industry classification. Then, we developed and verified the research model of each industry classification. The results of this study can be summarized as follows. Firstly, it is reflected in the technology valuation model and can be used to estimate the objective economic value of technology. The duration and the cost from the technology development stage to the commercialization stage is a critical factor that has a great influence on the amount of money to discount the future sales from the technology. The results of this study can contribute to more reliable technology valuation because it estimates the commercialization duration and cost scientifically based on past data. Secondly, we have verified models of various fields such as statistical model and data mining model. The statistical model helps us to find the important factors to estimate the duration and cost of technology Commercialization, and the data mining model gives us the rules or algorithms to be applied to an advanced technology valuation system. Finally, this study reaffirms the importance of commercialization costs and durations, which has not been actively studied in previous studies. The results confirm the significant factors to affect the commercialization costs and duration, furthermore the factors are different depending on industry classification. Practically, the results of this study can be reflected in the technology valuation system, which can be provided by national research institutes and R & D staff to provide sophisticated technology valuation. The relevant logic or algorithm of the research result can be implemented independently so that it can be directly reflected in the system, so researchers can use it practically immediately. In conclusion, the results of this study can be a great contribution not only to the theoretical contributions but also to the practical ones.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.