• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.169-175
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• 2001

Most of studies on automated storage/retrieval (AS/R) system assumed that storage capacity is given, although it is a very important decision variable in the design phase. We propose a simple algorithm to estimate the required storage capacity, i.e., number of aisles and number of openings in vertical and horizontal directions in each aisle, of an AS/R system under stochastic demand, in which storage requests occur endogenously and exogenously while the retrieval requests occur endogenously from the machines. Two design criteria, maximum permissible overflow probability and maximum allowable storage/retrieval (S/R) machine utilization, are used to compute the storage capacity. This model can be effectively used in the design phase of new AS/R systems.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.23-29
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• 1992

To compute the expected travel time of storage and retrieval (S/R) machine in automated warehousing systems most of the previous studies assumed that equal number of rack openings are required regardless of the nature of storage assignment rules. It is known that randomized storage assignment rule usually needs less storage to space than needed for full turnover-based assignment rule. The objective of this paper is compute the expected travel time of each assignment rule more equitably by taking into account the storage space required for each rule. First, the rack storage space is determined which satisfies a given service level. Then based on the standard Economic Ordering Quantity model, trade-off analysis is carried out which relates the storage space to the expected travel time of the S/R machine. Finally, example problems are solved to compare the performance of each assignment rule under varying conditions of demand pattern and service level.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.165-175
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• 2015

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink(R) and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.65-70
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• 2023

In this paper, we propose a CCTV storage space securing model using YOLO v3. CCTV is installed and operated in various parts of society for disasters, disasters and safety such as crime prevention, fire prevention, and monitoring, and the number of CCTV is increasing and the quality of the video quality is improving. Due to this, as the number and size of image files increase, it is difficult to cope with the existing storage space. In order to solve this problem, we propose a model that detects specific objects in CCTV images using YOLO v3 library and deletes unnecessary frames by saving only the corresponding frames, thereby securing storage space by reducing the size of the image file, and thereby Periodic images can be stored and managed. After applying the proposed model, it was confirmed that the average image file size was reduced by 94.9%, and it was confirmed that the storage period was increased by about 20 times compared to before the application of the proposed model.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1864-1873
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• 2018

Energy shifting is an innovative method used to obtain the highest profit from the operation of energy storage systems (ESS) by controlling the charge and discharge schedules according to the electricity prices in a given period. Therefore, in this study, we propose an optimal charge and discharge scheduling method that performs energy shift operations derived from an ESS efficiency model. The efficiency model reflects the construction of power conversion systems (PCSs) and lithium battery systems (LBSs) according to the rated discharge time of a MWh-scale ESS. The PCS model was based on measurement data from a real system, whereas for the LBS, we used a circuit model that is appropriate for the MWh scale. In addition, this paper presents the application of a genetic algorithm to obtain the optimal charge and discharge schedules. This development represents a novel evolutionary computation method and aims to find an optimal solution that does not modify the total energy volume for the scheduling process. This optimal charge and discharge scheduling method was verified by various case studies, while the model was used to realize a higher profit than that realized using other scheduling methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.867-874
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• 1994

The complex nature of low flow transport and transformation of nonconservative pollutants in natural streams has been investigated using a numerical solution of a proposed mathematical model that is based on a pair of mass balance equations describing the advection, dispersion, decay and mass exchange mechanisms in streams and in storage zones. In the present study, a mathematical model (named "Storage-Transformation Model") has been developed to predict adequately the non-Fickian nature of mixing and transformation mechanisms for decaying substances in natural streams under low flow conditions. Comparisons of the computed concentration-time curves with the measured data show that the Storage-Transformation Model yields better agreements in general shape, peak concentration and time to peak than the conventional 1-D dispersion model. The proposed model shows significant improvement over the 1-D dispersion model in predicting natural transport and transformation processes in streams through pools and riffles.

• Journal of Biosystems Engineering
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• v.20 no.3
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• pp.275-287
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• 1995

The rice processing complex(RPC) consisted of the rice handling, drying, storage, and milling processes. It has been established at 83 locations domestically by April 1994, and 200 of RPC will be built more throughout the country. Therefore, this study has been performed to achieve two objectives as the followings : 1) Development of mathematical models which can assess the requirement of electricity, fuel, and labor for four model systems of rice processing complex. 2) Development of a computer simulation model which produce the improved designs of RPC by the evaluation results of energy requirements of four RPC models. The results from this study are summarized as follows : 1) Mathematical models were developed on the basis of result of mass balance analysis and required power of machines for each process. 2) A computer simulation model was developed, which can produce the improved designs of RPC by the evaluation results of energy requirements. The computer simulation model language was BORLAND $C^{++}$. 3) The results of simulation showed that total energy requirements were ranged from 75.94㎾h/t to 124.30㎾h/t. 4) From the results of computer analysis of energy requirement classified by drying type, it was found that energy requirement of the drying type A{paddy rice (PR) for storage-natural air drying(15%), PR for milling-heated air drying(16%)} were less than that of the drying type B{1 step-natural air drying(PR for storage : 18%, PR for milling : 20%), 2 step-heated air drying(PR for storage : 15%, PR for milling : 16%)}. 5) The energy efficient drying method is that all the incoming rough rice to RPC should be dried by national air drying systems. If it is more than the capacity of national air drying system, the amount of surplus rough rice is recommended to be dried by the heated air drying method.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.1-21
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• 2024

This study investigates the characteristics of visual working memory (VWM) representations by examining two theoretical models: the integrated-object and the parallel-independent feature storage models. Experiment I involved a change detection task where participants memorized arrays of either orientation bars, colored squares, or both. In the one-feature condition, the memory array consisted of one feature (either orientations or colors), whereas the two-feature condition included both. We found no differences in change detection performance between the conditions, favoring the integrated object model over the parallel-independent feature storage model. Experiment II employed a recall task with memory arrays of isosceles triangles' orientations, colored squares, or both, and one-feature and two-feature conditions were compared for their recall performance. We found again no clear difference in recall accuracy between the conditions, but the results of analyses for memory precision and guessing responses indicated the weak object model over the strong object model. For ongoing debates surrounding VWM's representational characteristics, these findings highlight the dominance of the integrated object model over the parallel independent feature storage model.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.353-362
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• 2018

The purpose of this study is to suggest a structure plan for improving the utilization of inactive storage in the dam for overcoming the drought. Inactive storage in the dam is composed of the emergency storage and dead storage. The emergency storage can be used for the case of emergency such as drought. But, in general, the dead storage for sedimentation is not used even for the emergency. Therefore, this study considers the part of dead storage that the sedimentation is not progressed yet can be used during the severe drought period and is called "drought storage in a dam". The accurate Sediment Level(SL) analysis for the computation of the drought storage should be performed and so the present and future SL in the dam reservoir is estimated using SED-2D linked with RMA-2 model of SMS. After the consideration of additionally available storage capacity based on the estimated SL, the drought storage is finally determined. Present data based on historical data, future predicted future climate factors by Representative Concentrarion Pathways(RCP) 8.5 scenario. Then, using the TANK model, dam inflows were determined, and future period such as SL and drought storage were suggested. As the results, we have found that the available drought storage will be reduced in the future when we compare the present drought storage with the future one. This is due to a increase variability of climate change. Therefore, we should take the necessary study for the increase of available drought storage in the future.

• Food Science and Preservation
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• v.17 no.1
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• pp.16-22
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• 2010

To maintain the storage quality of agricultural products, temperature uniformity during cold storage, which is affected by fan flow rate and product arrangement, is important. We simulated and validated a CFD (Computational Fluid Dynamics) model that can predict both airflow and temperature distribution in a cold storage environment. Computations were based on a commercial code (FLUENT 6.2) and two turbulence models. The standard k-$\varepsilon$ model and the Reynolds stress model (RSM) were chosen to improve the accuracy of CFD prediction. To obtain comparative data, the temperature distribution and velocity vector profiles were measured in a full-scale cold storage facility and in a 1/5 scale model. The agricultural products domain in cold storage was modeled as porous for economical computation. The RSM prediction showed good agreement with experimental data. In addition, temperature distribution was simulated in the cold storage rooms to estimate the uniformity of temperature distribution using the validated model.