• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.73-81
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• 2014

This study proposed development method for practical use techniques compromise data storage layer, data processing layer, data analysis layer, visualization layer. Data of storage, process, analysis of each phase can see visualization. After data process through Hadoop, the result visualize from Mahout. According to this course, we can capture several features of customer, we can choose recommendation of financial item on time. This study introduce background and problem of big data and discuss development method and case study that how to create big data has new business opportunity through financial item recommendation case.

• Proceedings of the Technology Innovation Conference
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• 1998.06a
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• pp.208-215
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• 1998

Since the advent of conceptual prototype and seminal application, the notion of national systems of innovation(NSI) has drawn an increasing recognition. Although the morphological entanglement is still ubiquitous and the theoretical underpinning is fragile, NSI seems to be the last step toward an increasingly complex and encompassing concept of innovation research. Inevitably, NSI necessitates the comparative analysis in that it normatively attempts to draw best practices. Unfortunately, national profiles are too complex and diverse to derive a unified, concrete representation of the system, posing the problem of defining and modelling NSI for international comparison. This paper aims at providing an inductive taxonomy of NSI based on R&D structure of OECD member economies. Based on the similarity among national profiles, clustering method was applied to identify seven clusters such as (1) enterprise-government funding and enterprise-education performing group, (2) enterprise-government funding and balanced performing group, (3) balanced funding and enterprise-education performing group, (4) balanced funding and performing group, (5) enterprise-dominating group, (6) government-education dominating group and (7) government-education funding and education performing group. This paper by nature is descriptive and exploratory. R&D structure represents a static snapshot of innovative performance since it accounts for only the input side of NSI and thus may not offer convincing explanations of the holistic innovation system. A more detailed and extensive analysis on the economic/technological performance across clusters will shed light on the promising avenue to future research.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.113-116
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• 2008

This work presents numerical optimization for design of staggered arrays of dimples printed on opposite surfaces of a cooling channel with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing and dimple depth to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier-Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-mean clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.7
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• pp.235-238
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• 2015

IEEE 802.15.4 is the well-established standard enabling wireless connectivities among wireless sensor nodes. However, the wireless sensor networks based on IEEE 802.15.4 are inherently vulnerable to hidden nodes collision because the wireless sensor nodes have very limited communication range and battery life time. In this paper, we propose the advanced method of mitigating hidden nodes collision in IEEE 802.15.4 base wireless sensor networks by clustering sensor nodes according to channel quality information. Moreover, we deal with the problem of resource allocation for each cluster.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

• Korean Management Science Review
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• v.18 no.2
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• pp.125-134
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• 2001

This paper is concerned with the development of a GIS-based distribution system with service consideration. The proposed model could be used for a wide range of logistics applications in planning, engineering and operational purpose for logistics system. This research addresses the formulation of those complex prob1ems of two-echelon logistics system to plan the incorporating supply center locations and distribution problems based on GIS. We propose an integrated logistics model for determining the optimal patterns of supply centers and inventory allocations (customers) with a three-step sequential approach. 1) First step, Developing GIS-distance model and stochastic set-covering program to determine Optimel pattern of supply center location. 2) Second step, Optimal sector-clustering to support customers. 3) Third step, Optimal vehicle rouse scheduling based on GIS, GIS-VRP In this research we developed GUI-tree program, the GIS-VRP provide the vehicle to users and freight information in real time. We applied a set of sample examples to this model and demonstrated samp1e results. It has been found that the proposed model is potentially efficient and useful in solving multi-depot problem through examples. However the proposed model can provide logistics decision makers to get the best supply schedule.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• Genomics & Informatics
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• v.5 no.3
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• pp.95-101
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• 2007

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n<

• Journal of the Economic Geographical Society of Korea
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• v.17 no.4
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• pp.646-659
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• 2014

Political and academic concerns on creative economy have been increased, despite of the debates on its concepts and socioeconomic implications. This article is an exploratory study about the mechanisms and the sources in which creative economy works. Due to ICT revolution and expansion of individual's networking competency, collective knowledge created by networking and city/region in which the collective knowledge has been embedded became the parts and parcels of creative economy. Three sources of creative city is as follows: regional peculiarity and locality from industrial clustering, intensity of urban networks(openness), and value orientations in regional problem solving.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.157-162
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• 2008

This paper presents a learning method of tagging ontology using large tagging data such as a folksonomy, which stands for classification structure informally created by the people. There is no common agreement about the semantics of a tagging, and most social web sites internally use different methods to represent tagging information, obstructing interoperability between sites and the automated processing by software agents. To solve this problem, we need a tagging ontology, defined by analyzing intrinsic attributes of a tagging. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tagging ontology is also suggested as an applying field.