• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.3
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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.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.221-228
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• 2005

Data storage was thought to be inside of or next to server cases but advances in networking technology make the storage system to be located far away from the main computer. In Internet era with explosive data increases, balanced development of storage and transmission systems is required. SAN(Storage Area Network) and NAS(Network Attached Storage) reflect these requirements. It is important to know the capacity and limit of the complex storage network system to got the optimal performance from it. The capacity data is used for performance tuning and making purchasing decision of storage. This paper suggests an analytic model of storage network system as queuing network and proves the model though simulation model.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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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.

• Journal of Korea Water Resources Association
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• v.37 no.4
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• pp.257-271
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• 2004

To evaluate behavior in representing solute transport in natural streams, the storage zone model of the axially periodic transient storage zones is developed. The periodic transient storage zone model and continuous storage zone model are verified using the parameters and the tracer concentration vs. time curves observed in laboratory channels. The periodic storage zone model best fit the measured concentration vs. time curves, while the continuous storage model fails to describe some fluctuations and the plateau region of the tail occurring in a discontinuous transient storage system. Dispersion data from Shingobee River, Minnesota, U. S. A. show that the concentration curves simulated by the proposed model fit the observed concentration curves well.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.2
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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.

• IE interfaces
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• v.23 no.4
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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.