• The Journal of Fisheries Business Administration
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• v.40 no.1
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.11-16
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• 2005

Recently, there are a lot of endeavor to implement a fuzzy theory to artificial neural network. Goh proposed the fuzzy single layer perceptron algorithm and advanced fuzzy perceptron based on the generalized delta rule to solve the XOR Problem and the classical Problem. However, it causes an increased amount of computation and some difficulties in application of the complicated image recognition. In this paper, we propose an enhanced fuzzy single layer Perceptron using the dynamic adjustment of threshold. This method is applied to the XOR problem, which used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for image application. In a result of experiment, it does not always guarantee the convergence. However, the network show improved the learning time and has the high convergence rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.8 no.2
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• pp.177-184
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• 1988

The method of least squares has been applied to the static data, but it was not applications for the processing of observed values accompaning real-time variation. In this paper, having been considered all observations to be the function of time, leveling nets were analized dynamically by introducing the concept of time to conventional method of least squares. As a results, the method of dynamic least squares was well applicable to the adjustment of leveling nets.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.372-378
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• 2010

This paper propose the fast motion estimation algorithm with adaptive search range adjustment using motion activities of temporal and spatial neighbor blocks. The existing fast motion estimation algorithms with adaptive search range adjustment use the maximum motion vector of all blocks in the reference frame. So these algorithms may not control a optimum search range for slow moving block in current frame. The proposed algorithm use the maximum motion vector of neighbor blocks in the reference frame to control a optimum search range for slow moving block. So the proposed algorithm can reduce computation time for motion estimation. The experiment results show that the proposed algorithm can reduce the number of search points about 15% more than Simple Dynamic Search Range(SDSR) algorithm while maintaining almost the same bit-rate and motion estimation error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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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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• v.7 no.4
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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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.5
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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.