• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.979-986
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• 2016

At present, electric power industry around the world are being gradually changed to a new paradigm, such as electrical energy storage system, the wireless power transmission. Demand for ESS, the core technology of the new paradigm, has been growing worldwide. However, it is essential to estimate the optimal capacity of ESS facilities for frequency regulation because the benefit would be saturated in accordance with the investment moment and the increase of total invested capacity of ESS facilities. Hence, in this paper, the annual optimal mathematical investment model is proposed to estimate the optimal capacity and to establish investment plan of ESS facility for frequency regulation. The optimal mathematical investment model is newly established for each season, because the construction period is short and the operation effect for the load by seasons is different unlike previous the mathematical investment model. Additionally, the marginal operating cost is found by new mathematical operation model considering no-load cost and start-up cost as step functions improving the previous mathematical operation model. ESS optimal capacity is established by use value in use iterative methods. In this case, ESS facilities cost is used in terms of the value of the beginning of the year.

• Journal of Preventive Veterinary Medicine
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• v.42 no.4
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1996-2000
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• 2006

현재 국내 주요 하천의 홍수예경보시스템 운영과 다목적댐의 홍수조절관리를 위하여 수문학적 모형의 하나인 저류함수모형(storage function model)을 사용하고 있다. 저류함수모형은 산지가 많은 유역에 적합하도록 개발된 모형으로, 계산절차가 간편하고 홍수유출의 비선형성을 고려할 수 있는 방법이므로 선형모형보다 합리적이라고 알려져 있다. 그러나 실제 홍수사상에 저류함수모형을 적용하기 위해서는 적절한 매개변수의 적용이 필요하다. 현재까지 저류함수모형의 매개변수를 보정하기 위한 연구가 많이 되었지만, 실질적으로 보정된 매개변수를 실제 홍수사상에 적용함에 있어서는 많은 어려움이 존재한다. 따라서 이러한 문제점을 해결하고자 본 연구에서는 저류함수 모형 중 유역유출 매개변수를 첨두유량에 대한 상대민감도분석을 통하여 매개변수의 경계조건을 설정하고, 이 경계조건을 바탕으로 최적화기법(optimization technique)을 사용하여 과거 홍수사상에 대하여 보정을 수행하였다. 그리고 보정된 매개변수를 모의 홍수사상에 적용하기 위한 최적매개변수(optimal parameter) 결정을 위한 방법들을 제시 및 적용하여 비교 분석하였다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.56-64
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• 1998

Multi-layered perceptrons that are a nonlinear neural network model, have been widely used for various applications mainly thanks to good function approximation capability for nonlinear fuctions. However, for digital hardware implementation of the multi-layere perceptrons, the quantization scheme using "look-up tables (LUTs)" is commonly employed to handle nonlinear signmoid activation functions in the neworks, and thus requires large amount of storage to prevent unacceptable quantization errors. This paper is concerned with a new effective methodology for digital hardware implementation of multi-layered perceptrons, and proposes a "piecewise affine approximation" method in which input domain is divided into (small number of) sub-intervals and nonlinear sigmoid function is linearly approximated within each sub-interval. Using the proposed method, we develop an expression and an error backpropagation type learning algorithm for a multi-layered perceptron, and compare the performance with the quantization method through Monte Carlo simulations on XOR problems. Simulation results show that, in terms of learning convergece, the proposed method with a small number of sub-intervals significantly outperforms the quantization method with a very large storage requirement. We expect from these results that the proposed method can be utilized in digital system implementation to significantly reduce the storage requirement, quantization error, and learning time of the quantization method.quantization method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.65-70
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• 2022

As fast storage has become popular, the memory management system designed for hard disks needs to be reconsidered. In this paper, we observe that memory access latency is sensitive to the page size when fast storage is adopted. We find the reason from the TLB miss rate, which has the increased impact on the memory access latency in comparison with the page fault rate, and there is trade-off between the TLB miss rate and the page fault rate as the page size is varied. To handle such situations, we model the page fault rate and the TLB miss rate accurately as a function of the page size. Specifically, we show that the power fit and the exponential fit with two terms are appropriate for fitting the TLB miss rate and the page fault rate, respectively. We validate the effectiveness of our model by comparing the estimated values from the model and real values.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.2
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• pp.29-38
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• 1982

Nonlinear hydrological model containing the nonlinearity of effective rainfall, lag time and runoff is presented. In the evaluation of rainfall excess, the polynomial fitting method for total rainfall, 5 day antecedant rainfall and direct runoff is developed. In the application to actual watershed, the estimated model parameters of nonlinear lag model reflecting the nonlinearity of lag time are compared with the parameters, by both the fitting method and the correlation, model which are the modified version of the storage function model. The Successive Approximation Method in mathematical solution and Newton-Rhapson method in numerical solution are found to be superior to the conventional numerical graphic method in the analysis of nonlinear processes.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.345-353
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• 2008

In order to design storage-based non-point source management facilities, the aspect of statistical features of the entire precipitation time series should be considered since non-point source pollutions are delivered by continuous rainfall runoffs. The 3-parameter mixed exponential probability density function instead of traditional single-parameter exponential probability density function is applied to represent the probabilistic features of long-term precipitation time series and model urban stormwater runoff. Finally, probability density functions of water quality control basin overflow are derived under two extreme intial conditions. The 31-year continuous precipitation time series recorded in Busan are analyzed to show that the 3-parameter mixed exponential probability density function gives better resolution.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.47-52
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• 2010

Many industrial accidents have occurred over the years in the manufacturing and construction industries in Korea. However, as the service industry has increased continuously, the share of the accident rate in the service industry was 39.07% in 2009, while the manufacturing industry share was 33.73%. The service industry share overtook the manufacturing industry share for the first time. Therefore, this research considers prevention of industrial accidents in the service industry as well as manufacturing and construction industries. This paper describes a procedure and a method to estimate efficient accident rate forecasting and estimated zero accident time in the service industry in order to prevent industrial accidents in the transportation, storage, and telecommunication divisions. This paper proposes a model using an analytical function for the sake of very efficient accident rate forecasting. Accordingly, this paper has develops a program for accident rate forecasting, zero accident time estimating, and calculation of achievement probability through MFC (Microsoft Foundation Class) software Visual Studio 2008 in the transportation, storage, and telecommunication divisions. In results of this paper, ARIMA (Auto Regressive Integrating Moving Average) is regarded as a very efficient forecasting model for the transportation, storage, and telecommunication division. In testing this model, value minimizing the Sum of Square Errors (SSE) was calculated as 0.2532. Finally the results of this paper are sure to help establish easy accident rate forecasting and strategy or method of zero accident time in the service industry for prevention of industrial accidents.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.387-402
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• 2008

In this thesis we propose a two-stage periodic inspection model for maintaining the reliability of a system in long-term storage. There are two types of tests available; a fallible test and an error-free test. The system is overhauled at detection of failure or when the storage reliability after inspection becomes less than or equal to the prespecified value. The expected cost per unit time until overhaul is derived and a procedure for minimizing the expected cost is suggested. The two-stage periodic inspection model is compared with the one-stage periodic inspection model for various parameters of the cost function when the failure time follows exponential and Weibull distributions. The proposed model is then applied to an existing missile system for comparison with the current inspection policy.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.335-345
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• 2021

The one-dimensional solute transport models have been developed for recent decades to predict behavior and fate of solutes in rivers. Transient storage model (TSM) is the most popular model because of its simple conceptualization to consider the complexity of natural rivers. However, the TSM is highly dependent on its parameters which cannot be directly measured. In addition, the TSM interprets the late-time behavior of concentration curves in the shape of an exponential function, which has been evaluated as not suitable for actual solute behavior in natural rivers. In this study, we suggested a stochastic approach to the solute transport analysis. We delineated the model development and model application to a natural river, and compared the results of the proposed model to those of the TSM. To validate the proposed model, a tracer test was carried out in the 4.85 km reach of Gam Creek, one of the first-order tributaries of Nakdong River, South Korea. As a result of comparing the power-law slope of the tail of breakthrough curves, the simulation results from the stochastic storage model yielded the average error rate of 0.24, which is more accurate than the 14.03 and 1.87 from advection-dispersion model and TSM, respectively. This study demonstrated the appropriateness of the power-law residence time distribution to the hyporheic zone of the Gam Creek.