• Journal of Music and Human Behavior
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• v.14 no.2
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• pp.91-105
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• 2017

Community music therapy (CoMT) has been recently developed and expands the opportunities for music therapy. The concept of CoMT is introduced in this article, and its three attributes of community, music, and health are reviewed. This study specified each attribute of CoMT: a community (a group of people, a field where members of a group interact with each other), music (a substance of interaction, collective music-making), and health (motivation and goal of interaction, relational and social well-being). The application and interactions of the three attributes of CoMT are introduced as in the concept of community music, music and health, and community health. How CoMT can be applied to the field of music therapy is also detailed and based on the concept of CoMT and its relationship with the attributes, the CoMT was reconstructed as CoMuHeal in this study. Future studies are needed to propose how music therapy approaches can be developed to provide music for well-being and better health in the community and how CoMT can be applied in collaboration with other professional fields.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.1-9
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• 2015

This paper proposes CAC method that is more efficient for RRM using game theory combined with Multiple Attribute Decision Making(MADM). Because users request services with different Quality of Service(QoS), the network preference values to alternative networks for each service are calculated by MADM methods such as Grey Relational Analysis(GRA), Simple Additive Weighting(SAW) and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS). According to a utility function representing preference value, non-cooperative game is played, and then network provider select the requested service that provide maximum payoff. The appropriate service is selected through Nash Equilibrium that is the solution of game and the game is played repeated. We analyze two overlaid networks among four Wireless LAN(WLAN) systems with different properties. Simulation results show that proposed MADM techniques have same outcomes for every game round.

• Journal of the Korean earth science society
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• v.32 no.1
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• pp.84-98
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• 2011

The study investigated the linguistic characteristics of middle school students' writing on the themes of earth science through analysis of its genre and register. Data for analysis included $7^{th}$ grade and $9^{th}$ grade students' writings about 'global warming' and 'classification of rocks'. The results of this study include: First, many students were not accustomed to writing in genre, especially exposition genre. Second, in terms of ideational meaning, the material verbs representing action or doing were more dominant than relational verbs that are related to the attribute or definition of things, and additional logical relations were predominant. Third, regarding interpersonal meaning, agents, emotions, subjective opinions appeared in the writings and students did not express their ideas conclusively and revealed feelings of doubt and uncertainty about their knowledge. Fourth, as for textual meaning, most students listed fragments of information using additional conjunctions in simple structures and were not accustomed to writing texts with organizing structures, logical patterns, cohesion, and coherence. From these results, we argued that the scientific writings should be emphasized in science learning that aims to foster scientific literacy. In addition, we discussed the necessity of improving science teachers' perceptions on scientific writing as well as setting up a specific plan in the national curriculum.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.