• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제26권1호
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• pp.73-84
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• 2020

Purpose: Though Korean healthcare services have been upgraded, infection and fire had been broken out in general hospitals. And higher concerns about quality assessment made it to clinical laboratory design guideline studies. So, this study investigates the facilities, equipment and personnel of laboratory medicine focusing on more than five hundred fifty bed hospital, and contributes to make guidelines for safety and efficiency in lab. Methods: Questionnaires to supervisor technologist and field surveys to medical laboratories in korean hospitals have been conducted for the data collection. 16 answers have been analysed statistically by MS Excel program. Results: Most of the sample tests such as hematology, clinical chemistry, immunology, transfusion, urinalysis, microbiology and molecular diagnosis are performed by more than 80% in large sized general hospital laboratory. In the test methods, automatic analyzers are used up to 80%, total laboratory automation up to 43% in clinical chemistry and immunology, and manual tests in all sorts of the test. There are placed in single lab or two and three labs above the ground, which are all in semi-open lab. There is some correlation with the number of specimens and the number of lab people depending on the number of hospital beds. Laboratory environment shows that work distance is good, but evacuation path width, visibility, separation of staff area from automatic analyzer, and equipment installations are needed to have more spaces and gears. Most of the infection controls are equipped with mechanical ventilation, air-conditioning, washbasin and wastewater separation, BSC installation and negative pressure lab room. Implications: Although the laboratory space area is calculated considering the number of hospital beds, type of tests and number of staff, hospital's expertise and the samples numbers per year should be taken into account in the planning of the hospital.

• Safety and Health at Work
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• 제9권1호
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• pp.95-100
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• 2018

Background: Respiratory protection equipment (RPE) is the last resort to control exposure to workplace air pollutants. A comprehensive respiratory protection program (RPP) ensures that RPE is selected, used, and cared properly. Therefore, RPP must be well integrated into the occupational health and safety requirements. In this study, we evaluated the implementation of RPP in Iranian petrochemical industries to identify the required solutions to improve the current status of respiratory protection. Methods: This cross-sectional study was conducted among 24 petrochemical industries in Iran. The survey instrument was a checklist extracted from the Occupational Safety and Health Administration respiratory protection standard. An index, Respiratory Protection Program Index (RPPI), was developed and weighted by analytic hierarchy process to determine the compliance rate (CR) of provided respiratory protection measures with the RPP standard. Data analysis was performed using Excel 2010. Results: The most important element of RPP, according to experts, was respiratory hazard evaluation. The average value of RPPI in the petrochemical plants was $49{\pm}15%$. The highest and lowest of CR among RPP elements were RPE selection and medical evaluation, respectively. Conclusion: None of studied petrochemical industries implemented RPP completely. This can lead to employees' overexposure to hazardous workplace air contaminants. Increasing awareness of employees and employers through training is suggested by this study to improve such conditions.

• 한국정밀공학회지
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• 제28권12호
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• pp.1434-1440
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• 2011

In this paper, we assessed muscular activities of lower limbs and foot pressure for car and bus drivers according to operating three electronic pedals that we developed. To analyze drivers' physical exhaustion, muscular fatigue of lower limbs was evaluated. Eleven car drivers and six urban bus drivers were participated in this experiment. The virtual driving system was used for the real driving environment. The virtual driving system was comprised of a spring seat, a steering wheel, pedals (clutch, excel and brake pedals), a manual transmission and a virtual driving simulation. For the real vibration like situation on the road, six degree of freedom motion base system was used. Measured muscles were rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA) and gastrocnemius (Gn) muscles. For the quantitative muscular activities, integrated electromyography (IEMG) was analyzed. Muscular fatigues also were analyzed through the analysis of the median frequency. In addition, foot pressures were analyzed and compared through the peak and averaged pressure during the operating three developed electronic pedals. The experiments are conducted with total 17 drivers, 11 general public and 6 drivers. As a result of the analysis, electromyogram and fatigue analysis through intermediate frequency reduction for pedal-1 more efficient than other pedals. And foot pressure also was decreased. Consequently, we suggested the most efficient pedal and method to minimize the amount of cumulative fatigue.

• 품질경영학회지
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• 제49권4호
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• 한국산업보건학회지
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• 제30권4호
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• pp.364-375
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• 2020

Objectives: The purpose of this study is to suggest a direction for consumer-oriented curriculum development by analyzing the priorities of subjects and their preference for educational methods. Methods: The participants included 773 management supervisors and education practitioners in the workplace, and a survey was conducted from April 17 to August 30, 2019. Frequency analysis, t-test, Borich's Needs Analysis, and Locus for Focus Model analysis were performed using Microsoft Excel 2019 and IBM SPSS 21.0. Results: The highest perceived priorities for education subjects were as follows: ① 'CPR and First Aid Practice' and 'Occupational Disaster Prevention and First Aid Basics' in the manufacturing industry; and ② 'Emotional Labor and Job Stress Prevention', 'Occupational Disaster Prevention and First Aid basics, and 'Musculoskeletal Disorder Prevention' in the service industry. 'Collective education' was the most preferred method of education. 'School-type' was preferred for the seating arrangement, and the proper number of trainees was considered to be about 30. Respondents said the contents of the education was a top priority when they applied for education, and curriculum and appropriate textbooks should be considered in calculating the cost of education. Conclusions: Practical subjects and subjects related to major hazards by industry were required for management supervisor education. It was proposed in this study that the contents and operating methods of education be changed so that workers can easily comprehend essential subjects such as the Occupational Safety and Health Act. In conclusion, it is necessary to design the curriculum and apply educational methods suitable for each subject's characteristics in consideration of the priorities for subjects reflected in the needs of trainees.

• 품질경영학회지
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• 제50권4호
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• pp.751-776
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• 2022

Purpose: The purpose of this paper is to present implications by analyzing research trends on CSR, CSV and ESG by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on CSR, CSV and ESG. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "CSR", "CSV" and "ESG" as search terms, and the Korean abstracts and keyword were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, CSR 2,847 papers, CSV 395 papers, ESG 555 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; CSR, CSV, and ESG studies showed that research slowed down somewhat before 2010, but research increased rapidly until recently in 2019. Research have been found to be heavily researched in the fields of social science, art and physical education, and engineering. As a result of the study, there were many keyword of 'corporate', 'social', and 'responsibility', which were similar in the word cloud analysis. Looking at the frequent keyword and word cloud analysis by field and year, overall keyword were derived similar to all keyword by year. However, some differences appeared in each field. Conclusion: Government support and expert support for CSR, CSV and ESG should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to them. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• 한국재난정보학회 논문집
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• 제19권1호
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• pp.231-240
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• 2023

Purpose: The purpose of this study is to analyze air quality and carbon emissions in developing countries, particularly Myanmar, and explore the impact of transportation on CO2 emissions during peak hours relative to free-flow conditions. Method: This study conducted a traffic survey in two major cities in Myanmar to quantify carbon dioxide emissions from the transportation sector, using IPCC's tier 1 and tier 2 approaches, with statistical analysis performed using Python 3 and Microsoft Excel for comparative analysis of critical factors in CO2 emissions. Result: The result of this study is an estimate of the vehicle kilometers traveled (VKT) and fuel consumption in Yangon city for the year 2019, based on data from various sources including the Myanmar Statistical data base, YUTRA project survey, and Ministry of Electric and Energy. The study also analyzes the average travel time index (TTI) for the four roads in Yangon, which indicates the impact of congestion on vehicle travel time and CO2 emissions. Overall, the study provides important insights into the transport sector in Yangon city and can be used to inform policies aimed at reducing greenhouse gas emissions and improving traffic conditions. Conclusion: The study concludes that congestion plays a significant role in increasing fuel use and emission levels in the road transport sector in Myanmar. The analysis provides valuable insights into the impact of the sector on the environment and emphasizes the importance of addressing congestion to reduce fuel use and emissions. However, the study's scope is limited to Yangon city and Mandalay city, and some mean values may not accurately represent the entire country and other developing countries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1807-1822
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• 2023

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.

• 해양환경안전학회지
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• 제19권2호
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• pp.171-178
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• 2013

이 연구는 필리핀 연안수역의 해양안전과 선박교통관제서비스에 대한 설문조사 결과의 일부를 나타낸 것이다. 이 연구는 응답자의 경력과 육 해상 경험, 친숙해역, 위험요소별 위험지역별 선박운항자의 주관적 위험인식을 조사하였다. 설문은 202명이 응답해 주었고 설문 데이터는 엑셀 프로그램과 통계 프로그램을 이용하여 분석하였다. 전체 응답자의 97 %가 다양한 종류와 크기의 선박에서 승선한 경험이 있었고 88 %는 선박 항해에 직접적으로 종사한 사람이었으며 마닐라 지역(NCR지역)에서 가장 높은 응답률이 있었다. 위험요소별 위험지역별 위험인식 부분에서 위험수준 3단계 '때때로 위험 증가'와 위험수준 4단계 '자주 위험 증가'라는 높은 위험 지표를 보였다. 이 연구에서 가장 높은 위험 요소는 위험수준 5단계 '매우 자주 위험 증가'에 해당되는 "법과 규정의 위반" 으로 나타났다. 그리고 전체 친숙 해역 중 가장 높은 위험 인식을 보인 마닐라 베이 지역(NCR 지역)에서도 역시 위험수준 5단계로 나타났다. 그러므로 각 지역내에서 실행가능한 법과 규정의 광범위한 검토(해상교통체계와 구조물들의 강화, 이해당사자들의 교육(국가의 혼잡 수역, 특히 마닐라 베이 지역))의 실행이 이 연구에 의해 권고된다. 이 연구의 궁극적인 목적은 안전 관련 정보를 수집 분석하여 국내 해상교통안전의 개선과 향상의 지침으로 활용될 기술과 모델을 개발하는 것이다.

• Journal of Construction Engineering and Project Management
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• 제5권1호
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.