• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.668-682
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• 2017

Alignments of railways recently constructed in Korea have been straightened due to the advent of high-speed rail, which means increasing the numbers of tunnels and bridges. Overbreak during tunnel construction may be unavoidable, and is very influential on overall stability. Over-excavation in tunneling is also one of the most important factors in construction costs. Overbreak problems around crown areas have decreased with improvements of excavation methods, but overbreak problems around bottom areas have not decreased because those areas are not very influential on tunnel stability compared with crown areas. The filling costs of 10 cm thickness of overbreak at the bottom of a tunnel are covered under construction costs by Korea Railway Authority regulations, but filling costs for more than the covered thickness are considered losses of construction cost. The filling material for overbreak bottoms of tunnels should be concrete, but concrete and mixed granular materials with fractured rock are also used for some sites. Tunnels in which granular materials with fractured rock are used may have a discontinuous section under the concrete slab track. The discontinuous section influences the propagation of waves generated from train operation. When the bottom of a tunnel is filled with only concrete material, the bottom of the tunnel can be considered as a continuous section, in which the waves generated from a train may propagate without reflection waves. However, a discontinuous section filled with mixed granular materials may reflect waves, which can cause resonance of vibration. The filled materials and vibration propagation characteristics are studied in this research. Tunnel bottom filling materials that have ratios of granular material to concrete of 5.0 %, 11.5 %, and 18.0 % are investigated. Samples were made and tested to determine their material properties. Static numerical analyses were performed using the FEM program under train operation load; test results were found to satisfy the stability requirements. However, dynamic analysis results show that some mixed ratios may generate resonance vibration from train operation at certain speeds.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.61-77
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• 2019

If a new utility tunnel is planned for high density existing urban areas in Korea, a rational decision-making process such as the determination of optimum design capacity by using the feasibility evaluation system based on quantitative evaluation indexes and the economic evaluation is needed. Thus, the previous study presented the important weight of individual higher-level indexes (3 items) and sub-indexes (16 items) through a hierarchy analysis (AHP) for quantitative evaluation index items, considering the characteristics of each urban type. In addition, an economic evaluation method was proposed considering 10 benefit items and 8 cost items by adding 3 new items, including the effects of traffic accidents, noise reduction and socio-economic losses, to the existing items for the benefit cost analysis suitable for urban utility tunnels. This study presented a quantitative feasibility evaluation method using the important weight of 16 sub-index items such as the road management sector, public facilities sector and urban environment sector. Afterwards, the results of quantitative feasibility and economic evaluation were compared and analyzed in 123 main road sections of the Seoul. In addition, a comprehensive evaluation method was proposed by the combination of the two evaluation results. The design capacity optimization program, which will be developed by programming the logic of the quantitative feasibility and economic evaluation system presented in this study, will be utilized in the planning and design phases of urban community zones and will ultimately contribute to the vitalization of urban utility tunnels.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Journal of Korean Academy of Nursing
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• v.24 no.3
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• pp.413-431
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• 1994

The purpose of this study was to build a substantive theory about the experience of the family whose child has died of cancer The qualitative re-search method used was grounded theory. The interviewees were 17 mothers who had cared for a child who had died of cancer Traditionally in Korea, mothers are the care givers in the family and are considered sensitive to the family's thoughts, feelings. The data were collected through in-depth interviews by the investigator over a period of nine months. The data were analyzed simultaniously by a constant comparative method in which new data are continuously coded into categories and properties according to Strauss and Corbin's methodology. The 16 concepts which were found as a result of analyzing the grounded data were, -left over time, the empty place, meaninglessness, inner sadness, situational sadness, heartache, physical pain, guilt, resentment, regret, support / stigmatization, finding meaning in the death, changing attitudes about life and living, changing attitudes about health, changing religious practice and changing family relations. Five categories emerged from the analysis. They were emptiness, consisting of left over time, the empty place and meaninglessness ; sadness, consisting of inner sadness and situational sadness ; pain, consisting of heartache and physical pain ; bitterness, consisting of guilt, resentment, regret, sup-port / stigmatization and finding meaning in the death : and transition, consisiting of changing attitudes about life and living, changing attitudes about health, changing religious practice and changing family relations. These categories were synthesized into the core concept, -the process of filling the empty space. The core phenomenon was emptiness. Emptiness varied with the passing of time, was perceived differently according to support / stigmatization and finding meaning in the death, was followed by sad-ness, pain, and bitterness, and finally resulted in changes in attitudes about life and living and about health, and in changes in religious practice and family relations. The process of filling the empty space proceeded by ① accepting realty, ② searching for the reason for the child's death, ③ controlling the bitter feelings, ④ reconstructing the relationships ameng death, illness and health and ⑤ filling the emptiness by resolving causes of child's death, adopting, having another child or with work. Six hypotheses were derived from the analysis. ① The longer the bereavement, the mere the empty space becomes filled. ② The longer the hospitalization, the more sup-port the family needs. ③ The more the sadness, pain and bitterness are expressed, the mere positive changes emerge. ④ Family support faciliates the process of filling the empty space. ⑤ Higher family cohesiveness faciliates the process of filling the empty space. ⑥ The greater the variety of reasons attributed to the child's death, the greater the variety of patterns of change. Four propositions related to emptiness and bitter-ness were developed. ① When the sense of emptiness is great and bitterness is manifested by severe feelings of guilt and resentment, the longer the process of fill-ing the empty space. ② When the sense of emptiness is great and the family is highly motivated to get rid of the bitterness, the shorter the process of filling the empty space. ③ When the sense of emptiness is less and bitter-ness is manifested by severe feelings of guilt and resentment, the process of filling the empty space is delayed. ④ When the sense of emptiness is less and the family is highly motivated to get rid of the bitterness, the process of filling the empty space goes on to completion. Through this substantive theory, nurses under-stand the importance of emptiness and bitterness in helping the family that has lost a child through cancer fill the empty space. Further research to build substantive theories to explain other losses may con-tribute to a formal theory of how family health is restored after human tragedies are experienced.

• Tuberculosis and Respiratory Diseases
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• v.65 no.6
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• pp.487-494
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• 2008

Background: Chromosome 17p allele losses and mutations of p53 gene are the most common genetic abnormalities in lung cancer. The purposes of this study were to evaluate the factors associated with p53 protein overexpression and to evaluate its prognostic value in patients with pathologic stage I non-small cell lung cancer (NSCLC). Methods: This is a retrospective review for the patients who underwent surgical resection at Samsung Medical Center between Jan 2003 and Jun 2004. Immunohistochemical staining for p53 protein was performed on tumor tissues from patients with lung cancer. The p53 overexpression was evaluated in relation to age, sex, smoking history, histology and pathologic stage by univariate and multivariate analyses. The disease-free survival (DFS), disease-specific survival (DSS) and overall survival (OS) were analyzed using the Kaplan-Meier methods and the differences in DFS, DSS and OS were assessed by using the log-rank tests. Results: A total of 125 patients were included in the analysis and a median frequency of p53 expression in tumor tissue was 10%. The p53 overexpression (${\geq}10%$) was more common in squamous cell carcinoma (66%) than in adenocarcinoma (38%, p=0.002). The p53 overexpression was more common in pathologic stage IB (59%) than in IA (38%, p=0.002). Patients with p53-overexpressing tumor (27 years) smoked more years compared with those without it (20 years, p=0.032). Smoking history ${\geq}25$ pack-years was more common in patients with p53 overexpression (58%) than in those without it (38%, p=0.024). In the multivariate analysis, only histology was significantly associated with p53 overexpression. However, there were no significant differences of DFS, DSS and OS in relation to p53 status. Conclusion: The p53 overexpression was associated with histology, pathologic stage and smoking history in patients with pathologic stage I NSCLC. However, the p53 overexpression was not associated with patient's survival.

• Korean Journal of Agricultural Science
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• v.20 no.1
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• pp.68-87
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• 1993

Data acquisition system and computer program developed in this study could be well used in engine vibration analysis. The system and program developed were also operated to be able to control measuring interval, number of channels, number of data. The flywheel was specially studied to provide the proper weight with rubber damper for the engine design at low level of vibration. This study was conducted to obtain basic data which affect the engine vibration. The experiment of this study was performed on original weight flywheel, weight-reduced flywheel, weight-reduced and rubber-coated flywheel, weight-reduced and damper-attached flywheel. Avarage of peak value, maximum vibration, power spectrum density based on FFT analysis are major factors of this experiment. Results were obtained as follows : 1. When rubber was inserted in the flywheel rim of which weight was reduced from 32.2kgf to 24.4kgf, maximum vibration of the engine was decreased 48.3% at X axis, 35.5% at Y axis and 34.6% at Z axis in comparison with the flywheel of original weight. 2. When the flywheel of rubber damper was compared with the original flywheel, the average of absolute vibration for rubber damped flywheel was decreased at X, Y, Z axis and especially its decreasing rate was so high at X-axis comparing with the other flywheel, which implied that rubber damper was very useful to reducing the vibration of the engine at X axis. 3. Hysteresis losses of X, Y, Z axis were greatly decreased in the flywheel with rubber damper on rim. 4. Damped oscillation effect on X and Y axis vibration above average peak vibration by the flywheel of rubber damper on rim was larger than those by the other flywheels. 5. Power spectrums of vibration at real and imaginery part were bi-mode type. The vibration frequency of rubber dampered flywheel which weight is decreased was slightly increased as compared with original flywheel.

• Journal of Plant Biotechnology
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• v.33 no.3
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• pp.153-160
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• 2006

Brassica rape is an important species used as a vegetable, oil, and fodder worldwide. It is related phylogenically to Arabidopsis thaliana, which has already been fully sequenced as a model plant. The 'Multinational Brassica Genome Project (MBGP)'was launched by the international Brassica community with the aim of sequencing the whole genome of B. rapa in 2003 on account of its value and the fact that it has the smallest genome among the diploid Brassica. The genome study was carried out not only to know the structure of genome but also to understand the function and the evolution of the genes comprehensively. There are two mapping populations, over 1,000 molecular markers and a genetic map, 2 BAC libraries, physical map, a 22 cDHA libraries as suitable genomic materials for examining the genome of B. rapa ssp. pekinensis Chinese cabbage. As the first step for whole genome analysis, 220,000 BAC-end sequences of the KBrH and KBrB BAC library are achieved by cooperation of six countries. The results of BAC-end sequence analysis will provide a clue in understanding the structure of the genome of Brassica rapa by analyzing the gene sequence, annotation and abundant repetitive DHA. The second stage involves sequencing of the genetically mapped seed BACs and identifying the overlapping BACs for complete genome sequencing. Currently, the second stage is comprises of process genetic anchoring using communal populations and maps to identify more than 1,000 seed BACs based on a BAC-to-BAC strategy. For the initial sequencing, 629 seed BACs corresponding to the minimum tiling path onto Arabidopsis genome were selected and fully sequenced. These BACs are now anchoring to the genetic map using the development of SSR markers. This information will be useful for identifying near BAC clones with the seed BAC on a genome map. From the BAC sequences, it is revealed that the Brassica rapa genome has extensive triplication of the DNA segment coupled with variable gene losses and rearrangements within the segments. This article introduces the current status and prospective of Korea Brassica Genome Project and the bioinformatics tools possessed in each national team. In the near future, data of the genome will contribute to improving Brassicas for their economic use as well as in understanding the evolutional process.