• 한국콘텐츠학회논문지
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• 제7권5호
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• pp.1-9
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• 2007

토폴로지 정보는 망에서 계층적으로 구성될 수 있으며, 토폴로지 정보를 요약하는 과정을 토폴로지 요약이라 한다. 이는 라우팅과 네트워크의 확장성에 매우 중요한 요소이다. 특히, 사설망 인터페이스에서 라우팅 알고리즘과 토폴로지 요약 알고리즘은 네트워크의 성능에 중요한 변수가 된다. 따라서 본 논문에서는 사설망 인터페이스에서 토폴로지 요약을 위한 라우팅 시뮬레이터를 설계 및 구현하였다. 그리고 구현된 시뮬레이터를 사용하여 기존 토폴로지 요약 알고리즘의 성능을 분석하였다. 구현된 사설망 인터페이스 시뮬레이터는 토폴로지 요약 알고리즘 개발에 유용하게 사용될 수 있다.

• 대한가정학회지
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• 제40권6호
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• pp.133-144
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• 2002

The purpose of this study was to investigate the characteristics of the social networks and its relationship with the psychological well-being of middle-aged men. The participants were 314 men who were married living in Pusan, aged between 40 and 59, having occupation and children. Data were collected by questionnaire which consists of Social Networks Scale and Psychological Well-being Scale. The major results of the study were summarized as follows; First, the range of the social contact with men's own kin was wider than women's. In the contact frequency of midge-aged men, primary networks were shown more frequent contact than the secondary networks. In the characteristics of interactive function of social networks, kinship of the middle- aged men, their partners and friendship networks were the most important among the six networks, which agrees the fact that kin are still the primary source of social support. Second, the level of psychological well-being was lower than family-related satisfaction and work-related satisfaction. In the work-related satisfaction, the level of job satisfaction was lower than the other sub-categories. Psychological well-being of middle-aged men were affected by Social economic status as well.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.1147-1150
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• 2008

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of the support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, The disaggregation means that the yearly PE data divides into the monthly PE data. 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 disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily 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 GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily 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.

• 대한가정학회지
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• 제46권8호
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• pp.85-95
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• 2008

This study examined the revictimization of wives from repeated husband violence and how that affected their depression. It also explored whether social support networks can have moderating effects. Sixty-four wives participated in the research group, 72.3% of whom had experienced repeated verbal violence, and 29.2% experienced repeated physical violence since their husbands participated in the correction and rehabilitation program for family violence perpetrators. Revictimization from repeated husband-to-wife violence was proven to significantly influence wife depression. To moderate the harmful effects of repeated domestic violence on depression, social support networks were observed to provide protective reinforcements. However, the findings of this study did not support the notion that social support networks have moderating effects on wife depression, while a strong negative relationship was established between professional networks of social support networks. Based on these results, the research discussion here advocates for an intervention that promotes psychological health to wives who are exposed to repeated domestic violence.

• 컴퓨터교육학회논문지
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• 제6권3호
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• pp.47-56
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• 2003

We describe a new method for removing non-linear phased array antenna aberration called "squint" problem. To develop a compensation scheme. theoretical antenna and artificial neural networks were used. The purpose of using the artificial neural networks is to develop an antenna system model that represents the steering function of an actual array. The artificial neural networks are also used to implement an inverse model which when concatenated with the antenna or antenna model will correct the "squint" problem. Combining the actual steering function and the inverse model contained in the artificial neural network, alters the steering command to the antenna so that the antenna will point to the desired position instead of squinting. The use of an artificial neural network provides a method of producing a non-linear system that can correct antenna performance. This paper demonstrates the feasibility of generating an inverse steering algorithm with artificial neural networks.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.1560-1563
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of support vector machine neural networks model (SVM-NNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of SVM-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.133-133
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• 2021

The performance of radar Quantitative Precipitation Estimation (QPE) using Long Short-Term Memory (LSTM) networks in hydrological applications depends on either the quality of data or the three-dimensional CAPPI structure from the weather radar. While radar data quality is controlled and enhanced by the more and more modern radar systems, the effect of CAPPI structure still has not yet fully investigated. In this study, three typical and important types of CAPPI structure including inverse-pyramid, cubic of grids 3x3, cubic of grids 4x4 are investigated to evaluate the effect of CAPPI structures on the performance of radar QPE using LSTM networks. The investigation results figure out that the cubic of grids 4x4 of CAPPI structure shows the best performance in rainfall estimation using the LSTM networks approach. This study give us the precious experiences in radar QPE works applying LSTM networks approach in particular and deep-learning approach in general.