• 수산경영론집
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• 제40권1호
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• pp.133-152
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• 2009

The purpose of this paper is to model price formation and analyze demand structures for fishes under the restriction of Korean fisheries regulations. This study suggests the model that the price of fish is formed by its quantity, expenditure, and habit persistence. In economic literature, such a fishery market demand is called the inverse demand with dynamic habit persistence. Based upon a static differential price formation model, the paper has generalized it dynamically incorporating habit persistence effects. The empirical results show that all the species have values less than one and (-) sign of price flexibilities, thus being price inflexible. The estimated habit adjustment coefficients are significant at the level of 1%. Especially, TAC species have the smaller values of them than those of other main fish species. The contribution and results are summarized as follows. First, the fishery market demand has a strong dynamic effects from habit persistence. Second, the fishery market demand structure could be analyzed in a way different from the ordinary demand analysis, which is based upon price flexibility, scale flexibility, and cross adjustment flexibility. Third, the limitation of this paper is that it ignores the increasing stock effects by catching restrictions, thus raising consumers' benefit in the future.

• 대한산업공학회지
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• 제32권3호
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• pp.210-218
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• 2006

Traditional statistical process control (SPC) applied to discrete part industry in the form of control charts can look for and eliminate assignable causes by process monitoring. On the other hand, engineering process control (EPC) applied to the process industry in the form of feedback control can maintain the process output on the target by continual adjustment of input variable. This study presents controlling and monitoring rules adopted by variable sampling interval (VSI) to change sampling intervals in a predetermined fashion on the predicted process levels under integrated EPC and SPC systems. Twelve rules classified by EPC schemes(MMSE, constrained PI, bounded or deadband adjustment policy) and type of sampling interval combined with EWMA chart of SPC are proposed under IMA (1,1) disturbance model and zero-order (responsive) dynamic system. Properties of twelve control rules under three patterns of process change (sudden shift, drift and random shift) are evaluated and discussed through simulation and control rules for integrated VSI EPC and SPC systems are recommended.

• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.11-16
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• 2005

최근에 퍼지 이론을 인공 신경망에 접목하여 개선된 성능을 보이려는 경향이 많다. Goh는 퍼지단층 퍼셉트론 알고리즘과 일반적인 델타 규칙(Generalized delta rule)에 기반한 개선된 퍼지 퍼셉트론을 제안하여 Exclusive-OR(XOR) 문제 등을 해결하였다 그러나 이 방법은 계산량의 증가와 복잡한 영상인식에 적응하기에는 어려움이 있다. 논문에서는 동적 역치조정에 의한 개선된 퍼지 단층 퍼셉트론을 제안한다. 제안된 방법은 페턴인식의 벤치마크로 사용되는 XOR문제에 적용된다. 또한 영상 응용영역으로서 디지털 영상의 인식에 적용한다. 실험결과에서 항상 수렴하지는 않지만 그러나 제안된 모델은 학습시간의 개선과 높은 수렴율을 보였다.

• 대한토목학회논문집
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• 제8권2호
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• pp.177-184
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• 1988

지금까지 관측(觀測)값의 처리(處理)에 적용(適用)해 왔던 최소(最小)제곱법(法)은 정적(靜的)인 data의 처리(處理)에는 적합(適合)하나, 시간(時間)의 변화(變化)가 수반(隨伴)된 관측치(觀測値)의 처리(處理)에는 부적당(不適當)하다. 본(本) 연구(硏究)는 모든 관측(觀測)값이 시간(時間)의 함수(函數)인 것을 고려(考慮)해서 종래(從來)의 최소(最小)제곱법(法)의 일반식(一般式)에 시간(時間)의 개념(槪念)을 도입(導入)하여 동적(動的)으로 수준망(水準網)을 해석(解析)하였다. 이 경우(境遇) 관측(觀測) data를 동적최소(動的最小)제곱법(法)으로 처리(處理)한 결과(結果) 정밀(精密)한 조정(調整)값을 얻었다.

• 콘크리트학회논문집
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• 제17권6호
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• 한국전자통신학회논문지
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• 제5권4호
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• pp.372-378
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• 2010

시 공간적으로 인접한 주변 블록들의 움직임 정보들을 이용하여 적응적으로 탐색 범위를 조절하는 고속 움직임 추정 기법을 제안한다. 기존의 탐색 범위 조절을 통한 고속 움직임 추정 기법들은 참조 프레임에 있는 모든 블록들의 움직임 벡터들 중에서 최댓값을 이용하여 현재 블록의 탐색 범위 조절을 하기 때문에 움직임이 작은 블록들에 대해서는 최적의 탐색 범위 조절을 못할 수 있다. 본 논문에서는 참조 프레임과 현재 프레임에서 현재 블록과 인접한 주변 블록들의 움직임 정보를 동시에 고려하여 최적의 탐색 범위를 결정하여 움직임 추정을 고속으로 할 수 있는 움직임 추정 기법을 제안한다. 시간적으로 인접된 블록들 즉 참조 프레임에 있는 주변 블록들의 움직임 정보를 이용하면 움직임이 작은 블록의 탐색 범위의 크기를 적절하게 작게 할 수 있어 고속의 움직임 추정을 할 수 있다. 실험을 통하여 본 논문에서 제안한 시 공간적 주변 블록들의 움직임 정보를 이용하는 탐색 범위 조절을 통한 고속 움직임 추정 기법이 비트율과 움직임 추정 오차는 거의 비슷하게 유지하면서 기존의 방법인 Simple Dynamic Search Range(SDSR) 기법보다 탐색 점의 수를 약 15% 더 감소시킬 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2020-2037
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• 2017

In order to improve the energy efficiency of n-Epidemic routing protocol in opportunistic networks, in which a stable end-to-end forwarding path usually does not exist, a novel adjustment strategy for parameter n is proposed using learning atuomata principle. First, nodes dynamically update the average energy level of current environment while moving around. Second, nodes with lower energy level relative to their neighbors take larger n avoiding energy consumption during message replications and vice versa. Third, nodes will only replicate messages to their neighbors when the number of neighbors reaches or exceeds the threshold n. Thus the number of message transmissions is reduced and energy is conserved accordingly. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce and balance energy consumption. Furthermore, the key metric of delivery ratio is improved compared with the original n-Epidemic routing protocol. Obviously the proposed scheme prolongs the network life time because of the equilibrium of energy consumption among nodes.

• 한국측량학회지
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• 제35권5호
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• pp.389-404
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• 2017

The main purpose of this presented investigation is to build up the BHMS based on GNSS. This proposed monitoring system can conduct the deflection and dynamic characteristics analysis by using only GNSS positioning solution. The general bridge monitoring system being operated recently is composed of a combination of various sensors that are able to conduct deflection monitoring and dynamic characteristics monitoring analysis at the same time. However, GNSS based BHMS has the unique procedure in terms of data analysis. In the other words, GNSS positioning solution is firstly applied to deflection monitoring analysis then, this deflection analysis can be sequentially reflected in the dynamic characteristics. Unfortunately, the adjustment result of GNSS positioning solution estimated through various options and conditions and the process of monitoring analysis has not been fulfilled systematically. This means that different results or analysis value are presented according to the methodology and officers. Most of researches have been focusing on deflection monitoring analysis and some investigation regarding to dynamic characteristics is recently introduced. Moreover, it is not still reported the systematic investigation with regards to proper filtering and analysis methodology. This study was carried out based on a large amount of data, from this, various variables not reported yet are actively considered. Therefore, specific software for both monitoring analysis have been developed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• 대한기계학회논문집B
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• 제28권5호
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• pp.560-568
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• 2004

We performed the numerical study on the characteristics of the 3-D airflow and dynamic cross contamination in the photolithography process cleanroom. The nonunifurmity, the deflection angle and the global cross contamination were used for analyzing the characteristics and performances of cleanroom. From the numerical results, we knew that the airflow characteristics of the cleanrooms are largely affected by the porosity of panel and the adjustment of dampers and the global cross contamination varies with the location of source and the passage of time through the concentration ratio.