우리나라는 삼면이 바다로 이루어져 있고, 이에 따라 많은 해양 시설로 인한 위험유해물질이 배출되고 있으나, 배출관리 및 규제 시스템이 미비한 상황이다. 따라서, 위험유해물질(HNS) 관리를 위하여 효율적으로 데이터를 수집할 수 있는 시스템이 필요하 다. 본 연구에서는 HNS 데이터를 효율적으로 관리 및 저장하기 위한 데이터 표준화 시스템을 설계하고 이의 표준화 방안을 제시하고 자 한다.

As various accidents have occurred in underground spaces, we aim to improve the quality validation standards and methods as specified in the Regulations on Producing Integrated Map of Underground Spaces devised by the Ministry of Land, Infrastructure and Transport of the Republic of Korea for a high-quality integrated map of underground spaces. Specifically, we propose measures to improve the quality assurance of pipeline-type underground facilities, the so-called life lines given their importance for citizens’ daily activities and their highest risk of accident among the 16 types of underground facilities. After implementing quality validation software based on the developed quality validation standards, the adequacy of the validation standards was demonstrated by testing using data from two-dimensional water supply facilities in some areas of Busan, Korea. This paper has great significance in that it has laid the foundation for reducing the time and manpower required for data quality inspection and improving data quality reliability by improving current quality validation standards and developing technologies that can automatically extract errors through software.

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real- world scenarios.

We performed a study to examine the association between diet quality and nonalcoholic fatty liver disease (NAFLD). Our study included 3,586 women aged 40-64 years who participated in the sixth Korea National Health and Nutrition Examination Survey. The study subjects were classified into the NAFLD group (n=816) and the normal group (n=2,770) using the hepatic steatosis index. The anthropometric indices, blood profiles, and dietary intake data of the subjects were obtained. The waist circumference, body mass index, and the serum levels of triglycerides, fasting blood sugar, HbA1c, and systolic and diastolic blood pressures were higher in the NAFLD compared to the normal groups (p<0.001, respectively). The intakes of protein (g/kg body weight, p<0.001), potassium (p<0.001), and vitamin A (p=0.006) were significantly lower in the NAFLD group. It was observed that the higher the total Korean Healthy Eating Index score, the lower the risk of NAFLD. A reverse relationship was shown between the NAFLD risk and the intakes of total fruits, total vegetables, vegetables excluding Kimchi and pickled vegetables, meat, fish, eggs and beans. Therefore, it is recommended that middleaged women in Korea increase their intakes of fruits, vegetables, and foods high in protein for the proper management of NAFLD.

사출성형공정은 열가소성 수지를 가열하여 유동상태로 만들어 금형의 공동부에 가압 주입한 후에 금형 내에서 냉각시키는 공정으로, 금형의 공동모양과 동일한 제품을 만드는 방법이다. 대량생산이 가능하고, 복잡한 모양이 가능한 공정으로, 수지온도, 금형온도, 사출속도, 압력 등 다양한 요소들이 제품의 품질에 영향을 미친다. 제조현장에서 수집되는 데이터는 양품과 관련된 데이터는 많은 반면, 불량품과 관련된 데이터는 적어서 데이터불균형이 심각하다. 이러한 데이터불균형을 효율적으로 해결하기 위하여 언더샘플링, 오버샘플링, 복합샘플링 등이 적용되고 있다. 본 연구에서는 랜덤오버샘플링(ROS), 소수 클래스 오버 샘플링(SMOTE), ADASTN 등의 소수클래스의 데이터를 다수클래스만큼 증폭시키는 오버샘플링 기법을 활용하고, 데이터마이닝 기법을 활용하여 품질예측을 하고자 한다.

Ball stud parts are manufactured by a cold forging process, and fastening with other parts is secured through a head part cutting process. In order to improve process quality, stabilization of the forging quality of the head is given priority. To this end, in this study, a predictive model was developed for the purpose of improving forging quality. The prediction accuracy of the model based on 450 data sets acquired from the manufacturing site was low. As a result of gradually multiplying the data set based on FE simulation, it was expected that it would be possible to develop a predictive model with an accuracy of about 95%. It is essential to build automated labeling of forging load and dimensional data at manufacturing sites, and to apply a refinement algorithm for filtering data sets. Finally, in order to optimize the ball stud manufacturing process, it is necessary to develop a quality prediction model linked to the forging and cutting processes.

In this study, as part of the paradigm shift for manufacturing innovation, data from the multi-stage cold forging process was collected and based on this, a big data analysis technique was introduced to examine the possibility of quality prediction. In order for the analysis algorithm to be applied, the data collection infrastructure corresponding to the independent variable affecting the quality was built first. Similarly, an infrastructure for collecting data corresponding to the dependent variable was also built. In addition, a data set was created in the form of an independent variable-dependent variable, and the prediction accuracy of the quality prediction model according to the traditional statistical analysis and the tree-based regression model corresponding to the big data analysis technique was compared and analyzed. Lastly, the necessity of changing the manufacturing environment for the use of big data analysis in the manufacturing process was added.

This study aimed to assess the nutritional quality of breakfast among Korean school-aged children and adolescents depending on eating together as a family, based on the 2013-2014 Korea National Health and Nutrition Survey. One day 24-hour recall data of 1,831 children and adolescents aged from 6 to 17 years were collected. The nutritional quality of breakfast was analyzed and compared between Family Breakfast Group (FBG, n=1,410) and Eating-alone Breakfast Group (EBG, n=421). The results showed that age, family structure, number of family members, and frequency of breakfast were associated with eating breakfast as a family. The calorie intake from breakfast explained 19% and 16% of the daily intake for FBG and EBG, respectively. The percentages of children and adolescents consuming Vitamin A, Vitamin B1, Vitamin B2, Vitamin C, Niacin, and Iron less than 1/4 of the Estimated Average Requirements were significantly lower in FBG than in EBG. The average numbers of serving for “Grains” and “Vegetables” food groups and the average Dietary Diversity Score were significantly higher in FBG than in EBG. Overall, the results indicated that eating breakfast as a family is positively associated with nutritional quality of breakfast among Korean school-aged children and adolescents.

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

This study used the methods of decision tree analysis, association rule analysis, and Kano’s model to explore the behavior patterns of mainland China tourists staying at the international tourist hotels in Taiwan. To this end, the data of their demographics, travel variables, overall satisfaction with the lodging experience, different service quality perceptions, and loyalty intentions were included. The decision tree analysis showed that a tourist’s overall satisfaction with the lodging experience, satisfaction with the quality of core intangible services, and certain demographic characteristics are three important determinants of tourist loyalty towards the hotels. In terms of the effect of demographics, the customers’ monthly income and length of stay at the hotel are two main determinants in this study. In addition, if the customer perceptions of different parts of hotel service quality are taken into account, among the five hotel service quality domains, core intangible services from the receptionist, housekeeping personnel, and food & beverage personnel are found to be important influences on hotel customer loyalty intention. In other words, high quality intangible services are important for luxury hotels to demonstrate their unique ability to help customers experience the service quality that creates loyalty intentions. With regard to the association rule analysis, the results showed that core intangible service aspects from the receptionist, housekeeping personnel, and food & beverage personnel are strongly associated with customer loyalty intentions, as are the tangible aspects of the reception and hotel room facilities. The former indicated that reception in the hotel lobby could be considered one of the most important servicescapes because of its impact in forming many of the first impressions of hotel guests, while the latter is treated as core offerings in hotels that would be encountered by most hotel customers. If the tourists are mainly from package tours, the intangible services and tangible facilities of these areas are the important areas to create customer satisfaction. However, if the tourists are mainly independent tourists because they have more time and free choice to stay at the hotel longer than the package tour tourists, the intangible services and tangible facilities of the entertainment or business centers would be even more important to these tourists than to the package tour tourists. With regard to Kano’s model analysis, the results showed that, based on mainland China tourists’ perceptions, most of the service elements fit into the category of one-dimensional quality attributes. This means that these service elements are positively and linearly related to customer satisfaction, and the greater fulfillment of the attribute results in a greater degree of satisfaction. This also means that hotels should make more effort to innovate their intangible services and tangible facilities to create business advantages in the market.

기후 자료를 정확히 분석하고 미래 환경을 예측하기 위해서는 이들 자료에 대한 검증과 품질 관리 과정이 필요하다. 본 연구에서는 우리나라에서 구축한 기후 자료의 품질을 효과적으로 관리하기 위하여, 기후 자료의 시각화 도구를 개발하고, 이를 이용한 품질 관리 방안을 제시하였다. 본 연구에서 개발한 HTML5 기반의 시각화 도구를 이용하여, 인터넷 환경에서 다수의 전문가들이 효과적으로 기후 자료를 관리할 수 있게 되었다. 기후자료의 시각화 도구를 활용하여 기후자료의 품질 관리 업무에 적용한 결과, 다양한 종류의 품질 관리 검사를 효과적으로 수행할 수 있었다. 본 연구의 결과는 향후 기상청의 기후 자료 품질 관리 업무에 활용될 수 있을 것으로 예상된다.

성공적인 국제 Argo 사업을 통해 얻어진 고해상도의 수온 및 염분 프로파일들을 이용하여 준실시간으로 3차원 전지구 해양 상태의 재현이 가능해졌으며, 전지구 해양 자료 분석 연구에 대한 관심 또한 크게 증가하였다. 본 연구에서는 XBT와 Argo 자료의 계통 오차, 열염분 해수면 높이 및 인공위성 관측 자료와의 해수면 수지 불일치, 해양 열용량 변동 및 객관 분석장 개발 현황 등 최근의 자료 분석 연구의 주요 쟁점들에 관해 정리하였다. 특히 동아시아 해역에서의 지역 규모 해양 기후 변화 분석에 앞서 매우 신중한 해양 자료의 품질 관리 및 전구 규모의 해양 변동에 관한 이해가 필요하다는 것을 강조하였다.