• Information Systems Review
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• v.18 no.4
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• pp.89-106
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• 2016

The latest movement in live video streaming service is mobile personal broadcasting (MPB), which refers to consumers accessing the service through social media with mobile devices, such as smartphones and tablet PCs. This service is possible through the advancements in mobile video technology and platforms. Features such as enhanced user interaction, personalization, and real-time broadcasting, combined with a greater variety of content, have led to the development of MPB. The increase in MPB users calls for research, including that on the hedonic motivational angle. This study aims to assess MPB users' intrinsic motives through the hedonic-motivation system adoption model (HMSAM) using seven factors: joy, temporal dissociation, escapism, focused immersion, perceived ease of use, perceived usefulness and intention to watch. Survey data collected from 154 samples were analyzed with statistical techniques, such as structural equation modeling. Results showed that time dissociation, escapism, and perceived ease of use have a positive relationship with heightened enjoyment. Joy significantly affects focused immersion and intention to watch. Escapism also had a statistically significant influence on focused immersion. This study contributes to the advancement of the MPB study under the HMSAM theoretical framework and offers practical suggestions to managers to enhance MPB content viewership.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.427-436
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• 2016

Due to disasters such as earthquakes and landslides in urban areas, persons have been buried inside collapsed buildings and structures. Rescuers have mainly utilized detection equipment by applying sound, video and electric waves, but these are expensive and due to the directional approaches onto the collapsed site, secondary collapse risk can arise. In addition, due to poor utilization of such equipment, new human detection technology with quick and high reliability has not been utilized. To address these issues, this study develops a wireless signal-based human detection module that can be loaded into an Unmanned Aerial Vehicle (UAV). The human detection module searches for the 3D location for buried persons by collecting Wi-Fi signal and barometer sensors data transmitted from the mobile phones. This module can gain diverse information from mobile phones for buried persons in real time. We present a development framework of the module that provides 3D location data with more reliable information by delivering the collected data into a local computer in the ground. This study verified the application feasibility of the developed module in a real collapsed area. Therefore, it is expected that these results can be used as a core technology for the quick detection of buried persons' location and for relieving them after disasters that induce building collapses.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.71-90
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• 2012

Although it has been studied for a long time in various disciplines, most of control theories remain being developed by analyzing relatively simple tasks. Even recent research on control of information systems development explains only a small part of control phenomena observed in the real world projects. This research focuses on identifying and analyzing the concepts and structures in order to make them useful for understanding and explaining control of information systems development comprehensively This investigation utilizes the complementary relationship between views on control from organizational and economic perspectives. A conceptual framework developed by integrating previous research on control allows us to analyze the development of information systems for control purposes. The results of discussion about control mechanisms and network can be used as guidelines for designing control systems in real projects. Analysis of control networks shows that control of development projects requires quite complex networks intertwining a variety of controllers and controlees. The results of this research are expected to contribute to correcting the unbalanced status of IS research which has emphasized too heavily on planning and implementation, and deepening and widening our understanding about controlling development projects. Practitioners can use the results as guidelines for designing control mechanisms and networks, and get alerted by them about the agency risks inherent in outsourced developments.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.15-24
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• 2018

Despite the recognition of the need for productivity information and its importance, the feedback of productivity information is not well-established in the construction industry. Effective use of productivity information is required to improve the reliability of construction planning. However, in many cases, on-site productivity information is hardly management effectively, but rather it relies on the experience and/or intuition of project participants. Based on the literature review and expert interviews, the authors recognized that one of the possible solutions is to develop a systematic approach in dealing with productivity information of the construction job-sites. It is required that the new system should not be burdensome to users, purpose-oriented information management, easy-to follow information structure, real-time information feedback, and productivity-related factor recognition. Based on the preliminary investigations, this study proposed a framework for a novel system that facilitate the effective management of construction productivity information. This system has utilized Sketchup software which has good user accessibility by minimizing additional data input and related workload. The proposed system has been designed to input, process, and output the pertinent information through a four-stage process: preparation, input, processing, and output. The inputted construction information is classified into Task Breakdown Structure (TBS) and Material Breakdown Structure (MBS), which are constructed by referring to the contents of the standard specification of building construction, and converted into productivity information. In addition, the converted information is also graphically visualized on the screen, allowing the users to use the productivity information from the job-site. The productivity information management system proposed in this study has been pilot-tested in terms of practical applicability and information availability in the real construction project. Very positive results have been obtained from the usability and the applicability of the system and benefits are expected from the validity test of the system. If the proposed system is used in the planning stage in the construction, the productivity information and the continuous information is accumulated, the expected effectiveness of this study would be conceivably further enhanced.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.65-78
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• 2009

The rapid changes of financial environment have increased the need and demand for personal financial advisory service from financial experts. In particular, as the individual customers want to get more customized financial services, the financial institutions created the private banking (PB) sector and have constantly expanded their PB services. However, it remains still problematic that the private banking system requires high costs so that the number of eligible customers who can have proper PB services is quite limited. To solve this problem, we propose an intelligent agent that can provides specialized and customized personal financial advisory services to the customers with low costs. The proposed agent systemizes and structures the information and knowledge of financial experts in private banking services so that individual customers can easily access to high-quality PB services when they need. On the first attempt we develop a framework of U-smart PB, an intelligent agent for personal financial management based on different scenarios related to personal financial decisions, and derive its core services. This system not only provides information simply, but also proposes to support personal investment decisions technically as an intelligent agent that embodies real-time customized financial management in a ubiquitous environment, regardless of time and place.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.361-366
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• 2017

As the recent development of computing architecture and application software technology, real world simulation, which is the ultimate destination of computer simulation, is emerging as a practical issue in several research sectors. In this paper, metal plate motion in a square shock tube for small time interval was calculated using a supercomputing-based fluid-structure-combustion multi-physics simulation tool called Illinois Rocstar, developed in a US national R amp; D program at the University of Illinois. Afterwards, the simulation results were compared with those from experiments. The coupled solvers for unsteady compressible fluid dynamics and for structural analysis were based on the finite volume structured grid system and the large deformation linear elastic model, respectively. In addition, a strong correlation between calculation and experiment was shown, probably because of the predictor-corrector time-integration scheme framework. In the future, additional validation studies and code improvements for higher accuracy will be conducted to obtain a reliable open-source software research tool.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.158-167
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• 2016

This study evaluates potential sources of errors in k-NN(k-nearest neighbor) algorithm such as procedures, variables, and input data. Previous research has been thoroughly reviewed for understanding fundamentals of k-NN algorithm that has been widely used for short-term traffic states prediction. The framework of this algorithm commonly includes historical data smoothing, pattern database, similarity measure, k-value, and prediction horizon. The outcomes of this study suggests that: i) historical data smoothing is recommended to reduce random noise of measured traffic data; ii) the historical database should contain traffic state information on both normal and event conditions; and iii) trial and error method can improve the prediction accuracy by better searching for the optimum input time series and k-value. The study results also demonstrates that predicted error increases with the duration of prediction horizon and rapidly changing traffic states.