• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.79-85
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• 2023

Recently, in the semiconductor process, large companies are seeking process changes from memory semiconductors to the foundry due to the increase in demand due to the 4th industry. industry is expanding. The characteristics of special gases and precursors, which are raw materials used to produce these semiconductor chips, are toxic, pyrophoric, inflammable, and corrosive. These semiconductor raw materials are operated in a closed system and do not leak to the outside during normal times, but when leaked, they spread to the inside of the gas box, and when proper ventilation is not provided inside the gas box, they spread to the outside, causing fires, explosions, or toxic substances. It can lead to major accidents such as leakage. Recently, there have been cases of accidents in which hazardous materials leaked from the closed system of the semi conductor process and spread to the inside and outside of the gas box. . In this study, we propose preventive measures based on the case of an accident in which raw material leaked from the VCR fitting, which is the connection part of the semiconductor raw material transfer pipe, and spread to the outside of the gas box.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.453-453
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• 2021

도시지역 비점오염원관리, 물순환 회복, 침투 및 증발산량 증가, 열섬현상 저감을 위한 주요한 방안으로 저영향개발(low impact development, LID)과 그린인프라 기법의 적용되고 있다. LID 시설은 소규모 분산형 시설로써 넓은 지역에 많고 다양한 시설들이 적용되어 시설의 개수가 많으며, 수질 및 토양 내 기성제품에 대한 센서들의 가격은 고가로 형성되어 있어 기기의 경제성 및 유지관리 등 적용하는데 제한적이다. 따라서 과거 모니터링 자료를 기반으로 오염물질들과의 상관성 분석을 통하여 계측이 어려운 항목들을 계측가능한 항목들로부터 예측 가능하며, 선정된 항목들에 대한 비용효율적인 센서를 개발하여 실시간 LID 모니터링이 가능한 비용효율적 모니터링을 개발하였다. 공주대학교 천안캠퍼스의 LID 시설들은 2013년에 조성되어 현재까지 시설이 운영되고 있으며, 5년이상의 과거 강우시 모니터링 자료들을 이용하여 오염물질 상관성 분석을 수행가능 하기에 대상지로 선정하였다. 오염물질 상관성 분석은 2013년부터 2017년도에 침투도랑에서 수행된 강우시 모니터링 자료를 활용하여 각 오염물질들의 상관성을 분석을 수행하였다. 침투도랑 내 유입되는 평균 유입수는 TSS 286.1±318.3 mg/L, BOD 22.6±39.5 mg/L, TN 8.96±5.85 mg/L, TP 1.01±1.11 mg/L로 나타났다. 겨울철에 비해 여름철에서의 오염물질의 유입농도가 높은 것으로 분석되었다. 이는 여름철 고온건조로 인한 노면 내 차량의 주행으로 인한 중금속, 폐타이어 등과 장마철 강우 시 유출된 토사로 인하여 유입수의 농도가 높은 것으로 분석되었다. 오염물질 부하량은 TSS와 COD 0.66으로 유의성이 높은 것으로 나왔으며, COD와 TSS, TP, TN 등 유의성이 높은 것으로 분석되었다. Arduino와 Raspberry PI를 활용하여 저비용 센서와 LTE 모뎀통신과 데이터 베이스 연결하여 개발된 프로그램을 통해서 무선으로 LID 시설에 대한 모니터링을 침투화분2와 식생체류지에 조성하였다. 전력공급이 어려운 식생체류지의 경우 태양열(Solar system) 시스템과 보조 전력 배터리를 조성하여 장마철이나 장기적인 악천후로 인한 전력을 생산하지 못할 경우 보조전력배터리에서 전력을 제공하여 지속적인 모니터링이 이루어지도록 설계하였다. 토양함수량, 토양온도와 Conductivity 등 3종류의 센서를 적용하였으며, 프로그램은 현재 2단계를 통한 2차수정을 통하여 프로그램을 구축하였다. 오차, 오작동, 계측값에 대한 검·보정 작업이 필요하다. 또한 대기자료의 구축을 통해 보다 토양과 LID 시설에 대한 영향분석이 필요한 것으로 사료된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.655-664
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• 2008

Lean Construction recommends implementing site production management by measuring the reliability of daily production tasks, collecting the causes for failure of incomplete tasks, and identifying problems in the existing process based on the collected data. Although many research projects have been performed to introduce Lean Construction in Korea, there are not many cases in which day-to-day site production operations has been stringently evaluated based on such methods. This paper introduces three cases in which such techniques were implemented on a residential, civil infrastructure and plant project. On each project, process maps were created with the superintendents and daily production meetings were held for two weeks. Consequently, the average PPC for the three projects was 79% for PPC and 16% for PAT. In addition, the majority of the failures were due to 'Directive/Plan' and 'Prerequisites.' The results show that project stakeholders (owners, contractors, etc.) lack the ability to plan ahead and keep to their plans, and also lack the capability to synchronize workflow between themselves. The results also reveal that project participants need to be more proactive in solving process problems on site and also need to be better educated in Lean concepts and methodologies.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.3
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• pp.129-147
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• 2009

The ultimate goal of classification systems is to provide tools for information management and services through collocation of information objects in similar topics. The National Research Council for Economics, Humanities, and Social Sciences(NRCS) aims to organize the research products from 23 research institutes. To manage and organize the research products effectively, the standard classification system has been developed in conjunction of users' survey and the Business Reference Model(BRM). Although the standard classification system consists of users' perspectives and the aspects of organizational functions, there are limits to apply the system into classification practices. In this study, the proposed hybrid approach is to combine a clustering approach with 1,884 keywords from the titles of research products between 2007 and 2008. The clustering approach is performed in a heuristic way according to the KDC due to the lack of digital full texts of research products. The results of this study proposed a revised standard classification system for NRCS with 16 headings and 90 sub-headings. The revised standard classification system will play an important role in managing research products effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.276-279
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• 2022

The 4th industrial revolution refers to the transition to a knowledge society in which the production speed of knowledge is fast and the proportion of the knowledge industry is greatly increased. The reorganization of the industrial structure and the change of occupations and jobs due to new technologies are bringing about changes in education, and the development of digital technology has made education that is borderless, individual and dynamic, becoming the new standard of education. With these changes, interest in nano-degrees on new technologies or micro-degrees focused on core courses rather than regular course degrees is increasing. As a representative example, Udacity in the United States has opened and operated online nanodegree courses related to jobs, and collaborates with major companies to develop and educate core training courses necessary for companies, thereby efficiently supporting companies in securing talent. With the revitalization of online vocational and job training, an environment has been prepared in which individuals can set goals for vocational competency development and continue their portfolio-based sustainable learning. However, for effective vocational education, automated and personalized educational content design should be preceded. To this end, in this paper, we propose a personalized career and course map recommendation system in the era of online learning

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.383-384
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• 2022

Following the development of technologies such as digital twin, IoT, and AI after the 4th industrial revolution, decision-making problems are being solved based on high-dimensional data analysis. This has recently been applied to the port logistics sector, and a number of studies on big data analysis, deep learning predictions, and simulations have been conducted on container terminals to improve port productivity. These high-dimensional data analysis techniques generally require a large number of data. However, the global port environment has changed due to the COVID-19 pandemic in 2020. It is not appropriate to apply data before the COVID-19 outbreak to the current port environment, and the data after the outbreak was not sufficiently collected to apply it to data analysis such as deep learning. Therefore, this study intends to present a port data augmentation method for data analysis as one of these problem-solving methods. To this end, we generate the container stowage situation of the yard through a generative adversarial neural network model in terms of container terminal operation, and verify similarity through statistical distribution verification between real and augmented data.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.91-98
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• 2023

The amount of power generation of a solar plant has a high correlation with weather conditions, geographical conditions, and the installation conditions of solar panels. Previous studies have found the elements which impacts the amount of power generation. Some of them found the optimal conditions for solar panels to generate the maximum amount of power. Considering the realistic constraints when installing a solar power plant, it is very difficult to satisfy the conditions for the maximum power generation. Therefore, it is necessary to know how sensitive the solar power generation amount is to factors affecting the power generation amount, so that plant owners can predict the amount of solar power generation when examining the installation of a solar power plant. In this study, we propose a polynomial regression analysis method to analyze the relationship between solar power plant's power generation and related factors such as weather, location, and installation conditions. Analysis data were collected from 10 solar power plants installed and operated in Daegu and Gyeongbuk. As a result of the analysis, it was found that the amount of power generation was affected by panel type, amount of insolation and shade. In addition, the power generation was affected by interaction of the installation angle and direction of the panel.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.6
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• pp.193-204
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• 2022

In a smart factory environment in a small and medium-sized enterprise (SME) environment, sensors and actuators operating on actual manufacturing lines, programmable logic controllers (PLCs) to manage them, human-machine interface (HMI) to control and manage such PLCs, and consists of operational technology server to manage PLCs and HMI again. PLC and HMI, which are in charge of control automation, perform direct connection with OT servers, application systems for factory operation, robots for on-site automation, and production facilities, so the development of security technology in a smart factory environment is demanded. However, smart factories in the SME environment are often composed of systems that used to operate in closed environments in the past, so there exist a vulnerable part to security in the current environment where they operate in conjunction with the outside through the Internet. In order to achieve the internalization of smart factory security in this SME environment, it is necessary to establish a process according to the IEC 62443-4-1 Secure Product Development Life cycle at the stage of smart factory SW and HW development. In addition, it is necessary to introduce a suitable development methodology that considers IEC 62443-4-2 Component security requirements and IEC 62443-3 System security requirements. Therefore, this paper proposes an application plan for the IEC 62443 based development security process to provide security internalization to smart factories in an SME environment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.226-235
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• 2023

This study aimed to supplement the shortcomings of the Multiple-sensor-based Frost Observation System (MFOS). The developed frost observation system is an improvement of the existing system. Based on the leaf wetness sensor (LWS), it not only detects frost but also functions to predict surface temperature, which is a major factor in frost occurrence. With the existing observation system, 1) it is difficult to observe ice (frost) formation on the surface when capturing an image of the LWS with an RGB camera because the surface of the sensor reflects most visible light, 2) images captured using the RGB camera before and after sunrise are dark, and 3) the thermal infrared camera only shows the relative high and low temperature. To identify the ice (frost) generated on the surface of the LWS, a LWS that was painted black and three sheets of glass at the same height to be used as an auxiliary tool to check the occurrence of ice (frost) were installed. For RGB camera shooting before and after sunrise, synchronous LED lighting was installed so the power turns on/off according to the camera shooting time. The existing thermal infrared camera, which could only assess the relative temperature (high or low), was improved to extract the temperature value per pixel, and a comparison with the surface temperature sensor installed by the National Institute of Meteorological Sciences (NIMS) was performed to verify its accuracy. As a result of installing and operating the MFOS v2, which reflects these improvements, the accuracy and efficiency of automatic frost observation were demonstrated to be improved, and the usefulness of the data as input data for the frost prediction model was enhanced.