• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.137-152
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• 2012

In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.552-560
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• 2018

Since 1960's, the large conglomerates of South Korea have grown due to the corporate-centered, fast-paced growth drive, while the small and medium-sized businesses supported the country's economy as the subordinate structure of these conglomerates. Due to the globalization of the business environments, the focus of competition shifted from competitions between individual companies to one between networks of companies. Therefore, more emphasis is now put on the capabilities of the cooperation networks between companies rather than the capabilities of individual companies. Therefore, in this study, the author examined the influence of the win-win growth strategy elements through cooperation with small and medium-sized businesses upon corporate performance. This study was conducted with the workers of small and medium-sized businesses that have previous cooperation experiences with South Korean conglomerates over the period from March 2 to May 17, 2018. For this, a total of 515 questionnaires were retrieves to obtain the data for analysis. The analysis was conducted using SPSS 22.0 and AMOS 18.0. The analytical processes that were taken included exploratory factor analysis, confirmatory factor analysis, confidence analysis, correlation analysis, and structural equation analysis model. The results of the analysis showed that, first of all, the win-win growth strategy factors that affected the strategic performance, which is a part of cooperate performance were, respectively, harmonization with the goals, production technical support, and quality system. Second, the win-win growth strategy factors that affected the financial performance, which is a part of corporate performance, turned out to be harmonization with the goals, quality system, and incentive. With the results of this study, it was shown that the elements such as harmonization with the goals, production technical support, quality systems, and incentives were key infrastructural factors that affected the corporate performance directly. On the other hand, its implication is that informative or knowledge-related factors, such as joint knowledge creation, do not have their own added values, while they are not too much likely to affect corporate performances for the moment.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• Archives of design research
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• v.20 no.3 s.71
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• pp.279-288
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• 2007

Today, design is not free from fashion, which emerges and vanishes temporarily, and aims at equalization. As a result, products quickly become obsolete because of fashion. This means that the span of products is determined by a social concept, which is not clarified, regardless of their functions. Usable products will gradually disappear from us and it will cause serious environmental problems, unless we can find out measures against fashion. As such, it is important to study the characteristics of the shaker's design in this circumstance. The Shaker's community has a distinguishable difference from other general societies. Temporary fashion and misled information cannot interfere with their consciousness. Religion, the life and the principle of design have developed on the same level in their community. Especially, any decoration or the difference of materials is not allowed in shaker's design. It reflects their thinking that all people are equal in the sight of God. Therefore, any decoration for social and economical superiority can not be used. Through this consciousness, they can be free from fashion or decoration. They, also, believe that they can reach perfection through practicality and simplicity. The reason why shaker's design is not disturbed by fashion is that their belief is involved in their design. Consequently, if religious or conscious contents are primarily set up, design can be free from fashion and products can be used for a long time.

• Journal of Aquaculture
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• v.19 no.1
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• pp.25-32
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• 2006

An experiment was carried out to investigate changing of water quality during the seed production of dark-banded rockfish Sebastes inermis in large scale tanks. Ten broodstock of dark-banded rockfish were held in three circular tanks (diameter 6.5 m; depth 2 m; water volume 50 ton) each (stocking density $0.061kg/m^3$). During the experiment the temperature ranged from 14.2 to $16.1^{\circ}C$. The fingerlings were 134 with rotifers only during 1 to 9 days after parturition, rotifers with Artemia nauplii during 10 to 20 days after parturition, Artemia nauplii only during 21 to 35 days after parturition, Artemia nauplii with commercial diet during 36 to 80 days after parturition and commercial diet only during 81 to 85 days after parturition. Water quality (dissolved oxygen, pH, $NH_4^+-N,\;NO_2^--N,\;NO_3^--N\;and\;PO_4^{3-}-P$) in rearing tanks measured every 5 days in long term monitoring investigation or every 2 hours in diurnal monitoring investigation. In 85 days after parturition, the body weight of fish grew up to 0.88 f and specific growth rate was 8.0%/day in body weight. In long term monitoring investigation, with the increase of the amount of supplied commercial diet, the concentration of dissolved oxygen (DO) and pH decreased, but the concentration of $NH_4^+-N\;(4.5\;to\;76.3{\mu}M),\;NO_2^--N\;(0.02\;to\;0.06{\mu}M),\;NO_3^--N\;(3.0\;to\;5.9{\mu}M)$, and $PO_4^{3-}-P\;(0.41\;to\;0.59{\mu}M)$ increased. In the diurnal monitoring investigation, the concentration of $NH_4^+-N$ showed great fluctuation and ranged from 3.0 to $9.1{\mu}M$ when fed rotifers, 16.3 to $45.8{\mu}M$ when fed Artemia nauplii and 36.5 to $120.1{\mu}M$ when fed commercial diet. After daily feeding with each of feed, the amount of dissolved inorganic nitrogen (DIN) and phosphorus (P) wastage were 7.0 g and 0.7 g when fed rotifers, 24.7 g and 0.7 g when fed Artemia nauplii and 140.9 g and 2.2 g when 134 commercial diet. The amount of DIN and phosphorous wastage during 134 commercial diet was significantly higher than that of fed rotifer and Artemia nauplii (P<0.05). Results will provide valuable information far water quality management and culture of dark-banded rockfish in commercial seed production systems.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.139-151
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• 2014

It is increasingly supported by scientific evidence that greenhouse gas caused by human activities is changing the global climate. In particular, the changing climate has affected human health, directly or indirectly, and its adverse impacts are estimated to increase in the future. In response, many countries have established and implemented a variety of mitigation and adaptation measures. However, it is significant to note that climate change will continue over the next few centuries and its impacts on human health should be tackled urgently. The purpose of this paper is to examine domestic policies and research in health sector in adaptation to climate change. It further aims to recommend future research directions for enhanced response to climate change in public health sector, by reviewing a series of adaptation policies in the selected countries and taking into account the general features of health adaptation policies. In this regard, this study first evaluates the current adaptation policies in public health sector by examining the National Climate Change Adaptation Master Plan(2011~2015) and Comprehensive Plan for Environment and Health(2011~2020) and reviewing research to date of the government and relevant institutions. For the literature review, two information service systems are used: namely, the National Science and Technology Information Service(NTIS) and the Policy Research Information Service & Management(PRISM). Secondly, a series of foreign adaptation policies are selected based on the global research priorities set by WHO (2009) and reviewed in order to draw implications for domestic research. Finally, the barriers or constraints in establishing and implementing health adaptation policies are analyzed qualitatively, considering the general characteristics of adaptation in the health sector to climate change, which include uncertainty, finance, technology, institutions, and public awareness. This study provides four major recommendations: to mainstream health sector in the field of adaptation policy and research; to integrate cross-sectoral adaptation measures with an aim to the improvement of health and well-being of the society; to enhance the adaptation measures based on evidence and cost-effectiveness analysis; and to facilitate systemization in health adaptation through setting the key players and the agenda.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.591-597
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• 2011

Forest carbon stock changes in a national forest were assessed by CBM-CFS3 model with different management scenarios to support decision making for a long term forest planning. Management scenarios were composed with 4 different levels of timber harvesting - current harvesting level (scenario1), 30% increment in each period (scenario2), 3 times increment (scenario3), and 5 times increment (scenario4). For each scenarios, changes in total carbon stocks, carbon stocks of each carbon pools, carbon stocks of harvested wood products (HWP) and age class structure were estimated over 100-year planning horizon. The estimated total carbon stock including HWP at the end of final period (100 years) was 433.1 tC/ha under scenario 1, but the age class structure has skewed right to the upper classes, which is not desirable for sustainable forest management. Under the scenario 4, however, the total carbon stock decrease to 385.5 tC/ha and the area of old growth forest show a significant decline. The estimated total carbon stock under scenario 2 and 3 were 411.7 tC/ha and 410.5 tC/ha respectively, and it was able to maintain the initial level of the forest carbon stocks during the planning horizon. Also the age class structures under the scenario 2 and 3 were evenly distributed from class 1 to class 8. Overall, scenario 2 and 3 were the most acceptable forest management options, in terms of carbon stock changes and age class structure.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.