• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Korean Journal of Heritage: History & Science
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• v.52 no.2
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• pp.80-105
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• 2019

With the increasing complexity and irregularity of disaster types, the need for cultural asset preservation and management from a proactive perspective has increased as a number of cultural properties have been destroyed and damaged by various natural and humanistic factors. In consideration of these circumstances, the Cultural Heritage Administration enacted an Act in December 2005 to enforce the regular commission of surveys for the systematic preservation and management of cultural assets, and through a recent revision of this Act, the investigation cycle has been reduced from five to three years, and the object of regular inspections has been expanded to cover registered cultural properties. According to the ordinance, a periodic survey of city- or province-designated heritage is to be carried out mainly by metropolitan and provincial governments. The Seoul Metropolitan Government prepared a legal basis for commissioning regular surveys under the Seoul Special City Cultural Properties Protection Ordinance 2008 and, in recognition of the importance of preventive management due to the large number of cultural assets located in the city center and the high demand for visits, conducted regular surveys of the entire city-designated cultural assets from 2016 to 2018. Upon the first survey being completed, it was considered necessary to review the policy effectiveness of the system and to conduct a comprehensive review of the results of the regular surveys that had been carried out to enhance the management of cultural assets. Therefore, the present study examined the comprehensive management status of the cultural assets designated by the Seoul Metropolitan Government for three years (2016-2018), assessing the performance and identifying limitations. Additionally, ways to improve it were sought, and a DB establishment plan for the establishment of an integrated management system under the auspices of the Seoul Metropolitan Government was proposed. Specifically, survey forms were administered under the Guidelines for the Operation of Periodic Surveys of National Designated Cultural Assets; however, the types of survey forms were reclassified and further subdivided in consideration of the characteristics of the designated cultural assets, and manuals were developed for consistent and specific information technologies in respect of the scope and manner of the survey. Based on this analysis, it was confirmed that 401 cases (77.0%) out of 521 cases were generally well preserved; however, 102 cases (19.6%) were found to require special measures such as attention, precision diagnosis, and repair. Meanwhile, there were 18 cases (3.4%) of unsurveyed cultural assets. These were inaccessible to the investigation at this time due to reasons such as unknown location or closure to the public. Regarding the specific types of cultural assets, among a total of 171 cultural real estate properties, 63 cases (36.8%) of structural damage were caused by the failure and elimination of members, and 73 cases (42.7%) of surface area damage were the result of biological damage. Almost all plants and geological earth and scenic spots were well preserved. In the case of movable cultural assets, 25 cases (7.1%) among 350 cases were found to have changed location, and structural damage and surface area damage was found according to specific material properties, excluding ceramics. In particular, papers, textiles, and leather goods, with material properties that are vulnerable to damage, were found to have greater damage than those of other materials because they were owned and managed by individuals and temples. Thus, it has been confirmed that more proactive management is needed. Accordingly, an action plan for the comprehensive preservation and management status check shall be developed according to management status and urgency, and the project promotion plan and the focus management target should be selected and managed first. In particular, concerning movable cultural assets, there have been some cases in which new locations have gone unreported after changes in ownership (management); therefore, a new system is required to strengthen the obligation to report changes in ownership (management) or location. Based on the current status diagnosis and improvement measures, it is expected that the foundation of a proactive and efficient cultural asset management system can be realized through the establishment of an effective mid- to long-term database of the integrated management system pursued by the Seoul Metropolitan Government.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• The Korean Society of Law and Medicine
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• v.23 no.4
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• pp.29-74
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• 2022

As social disasters occur under the Disaster Management Act, which can damage the people's "life, body, and property" due to the rapid spread and spread of unexpected COVID-19 infectious diseases in 2020, information collected through inspection and reporting of infectious disease pathogens (Article 11), epidemiological investigation (Article 18), epidemiological investigation for vaccination (Article 29), artificial technology, and prevention policy Decision), (3) It was used as an important basis for decision-making in the context of an infectious disease crisis, such as promoting vaccination and understanding the current status of damage. In addition, medical policy decisions using infectious disease data contribute to quarantine policy decisions, information provision, drug development, and research technology development, and interest in the legal scope and limitations of using infectious disease data has increased worldwide. The use of infectious disease data can be classified for the purpose of spreading and blocking infectious diseases, prevention, management, and treatment of infectious diseases, and the use of information will be more widely made in the context of an infectious disease crisis. In particular, as the serious stage of the Disaster Management Act continues, the processing of personal identification information and sensitive information becomes an important issue. Information on "medical records, vaccination drugs, vaccination, underlying diseases, health rankings, long-term care recognition grades, pregnancy, etc." needs to be interpreted. In the case of "prevention, management, and treatment of infectious diseases", it is difficult to clearly define the concept of medical practicesThe types of actions are judged based on "legislative purposes, academic principles, expertise, and social norms," but the balance of legal interests should be based on the need for data use in quarantine policies and urgent judgment in public health crises. Specifically, the speed and degree of transmission of infectious diseases in a crisis, whether the purpose can be achieved without processing sensitive information, whether it unfairly violates the interests of third parties or information subjects, and the effectiveness of introducing quarantine policies through processing sensitive information can be used as major evaluation factors. On the other hand, the collection, provision, and use of infectious disease data for research purposes will be used through pseudonym processing under the Personal Information Protection Act, consent under the Bioethics Act and deliberation by the Institutional Bioethics Committee, and data provision deliberation committee. Therefore, the use of research purposes is recognized as long as procedural validity is secured as it is reviewed by the pseudonym processing and data review committee, the consent of the information subject, and the institutional bioethics review committee. However, the burden on research managers should be reduced by clarifying the pseudonymization or anonymization procedures, the introduction or consent procedures of the comprehensive consent system and the opt-out system should be clearly prepared, and the procedure for re-identifying or securing security that may arise from technological development should be clearly defined.

• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.302-314
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• 2023

This study aims to explore the direction for Korea's effective response to Target 3 (30by30), which can be said to be the core of the Kunming-Montreal Global Biodiversity Framework (K-M GBF) of the Convention on Biological Diversity (CBD), to find the direction of systematic OECM (Other Effective area-based Conservation Measures) discovery at the national level through a survey of global conceptual review and expert perception of OECM. This study examined ① the use of Korean terms related to OECM, ② derivation of determining criteria reflecting global standards, ③ deriving types of potential OECM candidates in Korea, and ④ considerations for OECM identification and reporting to explore the direction for identifying systematic, national-level OECM that complies with global standards and reflects the Korean context. First, there was consensus for using Korean terminology that reflects the concept of OECM rather than simple translations, and it was determined that "nature coexistence area" was the most preferred term (12 people) and had the same context as CBD 2050 Vision of "a world of living in harmony with nature." This study suggests utilizing four criteria (1. No protected areas, 2. Geographic boundaries, 3. Governance/management, and 4. Biodiversity value) that reflect OECM's core characteristics in the first-stage selection process, carrying out the consensus-building process (stage 2) with the relevant agencies, and adding two criteria (3-1 Effectiveness and sustainability of governance and management and 4-1 Long-term conservation) and performing the in-depth diagnosis in stage 3 (full assessment for reporting). The 28 types examined in this study were generally compatible with OECMs (4.45-6.21/7 points, mean 5.24). In particular, the "Conservation Properties (6.21 points)" and "Conservation Agreements (6.07 points)", which are controlled by National Nature Trust, are shown to be the most in line with the OECM concept. They were followed by "Buffer zone of World Natural Heritage (5.77 points)", "Temple Forest (5.73 points)", "Green-belt (Restricted development zones, 5.63 points)", "DMZ (5.60 points)", and "Buffer zone of biosphere reserve (5.50 point)" to have high potential. In the case of "Uninhabited Islands under Absolute Conservation", the response that they conformed to the protected areas (5.83/7 points) was higher than the OECM compatibility (5.52/7 points), it is determined that in the future, it would be preferable to promote the listing of absolute unprotected islands in the Korea Database on Protected Areas (KDPA) along with their surrounding waters (1 km). Based on the results of a global OECM standard review and expert perception survey, 10 items were suggested as considerations when identifying OECM in the Korean context. In the future, continuous research is needed to identify the potential OECMs through site-level assessment regarding these considerations and establish an effective in-situ conservation system at the national level by linking existing protected area systems and identified OECMs.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.21-30
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• 2022

Purpose: The purpose of this study is to evaluate the effectiveness of oral positioning stent, the BinkieRT^TM in radiation treatment for head and neck cancer patients in terms of tongue positions reproducibility, tongue doses and material properties. Materials and Methods: 24 cases using BinkieRT^TM during radiation treatments were enrolled. The tongue was contoured on planning CT and CBCT images taken every 3 days during treatment, and then the DSC and center of tongue shift values were analyzed to evaluate the reproducibility of the tongue. The tongue dose was compared in terms of dose distribution when using BinkieRT^TM and different type of oral stents (mouthpiece, paraffin wax). Randomly selected respective 10 patients were measured tongue doses of initial treatment plan for nasal cavity and unilateral parotid cancer. Finally, In terms of material evaluation, HU and relative electron density were identified in RTPS. Results: As a result of DSC analysis, it was 0.8 ± 0.07, skewness -0.8, kurtosis 0.61, and 95% CI was 0.79~0.82. To analyze the deviation of the central tongue shift during the treatment period, a 95% confidence interval for shift in the LR, SI, and AP directions were indicated, and a one-sample t-test for 0, which is an ideal value in the deviation(n=144). As a result of the t-test, the mean and SD in the LR and SI directions were 0.01 ± 0.14 cm (p→.05), 0.03 ± 0.25 cm (p→.05), and -0.08 ± 0.25 cm (p ←.05) in the AP direction. In the case of unilateral parotid cancer patients, the Dmean to the tongue of patients using BinkieRT^TM was 16.92% ± 3.58% compared to the prescribed dose, and 23.99% ± 10.86% of patients with Paraffin Wax, indicating that the tongue dose was relatively lower when using BinkieRT^TM (p←.05). On the other hand, among nasal cavity cancer patients, the Dmean of tongue dose for patients who used BinkieRT^TM was 4.4% ± 5.6%, and for those who used mouthpiece, 5.9% ± 6.8%, but it was not statistically significant (p→.05). The relative electron density of Paraffin Wax, BinkieRT^TM and Putty is 0.94, 0.99, 1.26 and the mass density is 0.95, 0.99 and 1.32 (g/cc), Transmission Factor is 0.99, 0.98, 0.96 respectively. Conclusion: The result of the tongue DSC analysis over the treatment period was about 0.8 and Deviation of the center of tongue shifts were within 0.2 cm, the reproducibility was more likely excellent. In the case of unilateral head and neck cancer patients, it was found that the use of BinkieRT^TM rather than Paraffin Wax or Putty can reduce the unnecessary dose irradiated to the tongue. This study might be useful to understand of BinkieRT^TM's properties and advantages. And also it could be another considered option as oral stent to keep the reproducibility of tongue and reducing dose during head and neck radiation treatments.

• Journal of Korean Home Economics Education Association
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• v.28 no.3
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• pp.47-63
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• 2016

The purpose of this study is to suggest the basic information for diverse and balanced research and development in this field with understanding research trends related to safety education in home economics. In order to so, this study makes population and sampling by targeting cases which refer to 'safety' on 15 papers of academic journals related to home economics registered in the National Research Foundation from 2001 to 2015, 244 papers related to safety education area and 179 master doctorate thesis by searching keyword as 'safety'. Analysis contents are research trends of papers related to safety education by year and by subject and research trends of safety education by area and by research method. As a result of the study, first, the number of research papers related to safety education by year on home economics curriculum repeated increase and decrease and there have been consistent studies conducted on safety education with 14-52 papers per every year and yearly average 28.2 papers. On the other hand, the most number of studies conducted in 2015 with 52 papers which are twice as much of 26 papers in 2014. This seems to be affected by the announcement of safety comprehensive countermeasures from government and the emphasis of safety subject on 2015 curriculum revision of the Ministry of Education. Second, with regards to research trends by topic, 137 papers are related to safety education (29%), 336 papers are related to safety actual condition (71%). Accidents and recognition had a greater percentage in a paper before 2009 (74.4%) and studies are increased after 2009 (from 21 papers to 53 papers) in terms of development or evaluation of safety education program, development of education materials, development of education method etc. Subject area dealt with the most on the research of safety actual condition is regarding safety accidents or effective variables (23.2%). Subject regarding the variables are researches related to factors influencing family violence, internet addiction, spouse violence, willingness to purchase unsafe food, age harassment, or suicidal attempt etc. Next, researches related to safety recognition (13.9%), safety knowledge and attitude (7.4%), safety behaviors (6.3%), safety consciousness (2.3%) show in sequence. Subject area dealt with the most on the researches regarding safety education is development and evaluation of safety education program (11%) and this appears the most in 2015 by year (21.5%). Third, with regards to eight areas of safety education, there are 143 papers regarding public safety (33.8%), 106 papers regarding violence and personal safety (25.1%), 93 papers regarding general subject on safety or whole safety area (22%) and 58 papers regarding drug and internet addiction (13.7%) in sequence. And there is no paper related to first aid and 1 paper is related to occupational safety (0.2%). Occupational safety area is less researched nevertheless its included in home economic curriculum as relative chapter. First aid does not directly correlate with home economics curriculum but should be studied in preparation for accident which could happen in practical class. Forth, with regards to research trends by research method, quantitative research (89.1%) is mostly used and both research study (70.4%) and experimental research (18.7%) are used the most frequently. In particular, researches on the actual condition of safety education and experimental studies for effectiveness verification take most of research method. As qualitative studies, there are phenomenological study (3.1%) and case study (3.1%) related to actual conditions of safety accidents. 10 papers (2.4%) are mixture of quantitative and qualitative research and some research conducted research study and experimental research at the same time (0.9%). With regards to subject of study, human environments (87.5%) are more than physical environments (12.5) and students (48.4%) are more than teachers and school parents (20.6%). As the subject of physical environments, school (6.5%) is the most but home environment is none. As a result of the study, research for the development of evaluation tool for evaluating safety education, occupational safety and lifelong education should be conducted from this time forward. In addition, the object of study shall be expanded to both human environments in terms of entire life and physical environments for home. An in-depth qualitative research should be needed by observing and meeting with each student.

• Journal of Preventive Medicine and Public Health
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• v.23 no.3 s.31
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• pp.324-337
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• 1990

To assess the effectiveness of the interventions in working environment and personal hygiene for the occupational exposure to the lead, 156 workers (116 exposed subjects and 40 controls) of a newly established battery factory were examined for their blood lead concentration (Pb-B) in every 3 months up to 18 months. Air lead concentration (Pb-A) of the workplaces was also checked for 3 times in 6 months interval from August 1987. Environmental intervention included the local exhaust ventilation and vacuum cleaning of the floor. Intervention of the personal hygiene included the daily change of clothes, compulsory shower after work and hand washing before meal, prohibition of cigarette smoking and food consumption at the work site and wearing mask. Mean Pb-B of the controls was $21.97{\pm}3.36{\mu}g/dl$ at the preemployment examination and slightly increased to $22.75{\pm}3.38{\mu}g/dl$ after 6 months. Mean Pb-B of the workers who were employed before the factory was in operation (Group A) was $20.49{\pm}3.84{\mu}g/dl$ on employment and it was increased to $23.90{\pm}5.30{\mu}g/dl$ after 3 months (p<0.01). Pb-B was increased to $28.84{\pm}5.76{\mu}g/dl$ 6 months after the employment which was 1 month after the initiation of intervention program. It did not increase thereafter and ranged between $26.83{\mu}g/dl\;and\;28.28{\mu}g/dl$ in the subsequent 4 tests. Mean Pb-B of the workers who were employed after the factory had been in operation but before the intervention program was initiated (Group B) was $16.58{\pm}4/53{\mu}g/dl$ before the exposure and it was increased to $28.82{\pm}5.66{\mu}g/dl$(P<0.01) in 3 months later (1 month after the intervention). The values of subsequent 4 tests remained between 26.46 and $28.54{\mu}g/dl$. Mean Pb-B of the workers who were employed after intervention program had been started (Group C) was $19.45{\pm}3.44{\mu}g/dl$ at the preemployment examination and gradually increased to $22.70{\pm}4.55{\mu}g/dl$ after 3 months(P<0.01), $23.68{\pm}4.18{\mu}g/dl$ after 6 months, and $24.42{\pm}3.60{\mu}g/dl$ after 9 months. Work stations were classified into 4 parts according to Pb-A. The Pb-A of part I, the highest areas, were $0.365mg/m^3$, and after the intervention the levels were decreased to $0.216mg/m^3\;and\;0.208mg/m^3$ in follow-up tests. The Pb-A of part II was decreased from $0.232mg/m^3\;to\;0.148mg/m^3,\;and\;0.120mg/m^3$ after the intervention. Pb-A of part III and W was tested only after intervention and the Pb-A of part III were $0.124mg/m^3$ in Jannuary 1988 and $0.081mg/m^3$ in August 1988. The Pb-A of part IV not stationed at one place but moving around, was $0.110mg/m^3$ in August 1988. There was no consistent relationship between Pb-B and Pb-A. Pb-B of the group A and B workers in the part of the highest Pb-A were lower than those of the workers in the parts of lower Pb-A. Pb-B of the workers in the part of the lowest Pb-A incerased more rapidly. Pb-B of group C workers was the highest in part I and the lowest in part IV. These findings suggest that Pb-B is more valid method than Pb-A for monitoring the health of lead workers and intervention in personal hygiene is more effective than environmental intervention.