• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.129-136
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• 2005

To reduce the noise in the noisy speech, it is desirable to change the parameters of the speech processing system according to the noise intensity to reproduce a good quality speech. This paper proposes an estimation method of noise intensity using a three layered neural network, which is able to learn the three graded speeches that is degraded by white noise or road noise. Experimental results demonstrate that the noise intensity could be estimated by the neural network. Even if the speakers and speech data are different from the training data, estimation rates for the noise intensity can be estimated by the neural network with an average accuracy of $95\%$ or more for white noise.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.353-361
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• 2001

Recently the boundary element method has been developed swiftly. The boundary element method is an efficient and accurate means for analysis of two dimensional elastic crack problems. This paper is concerned with the evaluation and the prediction of the stress intensity factor(SIF) for the crack emanating from the circular hole using boundary element method-neural network. The SIF of the crack emanating from the hole was calculated by using boundary element method. Neural network is used to evaluate and to predict SIF from the results of boundary element method. The organized neural network system (structure of four processing element) was learned with the accuracy 99%. The learned neural network system could be evaluated and predicted with the accuracy of 83.3% and 71.4% (in cases of SIF and virtual SIF). Thus the proposed boundary element method-neural network is very useful to estimate the SIF.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.9
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• pp.723-726
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• 2012

A current intensity convertible CLD chip was fabricated using small and large FET cell configuration. Pinch-off current of 8.82 mA and 11.56 mA were obtained for small and large cell in the CLD chip, respectively. Constant current was fairly maintained until the breakdown voltage of 60 V. Measured knee voltage, $V_k$ were 3.8 V and 4.5 V for small and large cell, respectively. We configured current amplifying chip with parallel connection of each cells, by connecting 8 individual large cells in parallel network, 92.0 mA of current was obtained. The pinch-off constant current of CLD chip was varied very linearly with respect to the number of parallel connected cell.

• Journal of Digital Convergence
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• v.6 no.4
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• pp.137-145
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• 2008

Embedded industry has emerged as one of the important industry for u-IT based application service. Technology development related to embedded hardware and software provides the base for the development of U-IT convergence industry. This study intends to suggest a research model to investigate the influence of industry, organization, technology factors on technology performance in the embedded companies. This study adopted research variables such as global network, local network, industry intensity, and technology capability as independent variables, with technology performance as a dependent variable from the existing literatures. The purpose of this study is to analyze the influence of industrial and organizational factors on technology performance of embedded companies SCM systems, including the moderating effect of government support. 94 companies data were collected by survey. The result of this empirical study is summarized as follows. First, local network and technology capability are the important determinants to influence technology performance of embedded companies in direct effect model. Second, global network and industry intensity has more positive influence on technology performance of embedded companies through a moderating variable of government support.

• Management & Information Systems Review
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• v.38 no.1
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• pp.1-21
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• 2019

This study focuses on (1) the relationship between the network intensity of top managers and the R&D investment of Korean firms, and (2) the moderating effects of the type (related-division vs. unrelated-division) and system (physical division vs. spin-offs) of corporate division on this relationship. The sample of this study was all type and/or system of corporate division implemented by Korean firms during 18-years (1999-2016) study periods. The results of multiple regression analyses as follow. First, as was expected in hypothesis 1 the network intensity of top managers has a strong positive linear relation with the R&D investment of Korean firms. Second, regarding the moderating effect of division type the results show that related-divisions significantly intensify the positive relationship of the network intensity of top managers with the R&D of Korean firms although unrelated-divisions did not. Third, in the analysis of moderating effect of corporate division system the results present the stronger positive moderating effect of spin-offs rather than physical divisions. The findings of the study implies that strong network intensity of top managers can be beneficial to long-term decision such as R&D investment of Korean firms. They accords to network theory that emphasize the importance of strong network effect among top managers based on their trust. The findings also implies that researchers and practitioners should consider organizational-level factors such as organizational structure, culture, corporate governance, etc as well as individual-level factors such as the characteristics and relationships of organizational members when making the decision for firm.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.95-107
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• 2023

Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, traffic accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of traffic accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline (B-spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten different models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have different effects such as increasing the speed limit would decrease traffic accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of traffic accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.

• Wind and Structures
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• v.15 no.1
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• pp.43-64
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• 2012

Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. A neural network-based model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle's region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively.

• Knowledge Management Research
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• v.19 no.3
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• pp.189-223
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• 2018

This paper examined the effect of innovation networks comprising research and development (R&D) collaboration on innovation performance of Korean pharmaceutical firms. As co-assigned patents and co-affiliated publications are common technical outcomes of successful R&D collaboration in the pharmaceutical industry, social network analysis technique was applied for analyzing innovation networks through patent and publication data. Results of Social network analysis indicated that a small set of highly innovative firms in the Korean pharmaceutical industry were actively involved in patenting and publishing. And the analysis of structural equation model found the followings: (1) R&D intensity significantly affected patenting, publication and new drug development, (2) the activity of patenting and publishing was positively related with the innovation performance measured by new drug development, and (3) R&D collaboration in terms of degree centrality of co-patent network played significant moderating roles on the relationships among R&D intensity, patenting, and new drug development. These findings are expected to be helpful to researchers as well as policy-makers to devise innovation-promoting policies in the Korean pharmaceutical industry. Discussions and limitations of the study are provided in the last part.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.5
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• pp.22-29
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• 2012

By using the cyclic properties of symmetric balance incomplete block design(BIBD) codes and arrayed-waveguide grating(AWG) routers, a compact optical CDMA network coder-decoder(codec) can be constructed. It can be observed that the various code families obtained by BIBD improve the BER performance compared to M-sequence code.