• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.27 no.3
• /
• pp.187-192
• /
• 2017

Objectives: It is difficult to identify exposure factors in the semiconductor industry due to low exposure levels to hazardous substances and because various processes take place in fabrication (FAB). Furthermore, a single worker often experiences a variety of job histories, so it is difficult to classify similar exposure groups (SEG) in the semiconductor industry. Therefore, we intend to develop a new exposure index, the period of working in FAB, that is applicable to the semiconductor industry. Methods: First, in specifying the classification of jobs, we clearly distinguished whether they were FAB workers or non-FAB workers. We checked FAB working hours per week through questionnaires administered to FAB workers. We derived an exposure index called FAB-Year that can represent the period of working in FAB. FAB-Year is an index that can quantitatively indicate the period of working in FAB, and one FAB-Year is defined as working in FAB for 40 hours per week for one year. Results: A total of 8,453 persons were surveyed, and male engineers and female operators occupied 90% of the total. The average total years of service of the subjects was 9.7 years, and the average FAB-Year value was 6.8. This means that the FAB-working ratio occupies 70% of total years of service. The average FAB-Year value for female operators was 8.4, for male facility engineers it was 7.7, and for male process engineers it was 3.5. A FAB-Year standardization value according to personal information (gender, job group, entry year, retirement year) for the survey subjects can be calculated, and standardized estimation values can be applied to workers who are not participating in the survey, such as retirees and workers on a leave of absence (LOA). Conclusions: This study suggests an alternative method for overcoming the limitations on epidemiological study of the semiconductor industry where it is difficult to classify exposure groups by developing a new exposure index called FAB-Year. Since FAB-Year is a quantitative index, we expect that various approaches will be possible in future epidemiological studies.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.16 no.1
• /
• pp.50-56
• /
• 2012

Purpose: Those who access to the nuclear medicine department are classified as radiation workers, temporarily access group, and occasional access group as defined by the atomic energy law. The radiation workers and temporarily access people wear a personal radiation dosimeter for checking their own radiation absorbed dose periodically. However, because of the sanitation workers, classified as temporarily access group, who are working in the nuclear medicine department are moved in a cycle with other departments and their works are changeful, it is hard to control their radiation absorbed dose. Thus, this study is going to examine the state of the sanitation worker's radiation absorbed dose, and then make sure whether they are classified as temporarily access group or not. Materials and methods: In the first instance, the first sanitation worker who works in vitro laboratory and PET room and the second sanitation worker who works in gamma camera rooms (invivo room) wore radiation dosimeter-OSL(Optically Stimulated Luminescence)- to measure their own radiation absorbed dose during work time from May to June 2011. Secondly, this study was taken place 5 places in gamma camera rooms, 2 places in PET bed room, operating room, waiting room and cyclotron room in PET and 4 places in vitro laboratory. And then to measure the radiation space dose rate, it is measured 10 times each of places as sanitation worker's work flow by using radiation survey meter. Results: The radiation absorbed dose on OSL of the first c who works in vitro laboratory and PET room and the second one who works in gamma camera rooms are 0.04, 0.02 mSv per month respectively. That means the estimated annual radiation absorbed doses are less than 1mSv as 0.48, 0.24 mSv/yr respectively. The radiation space dose rates as sanitation worker's work flow using survey meter are 0.0037, 0.0019 mSv/day, so the estimated annual radiation absorbed dose are 0.93, 0.47 mSv/yr respectively. The weighted exposure dose of first sanitation worker of each places are 1.62% in cyclotron room, 3.88% in waiting room, 2.39% in operating room, 81.01% in bed room of PET and 11.01% in vitro laboratory. The weighted exposure dose of second sanitation worker of each places are 45.22% in radiopharmaceutical laboratory, gamma 30.64% in camera rooms, 15.65% in waiting room, 8.49% in reading room. Conclusion: The annual radiation absorbed doses on OSL of both sanitation workers are less than 1 mSv per year and the annual radiation absorbed doses by using survey meter are less than 1mSv either, but close up to 1 mSv. Thus, to clarify whether the sanitation workers are temporarily access group or not, and to be lessen their s radiation absorbed dose, they should be educated about management of radiation and modified their work flow or work time appropriately, their radiation absorbed dose would be lessen certainly.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.27 no.3
• /
• pp.129-149
• /
• 2016

The purpose of this study is to examine in depth the personal experience of Instructor during National Competence Standards (NCS) Curriculum at Department of Library and Information Science. We conducted in depth interview (FGI) with participants who had recently experienced and data analysis was undertaken. We hope this study that an application of NCS would be activated fully in Library, educational institutes and qualifying examination institutes and that diverse feedbacks from related parties would makes leading to a better updated version of NCS development. As a result, First, The instructor had generally familiar with the background and purpose of the NCS. and they issued the inadequacies of job elements, non-reflection of the opinion on field education, the problems of classification of NCS. second, In the experience of NCS curriculum operating, There were the paperwork burden, Problems of methods of evaluation, evaluation period discrimination, the need to well communication with students. Third, In the problems on the NCS Curriculum operating, we found that there were The need on the proper Class size/hour, additional education, re-evaluation, Support system for NCS Curriculum operation, tools for practice, discrimination between relative evaluation and NCS evaluation, Enhancement for Competence/Standards. Fourth, On The ways of improving for NCS curriculum, We found that There were Class size, Support tools for practice, The improvement Competence/elements/standards based on LIS characteristic. The result of this study may contribute for improving the overall environment Based upon FGI analysis, several new directions for NCS education in the filed LIS curriculum are suggested.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.17-35
• /
• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.

• Korea Journal of Hospital Management
• /
• v.7 no.3
• /
• pp.63-83
• /
• 2002

This study is to survey the materials purchasing and inventory management status and the characteristics and opinions of the staff in charge of purchasing and inventory of the general hospitals in Busan area in order to contribute to the rationalization of its management through the grasp of actual situation and the presentation of desirable improvement plan for the materials purchasing and inventory management. The status of medical institute had been surveyed by the purchasing/ administration managers of total 26 general hospitals, and the purchasing/ management questionnaires had been commenced with 86 staff of the 26 hospitals. Its major survey results, after the analysis of 24 medical institute statuses (return rate of 92.30%) and 60 staff questionnaires (return rate of 69.76%), are as follows. First, post-purchasing evaluation system is not used actively, orders are being placed by phone or fax, and general merchandise is being purchased through free contracts in most of the hospitals participated in the survey. Second, as per the materials supplying methods, the requisition and delivery system is currently the most popular in the hospitals surveyed, however, both the requisition and delivery system and the par level transfer system are the most desired in the hospitals of more than 500 beds, and the par level transfer system is the most desired in the hospitals under 500 beds for the materials supplying system in the future. Third, as per the inventory management system that is desired the most in the future, the SPD and JIT types are preferred in the hospitals of more than 500 beds, the stockless strategy is preferred in the hospitals under 500 beds, the senior staff above section chief grade prefer the stockless strategy, and the junior staff prefer the ABC classification and SPD types. Fourth, The necessity of purchasing staff's training for the materials management is highly recognized but the effectiveness is not so much acknowledged, which is because such a training is thought to be so superficial and formal that it is not helpful much in the actual field. When summarizing the survey results as above, the materials purchasing and management system is differed for each group of hospitals according to the size of beds, and the more scientific management system is largely required by the general hospitals in Busan city. They also hope the introduction of joint purchasing system, materials management by the bar-code system, and positive execution of the market survey and training of the relevant staff for the management of purchasing affairs. So the more systematic purchasing and inventory management is regarded to be necessary through the introduction of scientific and specialized education of materials management, market survey, and post-purchasing evaluation system also through the computerization of materials purchasing and inventory management as soon as possible.

• International Commerce and Information Review
• /
• v.15 no.4
• /
• pp.97-122
• /
• 2013

Korea's trade balance in service showed surplus in 2012 on the basis of BPM5. This is recorded by 14 years since 1999. This owes to decrease of deficit in tourism balance, increase of surplus in construction and transportation, and shift from deficit to surplus, even in small portion, in personal cultural recreational services balance. While externally the global economic growth becomes inactive and the Korean Won has appreciated, internally Korean service industry is very weak and is not equipped with international competitiveness. This study intends to look into service surplus items and services deficit items and to present measures that will be able to strengthen competitiveness in service industry. As a short case study, German and Japan was benchmarked, as they are the countries which are developed on the basis of manufacturing like Korea. And in this study, by analyzing surplus items and deficit items in trade balance sheet, it is attempted to suggest policies which would be available for strengthening service industry. As the service industry is a highly value-added one, it is necessary to designate promising categories and intensively foster as strategic industry. Service industry has their own characteristics distinguished with manufacturing goods. It has very different logistics and payment system with manufacturing industry. It means there must be independent support systems which reflect the nature of industrial classification in service industry. It is necessary to provide export support system, to organize export market development group, to support marketing, to set common logistics center, to support diplomatic means, to provide legal service and so on.

• Journal of the Korean Society of Safety
• /
• v.33 no.6
• /
• pp.28-36
• /
• 2018

There was a difference in recognition of respirators according to the educational performance environment. they were showed higher recognition of respirators of group by internal and external mix trainer, less than 6 months, over 1hour, more than 5 times, variety of education. To identify the relationship between types of job classification(typical and atypical)and the levels of recognition of respirators, a total of 153 workers in a business workplace. mainly, typical workers showed higher recognition of respirators than atypical workers. Training of correct wearing showed high demands both typical and atypical workers. Descriptive statistics(SAS ver 9.2)was performed. the results of recognition of respirators were analyzed the mean and standard deviation by t-test, and anova, fit factor is used geometric means(geometric standard deviation), paired t-test, Wilcoxon analysis(P=0.05). Particulate filtering facepiece respirators (PFFR) is one of the most widely used items of personal protective equipments, and a tight fit of the respirators on the wearers is critical for the protection effectiveness. In order to effectively protect the workers through the respirators, it is important to find and evaluate the ways that can be readily applicable at the workplace to improve the fit of the respirators. This study was designed to evaluate effects of mask style (cup or foldable type) and donning training on fit factors (FF) of the respirators, since these are available at various workplace, especially at small business workplace. A total of 40 study subjects, comprised of employment type workers in metalworking industries, were enrolled in this study. The FF were quantitatively measured before and after training related to the proper donning and use of cup or foldable-type respirators. The pass/fail criterion of FF was set at 100. After the donning training for the cup-type mask, fit test were increased by 769%. but foldable-type mask was also increased after the donning training, the GM of FF for the foldable-type mask and it's increase rate were smaller as compared to the cup-type mask. Furthermore, the differences of the increase rates of the GM of FF in employment type of the subjects were not significantly for the foldable-type mask. These results imply that the raining on the donning and use of PFFR can enhance the protection effectiveness of cup or foldable-type mask, and that the training effects for the foldable-type mask is less significant than that for the cup-type mask. Therefore, it is recommended that the donning training and fit tests should be conducted before the use of the PFFR, and listening to workers opinion regularly.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.177-192
• /
• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.163-177
• /
• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.