• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.357-363
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• 2014

The development of information technology grows the society and allows the user to generate more information and the requirements. According to recent growing interest in digital convergence technologies, digital convergence can be utilized by a variety of methods as a new study. Electronic culture atlas would be digitized cultural information that represents a particular area on the map. In detail, spatial data like as point, line, and area can be used to represent the culture information. Using electronic culture atlas, also, it is possible to provide and utilize information which is associated with time, space(map) and subject(culture). Thus, electronic culture atlas could be used as a method for the humanities or area studies and spread research results by displaying on the culture atlas. In general, existing electronic culture atlas used bitmap image mostly as a base map. Using bitmap image, it is difficult to represent to provide three-dimensional information of specific area. Also, it happens problem such as breakage occurs during zoom-in operation. To solve this problems, we design and implement electronic culture atlas based on Google Earth which is three-dimensional map service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.67-73
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• 2016

Recently, wireless power transmission has attracted much interest and is the subject of much research in industry and academia. As its name implies, it is a technology which involves transferring power without wires. This paper presents the design of an ICT-based wireless power transmission system. The proposed system consists of a wireless transceiver unit and high-efficiency coil unit, which can increase both the transmission efficiency and the effective power distance. In particular, the wireless transceiver unit was designed to work with the ICT technique to enable real-time remote monitoring. Also, studies were done relating to the effect of reducing the standby power. The optimal frequency of IGBT devices used in industrial wireless power systems of 20[KHz] was utilized. The values of $23.9[{\mu}H]$ and $2.64[{\mu}F]$ were selected for L and C, respectively, through many field experiments designed to optimize the system design. In addition, an output current controlling algorithm was developed for the purpose of reducing the standby power. The results presented in this paper represent a 75[%] to 85[%] higher power transmission efficiency with a 10[%] increase in the effective power transmission distance compared with the existing systems. As a result, the proposed system exhibits a lower standby power and maintenance costs. Also, the designed wireless transceiver unit facilitates fault detection by means of user acquired data with the development of the ICT applied program.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.375-380
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• 2016

The proliferation of online social networking services (OSNSs) and smartphones has enabled people to easily make friends with a large number of users in the online communities, and interact with each other. This leads to an increase in the usage rate of OSNSs. However, individuals who have immersed into their digital lives, prioritizing the virtual world against the real one, become more and more isolated in the physical world. Thus, their socialization processes that are undertaken only through lots of face-to-face interactions and trial-and-errors are apt to be neglected via 'Add Friend' kind of functions in OSNSs. In this paper, we present a friend recommendation system based on the on/off-line contextual information for the OSNS users to have more serendipitous offline interactions. In order to accomplish this, we modeled both offline information (i.e., place visit history) collected from a user's smartphone on a 3D tensor, and online social data (i.e., friend relationships) from Facebook on a matrix. We then recommended like-minded people and encouraged their offline interactions. We evaluated the users' satisfaction based on a real-world dataset collected from 43 users (12 on-campus users and 31 users randomly selected from Facebook friends of on-campus users).

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.151-158
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• 2019

Patients with disabilities from various reasons such as disasters, injuries or chronic illness or elderly with limited body motion range due to aging are recommended to participate in rehabilitation programs at hospitals. But typically, it's not as simple for them to commute without help as they have limited access outside of the home. Also, regarding the perspectives of hospitals, having to maintain the workforce and have them take care of the rehabilitation sessions leads them to more expenses in cost aspects. For those reasons, in this paper, a home-based remote rehabilitation system using motion recognition is developed without needing help from others. This system can be executed by a personal computer and a stereo camera at home, the real-time user motion status is monitored using motion recognition feature. The system tracks the joint range of motion(Joint ROM) of particular body parts of users to check the body function improvement. For demonstration, total of 4 subjects with various ages and health conditions participated in this project. Their motion data were collected during all 3 exercise sessions, and each session was repeated 9 times per person and was compared in the results.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.503-513
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• 2005

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity($77\%$) and specificity($95\%$) are achieved for test protein pairs containing common domains with teaming sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.107-116
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• 2016

Industrial robots have been usually controlled using text-based programming languages provided by each manufacturer with its button-based TP(Teaching Pendent) terminal. Unfortunately, when we consider that people who manipulate TPs in manufacturing sites are mostly unskilled with no background knowledge about computer programming, these text-based programming languages using button-based interaction on manufacturing sites are too difficult for them to learn and use. In order to overcome the weaknesses of the text-based programming language, we propose a visual programming language that can be easily used on gesture-enabled devices. Especially, in our visual programming environment, each command is represented as a block and robots are controlled by stacking those blocks using drag-and-drop gestures, which is easily learnable even by beginners. In this paper, we utilize a widely-spread device, Tablet PC as the gesture-enabled TP. Considering that Tablet PC has limited display space in contrast to PC environments, we designed different kinds of sets of command blocks and conducted user tests. Based on the experiment results, we propose an effective set of command blocks for Tablet PC environment.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.1
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• pp.259-283
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• 2018

This research aims at understanding the differences between expert and general users' perceptions regarding publication preferences for Korean classics collations and translations in order to formulate future directions for these materials. For this purpose, an overview of changes in publishing in general as well as current status of collation/translation of Korean classics in particular are being identified. An online questionnaire was carried out in order to collect data regarding perceptions and preferences of expert users and general users of Korean classics. The results are based on the analyses of more than 1,000 responses. The analyses show that electronic books will not completely replace printed books and publishing both electronic and printed books in tandem for the time being is most preferable in order to satisfy varying user needs. Statistical analysis shows differences in terms of use value, value from possession, and readability of electronic and printed books in the two groups of users. However, as for the value of preservation by relevant institutions, there was a statistical difference between two groups towards printed books unlike their electronic equivalents. The research shows strong preference towards printed forms of classics collations and translations for the purpose of scholarly research and translation. Actual usage statistics reveal much heavy use of online database of classics translations compared to the use of available electronic books. For future publishing decisions for classics collations and translations will need to take into consideration of their special characteristics and symbolic nature. Proper representation of these materials into electronic format would require a standardized platform that enable various uses in different environments.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.73-85
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• 2014

This study analyzed the profile of a Facebook account to examine how personal information is revealed and what kinds of factors influence personal information revelation. Categories of user's profile on Facebook were analyzed and two dimensions were developed; the degree that how much personal information is revealed and the network limits that personal information is accessed. Main variables to determine personal information revelation are Facebook privacy concern and uses for social relationships along with gender, the duration of Facebook use, and average time of use. Data were collected from college students. Factor analysis produced two factors of Facebook privacy concern, Facebook privacy concern with users and Facebook privacy concern with the Facebook system. Regression analyses were performed to identify significant determinants of the degree of information revelation and the network limits of personal information. The results found out that the degree of personal information revelation is explained by gender, the duration of use, and use for social relationships while the network limit is explained by the duration of use and Facebook privacy concern with users. Worthy of notice is that use for social relationships and Facebook privacy concern with the Facebook system offset each other. The implications of the results are discussed. Additionally and finally the categories of profiles are graphically re-grouped to show how personal information revelation is associated with social relationship generation and maintenance.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.1-18
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• 2008

Because we can usually get many information through analyzing trajectories of moving objects on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. Also, because FNR-tree and MON-tree store the segment unit of moving objects, they can't support the trajectory of whole moving objects. In this paper, we propose an efficient trajectory index structures based on signatures on a spatial network, named SigMO-Tree. For this, we divide moving object data into spatial and temporal attributes, and design an index structure which supports not only range query but trajectory query by preserving the whole trajectory of moving objects. In addition, we divide user queries into trajectory query based on spatio-temporal area and similar-tralectory query, and propose query processing algorithms to support them. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently Finally, we show from our performance analysis that our trajectory index structure outperforms the existing index structures like FNR-Tree and MON-Tree.