• Journal of agricultural medicine and community health
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• v.24 no.1
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• pp.65-77
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• 1999

In order to estimate the utilization pattern of a rural health subcenter, and to identify the recognition for it among the farmhouse members in a suburban area, a questionnaire survey was carried out for objects of 696 population. The results observed were as follows: The annual utilization rate of rural health subcenter for a basic health service unit was 25.0 per 100 persons, and annual mean visiting times was 0.22 times. And the most frequent disease by annual health subcenter utilization illness was musculoskeletal disease(30.6%), and the next was respiratory disease(14.1%), gastrointestinal disease(13.9%) by order. Favorite reason for community health subcenter utilization were near distance from living place(49.6%), lower disease severity(18.9%) and lower medical cost(18.1%) by order. But disfavoring reasons for it were absence of specialist(20.2%), non effective treatment(19.2%) and insufficient equipment(14.7%) by order. And insufficient items about community health subcenter utilization were restriction of treatment limit(40.7%), lower reliance(22.5%) and difficulty in traffic(13.4%) by order. The results of logistic regression analysis suggested that statistically significant factors in health subcenter utilization was educational level. The desirable works for the health subcenter in a suburban area were disease control of elderly and disease preventing service. These results suggested that to increase the utilization of rural health subcenter in a suburban area and to promote the accessibility of rural residents to primary health care, there must be considered public relation about health subcenter, improvement of medical quality and change of priority about health subcenter's works.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.135-142
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• 2001

This study attempts to categorizes the factors of forest fire occurrences based on regional meteorologic data and general forest no characteristics of 18 cities and guns in Kangwon province. lo accomplish this goal, some statistical analyses such as analysis of variance, correspondence analysis and multidimensional scaling were adopted. To reveal the forest fires pattern of study region, a categorization process was conducted by employing the quantification approach which modified and quantified the metric-data of fire occurrence dates. Also, The fire occurrence similarity was compared by using multidimensional scaling for each study region. The major results are summarized as follows: It was found that the meteorological factors emerged as different to each region are average and maximum temperature, minimum dew point temperature and average and maximum wind speed. In the result of correspondence analysis representing relationships between fire causes and study regions, Kangrung is caused by arsonist, Chulwon, Hwachen and Yanggu caused by military factor, Sokcho and Chunchen caused by the debris burning, and Samchuk caused by general man-caused fires, respectively. Finally, the forest fire occurrence pattern of this study regions were divided into five areas such as, group I including Samchuk, Kangryung, Chunchen, Wonju, Hongchen and Hhoingsung, group II including Donghae, Taebaek, Yangyang and Pyongchang, group III including Jungsun, Chulwon and Whachen, group Ⅵ including Gosung, Injae and Yanggu, and group V including Shokcho and Youngwol.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.595-612
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• 2002

One of the most important issues in guaranteeing the high degree of QoS on mobile computing is how to reduce hand-off drops caused by lack of available bandwidth in a new cell. Each cell can request bandwidth reservation to its adjacent cells for hand-off calls. This reserved bandwidth can be used only for hand-offs, not for new calls. It is also important to determine how much of bandwidth should be reserved for hand-off calls because reserving too much would increase the probability of a new call being blocked. Therefore, it is essential to develop a new mechanism to provide QoS guarantee on a mobile computing environment by reserving an appropriate amount of bandwidth and call admission control. In this paper. bandwidth reservation and call admission control mechanisms are proposed to guarantee a consistent QoS for multimedia traffics on a mobile computing environment. For an appropriate bandwidth reservation, we propose an adaptive bandwidth reservation mechanism based on an MPP and a 2-tier cell structure. The former is used to predict a next move of the client while the latter to apply our mechanism only to the client with a high hand-off probability. We also propose a call admission control that performs call admission test only on PNC(Predicted Next Cell) of a client and its current cell. In order to minimize a waste of bandwidth caused by an erroneous prediction of client's location, we utilize a common pool and QoS adaptation scheme. In order evaluate the performance of our call admission control mechanism, we measure the metrics such as the blocking probability of new calls, dropping probability of hand-off calls, and bandwidth utilization. The simulation results show that the performance of our mechanism is superior to that of the existing mechanisms such as NR-CAT2, FR-CAT2, and AR-CAT2.

• Korean Journal of Occupational Health Nursing
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• v.8 no.2
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• pp.193-211
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• 1999

This study was carried out to investigate the management and support system affecting to the occupational health nursing services(OHNS) provided in group occupational health agencies(GOHA). Questionnaire was developed and distributed to 82 nurses who were working in GOHA and who agreed to participate in the survey. The results were as follow: 1. OH nurses responded were mostly in the age of twenty to thirties(89%), married(73.7%), technical college graduates(88.9%), worked in hospital(85.4%) and participated more than 1 year in group occupational health services (96.3%). 2. Fifty eight point four percent of the OH nurses worked in number of workplace more than 30 to less than 60 in the OHNS form. The figure of workplaces undertaken by nurses was ranged greatly from 9 to more than 100. Number of employees who cared by nurses were mostly under 5,000 peoples in 93.3%. The types of industry was mostly manufacturing and located in the order of factory complex area, suburban, urban and others. 3. Most OH nurses(87.8%) were fully involved in the OHNS for the SSE. Their working days to visit SSE was 5 days per week(77.8%) and one day in the GOHA at 41.3%. 4. The OH documents using by nurses were found in more than 23 different types. However, they were largely summarized in the types of 'Workplace Health Management Card', 'Personal Health Counselling Card', 'Daily Health Management Report', 'Visiting List of Workplace' and 'Sick Employee List'. 5. The items of laboratory test provided by GOHA were mostly achieved in the purpose of basic health examination. They were used to be the blood pressure check(98.8%), blood sugar test (98.8%), urine sugar and protein(91.4%), SGOT and SGPT(85.3% each), cholesterol (82.9%), hepa vaccine immunization(82.9%), r-GPT(81.7%), hemoglobin(79.3%) and triglyceride(75.5%). 6. The OH nurses(92.7%) followed the work pattern to visit the GOHA before and after small-scale enterprises(SSE) visit by car driven by nurses in 74.3%. They were payed by GOHA for transportation fees in certain amounts. However, nurse is the main person(75.0%) who covers up in case of traffic accident. If the GOHA has no transportation regulation for the formal workplace visit, data showed that nurses had been responsible to take charge(31.7%). 7. The personnel manager who takes in charge for nursing services was 'nurse' in 61.7% and 41.2% worked as the final decision maker related to nursing work. The OH nurses' opinions about factors affecting to the management were classified in the four areas such as 'Nature(Quality) of health professional'. 'Content of OHNS', 'Delivery system of the GOHS', and 'Others'. The factors were indicated highly in 'Authority as health professional', 'Level of perception of director on the OH' and 'Physical work condition for OHNS'. The things that this study suggests in the recommendation would be summarized in such as the management and supporting system working for SSE in the OHNS is necessary to reform thoroughly. The reconsidered aspects might be in the matters of number of workplaces undertaken by nurses, development of effectively practical health documents, preparation for guideline of the laboratory test in the workpleces, establishment of convenient and encouraging support system and cooperation between other health professionals with respect and skill.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.