• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.69-76
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• 2015

Although traffic information has been actively analyzed using big data, it has not been used as much with the consideration of driver characteristics. Among the various types of information, road signs can directly affect the driver. Road signs must provide the optimal information that enables drivers to reach their destinations with ease as well as information suitable for navigation systems. However, present road sign rules provide standardized information, regardless of the road type or size. This study suggests a method for providing road information that will help drivers determine their behavior. First, the minimum character size that can be used on a road sign for each design speed was obtained with respect to the visibility and decipherability of a road sign. Instead of conventional diagram-based direction guidance, a scenario using split-based direction guidance was created. To verify the effectiveness of the provided information, a three-dimensional simulated road environment was constructed, and a driving simulator was used for the test. At a simple plane intersection, the driver was not greatly influenced by directional guidance, but at a complex, three-dimensional intersection, the driver preferred summary-based directional guidance, which is instinctive guidance, over diagram-based guidance. On the basis of the test results, a secondary verification test that applied split-based guidance at a three-dimensional intersection confirmed that the driver had no problems in making decisions.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.141-146
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• 2005

Ship have cutout inner bottom and girder and floor etc. Ship's structure is used much, and structure strength must be situated, but establish new concept when high stress interacts sometimes fatally the area. There is no big problem usually by aim of weight reduction, a person and change of freight, piping etc. Because cutout's existence grow up in this place, and, elastic buckling strength by load causes large effect in ultimate strength. Therefore, stiffened perforated plate considering buckling strength and ultimate strength is one of important design criteria which must examine when decide structural concept at initial design. Therefore, and, reasonable buckling strength about perforated stiffened plate need to ultimate strength limited design . Calculated ultimate strength varied several web height and cutout's dimension, and thickness in this investigated data. Used program(ANSYS) applied F.E.A code based on finite element method.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.197-209
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• 2017

With the advent of age that people spend more time and money on leisure activities, there is increasing interest in professional sport games. The location of large scale sport facilities has substantial impacts on existing transportation pattern because the facility attracts and generates massive traffic volume within a short period of time. This study aims to develop a mode choice model of leisure trips of which the destinations are a sport facility. A structured SP (stated preference) survey questionnaires were developed through an experimental design, and professional sport spectators were asked to state their preference in the choice of transport mode to the sport facility. The survey results show that public transportation is preferred to passenger cars for their trip to big sports event, implying that the convenience of back home trip after the event is an important factor of their mode choice. This study is a rare research on the trip pattern to sports complex in Korea, which provides policy implications on the provision of mass transit including subway system to large scale sport complexes. And it is also expected that this study contributes to future researches on leisure trip pattern.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.722-728
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• 2012

Urban sewer systems has a limitation of capacity of rainwater storage and problem of occurrence of untreated sewage, so adopting a storage facility for sewer flooding prevention and urban non-point pollution reduction has a big attention. The Korea Ministry of Environment has recently introduced a new concept of "multi-functional storage facility", which is crucial not only in preventive stormwater management but also in dealing with combined sewer overflow and sanitary sewer discharge, and also has been promoting its adoption. However, reserving a space for a single large-scale storage facility might be difficult especially in urban areas. Thus, decentralized construction of small- and midium-sized storage facilities and its operation have been introduced as an alternative way. In this paper, we propose a model predictive control scheme for an optimized operation of distributed storage facilities and sewer networks. To this aim, we first describe the mathematical model of each component of networks system which enables us to analyze its detailed dynamic behavior. Second, overflow locations and volumes will be predicted based on the developed network model with data on the external inflow occurred at specific locations of the network. MPC scheme based on the introduced particle swarm optimization technique then produces the optimized the gate setting for sewer network flow control, which minimizes sewer flooding and maximizes the potential storage capacity. Finally, the operational efficacy of the proposed control scheme is demonstrated by simulation study with virtual rainstorm event.

• Journal of Legislation Research
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• no.53
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• pp.269-310
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• 2017

This article aims at the study on Improvement of legal System which is related to automated vehicles in the era of the 4th industrial revolution. Legal aspects of driving automation have two view points. One is to permit a automated vehicle, the other is to regulate the behavior of driver on the road. Signifying elements of the 4th industrial revolution are IoT, AI, big data, cloud computing etc. Automated vehicles are the imbodiment of those new ICT technologies. The vehicle management act(VMA) rules about vehicle registration and approval of vehicle types. VMA defines a automated vehicle as a vehicle which can be self driven without handling of driver or passenger. Vehicle makers can take temporary driving permission for testing and research the driving automation. Current definition of automated vehicle of VMA is not enough for including all levels of SAE driving automation. In the VMA must be made also a new vehicle safty standard for automated vehicle. In the national assembly is curruntly pending three draft bills about legislation of artificial intelligence. Driving automation and AI technologies must be parallel developed. It is highly expected that more proceeding research of driving automation can be realized as soon as possible.

• 한국노년학
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• v.29 no.3
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• pp.917-933
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• 2009

This study examined the factors affecting forms of long-term care service use by elderly and the forms of use are classified facility care service, home care service, and unused. It is used data from the 2nd pilot program for the Long Term Care Insurance scheme and it is analysed 5,497 cases. Multi-nominal regression is used. According to the results, women use formal service more than man do, and wowen use facility care than home care. Those who eligible for National Basic Livelihood Security System(NBLSS) are shown to have higher use of formal care(especially facility care) than the middle income class, and the low income class than the middle income class has lower use of formal care. In addition, higher the family care is available, lower the taking part in the service. The big cities and mid sized cities than rural are used the formal service and moreover mid sized cities are used facility care than home care. Furthermore, the level of care need is determinants of service use and function of ADL, IADL, and abnormal behavior is also determinants of formal service(especially facility care). But nursing need and rehabilitation need are not determinants of formal service use. Based on the results, the recommendations are developed and implemented for the improvement the elderly long-term care insurance.

• Information Systems Review
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• v.20 no.4
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• pp.59-78
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• 2018

With the advent of the various device types and marketing communication, customer's search and purchase behavior have become more complex and segmented. However, extant research on multichannel marketing effects of the purchase funnel has not reflected the specific features of device User Interface (UI) and User Experience (UX). In this study, we analyzed the marketing channel effects of multi-device shoppers using a unique click stream dataset from global online retailers. We examined device types that activate online shopping and compared the differences between marketing channels that promote visits. In addition, we estimated the direct and indirect effects on visits and purchase revenue through customer's accumulated experience and channel conversions. The findings indicate that the same customer selects a different marketing channel according to the device selection. These results can help retailers gain a better understanding of customers' decision-making process in multi-marketing channel environment and devise the optimal strategy taking into account various device types. Our empirical analyses yield business implications based on the significant results from global big data analytics and contribute academically meaningful theoretical framework using an economic model. We also provide strategic insights attributed to the practical value of an online marketing manager.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.49-58
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• 2019

There have been studies on the satisfaction, preference, and post occupancy evaluation of urban parks in order to reflect users' preferences and activities, suggesting directions for future park planning and management. Despite using questionnaires that are proven to be affective to get users' opinions directly, there haven been limitations in understanding the latest changes in park use through questionnaires. This study seeks to address the possibility of utilizing the thirdgeneration SNS data, Instagram and Google, to compare behavior patterns and trends in park activities. Instagram keywords and photos representing user's feelings with a specific park name were collected. We also examined reviews, peak time, and popular time zones regarding selected parks through Google. This study tries to analyze users' behaviors, emerging activities, and satisfaction using SNS data. The findings are as follows. People using park near residential areas tend to enjoy programs being operated in indoor facilities and to like to use picnic places. In an adjacent park of commercial areas, eating in the park and extended areas beyond the park boundaries is found to be one of the popular park activities. Programs using open spaces and indoor facilities were active as well. Han River Park as a detached park type offers a popular venue for excercises and scenery appreciation. We also identified companionship characteristics of different park types from texts and photos, and extracted keywords of feelings and reviews about parks posted in $3^{rd}$ generation SNS. SNS data can provide basis to grasp behavioral patterns and satisfaction factors, and changes of park activities in real time. SNS data also can be used to set future directions in park planning and management in accordance with new technologies and policies.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.