• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.189-193
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• 2003

금형가공에 있어서 밀링머신의 가공에서는 절삭가공의 잔유물인 버(Burr)가 생성되고, 이러한 버는 가공의 정밀도를 감소시킬 뿐만 아니라 후처리과정(Deburring)을 야기시킴으로 인해서 작업효율의 감소 및 생산성의 비효율적 낭비를 가져오게 된다. 따라서, 정밀도와 작업효율을 극대화하긴 위해서는 버의 생성원리를 파악하고, Exit Burr의 생성부분을 미리 예측하여 버의 생성을 최소화 할 수 잇는 작업 가공계획을 설계하여야 한다. (1)기존의 Burr Exit System에서는 피삭재의 단면형상인 Line과 Are처럼 단순한 형상뿐만 아니라, Line과 Are가 연결되어있는 복잡란 형상에 대해서도 버를 판별한다. 그리고, 가공 후 버가 생성되는 부분을 예측하고, 이때의 Exit Angle을 계산하여 이에 해당하는 기 실험결과 DataBase와 연동하여 생설될 버의 형상과 크기 등의 결과를 제공하여 준다. 더불어, 피삭재의 단면형상이 여러 가지 복합적인 형상으로 이루어져 있는 경우와 다양한 공구 경로까지 고려하여 실제가공과 거의 유사란 상황을 적용할 수 잇는 알고리즘으로 개발하였다. 본 논문에서는 이제까지 개발된 다양한 형상에 대한 Exit Burr 판별 알고리즘을 이용하여 임의형상을 가진 피삭재의 다중가공경로 상에서 발생 가능한 버를 예측하고, 버의 길이나 가공시간 들을 정?화 하여 최적화하는데 필요란 요소를 추출해 보고자 한다. 또한, 이를 인용하여 Face Windows에서의 버의 발생을 최소화 할 수 있는 최적 절삭가공 공구경로를 제시하여, 작업 효율성을 극대화하는 알고리즘을 Windows 응용 프로그램으로 구현하고자 한다.생성하기보다는 기존에 발생된 구매 지시의 우선적 사용과 기존 구매 지시의 납기 일자를 고객 납기에 가장 잘 맞출 수 있도록 변경하는 방안을 제시한다. 이렇게 함으로써 최대한 고객 납기를 만족하도록 계획을 수립할 수 있게 된다. 본 논문에서 제시하는 계획 모델을 사용함으로써 고객 주문에 대한 대응력을 높일 수 있고, 계획의 투명성으로 인한 전체 공급망의Bullwhip effect를 감소시킬 수 있는 장점이 있다. 동시에 이것은 향후 e-Business 시스템 구축을 위한 기본 인프라 역할을 수행할 수 있게 된다. 많았고 년도에 따른 변화는 보이지 않았다. 스키손상의 발생빈도는 초기에 비하여 점차 감소하는 경향을 보였으며, 손상의 특성도 부위별, 연령별로 다양한 변화를 나타내었다.해가능성을 가진 균이 상당수 검출되므로 원료의 수송, 김치의 제조 및 유통과정에서 병원균에 대한 오염방지에 유의하여야 할 것이다. 확인할 수 있었다. 이상의 결과에 의하면 고농도의 유기물이 함유된 음식물쓰레기는 Hybrid Anaerobic Reactor (HAR)를 이용하여 HRT 30일 정도에서 충분히 직접 혐기성처리가 가능하며, 이때 발생된 $CH_{4}$를 회수하여 이용하면 대체에너지원으로 활용 가치가 높은 것으로 판단된다./207), $99.2\%$(238/240), $98.5\%$(133/135) 및 $100\%$ (313)였다. 각각 두 개의 요골동맥과 우내흉동맥에서 부분협착이나 경쟁혈류가 관찰되었다. 결론: 동맥 도관만을 이용한 Off pump CABG를 시행하여 감염의 위험성을

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.475-481
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• 2019

A splint is one of assisting devices for the disabled with hemiplegia or contracture and is manually made by an experienced expert. Heated thermoplastic materials are continuously fitted to the affected part. This traditional method has a possible risk of low-temperature burn, quality variance of the splint due to the proficiency of maker. etc. While various approaches has been made using 3D printing technology in order to redeem those disadvantages, they still carry high cost issues with 3D scanners or accuracy issues with manual measurement. This research begins with symmetrical characteristics of human body and focuses on the preliminary study for the possibility of splint manufacturing with 3D printing technology based on geometric characteristics of unaffected arm. 3D right and left forearm models of healthy male adults were created by photogrammetry software and a series of digital images in order to measure the circumference and cross-sectional area of the forearm models at every 20mm from the elbow. The circumference and cross-sectional area showed tolerable levels of differences between both sides within subjects; The circumference and cross-sectional area showed very strong correlations between both sides within subjects. From these findings, the possibility of splint manufacturing with 3D printing technology could be confirmed based on the geometric characteristics of unaffected side.

• Research in Plant Disease
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• v.28 no.3
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• pp.143-151
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• 2022

In order to investigate the cause of wilting symptoms in red pepper field of Korea, the frequency of occurrence of red peppers showing wilting symptoms was investigated in pepper cultivation fields in Goesan, Chungcheongbuk-do for 5 years from 2010 to 2014. There was a difference in the frequency of wilting symptoms depending on the year of investigation, but the frequency of occurrence increased as the investigation period passed from June and July to August. During this period, Ralstonia solanacearum causing the bacterial wilt was isolated at a rate four times higher than Phytophthora capsica causing the Phytophthora late blight. In wilted peppers collected in Goesan of Chungbuk and Andong of Gyeongbuk in 2013 and 2014, R. solanacearum and P. capsici were isolated from 20.3% and 3.8% of the total fields, respectively. In the year with a high rate of wilting symptoms, the average temperature was high, and the disease occurrence date of the bacterial wilt, estimated with disease forecasting model, was also fast. The inconsistency between the number of days at risk of Phytophthora late blight and the frequency of occurrence of wither symptoms is thought to be due to the generalization of the use of cultivars resistant to the Phytophthora late blight in the pepper field. In our study, the wilting symptoms were caused by the bacterial wilt caused by R. solanacearum rather than the Phytophthora late blight caused by P. capsica, which is possibly caused by increasing cultivation of pepper varieties resistant to the Phytophthora late blight in the field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.109-117
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• 2023

Schedule delays present a major risk factor, as they can adversely affect construction projects, such as through increasing construction costs, claims from a client, and/or a decrease in construction quality due to trims to stages to catch up on lost time. Risk management has been conducted according to the importance and priority of schedule delay risk, but quantification of risk on the depth of schedule delay tends to be inadequate due to limitations in data collection. Therefore, this research used the BERT (Bidirectional Encoder Representations from Transformers) language model to convert the contents of aconstruction log, which comprised unstructured data, into WBS (Work Breakdown Structure)-based structured data, and to form a model of classification and quantification of risk. A process was applied to eight highway construction sites, and 75 cases of rain schedule delay risk were obtained from 8 out of 39 detailed work kinds. Through a K-S test, a significant probability distribution was derived for fourkinds of work, and the risk impact was compared. The process presented in this study can be used to derive various schedule delay risks in construction projects and to quantify their depth.

• Information Systems Review
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• v.22 no.4
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• pp.185-203
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• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.

• Journal of Environmental Policy
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• v.12 no.3
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• pp.21-47
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• 2013

The aim is to analysis landslide vulnerability in Inje, Korea, using GCI(Geospatial Correlative Integration) and probability rainfalls based on geographic information system (GIS). In order to achieve this goal, identified indicators influencing landslides based on literature review. We include indicators of exposure to climate(rainfall probability), sensitivity(slope, aspect, curvature, geology, topography, soil drainage, soil material, soil thickness and soil texture) and adaptive capacity(timber diameter, timber type, timber density and timber age). All data were collected, processed, and compiled in a spatial database using GIS. Karisan-ri that had experienced 470 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data, while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 3-day cumulative rainfalls of 449 mm. Results show that number of slope has comparatively strong influence on landslide damage. And inclination of $25{\sim}30^{\circ}C$, the highest correlation landslide. Improved previous landslide vulnerability methodology by adopting GCI. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing landslide mitigation policies.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Korean Journal of Food Science and Technology
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• v.37 no.3
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• pp.484-491
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• 2005

Quantitative microbial risk assessment (QMRA) analyzes potential hazard of microorganisms on public health and offers structured approach to assess risks associated with microorganisms in foods. This paper addresses specific risk management questions associated with Staphylococcus aureus in kimbab and improvement and dissemination of QMRA methodology, QMRA model was developed by constructing four nodes from retail to table pathway. Predictive microbial growth model and survey data were combined with probabilistic modeling to simulate levels of S. aureus in kimbab at time of consumption, Due to lack of dose-response models, final level of S. aureus in kimbeb was used as proxy for potential hazard level, based on which possibility of contamination over this level and consumption level of S. aureus through kimbab were estimated as 30.7% and 3.67 log cfu/g, respectively. Regression sensitivity results showed time-temperature during storage at selling was the most significant factor. These results suggested temperature control under $10^{\circ}C$ was critical control point for kimbab production to prevent growth of S. aureus and showed QMRA was useful for evaluation of factors influencing potential risk and could be applied directly to risk management.

• Journal of Genetic Medicine
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• v.5 no.1
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• pp.34-40
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• 2008

Purpose : Preeclampsia is a major cause of maternal and perinatal mortality and morbidity and is considered to be a multifactorial disorder involving a genetic predisposition and environmental factors. Endothelin-1 (ET-1) is a potent vasoconstrictor peptide, and alterations in the ET-1 system are thought to play a role in triggering the vasoconstriction seen with preeclampsia. The aim of this study was to examine the frequency of the 4 common single-nucleotide polymorphisms (SNPs) (c.1370T>G, c.137_139delinsA, c.3539+2T>C, and c.5665G>T) of the ET-1 gene in normotensive and preeclamptic pregnancies and to investigate whether these SNPs are associated with preeclampsia in pregnant Korean women. Methods : We analyzed blood samples from 206 preeclamptic and 216 normotensive pregnancies using a commercially available SNapShot kit and an ABI Prism 3100 Genetic analyzer. Results : There were no significant differences in genotype or allele frequencies of the 4 SNPs in the ET-1 gene between preeclamptic and normotensive pregnancies. The respective frequencies of the 3 haplotypes (TDTG, GDCT, and TICT; >10% haplotype frequency) were 61%, 13% and 13%, respectively, in preeclampsic pregnancies and 62%, 14% and 12%, respectively, in normotensive pregnancies. The frequencies of these haplotypes were similar for both groups. Using multiple logistic regression analysis, we did not observe an increase in the risk of preeclampsia for the 4 SNPs of the ET-1 gene under either a recessive or dominant model. Conclusion : This study suggests that the 4 SNPs of the ET-1 gene are not associated with an increased risk for preeclampsia in pregnant Korean women.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.66-73
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• 2016

Objectives : The purpose of this study was to investigate the characteristics of psychosocial factors related to functional dyspepsia(FD) and their effects on quality of life(QOL) in firefighters. Methods : This study examined data collected from 1,217 firefighters. We measured psychological symptoms by Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder questionnaire(GAD-7), Korean Occupational Stress Scale(KOSS), Ways of Coping checklist(WCCL), Rosenberg's Self-Esteem Scale(RSES) and World Health Organization Quality of Life Scale abbreviated version(WHOQOL-BREF). Chi-square test, independent t-test, Pearson's correlation test, logistic regression analysis, and hierarchical regression analysis were used as statistical analysis methods. Results : For the group with FD, the male participants showed significantly higher frequency(p=0.006) compared to the female participants. The group with FD had higher scores for depressive symptoms(p<.001), anxiety (p<.001), and occupational stress(p<.001), and did lower scores for self-esteem(p=.008), quality of life(p<.001) than those without FD. The FD risk was higher in the following KOSS subcategories: job demand(OR 1.94, 95% CI : 1.29-2.93), lack of reward(OR 2.47, 95% CI : 1.61-3.81), and occupational climate(OR 1.51, 95% CI : 1.01-2.24). In the hierarchical regression analysis, QOL was best predicted by depressive symptoms, self-esteem, and occupational stress. Three predictive variables above accounts for 42.0% variance explained of total variance. Conclusions : The psychosocial factors showed significant effects on FD, and predictive variables for QOL were identified based on regression analysis. The results suggest that the psychiatric approach should be accompanied with medical approach in future FD assessment.