• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.455-461
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• 2012

The paper explores the relationship among paradigms, ecosystem which need to establish broadcasting, information and communications industry as the core of national competitiveness in the digital convergence era, and proposes the future progressing strategies and policies directions that consider environmental factors which all mass media converged on Internet. This paper makes an attempt to contribute innovative & integrated perspectives in the industry.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.49-56
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• 2010

The Genetic Algorithm is one of the population surface modelling tool in the field of urban and environmental research based on the gridded population data. Taking the East-Hwasung area as the case, this study first builds a gridded population data based on the GIS databases as well as municipal population survey data. The study then constructs the attribute values of the explanatory variables by way of GIS tools. The regression model constructed with the same variables is also run as a comparative purpose at the same time. It is shown that the GenAlg output predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression model, indicating that it is a very useful interdisciplinary research tool to find optimal solutions in urban problems.

• Tunnel and Underground Space
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• v.18 no.1
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• pp.69-79
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• 2008

Dokdo is a volcano edifice originating from an oceanic island that was formed around 3 million to 2.2 million years ago, and it consists of Dongdo(eastern island) and Seodo(western island). Even though Dokdo is a small volcanic island, Dokdo has infinite potential value and significant economic, social, scientific, and technical aspects due to its resources, ecological and territorial value. In addition, it is of national interest with regards to the dispute with Japan over the dominium of Dokdo. A need to evaluate the ground stability of Dokdo, especially in Dongdo, has been seriously raised recently due to the various cracks caused by the progressive weathering and corrosion. This study dealt with the geology and geological layers of Dokdo and identified the status of ground cracks as the previous research to evaluate the ground stability of zones of concern in Dongdo. Also, this study analyzed the relationships between physical and mechanical properties with rock types. The results showed that the values of rock properties in Dokdo are lower contrary to the general rocks in Korea, and tuff was especially affected by the weathering and corrosion.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.230-240
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• 2009

This study proposes a theory of consumer experiences based on a cognitive science framework that serves as an alternative to the mainstream marketing paradigm of information processing and choice. The theory consists of three key theoretical constructs: experiential modules, primary vs. secondary experiences, and the hierarchy of experiential modules. Based on this theory, this study investigates the effect of experiential attributes and functional attributes on consumer's product evaluations, and the moderating role of consumer's knowledge. The main research findings are (1) the subjects react faster to sensory and affective stimuli(ex: experiential attributes) than they do to intellectual stimuli (ex: functional attributes), (2) the interaction modularity of attributes(functional vs. experiential) $\times$ tempo(normal vs. fast) $\times$ product knowledge(novice vs. expert) appear significantly with product evaluation as the dependent measure.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Tuberculosis and Respiratory Diseases
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• v.43 no.1
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• pp.108-112
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• 1996

Bronchial foreign body aspirations are uncommon in adults and usually have various underlying conditions, otherwise accidentally occurred in dental procedure, medical, surgical procedure. The most commonly aspirated objects were food materials, with peanuts leading the list. When bronchial foreign body was aspirated, no definite respiratory symptoms or foreign body is may not seen, then diagnosis is delayed. Therefore late complications was developed. The tendency to use fiberoptic bronchoscope for removal of foreign bodies in adults is increased. We report a rare case of bronchial pneumonia due to occult aspiration of a twig before 1 year with a review of the literatures.

• Convergence Security Journal
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• v.16 no.7
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• pp.101-111
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• 2016

The recent cyber threats are becoming real threats to our lives. This gloomy situation from cyber threats necessarily demands the establishment of the cyber threat information sharing system between the public and private area. Key countries, like the US, Japan and the UK, are stabilizing the cyber threat information sharing systems by founding exclusive organizations for sharing information and setting up and implementing relevant measures. In this thesis, I would like to propose the model for cyber threat information sharing in order to cope efficiently with the ever-intensifying cyber threats. My model would include key elements for the efficient information sharing, such as the clear designation of main operator of information sharing system, the management of collaboration system between the public and private sector, the build-up of the integrated and automated system and the supplementation of legal system including the grant of privilege, and so on.