• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.65-86
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• 2000

The estimates from an EM when it is applied to a large causal model of 10 or more categorical variables are often subject to the initial values for the estimates. This phenomenon becomes more serious as the model structure becomes more serious as the model structure becomes more complicated involving more variables. In this regard Wu(1983) recommends among others that EMs are implemented several times with different sets of initial values to obtain more appropriate estimates. in this paper a new approach for initial values is proposed. The main idea is that we use initials that are calibrated to data. A simulation result strongly indicates that the calibrated initials give rise to the estimates that are far closer to the true values than the initials that are not calibrated.

• ETRI Journal
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• v.30 no.4
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• pp.495-505
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• 2008

This paper presents a parallel processing searcher structure for the initial synchronization of a direct sequence ultra-wideband (DS-UWB) system, which is suitable for the digital implementation of baseband functionalities with a 1.32 Gsample/s chip rate analog-to-digital converter. An initial timing acquisition algorithm and a data demodulation method are also studied. The proposed searcher effectively acquires initial symbol and frame timing during the preamble transmission period. A hardware efficient receiver structure using 24 parallel digital correlators for binary phase-shift keying DS-UWB transmission is presented. The proposed correlator structure operating at 55 MHz is shared for correlation operations in a searcher, a channel estimator, and the demodulator of a RAKE receiver. We also present a pseudo-random noise sequence generated with a primitive polynomial, $1+x^2+x^5$, for packet detection, automatic gain control, and initial timing acquisition. Simulation results show that the performance of the proposed parallel processing searcher employing the presented pseudo-random noise sequence outperforms that employing a preamble sequence in the IEEE 802.15.3a DS-UWB proposal.

• Communications of the Korean Mathematical Society
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• v.23 no.4
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• pp.555-565
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• 2008

We will derive the asymptotic expansions of solutions of the heat equation with hyperfunctions initial data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.237-240
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• 1997

This paper is to arrange systematically the geometrical & physical data for real ships and to develop the graphic user interface program for initial hull design using NFHFD, which save the distributed information about hull form database and can output multi-variables.

• Bulletin of the Korean Mathematical Society
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• v.50 no.3
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• pp.753-760
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• 2013

We consider the global existence of strong solutions of the 3D incompressible Navier-Stokes equations in a bounded Lipschitz do-main under Dirichlet boundary condition. We present by a very simple argument that a strong solution exists globally when the product of $L^2$ norms of the initial velocity and the gradient of the initial velocity and $L^{p,2}$, $p{\geq}4$ norm of the forcing function are small enough. Our condition is scale invariant and implies many typical known global existence results for small initial data including the sharp dependence of the bound on the volumn of the domain and viscosity. We also present a similar result in the whole domain with slightly stronger condition for the forcing.

• Journal of Ocean Engineering and Technology
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• v.22 no.4
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• pp.1-7
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• 2008

The primary aim of the paper is to solve an unstable axisymmetric initial value problem of wave propagation when given initial data that is deteriorated by noise such as measurement error. To overcome the instability of the problem, Tikhonov's regularization, known as a non-iterative numerical regularization method, is introduced to solve the problem. The L-curvecriterion is introduced to find the optimal regularization parameter for the solution. It is confirmed that fairly stable solutions are realized and that they are accurate when compared to the exact solution.

• Journal of the military operations research society of Korea
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• v.22 no.1
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• pp.81-96
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• 1996

In a relational database environment, partitioning of data is directly concerned with the amount of data that needs to be required in a query or transaction. In this paper, we consider non-overlapping, vertical partitioning. Vertical partitioning algorithm in this paper is composed of two phases. In phase 1, we cluster the attributes with zero-one integer program that maximize affinity among attributes. The result of phase 1 is called 'Initial Fragments'. In phase 2, we modify Initial Fragments that is not directly considered by cost factors, making use of a transaction-based partitioning method. A transaction-based partitioning method is partitioning attributes according to a set of transactions. In this phase we select logical accesses which needs to be required in a transaction as comparison criteria. In phase 2, proposed algorithm consider only small number of modification of Initial Fragments in phase 1. This algorithm is so insensible to number of transactions and of attributes that it can applied to relatively large problems easily.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.3-11
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• 2015

Apartment developers consider the initial sales rate as an important indicator for their success of apartment development projects. They tried to achieve a secure level of initial sales rate. In spite of its importance, there is little research on the initial sales rate because of the difficulties in gathering proper data for analysis. This study, however, collects the data in initial sales rates in Su-won from various sources such as construction companies, marketing companies, sales companies and so on. By using this rare data, this study analyses the initial contract rate of apartment and estimates the initial contract rate by sales price. The result of this study shows that important of land area ratio, brand, and distance to park. It is expected that the proposed model will be used for apartment developers in sales planning phase.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.