• Journal of Korean Society of Rural Planning
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• v.13 no.1 s.34
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• pp.97-109
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• 2007

The purpose of this research was to investigate and analyse how Community Forest Initiatives as urban fringe management initiatives made alliances with a variety of interest groups, enrol them in the urban fringe management processes using the Actor Network Theory. The Thames Chase Community Forest Initiative was selected and its area of operation included a $97 km^2$ area of green-belt area in East London. It was a instrument far improving and protecting the unique characteristics of the countryside landscape from urban developments as well as evaluating the impact of forestry inclusion in land use planning in the urban fringe. It was operated through a tiered structure comprising the Thames Chase Joint Committee and the management team. They employed a variety of devices to speak with one voice to bring about an effective operation process and to secure the enrolment of a variety of interest groups in its operational processes. Of note, the initiative's actor network impacted on improvement to and management of the countryside landscape despite not owning any land itself. As a result, when urban fringe management initiatives will be launched in South Korea to achieve a more effective and efficient urban fringe management, local councillors and representatives from public and non-government bodies should be more responsive to local communities' views and needs and work more vigorously on their behalf through lobbying, seeking media support, and so on. Moreover, better understanding and communication between local authorities' officers and management initiatives' teams are essential to avoid duplication of work practice.

• Korean Journal of Environment and Ecology
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• v.25 no.3
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• pp.412-420
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• 2011

This study was conducted to quantitatively analyze the temporal change of green space network at multi-scales from 1975 to 2006 with the application of graph theory in Seoul, Korea. Remarkable change of connectivity was detected in green space networks at the scale ranging from 1,000 ~ 1,600 m during 30 years. Green spaces and their networks have been restoring after 1990 since forest areas had been fragmented in 1975. In 2006, we identified the important core habitat areas that can sustain diverse wildlife species and stepping stones composed of small patches that can link these core habitat areas. Green spaces showed high correlation with the relative importance value of green space connectivity. So, this study could graphically represent green space networks of Seoul City. Green spaces of core areas distributed at the northern and southern boundary, and those of stepping stones possessing the high value of betweenness centrality consisted at the middle, eastern and western boundary. These results indicate that green space network can be graphically and quantitatively explained by degree centrality, betweenness centrality and relative importance value of connectivity with the application of graph theory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.51-59
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• 1999

This study aims at the development of the techniques for the rainfall forecasting in river basins by applying neural network theory and compared with results of Multivariate Model (MVM). This study forecasts rainfall and compares with a observed values in the San Chung gauging stations of Nakdong river basin for the rainfall forecasting of river basin by proposed Neural Network Model(NNM). For it, a multi-layer Neural Network is constructed to forecast rainfall. The neural network learns continuous-valued input and output data. The result of rainfall forecasting by the Neural Network Model is superior to the results of Multivariate Model for rainfall forecasting in the river basin. So I think that the Neural Network Model is able to be much more reliable in the rainfall forecasting.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.8 no.2
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• pp.35-43
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• 1990

In this paper a two dimensional network adjustment theory is developed to analyze the plane trilateration network of single triangle network, of quadrilateral network, of polygon trilateration network and of combined network. The characteristics of error were analysed by developing an error propagation equation for each form of plane trilateration network. In case of combined network, the result of error analysis was represented by error ellipses and gross error detection was carried out by data snooping method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1183-1188
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• 2004

The inverse heat conduction problem (IHCP) is a problem of estimating boundary condition from temperature measurement at one or more interior points. Neural networks are general information processing systems inspired by the connectionist theory of human brain. By properly training the network by the learning rule, the neural network method can handle many non-linear or other complex problems. In this work, neural network is applied to complicated inverse heat conduction problems. Efficiency of the procedure is enhanced by incorporating the radial basis functions (RBF). The RBF is trained faster than other neural network and can find smooth solution. In order to demonstrate the effectiveness of the current scheme, a typical one-dimensional IHCP is considered. At one surface, the temperature as well as the heat flux is known. The unknown temperature of interest is estimated on the other side of the slab. The results from the proposed method based on RBF neural network are compared with the conventional method.

• International Journal of High-Rise Buildings
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• v.8 no.1
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• pp.49-56
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• 2019

Against the background of the rapid development of the Shanghai Metro network, this paper attempts to establish an analytical approach to evaluate the impact of multiple transport network accessibilities on commercial space use in metro station areas. Ten well-developed metro station areas in central Shanghai are selected as samples. Commercial space floor area and visitors in these areas are collected. Using ArcGIS and Spatial Design Network Analysis, the Shanghai Metro network and road network are modeled to compute diversified transport accessibilities. Evidence from land use and commercial space floor area within a 0-to-500-meter buffer zone of stations is consistent with location and land-use theory: commercial land use is concentrated closer to stations. Correlation analysis suggests that hourly visitors to the shopping mall are mainly influenced by metro network accessibility, while retail stores and restaurants are affected by both metro and pedestrian accessibility.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.3
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• pp.315-320
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• 2009

The purpose of this study is to predict the roundness of Numerical Control Machining so that helps the operator to choose the right machining conditions to produce a product within the given error limits. Learning of neural network is Backpropagation theory. From this study, the base was set to setup the database to produce precisely machined product by predicting the rate of error in the fabrication facility which does not have the environment to analyze it.

• Honam Mathematical Journal
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• v.31 no.2
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• pp.147-156
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• 2009

In this paper, we actually construct the simultaneous approximation by neural networks to a differentiable function. To do this, we first construct a polynomial approximation using the Fejer sum and then a simultaneous neural network approximation with the squashing activation function. We also give numerical results to support our theory.