• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.110-115
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• 2022

Accurate current-voltage modeling of solar cell systems plays an important role in power prediction. Solar cells have nonlinear characteristics that are sensitive to environmental conditions such as temperature and irradiance. In this paper, the output characteristics of photovoltaic module are accurately predicted by combining the artificial neural network and physical model. In order to estimate the performance of PV module under varying environments, the artificial neural network model is trained with randomly generated temperature and irradiance data. With the use of proposed model, the current-voltage and power-voltage characteristics under real environments can be predicted with high accuracy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.107-115
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• 2009

Traditionally, simulation method is used to test and evaluate the performance of communication protocol or functional elements for mobile communication service. In this paper, wireless network simulator is implemented using the C++ object-oriented programming language. This simulator can simulate wireless data services, like as ad-hoc networks, by considering the user's mobility. In this paper, the simulator includes network traffic model to reflect wireless data service and traffic source model to represent a user's mobility similar to real service environment and traffic characteristics can be reflected on the simulation, and also more accurate simulation results can be got through that. In addition, by using object-oriented techniques, new service feature or environment can be easily added or changed so that the developed mobile communication simulator can reflect the real service environment all the time. This simulator can be used in adjusting the characteristics of wireless data hosts following the mobility of the user, and also can be used in building new wireless ad-hoc network routing protocols.

• Journal of the Korean Home Economics Association
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• v.45 no.5
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• pp.75-83
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• 2007

The purpose of this study was to investigate the relationships between peer victimization by peers, bullying, and friendships, with a focus on friendship network, friendship quality, and friends' characteristics. The subjects were 678 fifth and sixth grade primary school children recruited from a public school in Bucheon city. The peer nomination index was used to assess peer victimization, bullying, and friendship network. In addition, children themselves reported on the quality of their friendships using the Friendship Quality Scale. The results showed that victimization by peers was influenced by friendship network size, support, and victimization of friends, whereas bullying was explained by support and the bullying behaviors of friends.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.265-268
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• 1998

This paper evaluates the VSN(Virtual switch Network) characteristics in the internal call processing of IMT-2000 switching system, which is composed of VSN instead of ATM switch network. In results, internal call establishment delay is increased approximately 5.4msec than the conventional ATM switching system. The evaluated condition is the load 0.8, and the 100km distance between VSNs. It is confirmed that the VSN has the potentiality in the practical implementation.

• 전기의세계
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• v.26 no.1
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• pp.63-70
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• 1977

This paper deals with computer analysis technique of the network having 3-terminal elements whose input and output characteristics are defined by nonuniform spacing function group on the volt-ampere space. Developing the algorithms to obtain the solutions of the network mentioned above by computer, we propose optimization technique, which can solve the normal form equations of the network defined in this paper and which involves mode analysis technique to be able to analyze the case that the function group has negative resistance characteristics.

• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.133-158
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• 1997

This paper analyzes the characteristics of technological knowledge flow-structure of Korean manufacturing in dynamic perspective. In doing that, the concept of the knowledge network is introduced which is defined as a set of industries and their interaction(knowledge flow) or linkage. The analysis of the inter-industrial knowledge flows is based on the technological similarity by using R&D researchers'academic background in the year of 1984, 1987, 1990. The analysis is carried out by such methodology as network analysis, indicator analysis and simple statistical analysis. And the final results are drawn both in absolute terms(dimension effect) and in relative terms (proportion effect) respectively. The main findings are as follow. First, the Korean manufacturing knowledge network appears to strengthen existing inter-industrial knowledge linkages rather than to construct new linkages. Second, the network seems to form a dualistic structure in that some high-technology sectors (knowledge production sectors) emerge along with traditional sectors (knowledge absorbing sectors). Third, since the mid-1980s, an inter-industrial fusion is witnessed among technologically intensive sectors, indicating that some sophisticated innovation modes are emerging in Korean manufacturing system.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.160-163
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• 1995

An optimal EEG and artifact classifier is proposed using neural network operating characteristics. The neural network operating characteristics are two dimensional parametric representations of the right and false identification probabilities of the network classifier. Since the EEG and EP signals acquired from multi -channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG), the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. Using the neural-network based classification, human expert's efforts and time can be substantially reduced. From experiments, the neural-network based classification performs as good as human experts: variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Korean Management Science Review
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• v.26 no.1
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• pp.183-196
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• 2009

Customer retention has been a pressing issue for companies to get and maintain the loyal customers in the competing environment. Lots of researchers make effort to seek the characteristics of the churning customers and the loyal customers using the data mining techniques such as decision tree. However, such existing researches don't consider relationships among customers. Social network analysis has been used to search relationships among social entities such as genetics network, traffic network, organization network and so on. In this study, a customer network is proposed to investigate the differences of network characteristics of churning customers and loyal customers. The customer networks are constructed by analyzing the real purchase data collected from a Korean cosmetic provider. We investigated whether the churning customers and the loyal customers have different degree centralities and densities of the customer networks. In addition, we compared products purchased by the churning customers and those by the loyal customers. Our data analysis results indicate that degree centrality and density of the churning customer network are higher than those of the loyal customer network, and the various products are purchased by churning customers rather than by the loyal customers. We expect that the suggested social network analysis is used to as a complementary analysis methodology with existing statistical analysis and data mining analysis.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.2
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• pp.299-322
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• 2020

The purpose of this study is to suggest reading education plan by exploring the characteristics of peer-relationship networks of elementary school students, and grasping the effects of those characteristics on the reading competencies. Social network analysis method was used, and centrality analysis, QAP correlation and QAP multiple regression analyses were conducted to examine the relationship between peers and reading competencies. The findings show that the help relationship rather than peer characteristics and friend relationship was related to reading competencies. However, since the friend relationship has an effect on the help relationship, it is also found that the relationship between the friend and the help relationship network should be considered in order to improve the reading competencies. This network analysis results are meaningful in reading education plan in the sense that they suggest a useful guideline for the formation of members ranging from individuals, small groups, to a whole class, and for periodical activities considering situation and learning purposes such as before, during, and after reading activities.

• Asian Journal of Innovation and Policy
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• v.6 no.1
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• pp.85-93
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• 2017

This paper focuses on the relationship between the characteristics of network and the productivity of scientists, which is rarely examined in previous studies. Utilizing a unique dataset from the Korean Citation Index (KCI), we examine the overall characteristics of the research network (e.g. distribution of nodes, density and mean distance), and analyze whether the network centrality is related to the scientific productivity. According to the results, firstly we have found that the collaborative research network of the Korean academics in the field of statistics and computer science is a scale-free network. Secondly, these research networks show a disciplinary difference. The network of statisticians is denser than that of computer scientists. In addition, computer scientists are located in a fragmented network compared to statisticians. Thirdly, with regard to the relationship between the researchers' network position and scientific productivity, a significant relation and their disciplinary difference have been observed. In particular, the degree centrality is the strongest predictor for the scientists' productivity. Based on these findings, some policy implications are put forward.