• 보건행정학회지
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• 제1권1호
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• pp.136-152
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• 1991

The study was conducted to project supply and demand of the physicians from year 1991 to year 2010 based on the analysis of supply and demand of the physicians up to year 1989. Results of the study will provide information for the physicians manpower planning of the 7th 5-year Economic Social Development Planning(1992-1996) and contribute to the overall health manpower planning for the 21the century. It is projected that physician will be oversupplied from the very near future based on the current productivity or underestimated based on the optimal productivity. Thus, it is desirable not to change size of training and education during the 7the 5-year planning period and re-examine the status of the physician manpower at the end of the 7th 5-year period taking into consideration medical services utilization pattern, patients' satisfaction, and physicians' productivity.

• 경영과학
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• 제22권1호
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• pp.179-197
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• 2005

This study examines market acceptance for DMB service, one of the touted new business models in Korea's next-generation mobile communications service market, using adoption end diffusion of innovation as the theoretical framework. Market acceptance for DMB service was assessed by predicting the demand for the service using the Bass model, and the demand variability over time was then analyzed by integrating the innovation adoption model proposed by Rogers (2003). In our estimation of the Bass model, we derived the coefficient of innovation and coefficient of imitation, using actual diffusion data from the mobile telephone service market. The maximum number of subscribers was estimated based on the result of a survey on satellite DMB service. Furthermore, to test the difference in diffusion pattern between mobile phone service and satellite DMB service, we reorganized the demand data along the diffusion timeline according to Rogers' innovation adoption model, using the responses by survey subjects concerning their respective projected time of adoption. The comparison of the two demand prediction models revealed that diffusion for both took place forming a classical S-curve. Concerning variability in demand for DMB service, our findings, much in agreement with Rogers' view, indicated that demand was highly variable over time and depending on the adopter group. In distinguishing adopters into different groups by time of adoption of innovation, we found that income and lifestyle (opinion leadership, novelty seeking tendency and independent decision-making) were variables with measurable impact. Among the managerial variables, price of reception device, contents type, subscription fees were the variables resulting in statistically significant differences. This study, as an attempt to measure the market acceptance for satellite DMB service, a leading next-generation mobile communications service product, stands out from related studies in that it estimates the nature and level of acceptance for specific customer categories, using theories of innovation adoption and diffusion and based on the result of a survey conducted through one-to-one interviews. The authors of this paper believe that the theoretical framework elaborated in this study and its findings can be fruitfully reused in future attempts to predict demand for new mobile communications service products.

• 자원ㆍ환경경제연구
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• 제24권1호
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• pp.225-249
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• 2015

우리나라 전력시장에서도 보다 선진화된 요금체계가 도입되어야 한다는 관점에서 가정부문에서의 선택적 전력요금제 도입이 논의되고 있다. 본 연구에서는 고정요금제, 실시간 요금제(RTP), 계시별 요금제(TOU)를 도입하였을 때의 효과를 분석하기 위해 행위자기반모형을 구축하였다. 시간대별 전력소비 유형이 다른 행위자를 설정하였으며, 전력수요와 전력가격을 연동시키기 위해 발전부문도 모형에 도입하였다. 분석 결과, 소비자 유형이 피크부하 때 덜 사용하는 경우에는 실시간 요금제인 RTP나 TOU를 택했을 때의 비용절감 효과가 컸으며, 특히 스마트 계량기 등을 이용하여 전력사용 시간을 최적화할 수 있는 경우에는 그 편익이 더욱 증가한 것으로 나타나 향후 스마트 전력소비를 위한 인프라 구축이 필요함을 알 수 있다.

• 산업경영시스템학회지
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• 제40권4호
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• 대한토목학회논문집
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• 제36권4호
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• pp.667-675
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• 2016

본 연구는 향후 교통수요관리를 위한 기초 자료로 활용하고자 가구별 차량보유패턴 및 보유구조를 분석하였다. 차량등록대수와 가구당 차량보유대수가 증가하는 현시점에 가구별 차량보유실태를 통해 보유패턴에 대해 알아 볼 필요가 있다. 또한 차량 증가 원인에 대해 분석 할 필요가 있을 것이다. 가구통행실태 조사의 결과를 바탕으로 승용차를 보유하고 있는 가구와 승용차 및 비승용차 보유하는 가구로 구분하여 연구를 진행하였고 주요 결과는 다음과 같다. 차량보유구조 패턴을 통해 가구당 차량보유대수가 2대 이하인 경우에는 승용차만을 보유하는 가구수가 많았으며 3대 이상의 경우 승용차만 보유하는 가구보다 비승용차와 함께 보유하는 가구 비율이 증가한다는 것을 알 수 있었다. 순서형 로짓 모형을 활용하여 가구별 차량 보유구조를 분석한 결과 가구 속성 자료 및 주거형태 변수에 따라 차량보유에 영향을 주는 요인들이 차이가 있는 것으로 나타났다.

• 패션비즈니스
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• 제10권6호
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• pp.63-78
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• 2006

The purpose of this research is to propose the scientific and rational establishment of research projects and directions for research by analyzing the research trends on clothing construction. The 689 papers in the field of clothing construction are selected among 5433 papers published within the recent ten years (1996-2005) in Journal of the Korean Home Economics Association, Journal of the Korean Society of Clothing and Textiles, Journal of the Korean society of costumes, Research Journal of the Costume Culture, Journal of the Korean Society of Clothing Industry and Journal of Fashion Business. The trend of researches on clothing construction is analyzed by classifying the topics by design, body type, pattern, size of apparel, fitting of clothing, protective clothing and functional clothing, and the others, and the results are as follows. The frequency order of the studies in the field of clothing construction is body type (32%) > pattern (24%) > size of apparel (13%) > protective clothing and functional clothing (10%) > the others (8%) > design-related clothing construction (6%) > fitting of clothing (4%) > sewing (3%). The major areas of research are body types, pattern, and sizes resulting from them. Most of researches are focused on women, and researches on men are relatively lacking. In addition to the deficiency of men-related research, Moreover, researches on characteristics of body types and on corresponding sizes of consumers in the target countries of export are necessary when the reality of Korean clothing and fashion industry that depends more on foreign demand than domestic demand is considered. For production of segmented and specialized results from clothing construction research, related tools such as CAD, 3D shape systems and dress form should be developed and utilized to contribute to precision of research results.

• 한국컴퓨터정보학회논문지
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• 제14권5호
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• pp.103-117
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• 2009

최근 MANET(Mobile Ad-Hoc Network)에서 보안요소를 추가한 라우팅 연구가 활발히 진행되어왔다. 그러나 기존 연구들은 secure 라우팅 또는 패킷 자체에 대한 악의적인 행위 탐지 중 어느 한 측면에 대해서만 연구가 되어왔다. 본 논문에서는 패킷 자체에 대한 악의적인 행위 및 라우팅 측면에서 보안 요소를 모두 고려한 SRPPnT(A Secure Routing Protocol in MANET based on Malicious Pattern on Node and Trust Level)를 제안한다. SRPPnT는 악의적인 행위가 이루어진 노드를 확인하여 각 노드에 대한 신뢰수준을 측정 후, 획득한 각 노드의 신뢰수준에 따라 라우팅 경로를 설정함으로써 패킷 및 라우팅 경로 설정에 대해 이루어질 수 있는 악의적인 행위를 효율적으로 대응할 수 있다. SRPPnT는 AODV(Ad-Hoc On-Demand Distance Vector Routing)를 기반으로 하였다. NS 네트워크 시뮬레이션 결과를 통해, 제안된 SRPPnT는 기존 프로토콜보다 네트워크 부하를 감소시킨 상태에서 악의적인 노드의 보다 정확하고 신속한 식별과 secure한 라우팅이 이루어짐을 확인하였다.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
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• pp.975-977
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• 2004

According to thin and light form-factor and additional function of today's electronic devices, it is required to decrease the pattern pitch of FPC. The high density demand is more and more important trend especially, for small size LCD module. Based on this requirement, the manufacturing process is advancing from subtractive method to super subtractive method.