• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.293-303
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• 2002

Recently, the use of the high strength materials and development of construction techniques have resulted in more flexible and longer spanning in the stadium systems. So the natural frequency of stadium structures are became low. Stadium stand could be led to significant dynamic response as like resonance due to spectator rhythmical activities. The accurate analysis of dynamic behavior of stadium systems and the precise investigation of the dynamic loads on stadium structures are demanded for effective design. It is desirable to apply measured dynamic loads created by spectator activities because these dynamic loads are not easy to express numerical formula. As the floor mesh of stadium stand is refined, the number of divided elements increases in numerical analysis. the rise of the number of elements makes the numbers of nodal points increased and numerous computer memory required. So it is difficult to analysis refine full model of stadium structures by using the commercial programs. In this study, the various dynamic loads induced by spectator movements are measured and analyzed. And a new modeling method that reduce the nodal points are introduced. Vibration analysis of stadium stands is executed to inspect accuracy and efficiency of proposed method in this paper.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.6 s.40
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• pp.55-66
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• 2004

Recently, many high-rise apartment buildings using the box system, composed of only reinforced concrete walls and slabs, have been constructed. In residential buildings such as apartments, vibrations occur from various sources and these vibrations transfer to neighboring residential units through walls and slabs. It is necessary to use a refined finite element model for an accurate vibration analysis of shear wall building structures. But it would take significant amount of computational time and memory if the entire building structure were subdivided into a finer mesh. Therefore, an efficient analytical method, which has only translational DOFs perpendicular to walls or slabs by the matrix condensation technique, is proposed in this study to obtain accurate results in significantly reduced computational time. If all of the DOFs except those perpendicular to walls or slabs in the shear wall structure eliminated using the matrix condensation technique at a time, the computational time for the matrix condensation would be significant. Thus, the modeling technique using super elements and substructuring technique is proposed to reduce the computational time for the matrix condensation. Dynamic analysis of 3-story and 5-story shear wall example structures were performed to verify the efficiency and accuracy of the proposed method. It was confirmed that the proposed method can provide the results with outstanding accuracy requiring significantly reduced computational time and memory.

• Korea Journal of Hospital Management
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• v.25 no.2
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• pp.45-56
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• 2020

Purpose: The purpose of this study is to investigate medical care behaviors influencing accuracy of the payment based New diagnosis-related groups (DRG) compared to fee for service (FFS) in hospitalized patients with medical illness. Methodology: In order to estimate the difference in medical costs between New DRG and FFS depending on medical care behaviors, medical records and hospital claims data (n=4,232) were utilized, which were collected from a single public hospital during the first-half of 2018. Data were analyzed by descriptive statistics, t-test, chi-square test, and multivariate binary logistic regression. Findings: The average difference in medical costs between New DRG and FFS were KRW 506,711±13,945 with incentives and KRW -51,506±12,979 without incentives, respectively. Forty-four point two percent (44.2%, n=1,872) of total subjects were shown to have negative compensation in overall medical costs with New DRG compared to the costs with FFS. Medical care behaviors that affected on the negative compensation were the presence of severe bed sores on admission, medical consultations, death, operations, medications and laboratory or imaging tests with unit price over KRW 100,000, hospital-acquired complications or underlying comorbidities, elderly patients (≧65 years), and hospitalized for more than average inpatient days defined by New DRG (p<0.001). The difference in average medical cost between New DRG and FFS for a group with mild illness was KRW -11,900±10,544, whereas it was KRW -196,800±46,364 for a group with severe illness (p<0.0001). Practical Implications: These findings suggest that New DRG payment model without incentives may incompletely cover the variation of medical costs in real clinical practice. Therefore, policy makers need to consider that the current New DRG reimbursement should be focused and refined to improve accuracy of payment on medical care resources utilized in severe and complex medical conditions.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.166-178
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• 2023

This study aims to introduce and assess CNN Deep Learning methods to analyze visual landscape images on social media with embedded user perceptions and experiences. This study analyzed visual landscape images by focusing on a healing place. For the study, seven adjectives related to healing were selected through text mining and consideration of previous studies. Subsequently, 50 evaluators were recruited to build a Deep Learning image. Evaluators were asked to collect three images most suitable for 'healing', 'healing landscape', and 'healing place' on portal sites. The collected images were refined and a data augmentation process was applied to build a CNN model. After that, 15,097 images of 'healing' and 'healing landscape' on portal sites were collected and classified to analyze the visual landscape of a healing place. As a result of the study, 'quiet' was the highest in the category except 'other' and 'indoor' with 2,093 (22%), followed by 'open', 'joyful', 'comfortable', 'clean', 'natural', and 'beautiful'. It was found through research that CNN Deep Learning is an analysis method that can derive results from visual landscape image analysis. It also suggested that it is one way to supplement the existing visual landscape analysis method, and suggests in-depth and diverse visual landscape analysis in the future by establishing a landscape image learning dataset.

• Archives of design research
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• no.18
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• pp.25-34
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• 1996

Industrial design development methods and processes have changed in accordance with Industrial Information Age. These days, problems are created by existing methods and evaluation of design value , all problems concerned with time and finances sitaution have been made a subject of discussion. Development of design processes have been changed by the development of problem recognition and solving tools, and dpsign tpchnulugy havp hppn replaced by computer technology,Thus. software design processes linking thoughtware to hardware are used in the solution of design problems with many parts. In this study, 3D Modeling samples are presented, 3D Modeling can realise ' Ideas' to '3Dimentional Virtual Ohjects'. These effect and value are anle to decisively influence the process of design problem conference-ebealuation-solution.Proxesses of actual modeling and rendering are made as follows. By compusition of simple 20 drawings and shaping them into 30 objects, 30 solid models can be made. To prssent effectivley, we can make a sample model by varying camera views,light sourses,materials and colours etc. This sample is evaluated by various cumposition, methods and PERT(Program Evaluation and Review Technique). This cuncrete sample (tentative plan)is changed within the CAD SYSTEM by design evaluation, and then converted to flowchart of mass productive conception through refined data. So, that tentative plan can be conformed to design desire actuillly, to the utmost degree. Finally, this design process can be proposed as il new method in cuntrast with current methods. The aim of this study is to suggest effective evaluation methods of design outcome among many evaluating elements.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.5
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• pp.335-347
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• 2013

In February 2008, high storm waves due to a developed atmospheric low pressure system propagating from the west off Hokkaido, Japan, to the south and southwest throughout the East Sea (ES) caused extensive damages along the central coast of Japan and along the east coast of Korea. This study consists of two parts. In the first part, we estimate extreme storm wave characteristics in the Toyama Bay where heavy coastal damages occurred, using a non-hydrostatic meteorological model and a spectral wave model by considering the extreme conditions for two factors for wind wave growth, such as wind intensity and duration. The estimated extreme significant wave height and corresponding wave period were 6.78 m and 18.28 sec, respectively, at the Fushiki Toyama. In the second part, we perform numerical experiments on wave-structure interaction in the Fushiki Port, Toyama Bay, where the long North-Breakwater was heavily damaged by the storm waves in February 2008. The experiments are conducted using a non-linear shallow-water equation model with adaptive mesh refinement (AMR) and wet-dry scheme. The estimated extreme storm waves of 6.78 m and 18.28 sec are used for incident wave profile. The results show that the Fushiki Port would be overtopped and flooded by extreme storm waves if the North-Breakwater does not function properly after being damaged. Also the storm waves would overtop seawalls and sidewalls of the Manyou Pier behind the North-Breakwater. The results also depict that refined meshes by AMR method with wet-dry scheme applied capture the coastline and coastal structure well while keeping the computational load efficiently.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Research in Community and Public Health Nursing
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• v.7 no.2
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• pp.384-399
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• 1996

The concept of parish nursing began in the late 1960s in the United States when increasing numbers of churches employed registered nurses (RNs) to provide holistic, preventive health care to the members of their congregations. Parish nursing role was developed in 1983 by Lutheran chaplain Granger Westberg, and provides care to a variety of church congregation of various denominations. The parish nurse functions as health educator, counselor, group facilitator, client advocate, and liaison to community resources. Since these activities are complementary to the population-focused practice of community health' CNSs, parish nurses either have a strong public health background or work directly with both baccalaureate-prepared public health nurses and CNSs. In a Midwest community in U.S.A., the Healthy People 2000(1991) objectives are being addressed in health ministries through a coalition between public health nurses and parish nurses. Parish nursing is in the beginning state in Korea and up untill now, there has been no research was conducted on concrete role of korean parish nurses. The main purpose of this study was to identify, classify and analyze activities of parish nurses. The other important objective of this study was to establish an effective approach and direction for parish nursing and provide a database for korean parish nursing model through analysis and' classification of the content of the nursing record which included nursing activities. This study was a descriptive survey research. The parish nurses were working in churches where the demonstration project developed on parish nursing. The study was done on all nursing records which were working in churches where the demonstration project developed on parish nursing. The study was done on all nursing records which were documented by parish nurses in three churches from March, 1995 to February, 1996. Namsan, Taegu Jeei and Nedang presbyterian churches in Taegu and Keimyung nursing college incooperated together for the parish nursing demonstration project. The data analysis procedure was as follows: First, a record analysis tool was developed and second, the data was collected, coded and analyzed, the classification for nursing activities was developed through a literature review, from which the basic analysis tool was produced and cotent validity review was also done. The classification of the activities of parish nurses showed 7 activitity categories. 7 activity categories consisted of visitation nursing, health check-ups, health education, referring, attending staff meetings, attending inservices and seminar, volunteers coordinating. The percentage of activities were as follows: Visitation nursing(A: 51.6%, B: 55%, C: 42.6%) Health check-ups(A: 13.5%, B: 12.1%, C: 22.3%) Health education(A: 13.5%, B: 13.2%, C: 18.2%) Referring(A: 1.4%, B: 4.2%, C: 2.4%) Attending staff meeting(A: 18.8%, B: 13.0%, C: 12.2%) Attending inservices and seminar(A: 1.5%, B: 2.2%, C: 2.1%) Volunteers coordinating(A: 0.3%, B: 0.4%, C: 0.0%) To establish and develope parish nursing delivery network in Korea, parish nurses role, activities and boundaries of practice should be continuously monitored and refined every 2 years. Also, It is needed to develope effective nursing recording system based on the need assessment research data of various congregation members. role, activities and boundaries of practice and arrangement of the working structure, continuing education, cooperation with community resources and structuring and organizing parish nursing delivery network. Also, It is needed to develope effective nursing recording system based on the need assessment research data of various congregation members.