• Korean journal of communication and information
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• v.66
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• pp.159-183
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• 2014

This study analyzed the competition among three game platforms, PC, Console and Mobile in aspect of user's gratifications using niche analysis. To do this, four gratification factors, 'transportability and accessibility', 'liveliness and variety', 'availability and economic feasibility', 'relationship', were assessed. To investigate competition level among three game platforms, niche breadth, niche overlap, competitive superiority of four gratification factors were analyzed. The results of niche analysis show that mobile platform appeared as a most competitive platform because it utilized diverse resources for game user gratification and had competitive superiority in 'transportability and accessibility' which appeared as most important factor determining user's gratification. However there was no game platform which had competitive superiority in all four gratification factors.

• The monthly packaging world
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• s.286
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• pp.67-71
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• 2017

• Structural Engineering and Mechanics
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• v.46 no.3
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• pp.403-416
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• 2013

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.466-469
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• 1996

In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.159-167
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• 1996

In this paper, a new competitive learning algorithm called dynamic competitive learning (DCL) is presented. DCL is a supervised learning mehtod that dynamically generates output neuraons and nitializes weight vectors from training patterns. It introduces a new parameter called LOG (limit of garde) to decide whether or not an output neuron is created. In other words, if there exist some neurons in the province of LOG that classify the input vector correctly, then DCL adjusts the weight vector for the neuraon which has the minimum grade. Otherwise, it produces a new output neuron using the given input vector. It is largely learning is not limited only to the winner and the output neurons are dynamically generated int he trining process. In addition, the proposed algorithm has a small number of parameters. Which are easy to be determined and applied to the real problems. Experimental results for patterns recognition of remote sensing data and handwritten numeral data indicate the superiority of dCL in comparison to the conventional competitive learning methods.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.82-91
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• 1997

In this paper, we analyze the dynamics of langevine competitive learning neural network based on its fokker-plank equation. From the viewpont of the stochastic differential equation (SDE), langevine competitive learning equation is one of langevine stochastic differential equation and has the diffusin equation on the topological space (.ohm., F, P) with probability measure. We derive the fokker-plank equation from the proposed algorithm and prove by introducing a infinitestimal operator for markov semigroups, that the weight vector in the particular simplex can converge to the globally optimal point under the condition of some convex or pseudo-convex performance measure function. Experimental resutls for pattern recognition of the remote sensing data indicate the superiority of langevine competitive learning neural network in comparison to the conventional competitive learning neural network.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.1-18
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• 2020

Fine dust is one of the top ten causes of deaths globally. More than 95% of the world's population are endangered by it. However, as the fine dust problem is difficult to address immediately, people should be informed of its risk and prepared to deal with it. This study explores the methods used to define, efficiently provide, and manage the complementary relationships between various types of media providing risk information utilizing the competitive characteristics of media in niche theory. A survey consisting of 348 Korean university students was conducted over 12 days, to analyze three factors: consumer perception of fine dust, media usage, and media use gratification. The response value for media gratification was substituted in the equation to derive the niche breadth, niche overlap, and competitive superiority. It was found that 1) for providing fine dust forecast and fine dust response guidance information, a smartphone application was the most effective; 2) smartphone applications were limited in providing additional information such as the severity and origin of fine dust, and hence, it is necessary to establish the functionality of the Internet and TV to complement smartphone applications. Thus, a system considering the above should be developed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.4
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• pp.113-131
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• 2015

Decision effectiveness depends on type of knowledge within team members generated by decision making process. Thus, organization in accordance with teams' experience and capability ultimately achieve their desired outcome. However, previous research has not addressed a mediating role between different knowledge type in decision making and product competitive advantages(pioneering advantage and product quality superiority). Based on the knowledge-based view, we model how different knowledge characteristics in decision making affect to acquire each of knowledge in decision making effectively and then to apply acquired knowledge in decision making. Anchored in a source-position-performance (SPP) framework (Day and Wensley's, 1988), we shed light on the effects of three knowledge characteristics dimensions in decision making process on knowledge management competences in decision making for a new product project. We also examine the relationship between two dimensions of NPD knowledge management competences, and product competitive advantages which consist of market pioneering advantage and product quality superiority. To test the relationships, the empirical analyses are conducted using a sample of team managers who participated in NPD projects. This study suggest that managers should increase their acquirability and applicability of knowledge by integrating complexity of diverse and new knowledge, developing codifiability of well-documented knowledge, and creating the sharing common knowledge among NPD team members. Thus, they are able to outrun major competitors in terms of pioneering advantage and product quality superiority perspective.

• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.