• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.217-225
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• 2019

National research and development(R&D) information is information generated in the process of performing R&D based on programs and projects issued by national government departments, and includes information from various research fields as ordered by various departments. Therefore, for efficient R&D information retrieval, it is necessary to build a national R&D terminology dictionary that can reflect the characteristics of such national R&D information. In this study, we propose a method for constructing a national R&D terminology dictionary by applying the classification of science and technology standards used to specify the research field in national R&D information. We will discuss the structural characteristics of national R&D project information and the usefulness of the project keyword, and explain the status of national R&D information by the National Standard Science and Technology Classification(NSSTC) Codes and the characteristics of the national R&D terminologies. Based on this, a method for building a national R&D terminology dictionary is defined in terms of the type and structure of the terminology dictionary, preliminary construction procedures, and refining rules. The national R&D terminology dictionary built on the basis of this study can be used in various ways such as expansion of search terms using Korean-English equivalent words and synonyms when searching national R&D information, clarifying the scope of search using NSSTC, and providing user convenience functions using term explanation information.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• Informatization Policy
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• v.29 no.1
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• pp.24-37
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• 2022

Amid rapid changes in the ICT environment attributed to the 4th Industrial Revolution, the development of information & communication technology, and COVID-19, the existing internet developed without considering security, mobility, manageability, QoS, etc. As a result, the structure of the internet has become complicated, and problems such as security, stability, and reliability vulnerabilities continue to occur. In addition, there is a demand for a new concept of the internet that can provide stability and reliability resulting from digital transformation-geared advanced technologies such as artificial intelligence and IoT. Therefore, in order to suggest a way of implementing the Korean future internet that can strengthen cybersecurity, this study suggests the direction and strategy for promoting the future internet that is suitable for the Korean cyber environment through analyzing important key factors in the implementation of the future internet and evaluating the trend and suitability of domestic & foreign research related to future internet. The importance of key factors in the implementation of the future internet proceeds in the order of security, integrity, availability, stability, and confidentiality. Currently, future internet projects are being studied in various ways around the world. Among numerous projects, Bright Internet most adequately satisfies the key elements of future internet implementation and was evaluated as the most suitable technology for Korea's cyber environment. Technical issues as well as strategic and legal issues must be considered in order to promote the Bright Internet as the frontrunner Korean future internet. As for technical issues, it is necessary to adopt SAVA IPv6-NID in selecting the Bright Internet as the standard of Korean future internet and integrated data management at the data center level, and then establish a cooperative system between different countries. As for strategic issues, a secure management system and establishment of institution are needed. Lastly, in the case of legal issues, the requirement of GDPR, which includes compliance with domestic laws such as Korea's revised Data 3 Act, must be fulfilled.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Spatial Information Research
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• v.18 no.1
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• pp.19-26
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• 2010

It is important to manage the underground facilities efficiently because they are directly related to the convenience and safety of citizens. But the fact that the different agencies take on a role of managing the underground facilities respectively makes it impossible. So Korean government has exerted great efforts to solve this problem through several projects. Korean government expects that the ubiquitous technologies such as sensor and wireless communication in the domain of the underground facilities management will be useful. It is main duty of u-City Management Center to monitor several situations about the underground facilities using ubiquitous technologies. But there is no consensus about what concrete situations it has to monitor even though u-cities under construction. Because local governments or related companies are in charge of carrying out these roles yet, there is no legal basis on which to do these tasks by the center. And it is needed to develop new technologies for u-services efficiently. Because technologies for u-service are vast, various and converged, so it necessary to develop them according to priority or in cooperation with other developers. In this paper, we classify the monitoring items for each facility which were investigated by a recent research. It is expected to make use of defining the role of the center and developing technologies with u-service.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.295-302
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• 2011

Ubiquitous is an information communication environment which peoples could connect on network without being conscious of the network and computer condition at anytime and anywhere. Presently, advanced technology, digital cities and eco-friendly cities using Ubiquitous technology are being focused through out the world. As recent information industry and communication technology have developed remarkably, studies on intelligent structures and structural maintenance methods by combining a general idea of connecting all objects on network by interpolating computers which is called Ubiquitous Computing, and USN(Ubiquitous Sensor Network) are being attempted. In this paper, a basic experiment on penetration of RF(Radio Frequency) sensor nodes using an experimental sample setting concrete and reinforcing bar as variables is studied to estimate the possibility of applying wireless sensor networks in structures. The spring of reinforcing bars, concrete thickness, intensity of radio frequency were set up as variables, transmitting and receiving distance were checked vertically and horizontally. Moreover, Spectrum Analyzer was used for checking the magnitude of the frequency used in order to measure the reduction of radio wave exactly. The radio wave reduction was numerically analyzed, and the influence of the wavelength was analysed as well. As a result, the penetration depth in concrete without reinforcing bars was 45cm, and in reinforced concretes which reinforcing bars placed at spacing of 5cm and 15cm, the penetration depth were 37cm and 45cm each. No influence on radio wave penetration depth were found when the spacing of reinforcing bars in the reinforced concrete structures becomes over 15cm.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.583-594
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• 1999

With the advance of VLSI and network technologies, it has now become a common practice to deploy a various forms of distributed computing environments. A study shows that a lot of network-aware computers are in an idle state for considerable amount of time depending on the types of users and time frames of the day. If we can take the full advantage of those idle computers, we can obtain the enormous combined processing power without further costly investment. In this paper, we present a distributed batch-processing system, called the Java Distributed Batch-processing System (JDBS), which allows us to execute CPU-intensive, independent jobs across a pool of idle workstations on top of extant distributed computing environments. Since JDBS is implemented using a Java programming language, it not only extends the scope of machine types that can be joined to the pool, but makes it a lot easier to build an entire system. Besides, JDBS is scalable and fault-tolerant due to its multi-cluster organization and intelligent strategies. A graphical user interface is also provided to facilitate the registration and unregistration, job submission, and job monitoring.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.175-201
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• 2023

In the midst of going through a non-face-to-face society, most of human activities narrowed down to the platform, restrictions on external activities are bringing the internal scalability of digital technology. Metaverse is virtually shifting reality and increasing the possibility of utilization in various areas. However, researches linked to the educational effects of metaverse, especially students with disabilities, are still an unknown area that lacks exploration. This paper focuses on the fact that metaverse-education is widening educational fields that meets the various needs of disabled students to realize social good and inclusive education, and communication effects such as resolving barriers to interaction are prominent. As a research method, examining literature research papers linked to AR/VR, metaverse with communication skills, interviews, articles, and columns by experts, and policy suggestions and implications for the special education was conducted. Although the limitations of research are confirmed, significant results are found on inclusive education, which provides educational maximizing effects and realizing human rights through direct immersive experience reflecting the Cone of Experience Theory. Hopefully follow-up studies on meta-edu for disabled students will be carried out in the future, and various interdisciplinary discussions are needed to carefully observe inclusive policies and benefits so that the socially vulnerable are not excluded from technologies in ICT society.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.