• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.6 no.1
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• pp.23-37
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• 2011

This paper is brought to carry out an empirical study whether evolution process of venture firm's scale is following the Gibrat's law; random evolution process, or Pareto law; self-organizing process. The empirical test, as attaching theoretical explanation, of this research utilize the serial data samples of 92 KOSDAQ listed companies from the year of 2005 through 2008. Summarizing the research results are as followed. First, Gini Coefficients representing the density of venture firm's scale has been constantly reduced since the year of 2005 in terms of number of employee, while these index increased during the same time period from the perspective of sales volume. Second, the evolution process of Korea venture firm's scale is following the Power Law related to Pareto Law. In particular, estimated Pareto coefficient, ${\alpha}$, is shown lower than 1 which is significant result. Third, the probability of joining in the top tier group of firm starting from the early stage growing is forecasted into 6.9%, the result which emphasize the starting scale of venture firm play an important role in long term evolution of venture firm.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.354-358
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• 2001

This paper presents a fuzzy neural network model which solves the underutilization problem. This fuzzy neural network has both stability and flexibility because it uses the control structure similar to AHT(Adaptive Resonance Theory)-l neural network. And this fuzzy nenral network does not need to initialize weights and is less sensitive to noise than ART-l neural network is. The learning rule of this fuzzy neural network is the modified and fuzzified version of Kohonen learning rule and is based on the fuzzification of leaky competitive leaming and the fuzzification of conditional probability. The similarity measure of vigilance test, which is performed after selecting a winner among output neurons, is the relative distance. This relative distance considers Euclidean distance and the relative location between a datum and the prototypes of clusters. To compare the performance of the proposed fuzzy neural network with that of Kohonen Self-Organizing Feature Map the IRIS data and Gaussian-distributed data are used.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.85-91
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• 2018

Most banks use only demographic information such as gender, age, occupation and address to segment customers, but they do not reflect financial behavior patterns of customers. In this study, we aim to solve the problems by using various big data in a bank and to develop customer segmentation method which can be widely used in many banks in the future. In this paper, we propose an approach of segmenting clustering blocks with bottom-up method. This method has an advantage that it can accurately reflect various financial needs of customers based on various transaction patterns, channel contact patterns, and existing demographic information. Based on this, we will develop various marketing models such as product recommendation, financial need rating calculation, and customer churn-out prediction based on this, and we will adapt this models for the marketing strategy of NH Bank.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.289-294
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• 2016

In recent times, as most people develop keen interest in health management, the importance of cultivating dietary habits to prevent various chronic diseases is emphasized. Subsequently, dietary management systems using a variety of mobile and web application interfaces have emerged. However, these systems are difficult to apply in real world and also do not provide personalized information reflective of the user's situation. Hence it is necessary to develop a personalized dietary management and recommendation method that considers user's body state information, food analysis and other essential statistics. In this paper, we analyze nutrition using self-organizing map (SOM) and prepare data about nutrition using clustering. We provide a substitute food recommendation method and also give feedback about the food that user wants to eat based on personalized criteria. The experiment results show that the distance between input food and recommended food of the proposed method is short compared to the recommended food results using general methods and proved that nutritional similar food is recommended.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.181-188
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• 2011

With the rapid proliferation of wireless networks and mobile computing applications, the landscape of the network security has greatly changed recently. Especially, Vehicular Ad Hoc Networks maintaining network topology with vehicle nodes of high mobility are self-organizing Peer-to-Peer networks that typically have short-lasting and unstable communication links. VANETs are formed with neither fixed infrastructure, centralized administration, nor dedicated routing equipment, and vehicle nodes are moving, joining and leaving the network with very high speed over time. So, VANET-security is very vulnerable for the intrusion of malicious and misbehaving nodes in the network, since VANETs are mostly open networks, allowing everyone connection without centralized control. In this paper, we propose a weighted intrusion detection method using rough set that can identify malicious behavior of vehicle node's activity and detect intrusions efficiently in VANETs. The performance of the proposed scheme is evaluated by a simulation study in terms of intrusion detection rate and false alarm rate for the threshold of deviation number ${\epsilon}$.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.42-51
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• 2017

Snowfall is an important natural hazard in Korea. In recent years, the socioeconomic importance of impact-based forecasts of meteorological phenomena have been highlighted. To further develop forecasts, we first need to analyze the climatic characteristics of each region. In this study, homogeneous regions for snowfall analysis were classified using a self-organizing map for impact-based forecast and warning services. Homogeneous regions of snowfall were analyzed into seven clusters and the characteristics of each group were investigated using snowfall, observation days, and maximum snowfall. Daegwallyeong, Gangneung-si, and Jeongeup-si were classified as areas with high snowfall and Gyeongsangdo was classified as an area with low snowfall. Comparison with previous studies showed that representative areas were well distinguished, but snowfall characteristics were found to be different. The results of this study are of relevance to future policy decisions that use impact-based forecasting in each region.

• Information Systems Review
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• v.10 no.2
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• pp.195-209
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.585-592
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• 2012

Food is crucial for the nutrition and survival of humans. Taste system is one of the fundamental senses. Taste cells detect and respond to five basic taste modalities (sweet, bitter, salty, sour, and umami). However, the cortical processing of taste sensation is much less understood. Recently, there were many efforts to observe the brain activation in response to taste stimulation using functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and optical imaging. These different techniques do not provide directly comparable data each other, but the complementary investigations with those techniques allowed the description and understanding of the sequence of events with the dynamics of the spatiotemporal pattern of activation in the brain in response to taste stimulation. The purpose of this study is the understanding of the brain activities to taste stimuli in sensory and affective aspects and the reviewing of the recent research of the gustotopic map by functional brain mapping.

• The Journal of the Korea Contents Association
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• v.6 no.4
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• pp.81-88
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• 2006

Ubiquitous has been a new issue in information technology field. Some people in GIS(Geographic Information Systems) and urban and regional planning have maintained that not only building three dimensional graphic environment in cyberspace is the key for ubiquitous, but also planners should plan and control the new space. They may believe that ubiquitous would be a mixture or/and combination of real-space and cyberspace. For strengthening their arguments, they should show the character of the space can be related to the three dimensional space and planning the space is possible. This study tried to critically analyze their assertion. After reviewing various articles and studies in multidisciplinary view, this challenging analysis shows those arguments need more sophisticated studies and can limit the character of cyberspace which has made the space prosperous.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.