• 한국농공학회논문집
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• 제54권5호
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• pp.43-47
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• 2012

A evaluation for the strength of rock includes a lot of uncertainty due to existence of discontinuity surface and weakness plain in the rock mass, so essential test results and other data for the resonable strength analysis are absolutely insufficient. Therefore, a analytical technique to reduce such uncertainty can be required. A probabilistic analysis technique has mainly to make up for the uncertainty to investigate the strength of rock mass. Recently, a artificial neural networks, as a more newly analysis method to solve several problems in the existing analysis methodology, trends to apply to study on the rock strength. In this study the unconfined compressive strength from basic physical property values of sedimentary rock, black shale and red shale, distributed in Daegu metropolitan area is estimated, using the artificial neural networks. And the applicability of the analysis method is investigated. From the results, it is confirmed that the unconfined compressive strength of the sedimentary rock can be easily and efficiently predicted by the analysis technique with the artificial neural networks.

• 대한지리학회지
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• 제45권2호
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• pp.256-274
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• 2010

본 연구의 목적은 국내 광고산업의 집적 특성과 광고제작의 공간적 네트워크를 고찰하는 것이다. 광고산업은 다른 비즈니스 서비스와 마찬가지로 서울의 집중도가 높기 때문에 서울 내에서의 집적 특성을 중점적으로 분석하였다. 업종별 종사자수의 지역별 비중과 더불어 로컬 모란 I와 입지계수 방법이 분석에 이용되었다. 광고산업의 강남지역 집중이 두드러지게 나타나는데, 이것은 신생 기업들의 강남지역 지향 입지와 국내 진입 외국계 광고대행사의 강남지역 선호와 관련이 있다. 또한 네트워크 분석 방법을 이용해 TV광고 제작의 공간적 네트워크를 분석했다. 광고대행사-광고제작사 네트워크 매트릭스를 구축하고, 이를 지역 매트릭스로 변환한 후, 중심성 분석과 중개 분석을 통해 광고제작의 공간적 네트워크를 고찰하였다. 이를 통해 강남 세부지역의 상이한 역할과 지위를 알 수 있는데, 강남지역 내부와 외부의 광고대행사들이 강남지역에 있는 광고제작사에 대부분의 광고제작을 아웃소싱하고 있다. 이러한 분석을 통해 광고산업이 강남지역이 광고제작의 목적지로서 뿐 아니라 강남지역에서 광고제작 네트워크가 순환되고 있음을 알 수 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.3-6
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• 1998

This paper considers the analysis of QoS(Quality of Service) routing mechanisms to support the NGIs(Next Generation Internets). NGIs are constructing high-speed IP layer networks to support all data services. To support real time multimedia services on NGIs, it is important to satisfy the required QoS parameters on networks. To support QoS requirements for NGI networks, new QoS routing methods are essential. In this paper, serveral new QoS routing algorithms are explained. Some problems for the high speed QoS routing will be explained and possible solutions are suggested.

• 지식경영연구
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• 제12권2호
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• pp.69-80
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• 2011

This paper reports on a structural view of a knowledge network comprised of clubs and organisationsexpressly concerned with cultural activities in a regional Australian city. Social network analysis showed an uneven distribution of power, influence, and prominence in the network. The network structure consisted of two modules of vertices clustered around particular categories of creative arts and these modules were linked most frequently by several organisations acting as communication hubs and boundary spanners. The implications of the findings include 'network weaving' for improving the network structure and developing a systemic approach for exploring the structures of social action that form community-based knowledge networks.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.669-673
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• 2008

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• 한국유체기계학회 논문집
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• 제5권4호
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• pp.47-53
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• 2002

The steam generator feedwater flow-rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow-rate in pressurized water reactors, may result in unnecessary plant power derating. The back-propagation network was used to generate models of signals for a pressurized water reactor Multiple-input, single-output hetero-associative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow-rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

• 한국정보통신학회논문지
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• 제17권9호
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• pp.2020-2028
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• 2013

본 논문에서는 복잡계 네트워크 이론에 기반하여 무선 애드혹 네트워크를 분석하고 네트워크 토폴로지를 구성하는 방법에 관하여 제안한다. 본 논문에서는 기존의 복잡계 네트워크 연구가 무선 통신 채널의 특성을 정확히 반영하지 못한 부분을 개선하였으며, 랜덤 그래프 이론을 무선 통신 환경을 고려하여 확장하였다. 주요 결과로서 복잡계 네트워크 이론에 기반한 네트워크 토폴로지 구성이 전체 애드혹 네트워크 성능에 미치는 영향을 분석하고 시뮬레이션을 통하여 검증하였다.

• Earthquakes and Structures
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• 제15권4호
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• pp.431-441
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• 2018

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.

• Journal of Communications and Networks
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• 제18권6호
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• pp.926-937
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• 2016

In this paper, we study the feasibility of receiver diversity for application to downlink cellular networks, where low-energy devices are equipped with information decoding and energy harvesting receivers for simultaneous wireless information and power transfer. We compare several options that are based on selection combining and maximum ratio combining, which provide different implementation complexities. By capitalizing on the Frechet inequality, we shed light on the advantages and limitations of each scheme as a function of the transmission rate and harvested power that need to be fulfilled at the low-energy devices. Our analysis shows that no scheme outperforms the others for every system setup. It suggests, on the other hand, that the low-energy devices need to operate in an adaptive fashion, by choosing the receiver diversity scheme as a function of the imposed requirements. With the aid of stochastic geometry, we introduce mathematical frameworks for system-level analysis. We show that they constitute an important tool for system-level optimization and, in particular, for identifying the diversity scheme that optimizes wireless information and power transmission as a function of a sensible set of parameters. Monte Carlo simulations are used to validate our findings and to illustrate the trade-off that emerge in cellular networks with simultaneous wireless information and power transfer.

• 한국안전학회지
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• 제39권1호
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• pp.70-81
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• 2024

With heavy rainfall due to extreme weather causing increasing damage, the importance of urban flood forecasting continues to grow. To forecast urban flooding accurately and promptly, a sewer network and surface grid with appropriate detail are necessary. However, for urban areas with complex storm sewer networks and terrain structures, high-resolution grids and detailed networks can significantly prolong the analysis. Therefore, determining an appropriate level of network simplification and a suitable surface grid resolution is essential to secure the golden time for urban flood forecasting. In this study, InfoWorks ICM, a software program capable of 1D-2D coupled simulation, was used to examine urban flood forecasting performance for storm sewer networks with various levels of simplification and different surface grid resolutions. The inundation depth, inundation area, and simulation time were analyzed for each simplification level. Based on the analysis, the simulation time was reduced by up to 65% upon simplifying the storm sewer networks and by up to 96% depending on the surface grid resolution; further, the inundation area was overestimated as the grid resolution increased. This study provides insights into optimizing the simplification level and surface grid resolution for storm sewer networks to ensure efficient and accurate urban flood forecasting.