• 대한예방한의학회지
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• 제13권1호
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• pp.1-11
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• 2009

To strengthen the R&D capability and the competitiveness of the Korean oriental medicine industry, an adequate supply of qualified R&D personnel including medical doctors of Korean oriental medicine is an important precondition. This study analyze current and future R&D manpower requirements including medical doctors in the Korean oriental medicine research area. Our analyses can be utilized for developing the government R&D manpower planning including the adequate supply of medical doctors for the Korean oriental medicine research. For the study, we conducted and analyzed a delphi survey of the experts, the principal investigators, with expertise in Korean oriental medicine research areas. The results of this study can be summarized as follows; First, in 2007 the Korean oriental medicine R&D personnel is currently under-supplied as many as 302 people including 111 medical doctors of Korean oriental medicine. The rate of under-supplied is 28.2%. Second, in 2017 the forecast shows that the R&D personnel in this area will be more severely under-supplied as many as 539 people including 185 medical doctor of Korean oriental medicine. The rate of under-supplied will be 32.6%. As a result, the confrontation of demand and supply forecasts shows that, in general, severe shortages of R&D manpower in the areas of Korean oriental medicine will result if there are not adequate manpower policy adjustment.

• 대한영양사협회학술지
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• 제25권1호
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• pp.44-58
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• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

• 한국산업융합학회 논문집
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• 제27권1호
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• pp.17-24
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• 2024

Recently, retail technology has been developed by the rapid evolution of e-commerce and a representative example is ESL technology. In this study, we investigate ESL technology, market status and forecasts, and analyze the competitive structure between relational companies. Market analysis refers to data from market reports of Marketsandmarkets and Research, and internet media. In ESL, the display field is predicted to account for 43% of the total market in 2026, and is converting from LCD to electronic paper. The segmented type is becoming more advanced into the full-graphic type, and CAGR of 18.7% for 3-7 inches and 20.6% for 7-10 inches is predicted. The demand for ESL is greatest in North America and Europe, but CAGR is the highest in the Asia-Pacific region at 19.1%. Since ESL technology has a lot of overlap with semiconductor and display technology, the Asia-Pacific region is relatively advantageous, and this has led to rapid growth of domestic companies. However, it is expected that competition from European companies that are actually owned by Chinese companies will increase in the future, so continuous technological development and new market development are necessary.

• 신재생에너지
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• 제20권1호
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• pp.95-101
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• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.

• 한국인공지능학회지
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• 제12권1호
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Asian Journal of Business Environment
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• 제14권3호
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• pp.1-11
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• 2024

Purpose: This study analyzes the impact of weather conditions, holidays, and sporting events on beer sales, providing insights for market strategy and inventory management in the beer industry. Research design, data and methodology: Beer types were classified into Lagers and Ales, with further subcategories. The study utilized weekly retail sales data from January 2018 to August 2020, provided by Nielsen Korea. An ARMAX model was employed for time-series analysis. Results: The analysis revealed that increasing temperatures positively influence sales of Pilsners and Pale Lagers. Conversely, higher precipitation levels negatively affect overall Lager sales. Among Ales, only Stout sales showed a significant decrease with increased rainfall. Sunshine duration did not significantly impact sales for any beer type. Humidity generally had little effect on beer sales, with the exception of Amber Lagers, which showed sensitivity to humidity changes. Holidays and sporting events were found to significantly boost sales across most beer types, although the specific impacts varied by beer category. Conclusions: This study offers a detailed analysis of how weather conditions and specific events influence different beer type sales. The findings provide valuable insights for breweries, beer processors, and retailers to optimize their market strategies and inventory management based on weather forecasts and seasonal events. By understanding the consumption patterns of each beer type in relation to environmental factors, businesses can better anticipate demand fluctuations and tailor their operations accordingly.

• 한국수자원학회논문집
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• 제55권11호
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• pp.941-953
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• 2022

본 연구에서는 한강 권역의 포함된 65개 지자체를 대상으로 5년간 월별 물이용량 자료를 수집, 분석하여 월 단위의 대표적인 생공용수 물이용 패턴을 도출하고, 물수급 네트워크 모델(MODSIM)과 기초자료를 이용하여 물이용 패턴을 반영함에 따른 유역 내 발생 가능한 물부족량의 차이를 비교하였다. 분석 결과, 특정 시기별 물이용 주기성을 파악하고, 월별 일정 비율 대비-3.120%~+4.322%의 증감 폭이 나타남을 확인하였다. 이를 물수급 모델에 적용할 경우, 28개 중권역 중 17개 유역에서 물부족량의 변화가 나타났고, 연간 동일한 수요량임에도 불구하고, 시기별 수요량의 비율을 적용함에 따라 최대 생공용수 부족량은 기존대비 약 8.0% 수준으로 감소할 것으로 분석되었다. 장래 기후변화, 사회적, 지역적 특성이 반영된 물이용 패턴에 대한 예측 연구와 연계한다면, 더욱 현실감 있는 물수급 전망과 신뢰도 높은 국가수자원계획 수립이 가능할 것이라고 판단된다.

• 대한토목학회논문집
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• 제28권5B호
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• pp.603-614
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• 2008

가정용수의 용도별 사용량은 주거형태, 거주자 생활양식, 주택구조 등의 내부적인 요인과 온도, 날씨, 수도요금 등과 같은 다양한 외부요인들의 영향을 받게 된다. 장기적으로는 거주민의 생활양식, 주거형태, 수도관련 시설의 변화 등에 따라 사용량이 점진적으로 변화하게 되며, 단기적으로는 기온이나 가뭄과 같은 기후조건이나 절수정책과 같은 인위적인 영향에 의해 사용량이 크게 변화하기도 한다. 용수수요에 영향을 미치는 인자에 대한 분석과 이해는 사용량의 변화원인과 경향을 설명해주고 여러가지 인자와의 상관관계를 분석함으로서 물사용패턴과 향후 수요를 예측하는데 도움을 준다. 본 연구에서는 가정용수의 용도별 사용량 실측자료를 바탕으로 사용패턴과 사용량에 미치는 영향인자를 분석하기 위하여 요일별, 월별, 계절별 사용량을 비교하고, 또한 가구특성, 기온 등의 외부인자에 의해 영향을 받는 용도에 대해서도 살펴보았다. 가정용수의 사용패턴을 분석한 결과, 토요일에 사용량이 가장 많고, 월간 중에는 7월의 사용량이 가장 높았으며, 월별 사용량의 첨두율은 평균대비 1.12로 나타났다. 기온에 따른 용도별 사용경향에서 총량용수는 $-14^{\circ}C{\sim}0^{\circ}C$까지 물사용량이 급격하게 증가하는 경향을 보였으나, $0^{\circ}C$이상에서는 큰 변화양상을 보이지 않았다. 반면에 세면, 세탁, 욕조용수는 $0^{\circ}C$이상에서도 점진적인 증가경향을 보였으나, 변기용수는 오히려 감소하는 경향을 보였다. 기온에 대한 용도별 사용량의 상관관계 분석결과 세면용수가 상관계수 0.73으로 상관성이 가장 높았으며, 변기용수는 -0.14로 매우 낮은 음의 상관성을 보였다. 각 인자별로 가정용수 사용량 원단위의 차이에 대한 유의성 여부를 검토하기 위하여 주택유형, 실거주인수, 가족구성, 건평, 수입, 맞벌이여부, 절수형변기, 비데기유무 등에 대하여 분산분석을 수행한 결과, 주택유형의 경우 욕조용수의 사용량에 차이가 발견되었고, 실거주인수는 싱크대, 변기, 기타, 총량용수에서 유의한 차이가 발견되었다. 특히 실거주인수는 세면과 욕조를 제외한 모든 용도별 사용량에서 유의한 차이를 보여 거주인수의 증감에 따라 용도별 사용량의 차이가 분명히 발생하며, 이는 향후 가구당 용수사용량을 설계하는 경우 평균 거주인수에 대한 신중한 검토가 필요할 것으로 판단되었다. 이러한 가정용수의 용도별 사용특성 분석결과는 수요예측, 수요관리 정책수립, 수도관련 기자재 및 시설의 규격결정 등에 기초자료로 활용될 수 있을 것으로 기대된다.

• Asian-Australasian Journal of Animal Sciences
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• 제31권7호
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• pp.968-975
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• 2018

Thailand is a country of native beef cattle resource farming. It has undergone rapid social and economic change in the past decade. Agricultural growth has been maintained by increasing the production of rice and cassava. Changing economic status also provides opportunities for beef cattle producers to meet increasing consumer demand for beef. Finishing beef cattle numbers in Thailand were about 1.0 M head in 2015. Beef produced in Thailand has exclusively been for domestic consumption. Only 1% of Thailand's beef cattle are for the premium market which is based on marbling score, 40% are sold into modern markets that consider muscling of cattle, and the remainder enter traditional markets. Cross-bred cattle for the premium market are raised within intensive systems. Most producers of premium beef are members of beef cooperatives, or have invested in their enterprises at high levels. Culled cow (native or cross-bred cattle) are mainly for small holder farm production. Malaysia, Indonesia, and other members of the Asian Economic Community (AEC) are set to become the largest beef market, which has been confirmed by 2015 through 2020 forecasts for consumption of beef that must increasingly be halal. These circumstances are likely to be challenging for beef producers in Thailand to gain a share of this market. Integration across all sectors involved in beef production in Thailand will be required.

• 한국ITS학회 논문지
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• 제11권3호
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• pp.92-101
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• 2012

최근 소득의 증가와 주5일제 근무확대로 인해 주말의 통행행태 및 수요가 크게 변화하고 있다. 이러한 주말통행은 평일통행과는 달리 주로 쇼핑, 여가, 레저 등과 연관되어 있어 주로 금요일 오후부터 일요일까지 주요 고속도로나 간선도로의 교통혼잡을 가중시키고 있는 것으로 나타났다. 따라서 주말통행수요예측이나 교통정책수립측면에서 주말통행에 영향을 미치는 요인을 분석하는 것은 매우 필수적일 것이다. 이에 본 논문에서는 2006년에 수행한 서울시 가구통행실태조사자료 중 동일가구에 실시한 평일 및 주말 통행조사자료를 이용하여 평일과 비교분석을 통해 주말통행의 특성을 분석 하였다. 또한 토빗모형을 개발하여 평일과 주말(토요일과 일요일)의 통행빈도에 영향을 미치는 요인을 규명하였다. 모형추정결과, 평일과 주말통행에 미치는 요인은 서로 다르게 나타났으며 주로 성별, 직업, 소득, 미취학아동수, 자가용보유 여부 등의 변수가 주말통행에 미치는 영향이 큰 것으로 분석되었다.