• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.849-864
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• 2022

In case of a specific sector being divided into sub-sectors, this study presents a process for estimating an input-output table, which is frequently used as basic data in fields of energy and environment economics. RAS method, which is universally used for this case, requires information on production, intermediate input sum, and intermediate demand sum for each sector in the new table. But in many cases, it is difficult to secure information on intermediate demand sum by sector. This study suggests a process for estimating a new input-output table without using information of intermediate demand sum in the case of sector separation, under the assumption that information of production value and intermediate input sum by sector are available. The key idea is that the values of many elements in the input-output table after disaggregation are the same as those in the table before disaggregation and that the sum of the elements after disaggregation, equals the values of the elements before disaggregation. The process of estimating the intemediate transaction matrix or the input coefficient matrix is presented by using these information instead of intermediate demand sum information. A small-scale simulation shows that the average error rate of the process proposed in this study is about 11.23% in estimating input coefficients, which is smaller than the 11.30% estimation error of RAS using the information of intermediate demand sum. However, since it is known in the literature that using additional information does not always improve estimation performance compared to not using it, additional research on various simulations is needed to apply the method of this study to reality.

• Journal of Digital Convergence
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• v.9 no.6
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• pp.137-151
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• 2011

This dissertation aims to empirically analyze the effect of cultivation of software industry on the local economy through Inter-regional Software Input-Output Analysis. The temporal range of analysis of effect of software industry on the local economy shall be for the year 2005 since analysis is made on the basis of the Regional Industrial Input-Output Table published by the Bank of Korea in 2005, and spatial domain shall be limited to the 16 metropolitan cities and provinces, which are the standards for each administrative zone. Results of analysis of this dissertation are as follows. Firstly, average inverse matrix coefficient of software industry for each region was computed to be 1.6248, which is lower than the average inverse matrix coefficient of 1.7979 for the entire industries. Secondly, among these, inverse matrix coefficient of software industry for each region on other industry within the same region was 0.1794, which is higher than that of entire industries at 0.1382. However, average inverse matrix coefficients of software industry for each region on self-industry within the same region and entire industries in other regions were found to be 1.0119 and 0.4335, respectively, which is lower than those of entire industries at 1.0982 and 0.5616, respectively. Thirdly, domestic produces induced by final demand items of software industry for each region was the highest for Seoul with 17.3309 trillion Korean won, accounting for 81.0% of the total, followed by Gyeonggi with 2.3370 trillion Korean won, 10.9% of the total. Fourthly, distribution ratios of domestic produces induced by final demand items of software industry for each region were found to be 19.1%, 72.1% and 8.8% with respect to the weight of consumption, investment and export, respectively, thereby illustrating very high level of distribution ratios of domestic produces being induced by investment in comparison to the distribution ratios of domestic produces being induced for the entire industries at 47.3%, 19.8% and 32.9%, respectively.

• Wind and Structures
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• v.6 no.2
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• pp.107-126
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• 2003

This paper describes a simple and practical approach through the application of Linear Stochastic Estimation (LSE) to reconstruct wind-induced pressure time series from the covariance matrix for structural load analyses on a low building roof. The main application of this work would be the reduction of the data storage requirements for the NIST aerodynamic database. The approach is based on the assumption that a random pressure field can be estimated as a linear combination of some other known pressure time series by truncating nonlinear terms of a Taylor series expansion. Covariances between pressure time series to be simulated and reference time series are used to calculate the estimation coefficients. The performance using different LSE schemes with selected reference time series is demonstrated by the reconstruction of structural load time series in a corner bay for three typical wind directions. It is shown that LSE can simulate structural load time series accurately, given a handful of reference pressure taps (or even a single tap). The performance of LSE depends on the choice of the reference time series, which should be determined by considering the balance between the accuracy, data-storage requirements and the complexity of the approach. The approach should only be used for the determination of structural loads, since individual reconstructed pressure time series (for local load analyses) will have larger errors associated with them.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3641-3655
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• 2016

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1246-1257
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• 2004

To find an optimal solution with genetic algorithm, it is desirable to maintain the population sire as large as possible. In some cases, however, the cost to evaluate each individual is relatively high and it is difficult to maintain large population. To solve this problem we propose a novel genetic algorithm based on fuzzy clustering, which considerably reduces evaluation number without any significant loss of its performance by evaluating only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly. We have used fuzzy c-means algorithm and distributed the fitness using membership matrix, since it is hard to distribute precise fitness values by hard clustering method to individuals which belong to multiple groups. Nine benchmark functions have been investigated and the results are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients as fitness distribution method.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.629-643
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• 2014

It is common to check the validity of an assumed model with the heavy use of diagnostics tools when conducting data analysis with regression techniques; however, outliers and influential data points often distort the regression output in undesired manner. Jang and Anderson-Cook (2013) proposed a graphical method called a firework plot for exploratory analysis that could visualize the trace of the impact of possible outlying and/or influential data points on individual regression coefficients and the overall residual sum of squares(SSE) measure. They developed 3-D plot as well as pair-wise plot for the appropriate measures of interest. In this paper, the approach was extended further to tell the strength of their approach; in addition, a more meaningful interpretation was possible by adding a measure not mentioned in their paper. This approach was applied to the mixture experiment because we felt that a detailed analysis of statistical measure sensitivity is required in a small experiment.

• Journal of Korea Water Resources Association
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• v.39 no.6 s.167
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• pp.511-520
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• 2006

Riverbank filtration is a natural process, using alluvial aquifers to remove contaminants and pathogens in river water for the production of drinking water. In Korea, most of the drinking water is supplied by surface water in-take. However, maintaining the quality of the drinking water becomes more and more difficult due to the increase of contamination. In riverbank filtration, the understanding of contaminant transport is an important task for the production of high quality drinking water and for the maintenance of facilities. In this paper, the transport behavior of hydrophobic organic contaminants is investigated when contaminants coexist with dissolved organic matter (DOM) and bacteria. In the developed model, the aquifer is thought of as a four phase system: two mobile colloidal phases, an aqueous phase, and a stationary solid matrix phase. The model equations are solved numerically for various situations. Results indicate that the presence of colloidal matters can enhance the mobility of contaminant significantly and that partitioning coefficients play an important role in the process.

• Analytical Science and Technology
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• v.7 no.2
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• pp.205-211
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• 1994

Acetic acid and succinic acid bonded polyamine-polyurea(CPPI and SAPPI) resins were synthesized from the reaction of polyethylenimine-polymethylenepolyphenylene isocyanate(PPI) resin as matrix polymer and chloroacetic acid and chlorosuccinic acid respectively. These resins were confirmed with infrared spectrometry and elemental analysis. The adsorption characteristics of the chromium(III) and dichromate(or chromate) ions on the resins were studied by measuring distribution coefficients($K_d$) with changing pH of the solution. It was thought that these ions were adsorbed by ion exchange mechanism. Chromium(III) and dichromate ion could be separated with stepwise elution method by changing pH of the eluent using SAPPI resin packed column($0.6cm(i.\;d.){\times}10cm(L.)$). Also, dichromate ion could be preconecntrated with CPPI resin column by a concentration factor of 50.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.6
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• pp.31-38
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• 2005

Aerodynamic influence coefficient linearly relates pressure with downwash in panel method for load analysis in which the viscosity of a flow is ignored and the compressibility cannot be taken into account in transonic region. Since the planform of an aerodynamic surface determines the coefficient, the panel method has a limit to the analysis of low Reynolds number flow. The accuracy of the pressure distribution can be improved by a direct correction to the pressure or a correction to the downwash, which is considered the change of camber or thickness, using the aerodynamic coefficients from wind tunnel test as constraints. A premultiplying correction method as well as a postmultiplying correction method is applied to a micro air vehicle to provide more accurate aerodynamic pressure for trim and load analyses. Theoretical aerodynamic pressure is obtained from the panel method. Correction factor matrix and correct pressure coefficient are computed for the conditions with two constraints in addition to single constraint. The postmultiplying correction method gives a better improvement in pressure distribution on micro air vehicle due to the flow characteristics on it.