• 한국산업보건학회지
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• 제21권4호
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• pp.201-208
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• 2011

Objectives: According to the compliance of the asbestos-related regulation, every building has to be inspected for asbestos presence before its abatement work. This study was performed for identifying the types and contents of asbestos in building bulk samples. Materials and Methods: Bulk samples were collected during the asbestos inspection in 2010. We grouped the bulk samples into the regulated asbestos containing materials(RACM), presumed asbestos containing materials(PACM), and construction products. Additionally, the types of asbestos in all bulk samples were identified by polarization microscopy(PLM). Results: The RACMs were from building, house, pipe and facility. The RACMs were found mainly building (72.1%) and house (93.7%). The contents of chrysotile in building, house and facility were 66.9% (1-90%), 89.7% (2-90%) and 11.0% (2-90%), respectively. PACMs were surfacing material, thermal system insulation (TSI), and miscellaneous material. The miscellaneous materials that showed a high detection rate (79.2%) were ceiling, roofing and wall materials. Among them, the roofing materials had high chrysotile content(9.7%, 2-21%), followed by wall (8.7%, 2-21%) and ceiling (3.4%, 1-17%). In the construction products, asbestos was found mainly in slate (92.6%, 2-21%), including chrysotile. The slate had high asbestos content (9.7%, 2-21%), followed by cement flat board (8.7%, 2-19%) and textile (3.4%, 1-17%) Conclusions: Utilizing these results, it would be contributed to construct a useful ACM database and prevent from asbestos exposure to workers in the asbestos abatement and maintenance works.

• 한국대기환경학회지
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• 제23권6호
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• pp.718-726
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• 2007

There are many varieties of asbestos: chrysotile, crocidolite, amosite, tremolite, actinolite, and anthophylite. These are widely used in construction materials, brake lining, textile, and so on. Even though non-asbestos fibers such as glassfiber and rockwool have manufactured because asbestos causes asbestosis, lung cancer, mesothelioma, etc., some bad effects of non-asbestos have been also reported. PCM (phase contrast microscopy) and PLM (polarized light microscopy) have been used to qualitatively analyze asbestoses. These techniques have serious drawbacks when identifying and separating various asbestoses. Recently scanning electron microscopy (SEM) equipped with energy dispersive X-ray analysis (EDX) has been known as an useful tool to analyze airborne particle since it provides physical and chemical information simultaneously. The purpose of the study was to classify both asbestos and non-asbestos fibers and finally to develop their source profiles by using the SEM/EDX. The source profiles characterized by 6 different types of asbestos fibers and 2 types of non-asbestos fibers had been developed by analyzing a total of 380 fibers. Analytical parameters used in this study were length, width, aspect ratio, and shape as physical information, and Na, Mg, Al, Si, K, Ca, Cr, Mn, Fe, and Cu as chemical information. All the parameters were intensively reviewed.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.33-40
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• 2013

Purpose: The goal of this study was to build an accurate digital factory that evaluates the performance of a factory using computer simulation. To achieve this goal, we evaluated the effect of worker-related variables on production in a simulation model using comparative analysis of two cases. Methods: The overall work process and worker-related variables were determined and used to build a simulation model. Siemens PLM Software's Plant Simulation was used to build a simulation model. Also, two simulation models were built, where the only difference was the use of the worker-related variable, and the total daily production analyzed and compared in terms of the individual process. Additionally, worker efficiency was evaluated based on worker analysis. Results: When the daily production of the two models were compared, a 0.16% error rate was observed for the model where the worker-related variables were applied and error rate was approximately 5.35% for the model where the worker-related variables were not applied. In addition, the production in the individual processes showed lower error rate in the model that included the worker-related variables than the model where the worker-related variables were not used. Also, among the total of 22 workers, only three workers satisfied the IFRS (International Financial Reporting Standards) suggested worker capacity rate (90%). Conclusions: In the daily total production and individual process production, the model that included the worker-related variables produced results that were closer to the real production values. This result indicates the importance of worker elements as input variables, in regards to building accurate simulation models. Also, as suggested in this study, the model that included the worker-related variables can be utilized to analyze in more detail actual production. The results from this study are expected to be utilized to improve the work process and worker efficiency.

• 한국대기환경학회지
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• 제24권3호
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• pp.346-356
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• 2008

Asbestos is the name of a group of minerals with long and thin fibers that originate naturally in the environment. Asbestos mainly affects lungs and the membrane that surrounds the lungs. In general, PCM (phase contrast microscopy) and PLM (polarized light microscopy) have been used to analyze asbestos fibers. However, these methods have often problems to over-estimate number concentration when counting real asbestos fibers. Moreover, there are many difficulties when separating and identifying various asbestos and non-asbestos fibers. In order to determine quantitative information on fibrous particles, source profiles for asbestos and non-asbestos fibers must be initially developed on the basis of their chemical compositions and physical parameters. In our study, a SEM/EDX was used to develop source profiles from known asbestos samples as reference samples. We could make the source profile matrix consisting of 6 types of asbestos fibers and 2 types of non-asbestos fibers by analyzing 380 fibers. Based on these profiles, a rule building expert system was developed by using the visual basic application (VBA). Various fibers were successfully classified by 2 simple rules in the EXCEL environment based on several visual steps such as inserting data, viewing results, and saving results. For a case study to test the expert system, samples from a construction materials and from various indoor environments such as a residental area, a preschool classroom, and an underground store were collected and analyzed. As a result of the survey, a total of 76 individual test fiber particles was well classified into 5 different types of particle classes; 9.3% of chrysotile, 15.4% of amosite, 0.8 of crocidolite, 4.2% of tremolite, 5.8% glass fiber, 21.1% of other fibers, and 43.5% of unknown fibers in terms of number concentration. Even though unknown portion was high, it will be decreased markedly when expanding fiber source profiles.

• Journal of Advanced Marine Engineering and Technology
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• 제40권1호
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• pp.28-33
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• 2016

배터리는 휴대폰, 전기자동차, 무인잠수정 등과 같은 분야에서는 주 동력원으로 사용되고, 일반 자동차에서는 시동기 또는 램프구동용으로 사용되며, 일반 선박에서는 비상전원으로 사용되고 있다. 2차 전지로는 납축전지와 리튬이온 배터리를 많이 사용하고 있으며, 납축전지는 가격이 비교적 저렴하고 안전하다. 리튬이온전지는 에너지 밀도가 높고 출력이 우수하며 수명이 긴 장점이 있으나 공기 중의 수분과 반응하여 폭발의 위험성을 가지고 있다. 그러나 최근에는 방수, 방염, 방진 기술의 발달에 힘입어 리튬배터리의 사용이 증가하고 있고, 특히 하이브리드 선박 및 전기추진 선박 등의 주동력원으로 사용될 만큼 그 사용범위가 점점 넓어지고 있으므로 좀 더 엄격한 배터리의 관리가 필요하다. 하이브리드 선박에서는 500kWh 이상의 대용량 동력원을 만들기 위하여 셀(Cell) 단위로 이루어진 수십 개의 리튬배터리가 들어 있는 팩들로 접속이 된 전원을 사용한다. 따라서 배터리 점검에 필요한 검출 전압, 전류 및 온도 데이터들을 관리용 서버로 보내 주는 유선 점검 및 감시시스템을 구현하는 데에는 많은 전선과 통신 모듈이 필요하다. 본 논문에서는 직렬통신 모듈보다 가격이 저렴하고 전선을 사용하지 않는 저 전력 블루투스(Bluetooth low energy, BLE) 무선통신 모듈과 전력선 모뎀을 사용하여 하이브리드 선박용 리튬배터리 저가형 점검 및 감시시스템을 구현하고자 한다. 배터리의 점검요소에는 잔존용량(State of charge, SOC)과 잔존수명(State of health, SOH)이 있으며, 제안한 시스템은 이들을 규칙적으로 점검하여 배터리의 수명 예측과 예방 정비를 할 수 있기 때문에 안전사고를 방지할 수 있을 것으로 전망된다.

• Asia pacific journal of information systems
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• 제23권3호
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.