• 한국농공학회지
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• 제26권3호
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• pp.90-98
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• 1984

In this paper, the anthor made a basic study of the storage function model and examined several constants in applying the storage function model to flood run-off analysis by dealing with the data in the Supyung and Hoyng Syung watershed, the applicabilities of the storage function model are examined by searching this optimum model parameters in two watersheds. The results are summarized as follows, 1) The optimum values of the exponential constants, P, in the storage function model showed to be 0.77 to 0.87 in two watersheds observed, therefore it was confirmed that the storage fumction model was approaching to the surface runoff model. 2) It was confirmed that the interval of variation of the storage constant, K, Showed to be larger than that of the exponential constant, p. 3) Relative erros in the discharge obtained by using the storage function model and the SDFP mothod showed to be 20 and 17 percent respectively to the observed discharge, therefore it was confirmed that the applicability of the storage function model using the SDFP method are excellent for runoff analysis. 4) A simple method is proposed for estimating the lag time in the storage function model.

• 한국수자원학회논문집
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• 제37권1호
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• pp.77-86
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• 2004

본 연구에서는 농업용 저수지에서 저수량 예측모형과 함께 저수지의 목표저수량 및 한계저수량을 유지하기 위한 저수지 운영방안을 제시하였다. 대상저수지인 금강저수지에서 1990년부터 200l년까지의 저수량 자료를 이용하여 갈수빈도해석을 적용하고, 2년빈도 한발저수량을 목표저수량(target storage)으로, 10년빈도 한발저수량을 한계저수량(critical storage)으로 설정하였다. 농업용 저수지의 운영의 효율화를 위해서는 우선 합리적인 방법을 통하여 장래 저수량을 예측하여야 한다. 예측된 저수량은 저수지 운영에 관한 계획을 수립하는데 기초자료로 활용될 수 있다. 본 연구에는 저수량 예측모형으로 ARIMA 모형과 자기회귀오차모형을 적용하였다. ARIMA 모형은 과거 저수량 자료만을 근거로 하여 저수량을 예측함으로서 예측정도가 상대적으로 낮은 것으로 나타난 반면, 자기회귀오차모형은 저수량과 관련 있는 설명변수들을 이용함으로써 예측의 효과를 높일 수 있었다. 농업용 저수지의 저수량은 이전 저수량, 강수량, 평균온도, 최고온도, 관개면적, 풍속, 습도의 영향을 받으므로 자기회귀오차모형을 적용하여 저수량과 저수량에 영향을 미치는 요인과의 관계를 분석하였다. 자기회귀오차모형에 의한 저수량 예측 관계식은 저수지의 연속방정식과 유사한 관계식으로 유도되어 실제 적용성이 높을 것으로 판단되며, 금광저수지에서 예측된 2002년도 저수량과 관측된 저수량을 비교한 결과, 양호한 예측결과를 보여 주었다.

• 디지털산업정보학회논문지
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• 제7권3호
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• pp.25-35
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• 2011

In the Semantic Web, storage models are required to efficiently store and retrieve metadata and ontology represented using OWL that can provide expressive power and reasoning support. In this paper, we propose an OWL storage model that can store and retrieve many restrictions and semantic relations defined on ontology with metadata. In addition, we propose some methods and rules to improve query processing efficiency of the proposed storage model. The proposed storage model can store and process large amounts of ontology and metadata because it consists of tables based on the relational database. And the proposed model can quickly provide more accurate results to users because of performing two different types of ontology reasoning and using the prime number labeling scheme to easily identify hierarchy relationships between classes or properties. The comparative evaluation results show that our storage model provides better performance than the existing storage model.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.221-228
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• 2005

데이터 저장장치는 서버의 내부나 근처에 있는 것으로 인식되어 왔으나 네트워크 기술의 발달로 저장장치 시스템은 주 전산기와 원거리에 떨어져 존재할 수 있게 되었다. 인터넷 시대에 데이터 량의 폭발적인 증가는 데이터를 저장하는 시스템과 이를 전송하는 시스템의 균형 있는 발전을 요구하고 있으며 SAN(Storage Area Network)이나 NAS(Network Attached Storage)은 이러한 요구를 반영하고 있다. 저장장치로부터 최적의 성능을 도출하기 위해서 복잡한 저장 네트워크의 용량과 한계를 파악하는 것이 중요하다. 파악된 데이터는 성능 조율의 기초가 되고 저장장치의 구매 시점을 결정하는데 사용될 수도 있다. 본 논문에서는 저장 네트워크 시스템의 큐잉 네트워크를 통한 분석적 모델을 제시한 다음, 이의 시뮬레이션하여 분석적 모델이 정당하다는 것을 입증한다.

• 대한산업공학회지
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• 제18권1호
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• pp.11-24
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• 1992

The performance of Storage assignment policies is traditionally evaluated with the storage capacity of and AS/RS taken as given. However, the storage capacity is closely related to the inventory model used in real situations. This paper presents a model of evaluating the performance of three storage policies(random storage, class-based storage, and full turnover-based storage) considering production lot-sizing simultaneously with storage assignment of inventory items. The objective of the model is to achieve a balance of warehouse throughput and space requirements such that a total of material handling cost, production ordering cost, and inventory holding cost is minimized. The effects of the parameters involved in the model are investigated on the performance of each storage policy through example problems.

• 한국수자원학회논문집
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• 제37권4호
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• pp.257-271
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• 2004

본류대를 따라 저장대가 주기적으로 존재하는 다중저장대모형을 개발하고 자연하천의 혼합거동을 해석하였다. 개발된 모형 및 하나의 저장대를 갖는 기존의 저장대모형을 비교하기 위하여 모형실험 결과를 이용하였다. 본 모형을 이용하여 구한 농도분포는 모형실험에서 수집된 시간에 따른 농도분포를 잘 재현하는 반면, 연속적인 저장대를 갖는 기존모형은 불연속적인 저장대 구조로 인해 발생하는 농도분포의 부차적인 융기부분을 정확히 재현하지 못하는 것으로 나타났다. 본 모형의 현장 적용성을 검토하기 위하여 새로운 모형을 미국 미네소타주에 위치한 Shingobee River에 적용하고 혼합거동을 해석한 결과 새로운 저장대 모형은 저장대가 존재하는 자연하천에서의 분산거동을 정확하게 모의하는 것으로 나타났다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.1454-1458
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• 2007

The determination of feasible design flood is the most important to control flood damage in river management. Model parameters should be calibrated using observed discharge but due to deficiency of observed data the parameters have been adopted by engineer's empirical sense. Storage constant in the Clark unit hydrograph method mainly affects magnitude of peak flood. This study is to estimate the storage constant based on the observed rainfall-runoff data at the three stage stations in the Imjin river basin and the three stage stations in the Ansung river basin. In this study four methods have been proposed to estimate the storage constant from observed rainfall-runoff data. The HEC-HMS model has been adopted to execute the sensitivity of storage constant. A criteria has been proposed to determine storage constant based on the results of the observed hydrograph and the HEC-HMS model.

• 한국자동차공학회논문집
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• 제22권2호
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• pp.60-66
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• 2014

A control oriented model of the Lean $NO_x$ trap (LNT) was developed to determine the timing of $NO_x$ regeneration. The LNT model consists of $NO_x$ storage and reduction model. Once $NO_x$ is stored ($NO_x$ storage model), at the right timing $NO_x$ should be released and then reduced ($NO_x$ reduction model) with reductants on the catalyst active sites, called regeneration. The $NO_x$ storage model simulates the degree of stored $NO_x$ in the LNT. It is structured by an instantaneous $NO_x$ storage efficiency and the $NO_x$ storage capacity model. The $NO_x$ storge capacity model was modeled to have a Gaussian distribution with a function of exhaust gas temperature. $NO_x$ release and reduction reactions for the $NO_x$ reduction model were modeled as Arrhenius equations. The parameter identification was optimally performed by the data of the bench flow reactor test results at space velocity 50,000/hr, 80,000/hr, and temperature of $250-500^{\circ}C$. The LNT model state, storage fraction indicates the degree of stored $NO_x$ in the LNT and thus, the timing of the regeneration can be determined based on it. For practical purpose, this model will be verified more completely by engine test data which simulate the NEDC transient mode.

• 대한산업공학회지
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• 제25권2호
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• pp.274-281
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• 1999

In this paper, we determine the required storage capacity of a unit-load automated storage/retrieval system(AS/RS) under random storage assignment(RAN) and n-class turnover-based storage assignment(CN) policies. For each of the storage policies, an analytic model to determine the optimal storage capacity of the AS/RS is formulated so that the total cost related to storage space and space shortage is minimized while satisfying a desired service level. A closed form of optimal solutions for the RAN policy is derived from the model. For the CN policy, an optimal storage capacity is shown to be determined by applying the existing iterative search algorithm developed for the full turnover-based storage(FULL) policy. Finally, an application of the approach to the standard economic-order-quantity inventory model is provided.

• 산업공학
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• 제23권4호
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• pp.311-318
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• 2010

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.