• Smart Structures and Systems
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• v.6 no.5_6
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• pp.439-459
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• 2010

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

• Journal of the Korean Society for Railway
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• v.18 no.6
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• pp.578-585
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• 2015

Track elastic components can be technically and economically efficient when integrated well into track superstructure of a railway network. In such cases, the elastic rail pad is larger than a 800m radius curve provides smooth rail branching and allows for high-speed operation ($V{\geq}160km/h$). High track resistance causes the tamping intervals to stand out because the constantly increasing share of the sleeper pad further extends the increase of the tamping interval and the long grinding period; the engineering and construction of the small curve radius track provides some measures for reducing the solid sounds. Installation of elastic mats under the ballast can have a good effect, particularly in the context of protection against dust during construction or extensive renovation measures when laying new lines. However, such a process requires special attention and proper installation.

• The Journal of the Petrological Society of Korea
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• v.25 no.3
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• pp.253-260
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• 2016

A moment magnitude 3.1 earthquake occurred in the Seoul metropolitan region (SMR), Korea, on 9 February 2010. The unexpected shaking attracted much attention and raised concerns about the seismic hazards and risks in the SMR, which was regarded as an area safe from any earthquake hazard. The SMR has a population of 25 million and is one of the largest metropolitan areas in the world. A shakemap for a scenario earthquake with magnitude 6.5 and focal depth 12 km implies that the SMR will be exposed to serious risk because of its large population and the high vulnerability of its buildings. Although the instrumentally recorded earthquakes discussed in this article cannot be classified as major events, they should not be discounted as insignificant. Considering the low seismicity, micro-earthquakes below the magnitude of a conventional seismic network can achieve would be used to estimate background information in the evaluation of earthquake hazards and risks.

• The Korean Journal of Franchise Management
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• v.12 no.4
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• pp.25-40
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• 2021

Purpose: Marketing communication with customers is essential for companies to survive and grow in a fiercely competitive environment. Advertising is one of the most readily accepted marketing communications by consumers. Unlike a company that owns a distribution network, advertising is vital for a franchise. In general, many advertising models select celebrities. Celebrities are more likely to attract audience attention and influence consumers' purchase intentions. Customers satisfied with advertising are more likely to stay loyal and buy again when the company launches a new product. In addition, customers form a brand image through advertisements. Therefore, in this study, the effect of the characteristics of the foodservice franchise advertising model characteristics on model satisfaction, brand image, and purchase intention. Research design, data, and methodology: The survey of this study was conducted among consumers over the age of 20 who had seen a restaurant franchise advertisement and visited a store within the last 2 months. 305 copies were collected for 7 days during the survey period, from October 21 to October 27, 2021. Among the collected questionnaires, 12 insincere questionnaires were excluded, and 293 were used for analysis. SPSS 25.0 and Smart PLS 3.0 were used as data collected to test the hypothesis. Result: As a result of the study, among the advertising model characteristics of a foodservice franchise, reliability, attractiveness, expertise, and closeness all had a significant positive (+) effect on model satisfaction. In addition, reliability and closeness were found to have a significantly positive (+) effect on brand satisfaction, but attractive and expertise did not significantly affect brand image. Finally, model satisfaction was found to have a significant positive (+) effect on brand image and purchase intention. Brand image also appeared to have a significant positive effect on purchase intention. Conclusions: Research Results First, this study verified the effect of a restaurant franchise advertising model using celebrities by using the characteristics of the advertising model. Second, this study developed a conceptual structure for model characteristics - model satisfaction - brand image - purchase intention. Third, the restaurant franchise marketer needs to select a model in consideration of the privacy of the advertising model. Fourth, consumers tend to equate advertising models with advertising

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.197-202
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• 2021

Biometric technology using finger veins is receiving a lot of attention due to its high security, convenience and accuracy. And the recent development of deep learning technology has improved the processing speed and accuracy for authentication. However, the training data is a subset of real data not in a certain order or method and the results are not constant. so the amount of data and the complexity of the artificial neural network must be considered. In this paper, the deep learning model of Inception-Resnet-v2 was used to improve the high accuracy of the finger vein recognizer and the performance of the authentication system, We compared and analyzed the performance of the deep learning model of DenseNet-201. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly. There is no preprocessing for the image in the finger vein authentication system, and the results are checked through EER.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.27-36
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• 2019

In this paper, we propose a method to find the most similar answer to the user's response from the question-answer database in order to avoid generating a redundant question in retrieval-based automatic question generation system. As a question of the most similar answer to user's response may already be known to the user, the question should be removed from a set of question candidates. A similarity detector calculates a similarity between two answers by utilizing the same words, paraphrases, and sentential meanings. Paraphrases can be acquired by building a phrase table used in a statistical machine translation. A sentential meaning's similarity of two answers is calculated by an attention-based convolutional neural network. We evaluate the accuracy of the similarity detector on an evaluation set with 100 answers, and can get the 71% Mean Reciprocal Rank (MRR) score.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.283-292
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• 2019

The loss of semantic knowledge and impairments in semantic associations by semantic category is gaining increasing attention, as indicators of early-stage cognitive decline. As such, we assigned semantic association task (SAT) to normal elderly (NE) and those with subjective memory impairment (SMI) or mild cognitive impairment (MCI) to examine their performance by semantic subcategories and the differences in error patterns. We found a significant difference in the number of correct response and reaction time under the SAT categories among the three groups, with the highest performance observed in 'function' and the lowest performance in 'superordinate' and 'part/whole'. Moreover, the error frequency was the lowest in NE, followed by those with SMI and MCI, with the latter two groups showing a significant increase in no-response. Our findings demonstrate the varying extent and process of impairments in the semantic network by category over different stages of cognitive decline. Thus, we proposed SAT performance as an indicator to detect and follow-up on cognitive decline in elderly with cognitive disorder.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.9-21
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• 2018

In South Korea, Jeju Island has a role as a test bed for electric vehicles (EVs). All conventional cars on the island are supposed to be replaced with EVs by 2030. Accordingly, how to effectively set up EV charging stations (EVCSs) that can charge EVs is an urgent research issue. In this paper, we present a case study on planning the locations of EVCS for Jeju Island, South Korea. The objective is to determine where EVCSs to be installed so as to balance the load of EVCSs while satisfying demands. For a public service with EVCSs by some government or non-profit organization, load balancing between EVCS locations may be one of major measures to evaluate or publicize the associated service network. Nevertheless, this measure has not been receiving much attention in the related literature. Thus, we consider the measure as a constraint and an objective in a mixed integer programming model. The model also considers the maximum allowed distance that drivers would detour to recharge their EV instead of using the shortest path to their destination. To solve the problem effectively, we develop a heuristic algorithm. With the proposed heuristic algorithm, a variety of numerical analysis is conducted to identify effects of the maximum allowed detour distance and the tightness of budget for installing EVCSs. From the analysis, we discuss the effects and draw practical implications.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Knowledge Management Research
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• v.20 no.3
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.