• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.316-321
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• 2007

We propose a quatitative annotation method for edges in Bayesian networks using given sets of condition-specific data. Bayesian network model has been used widely in various fields to infer probabilistic dependency relationships between entities in target systems. Besides the need for identifying dependency relationships, the annotation of edges in Bayesian networks is required to analyze the meaning of learned Bayesian networks. We assume the training data is composed of several condition-specific data sets. The contribution of each condition-specific data set to each edge in the learned Bayesian network is measured using the ratio of likelihoods between network structures of including and missing the specific edge. The proposed method can be a good approach to make quantitative annotation for learned Bayesian network structures while previous annotation approaches only give qualitative one.

• Journal of the Korean Society of Groundwater Environment
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• v.6 no.4
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• pp.180-187
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• 1999

Transmissivity is often estimated from specific capacity data because of the expense of conducting standard aquifer test to obtain transmissivity and the relative availability of specific capacity data. Most often, analytic expression relating specific capacity to transmissivity derived by Theis (1963). Brown (1963). and Logan (1964) are used in this analysis. The analytic solution typically used to predict transmissivity from specific capacity in alluvial aquifers assuming influence radius and/or storage coefficient of the aquifers. But those do not agree well with the measured transmissivity in fractured rock aquifers and in heterogeneous aquifers. Razack-Huntely (199l). Huntely-Steffey (1992). and Mace (1997) proposed emphirical rotations between specific capacity and transmissivity in heterogeneous alluvial aquifers. fractured rock aquifers, and karst aquifers. This study focuses on comparison between transmissivity and specific capacity data in volcanic rock aquifers of Jeju Island. Emphirical relation between the log of transmissivity and the log of specific capacity suggests they no linearly related (correlation coefficient 0.951) and the width of $\pm$0.25 log cycles in transmissivity includes 96.6% of data.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.590-609
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• 2010

The purpose of the study is to propose and evaluate Exploratory Spatial Data Analysis(ESDA) methods for examining age-specific population migration characteristics. First, population migration pyramid which is a pyramid-shaped graph designed with in-migration, out-migration, and net migration by age (or age group), was developed as a tool exploring age-specific migration propensities and structures. Second, various spatial statistics techniques based on local indicators of spatial association(LISA) such as Local Moran''s $I_i$, Getis-Ord ${G_i}^*$, and AMOEBA were suggested as ways to detect spatial dusters of age-specific net migration rate. These ESDA techniques were applied to age-specific population migration of Daegu Metropolitan City. Application results demonstrated that suggested ESDA methods can effectively detect new information and patterns such as contribution of age-specific migration propensities to population changes in a given region, relationship among different age groups, hot and cold spot of age-specific net migration rate, and similarity between age-specific spatial clusters.

• Language and Information
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• v.10 no.1
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• pp.21-46
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• 2006

This study proposes a Word Sense Disambiguation (WSD) algorithm, based on concept learning with special emphasis on statistically meaningful lowest frequency words. Previous works on WSD typically make use of frequency of collocation and its probability. Such probability based WSD approaches tend to ignore the lowest frequency words which could be meaningful in the context. In this paper, we show an algorithm to extract and make use of the meaningful lowest frequency words in WSD. Learning method is adopted from the Find-Specific algorithm of Mitchell (1997), according to which the search proceeds from the specific predefined hypothetical spaces to the general ones. In our model, this algorithm is used to find contexts with the most specific classifiers and then moves to the more general ones. We build up small seed data and apply those data to the relatively large test data. Following the algorithm in Yarowsky (1995), the classified test data are exhaustively included in the seed data, thus expanding the seed data. However, this might result in lots of noise in the seed data. Thus we introduce the 'maximum a posterior hypothesis' based on the Bayes' assumption to validate the noise status of the new seed data. We use the Naive Bayes Classifier and prove that the application of Find-Specific algorithm enhances the correctness of WSD.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.1
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• pp.77-88
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• 1993

In this paper, we propose a stereo vision system to solve correspondence problem with large disparity and sudden change in environment which result from small distance between camera and working objects. First of all, a specific feature is divided by predfined elementary feature. And then these are combined to obtain coded data for solving correspondence problem. We use Neural Network to extract elementary features from specific feature and to have adaptability to noise and some change of the shape. Fourier transformation and Log-polar mapping are used for obtaining appropriate Neural Network input data which has a shift, scale, and rotation invariability. Finally, we use associative memory to obtain coded data of the specific feature from the combination of elementary features. In spite of specific feature with some variation in shapes, we could obtain satisfactory 3-dimensional data from corresponded codes.

• Journal of Energy Engineering
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• v.13 no.4
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• pp.259-266
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• 2004

Specific heat mechanism of oxide fuel is contributed by lattice vibration, dilatation, conduction electron and defect and excess specific heat. Model of oxide fuel for specific heat consists of specific heat at constant pressure term, dilatation specific heat term and defect specific heat term. In this study experimental and published data on the specific heats of oxide nuclear fuels have been reviewed and analyzed to recommend the best fitting model. The oxide fuels considered in this paper were UO$_2$, mixed (U, Pu) oxides and spent fuel. The specific heat data of spent fuel has been replaced by that of simulated fuel.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.1
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• pp.17-24
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• 2013

In this paper, we present a multi-core processor including multimedia specific instructions to process multimedia data efficiently in the mobile environment. Multimedia specific instructions exploit subword level parallelism (SLP), while the multi-core processor exploits data level parallelism (DLP). These combined parallelisms improve the performance of multimedia processing applications. The proposed multi-core processor including multimedia specific instructions is implemented and tested using a Xilinx ISE 10.1 tool and SoCMaster3 testbed system including Vertex 4 FPGA. Experimental results using a fire detection algorithm show that multimedia specific instructions outperform baseline instructions in the same multi-core architecture in terms of performance (1.2x better), energy efficiency (1.37x better), and area efficiency (1.23x better).

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.644-648
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• 2002

Though it was difficult of globally monitor latent heat flux aver the ocean for many years, the situation is rapidly changing by the use of satellite data. Since a bulk formula is used to estimate turbulent heat flux using satellite data, we need wind speed, sea surface temperature and specific humidity data. However, it is not easy to accurately estimate specific humidity using satellite data. Now several algorithms for estimating specific humidity have been proposed and applied to construct latent heat flux data sets. Latent heat flux data sets derived from satellite data such as J-OFURO, HOAPS and GSSTF are available at present. Since the algorithm and used satellite data are not the same between them. the characteristics of each data set may be different. Therefore, it is important to clarify the difference between each data set and investigate the cause of the difference in latent heat flux estimates. In this paper we summarize the present state of the art with regard to the turbulent heat flux estimation by using satellite data. Also we present the comparison results of latent heat flux fields including not only satellite-derived flux fields but also analysis fields.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.899-910
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• 2016

Asymptotic tests are often not suitable for the analysis of sparse ordered contingency tables as asymptotic p-values may either overestimate or underestimate the true pvalues. In this pater, we describe permutation procedures in which we compute exact or resampling p-values for a weighted specific-category agreement in ordered $k{\times}k$ contingency tables. We use the weighted specific-category kappa proposed by $Kv{\dot{a}}lseth$ to measure the extent to which two independent raters agree on the specific categories. We carried out comparison studies between exact p-values, resampling p-values and asymptotic p-values using $3{\times}3$ contingency data (real and artificial data sets) and $4{\times}4$ artificial contingency data.