• Proceedings of the Korea Association of Information Systems Conference
• /
• 2000.11a
• /
• pp.25-35
• /
• 2000

A huge amount of financial information in large databases makes performance comparisons among organizations difficult or at least very time-consuming. This paper investigates whether neural networks in the form of self-organizing maps can be effectively employed to perform a competitive benchmarking in large databases. By using self-organizing maps, we expect to overcome problems associated with finding appropriate underlying distributions and functional forms of underlying data. The method also offers a way of visualizing the results. The database in this study consists of annual financial reports of 100 biggest Korean companies over the years 1998, 1999, and 2000.

• Journal of the Korean Regional Science Association
• /
• v.13 no.2
• /
• pp.55-74
• /
• 1997

This paper investigates the spatial characteristics of networks, which arise as a firm's strategy to enhance its competitiveness to cope with the changing economic environments characterized by technological changes and increasing competitiveness. The theoretical framework in this study proposes that networks emerge as a firm's strategies to promote its competitiveness through the vertical/horizontal disintegration of the production system. Futhermore, regional industries of networks. The study examines the types of cooperation and the spatial boundary of the computer industry networks in Korea. Questionnaire survey was conducted on 1, 128 computer companies which had more than 10 employees, with 126 questionnaires being used for analysis. In addition, newpaper articles were used to supplement the foregoing work on network characteristics. The review of these articles covers the period from Jan. 1994 to June 1996. Major findings of this study are as follows: The spatial range of cooperative networks varies according to the specific characters of cooperation(R & D, production, and seles). Intralocal networks are being developed in Kangnam and Youido area, the computer industry agglomeration clusres of Seoul. There are the regional differnces in the agents and contents of cooperation. In intra-national R & D and production networks, regional differnces in agglomeratins and non-agglomerations are not detercted. Most networks of this type are found between large firms and small firms. In contrast, foregn R & D and production networks, which are operated mostly by large firms, are found in Kangnam, Youido, and CBD. Intra-national and foreign productino networks are also focused in Kangnam, Youido, and CBD. Small firms are playing an active role in making this type of cooperation possible. In the perspective of localization-globalization, Korean computer industry can be analyzed in two respects: industrial and regional. The localization of small firms and the localization-globalization of large firms' networks are being developed in industrial contexts, while the localization-globalization of agglomerations and the localization of non-agglomerations networks are being developed in regional contexts. As networks for the localization-globalization of industry are growing in agglomerations, interfirm networks could be related to trends in the formation or intensification of industrial agglomerations. industrial agglomeration areas function as a facilitator of localization through subcontracts, intraregional network and interregional network. They also facilitate globalization via foregn networks. In non-agglomeratin areas, localization networks, which are connected with agglomeration areas via subcontracting, interregional R & D. or production cooperation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.5
• /
• pp.393-400
• /
• 2013

In conventional single-tier networks, downlink based association is the best association scheme for the uplink association because all macro base stations have the same physical specification. However, in uplink heterogeneous cellular networks, a downlink based cell association cannot be the best for uplink any more because of the difference of physical specification between the different base station. In this paper, we will propose a uplink based cell association scheme, and devise performance metric for describing a uplink performance in heterogeneous cellular networks. Then, we will discuss the necessity of the uplink based association by observing outage probability, delay constraint outage probability, delay constraint outage capacity.

• Journal of Families and Better Life
• /
• v.17 no.3
• /
• pp.159-170
• /
• 1999

This study analyzes the relationships between social networks and loneliness. Subjects of this study are male and female undergraduate students residing in Soul and Jeonju city Questionaires were and the obtained results were analyzed through SPSS PC+ The results are as follows 1) The size of general social networks doesnt's show difference between male and female students but at the of intimate social networks female students' one is bigger than male students' And female students' contact frequency via telephone is higher than male students' but the direct contact frequency did not show the difference: 2) The bigger the size of social networks is and the higher the contack frequency is the more the mount of social supports female students get. And the bigger the size of social networks is the more the amount of social support male students get. But among male students there are no relationships between the contact frequency and the amount of social supports 3) The size and co tact frequency of social networks is negatively related to loneliness among female and male students 4) The amount of social supports explaines the degree of loneliness at 23% among female students and 18% among male students.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.112-115
• /
• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.1423-1426
• /
• 2007

The goal of this research is to develop and apply the integrational model for the pan evaporation and the alfalfa reference evapotranspiration in Republic of Korea. Since the observed data of the alfalfa reference evapotranspiration using lysimeter have not been measured for a long time in Republic of Korea, PM method is used to assume and estimate the observed alfalfa reference evapotranspiration. The integrational model consists of staochastics and neural networks processes respectively. The stochastics process is applied to extend for the short-term monthly pan evaporation and alfalfa reference evapotranspiration. The extended data of the monthly pan evaporation and alfalfa reference evapotranspiration is used to evaluate for the training performance. For the neural networks process, the generalized regression neural networks model(GRNNM) is applied to evaluate for the testing performance using the observed data respectively. From this research, we evaluate the impact of the limited climatical variables on the accuracy of the integrational operation of stochastics and neural networks processes. We should, furthermore, construct the credible data of the pan evaporation and the alfalfa reference evapotranspiration, and suggest the reference data for irrigation and drainage networks system in Republic of Korea.

• Convergence Security Journal
• /
• v.7 no.4
• /
• pp.115-121
• /
• 2007

Due on failures of communication nodes for the wireless and wired networks, mobile agents may be blocked even if there is available service in the networks. To solve it, we propose migration policy with reordering of the paths to guarantee the migration of mobile agents and the paper will provide the extension with the autonomous migration of mobile agents.

• Journal of Korea Water Resources Association
• /
• v.34 no.4
• /
• pp.303-316
• /
• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.2193-2196
• /
• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Journal of the Korean Home Economics Association
• /
• v.35 no.3
• /
• pp.31-45
• /
• 1997

The main purpose of the study was to examine relationships of child development with maternal social networks and maternal perceptions of parenting difficulty. Subjects were 90 children, ages 4 and 5, with their mothers. Child development was measured by School Readiness Test, peer nomination, and social competency ratings by teachers. Mothers responded to a questionnaire regarding social networks and parenting difficulty. The major findings of the study include: 1) Employed mothers reported receiving less emotional support and listed more in-laws and work colleagues in their social network than unemloyed mothers. 2) Mothers who perceived receiving more emotional support from networks reported less difficulty in parenting, especially in providing cognitive stimulation and daily routine care to their children. 3) Children's age and maternal perceptions of easiness in providing cognitive stimulation were the most contributing factors for predicting children's learning readiness and social competency.