• The Korean Journal of Nuclear Medicine Technology
• /
• v.16 no.2
• /
• pp.3-6
• /
• 2012

Purpose : Samsung medical ceter shall find a cause of the interference factor and suggest a solution for it. Materials and Methods : A sample of $^{18}F$-FDG, radioactive pharmaceuticals produced by TRACERlab MX and FASTlab synthesizer. Gel-clot method uses Positive control tube and single test tube. Kinetic chromogenic method uses ENDOSAFE-PTS produced by Charles River. Results : According to Gel clot method of Endotoxin Tests at FASTlab, both turbidity and viscosity increased at 40-fold dilution and Gel clot was detected. In case of TRACERlab MX, Gel clot was detected in most of samples but intermittently not in a few of them. When using ENDOSAFE-PTS, sample CV (Coefficient of Variation) of FASTlab is 0% at all dilution rates whereas spike CV is 0% at 1-fold dilution, 0~35% at 10-fold, 3.6~12.9% at 20-fold, 5.2~7.1% at 30-fold, 1.1~17.4% at 40-fold, spike recovery; 0% at one-fold, 25 ~ 58% at 10-fold, 50 ~ 86% at 20-fold, 70~92% at 30-fold, and 75~120% at 40-fold. Sample CV of TRACERlab MX, is 0% at all dilution rates whereas spike CV is 1.4~4.8% at one-fold dilution, 0.6~19.9% at 10-fold, spike recovery; 35~72% at one-fold dilution and 77~107% at 10-fold. Conclusion : Gel clot does not seem to occur probably to H3PO4 which engages in bonding with Mg2+ion contributing gelation inside PCT. Dilution which is identical to reducing the amount of H3PO4, could remove interfering effects accordingly. Spike recovery was obtained within 70~150% - recommended values of supplier - at 40-fold dilution even in kinetic chromogenic method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.6
• /
• pp.652-657
• /
• 2018

Breeding control in a farm is a very important factor affecting milk productivity. Breeding management is important for the early detection of estrus, and reliable, automatic, more accurate, and faster monitoring of the timing of dairy cows is essential for farmers. This study measured the accuracy of estrus using the estrus indications, changes in activities, rumination activities, ruminal temperature, and pH. The biomedical information device S1 used in this study provided an estrus notice using the rumen temperature, pH, cow activities, and number of drinking estimations, which were inserted in the rumen through the oral route. The S2 device was used in the estrus notice for the rumen activities and cow activities. The data collected on the instrument were collected at intervals of 2 hours per day at the reference days (RD: -7~-3, +7~+ 3) +2), 7 days before insemination, and 7 days after insemination. The activities of the S1 device used in this paper increased with increasing number of insemination days (-1: $12.5{\pm}1.03/day$; 0: $12.9{\pm}1.73/day$) compared to the reference day (RD: $10.2{\pm}1.0/day$). The activities of the S2 device was also found to increase from the reference day to the insemination day (0: $63.0{\pm}3.66$) compared to the reference day (RD: $40.3{\pm}2.68$). The number of daily drinks in S1 decreased from the reference day (RD: $5.9{\pm}0.89/day$) to before the insemination day (-2: $5.6{\pm}0.98$; -1: $5.7{\pm}0.96$); +2: $6.0{\pm}0.73$). The number of daily drinks on the insemination day (0: $6.3{\pm}0.86$; +2: $6.0{\pm}0.73$) was similar to the reference day. The number of daily rumination in S2 decreased from the reference day (RD: $493.8{\pm}10.92$) to the insemination day (-1: $390.2{\pm}13.36$; 0: $354.1{\pm}16.71$).

• Fire Science and Engineering
• /
• v.31 no.5
• /
• pp.53-62
• /
• 2017

Electric power which is the energy source of economy and industries requires long distance transportation due to regional difference between its production and consumption, and it is supplied through the multi-loop transmission and distribution system. Prior to its actual use, electric power flows through several transformations by voltage transformers in substations depending on the characteristics of each usage, and a transformer has the structure consisting of the main body, winding wire, insulating oil and bushings. A transformer fire that breaks out in substations entails the primary damage that interrupts the power supply to houses and commercial facilities and causes various safety accidents as well as the secondary economic losses. It is considered that causes of such fire include the leak of insulating oil resulting from the destruction of bottom part of bushings, and the chain reaction of fire due to insulating oil that reaches its ignition point within 1 second. The smoke detector and automatic fire extinguishing system are established in order to minimize fire damage, but a difficulty in securing golden time for extinguishing fire due to delay in the operation of detector and release of gas from the extinguishing system has become a problem. Accordingly, this study was carried out according to needs of active mechanism to prevent the spread of fire and block the leak of insulating oil, in accordance with the importance of securing golden time in extinguishing a fire in its early stage. A bushings fireproof structure was developed by applying the high temperature shape retention materials, which are expanded by flame, and mechanical flame cutoff devices. The bushings fireproof structure was installed on the transformer model produced by applying the actual standards of bushings and flange, and the full scale fire test was carried out. It was confirmed that the bushings fireproof structure operated at accurate position and height within 3 seconds from the flame initiation. It is considered that it could block the spread of flame effectively in the event of actual transformer fire.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.44 no.6
• /
• pp.904-911
• /
• 2015

The objective of this study was to investigate the quality characteristics of traditional wine using an radio-frequency identification (RFID) system annexed to a fermenter. In this study, we proposed an RFID-based data transmission scheme for monitoring fermentation of traditional alcoholic beverages. The pH, total acidity, total sugar, soluble sugar, free sugar, alcohol content, and organic acids of were investigated and subjected to fermentation of traditional alcoholic beverages three times. The pH ranged from 7.98, 7.95, and 7.68 at day 0, decreased drastically to 3.31~2.96 at day 2, and then slowly increased to the end point, finally reaching 3.34 at day 20. Acidity tended to increase quickly with time, especially for all samples after day 2. The fermentation environment induced a sudden increase acidity in reactants and indicated a low pH. The total sugars during fermentation quickly decreased to the range of 20.3, 22.43, and 19.2% at day 2, and the slope of reduction steadily decreased to 5.1, 6.1, and 4.8% at day 10. On the other hand, the alcohol content showed the reverse trend as total sugars. The alcohol content also showed the same pattern as total acids, showing the highest alcohol content of 17.3% (v/v) on day 20. In this study on traditional wine fermentation using an RFID system, we showed that pH, soluble sugar, and alcohol content can be adopted as key indicators for quality control and standardization of traditional wine manufacturing.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.306-315
• /
• 2021

This study analyzed the effects of light-shielding curtains and halogens on spectrum when acquiring hyperspectral images in a greenhouse. The image data of tarp (1.4*1.4 m, 12%) with 30 degrees of angles was achieved three times with four conditions depending on 14 heights using the automatic image acquisition system installed in the greenhouse at the department of Southern Area of National Institute of Crop Science. When the image was acquired without both a light-shielding curtain and halogen lamp, there was a difference in spectral tendencies between direct light and shadow parts on the base of 550 nm. The average coefficient of variation (CV) for direct light and shadow parts was 1.8% and 4.2%, respective. The average CV value was increased to 12.5% regardless of shadows. When the image was acquired only used a halogen lamp, the average CV of the direct light and shadow parts were 2 .6% and 10.6%, and the width of change on the spectrum was increased because the amount of halogen light was changed depending on the height. In the case of shading curtains only used, the average CV was 1.6%, and the distinction between direct light and shadows disappeared. When the image was acquired using a shading curtain and halogen lamp, the average CV was increased to 10.2% because the amount of halogen light differed depending on the height. When the average CV depending on the height was calculated using halogen and light-shielding curtains, it was 1.4% at 0.1m and 1.9% at 0.2 m, 2 .6% at 0.3m, and 3.3% at 0.4m of height, respectively. When hyperspectral imagery is acquired, it is necessary to use a shading curtain to minimize the effect of shadows. Moreover, in case of supplementary lighting by using a halogen lamp, it is judged to be effective when the size of the object is less than 0.2 m and the distance between the object and the housing is kept constant.

• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.401-408
• /
• 2022

As research on a controlled environment system based on crop growth environment sensing for sustainable production of horticultural crops and its industrial use has been important, research on how to properly utilize soil moisture sensors for outdoor cultivation is being actively conducted. This experiment was conducted to suggest the proper method of utilizing the TEROS 12, an FDR (frequency domain reflectometry) sensor, which is frequently used in industry and research fields, for each orchard soil in three regions in Korea. We collected soils from each orchard where fruit trees were grown, investigated the soil characteristics and soil water retention curve, and compared TEROS 12 sensor calibration equations to correlate the sensor output to the corresponding soil volumetric water content through linear and cubic regressions for each soil sample. The estimated value from the calibration equation provided by the manufacturer was also compared. The soil collected from all three orchards showed different soil characteristics and volumetric water content values by each soil water retention level across the soil samples. In addition, the cubic calibration equation for TEROS 12 sensor showed the highest coefficient of determination higher than 0.95, and the lowest RMSE for all soil samples. When estimating volumetric water contents from TEROS 12 sensor output using the calibration equation provided by the manufacturer, their calculated volumetric water contents were lower than the actual volumetric water contents, with the difference up to 0.09-0.17 m³·m^-3 depending on the soil samples, indicating an appropriate calibration for each soil should be preceded before FDR sensor utilization. Also, there was a difference in the range of soil volumetric water content corresponding to the soil water retention levels across the soil samples, suggesting that the soil water retention information should be required to properly interpret the volumetric water content value of the soil. Moreover, soil with a high content of sand had a relatively narrow range of volumetric water contents for irrigation, thus reducing the accuracy of an FDR sensor measurement. In conclusion, analyzing soil water retention characteristics of the target soil and the soil-specific calibration would be necessary to properly quantify the soil water status and determine their adequate irrigation point using an FDR sensor.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1095-1105
• /
• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.1-23
• /
• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• The Korean Nurse
• /
• v.20 no.4 s.112
• /
• pp.27-38
• /
• 1981

오늘 저희에게 주어진 주제, 내일에 타당한 간호사업 및 간호교육의 향방을 어떻게 정하여야 하는가의 논의는 오늘날 간호계 주변에 일어나고 있는 변화의 실상을 이해하는 데서 비롯되어져야 한다고 생각하는 입장에서 먼저 세계적으로 건강관리사업이 당면한 딜레마가 어떠한 것이며 이러한 문제해결을 위해 어떠한 새로운 제안들이 나오고 있는가를 개관 하므로서 그 교육적 의미를 정의해 보고 장래 간호교육이 지향해야할 바를 생각해 보려 합니다. 오늘의 사회의 하나의 특징은 세계 모든 나라들이 각기 어떻게 전체 국민에게 고루 미칠 수 있는 건강관리체계를 이룩할 수 있느냐에 관심을 모으고 있는 사실이라고 봅니다. 부강한 나라에 있어서나 가장 빈궁한 나라에 있어서나 그 관심은 마찬가지로 나타나고 있읍니다. 보건진료 문제의 제기는 발달된 현대의학의 지식과 기술이 지닌 건강관리의 방대한 가능성과 건강 관리의 요구를 지닌 사람들에게 미치는 실질적인 혜택간에 점점 더 크게 벌어지는 격차에서 발생한다고 봅니다. David Rogers는 1960년대 초반까지 갖고 있던 의료지식의 축적과 민간인의 구매력 향상이 자동적으로 국민 건강의 향상을 초래할 것이라고 믿었던 순진한 꿈은 이루어지지 않았고 오히려 의료사업의 위기는 의료지식과 의료봉사간에 벌어지는 격차와 의료에 대한 막대한 투자와 그에서 얻는 건강의 혜택간의 격차에서 온다고 말하고 있읍니다. 균등 분배의 견지에서 보면 의료지식과 기술의 향상은 그 단위 투자에 대한 생산성을 낮춤으로서 오히려 장애적 요인으로 작용해온 것도 사실이고 의료의 발달에 따른 일반인의 기대 상승과 더불어 의료를 태성의 권리로 규명하는 의료보호사업의 확대로 야기되는 의료수요의 급증은 모두 기존 시설 자원에 압박을 초래하여 전래적 의료공급체제에 도전을 가해 왔으며 의료의 발달에 건 기대와는 달리 인류의 건강 문제 해결은 더욱 요원한 과제로 남게 되었읍니다. 현시점에서 세계인구의 건강문제는 기아, 영양실조, 안전한 식수 공급 및 위생적 생활환경조성의 문제에서부터 가장 정밀한 의료기술발달에 수반되는 의료사회문제에 이르는 다양한 문제를 지니고 있으며 주로 각개 국가의 경제 사회적 여건이 이 문제의 성격을 결정짓고 있다고 볼수 있읍니다. 그러나 건강 관리에 대한 요구는 영구히, 완전히 충족될 수 없는 요구에 속한다는 의미에서 경제 사회적 발달 수준에 상관없이 모든 국가가 공히 요구에 미치지 못하는 제한된 자원문제로 고심하고 있는 실정입니다. 또 하나의 공통된 관점은 각기 문제의 상황은 달라도 오늘날의 건강 문제는 주로 의료권 밖의 유전적 소인, 사회경제적, 정치문화적인 환경여건과 각기 선택하는 삶의 스타일에 깊이 관련되어 있다는 사실입니다. 따라서 오늘과 내일의 건강관리 문제는 의학적 견지에서 뿐 아니라 널리 경제, 사회, 정치, 문화적 관점에서 포괄적인 접근이 시도되어야 한다는 점과 의료의 고급화, 전문화, 일변도의 과정에서 소외되었던 기본건강관리체계 강화에 역점을 둔 다양하고 탄력성 있는 사업전개가 요구되고 있다는 점입니다. 다양한 건강관리요구에 적절히 대처할 수 있기 위한 그간 세계 각처에서 시도된 새로운 건강관리 접근과 그 제안을 살펴보면 대체로 4가지의 뚜렷한 성격들로 집약할 수 있을 것 같습니다. 그 첫째는 건강관리사업계획 및 그 수행에 있어 지역 사회의 적극적 참여를 유도하는 일, 둘째는 지역단위의 일차보건의료에서 부터 도심지 신예 종합병원, 시설 의료에 이르기까지 건강관리사업을 합리적으로 체계화하는 일. 셋째로 의료인력이용의 효율화 및 비의료인의 훈련과 협조 유발을 포함하는 효과적인 인력관리에 대한 제안과 넷째로 의료보험 및 각양 집단 의료유형을 포함하는 대체 의료재정 운영관리에 관련된 제안들을 들 수 있읍니다. 건강관리사업에 있어 지역사회 참여의 의의는 첫째로 사회 경제적인 제약이 모든 사람에게 가능한 최대한의 의료를 모두 고루 공급하기 어렵게 하고 있다는 점에서 제한된 정부재정과 지역사회가용자원을 보다 효율적으로 이용할 수 있게 하는 자조적이고 자율적인 지역사회건강관리체제의 구현에 있다고 볼 수 있으며 둘때로는 개인과 가족 및 지역민의 건강에 영향하는 많은 요인들은 실질적으로 의료권 외적 요인들로서 위생적인 생활양식, 식사습관, 의료시설이용 등 깊이 지역사회특성과 관련되어 국민보건의 실질적 향상을 위하여는 지역 주민의 자발적인 참여가 필수여건이 된다는 점 입니다. 지역 단위별 체계적인 의료사업의 전개는 제한된 의료자원의 보다 합리적이고 효율적인 이용을 가능하게 하며 요구가 있을때 언제나 가까운 거리에서 경제 사회적 제약을 받지 않고 이용할 수 있는 일차건강관리망을 통하여 건강에 관련된 정보를 얻으며 질병예방, 건강증진 및 기초적인 진료의 도움을 얻을 수 있고 의뢰에 대한 제2차, 제3차 진료에의 길은 건강관리사업의 질과 폭을 동시에 높고 넓게 해 줄 수 있는 길이 된다는 것입니다. 인력 관리에 관련된 두가지 기본 방향으로서는 첫째로 기존보건의료인력의 적정배치 유도이고 둘째는 기존인력의 역할확대, 조정 및 비의료인의 교육훈련과 부분적 업무대체를 들수 있으며 이러한 인력관리의 기본 방향은 부족되는 의료인력의 생산성을 높이고 주민들의 자조적 능력을 강화시킨다는 데에 두고 있음니다. 대체적 의료재정운영안은 대체로 의료공급과 재정관리를 이원화하여 주민의 경제능력이 의료수혜의 장애요소로 작용함을 막고 의료인의 경제적 동기에 의한 과잉치료처치에 의한 낭비를 줄임으로써 의료재정의 투자의 효과를 증대하는 데(cost-effectiveness) 그 기본방향을 두고 있다고 봅니다. 이러한 주변의료 사회적인 동향이 간호교육의 미래상에 끼치는 영향은 지대한 것이라 봅니다. 첫째로 장래 세계인구의 건강문제는 정치, 사회, 경제, 환경적인 의료권 밖의 요인들에 의해 더욱 크게 영향 받는다고 전제한다면 건강문제해결에 있어서도 전통적인 의료사업의 접근에서 더나아가 문제발생의 근원이 되는 생활개선이라는 차원에서 포괄적 접근을 생각하여야 하고 이를 위해선 정치, 경제, 사회전반에 걸친 깊이있는 이해과 주민의 생활환경에 직접 영향하는 교통수단, 통신망 mass media, 전력문제, 농업경영방법 및 조직적 사회활동 등 폭넓은 이해가 요구된다고 봅니다. 둘째로, 지역사회참여의 의의를 인정한다면 지역민의 자발적 참여를 효과적으로 유발시킬수 있고 의료집단과 각종 주민조직과 일반주민들 사이에서 협조적으로 일할수 있는 역량을 기르기위한 교육적 준비가 요구된다고 봅니다. 셋째로, 지역주민의 건강관리 자조능력 강화를 하나의 목표로 삼는다면 치료자에서 교육자로, 지도자에서 촉진자로, 제공자에서 지원자료의 역할의 변화 내지 다양화를 요구하게 될 것이므로 그에 대처할 수 있는 준비가 필요하다고 봅니다. 넷째로, 생각되어야 할 점은 지역중심건강관리사업을 지향하는 보건의료의 이념적 방향과 그에 상응하는 구체적 접근방법을 효율적으로 적용하기 위해서는 종횡으로 연결되는 의사소통체계의 정립과 민활한 정보교환이 이루어질 수 있어야 한다는 점에서 의사소통의 구심체로서 역할할 수 있는 역량을 함양해야 할 교육적 과제가 있다고 봅니다. 마지막으로 생각되어야 할 점은 지역중심으로 전개될 건강관리사업은 건강증진 및 질병예방적 측면과 질병진료 및 회복과 재활에 이르는 종합적이고 포괄적인 사업이어야 한다는 점에서 종래 공공 의료부문과 사설의료기관 사이에 나누어져 있던 예방의학과 치료의학의 통합 뿐 아니라 정부주축으로 이루어 지고 있는 지역사회개발사업 및 농촌지도사업과 종교 및 각종 민간인 집단이 벌이고있는 사업들과의 전체적인 통합적 접근이 이루어져야 한다고 생각하는 입장에서 종래 간호교육이 강조하지 않던 진료의 의무와 대외적 조직활동에 대한 보완적인 교육조치가 요구된다고 봅니다. 간호의 학문체계로서의 입장은 오랜 역사를 두고 논의의 대상이 되어왔으나 아직까지 뚜렷이 어떤 것이 간호 특유의 지식체계이며 건강문제에 관련하여 무엇이 간호특유의 결정영역이며 이 결정과 그 결과를 어떠한 방법으로 치료적 행위로 옮길 수 있는가에 대한 확실한 답을 얻지 못하고 있는 실정이라고 봅니다. 다만 근래에 제시된 여러 간호이론들 속에서 공통적으로 이야기되어지고 있는 개념들로선 우선 간호학문을 건강과 질병에 관련된 인간의 전인적이고 전체적인 상황을 다루는 학제적 과학으로서보는 입장이 있고 따라서 생물신체적인 면 외에 정신심리적, 사회경제적, 정치문화적 환경과의 상호작용 속에서 인간의 건강과 질병문제를 생각한다는 지향을 갖고 있다고 말할 수 있겠읍니다. 간호교육은 간호계 내적인 학문적, 이론적 체계화의 요구에 못지않게 대민봉사하는 전문직으로서의 사회적 책임을 감당해야하는 중요과제를 안고있어 변화하는 사회요구에 효과적으로 대처해 나가야 할 당면문제를 안고 있읍니다. 간효역할 확대, 보건진료원훈련 등 이러한 사회적 요구에 대응하려는 조치가 되겠읍니다. 이러한 시점에서 간호계가 분명히 짚고 넘어가야 할 사실은 이러한 움직임들이 종래의 의사들의 외업무공급을 연장 확대하는 입장에 서서 간호의 특수전문직 명목을 흐리게 할수있는 위험을 감수할 것인지 아니면 가능한 대체방안을 갖고 간호전문직의 독자적인 진로를 개척하면서 다각적인 도전을 받아들일 준비를 갖추든지 그 방향을 뚜렷이 해야할 일이라 생각합니다. 저로서는 이미 잘 훈련된 간호원들과 조산원들의 교육적, 경험적 배경을 기반으로 지역사회 최일선 건강관리요원으로 사회적 효능을 다 할수 있는 일차건강관리간호조직의 구현을 대체방안으로 제시하고 싶습니다. 간호원과 조산원들의 훈련된 역량과 건강관리체제의 구조적 변화를 효과적으로 조화시킨다면 대부분의 세계인구의 건강문제는 해결가능하다고 보는 입장입니다. 물론 정책과 의료와 행정적지원이 활성화되어지는 환경속에서만 그 기대하는 결과가 확대되리라는 점 부언하는 바입니다. 마지막으로 언급하고 싶은 점은 바로 오늘의 주제 ''교육의 동역자-선생과 학생''이라는 개념입니다. 특히 상회정의적 입장에서 보는 의료사업전개에 지역민 내지 의료소비자의 참여를 강조하는 현시점에 있어 교육자와 학생이 교육의 현장에서 서로 동역자로서 학습의 책임을 나누는 경험은 아주 시기적으로 적합하여 교육적으로 지대한 의미를 갖는 것이라고 생각합니다. 이에 수반되어져야 할 역할의 변화에 수용적인 자세를 갖고 적극 실제적용하려 노력하는 선생앞에서 자주적 결정을 행사해본 학생이야말로 건강관리대상자로 하여금 같은 결정권을 행사할수 있도록 촉구하여 주민의 자조적 역량을 기르고 의료사업의 민주화, 인간화를 이룩할 수 있는 길잡이가 될 수 있으리라 믿는 바입니다.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.1-21
• /
• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.