• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.49-53
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• 2022

본 논문은 기존의 도시개발정책에서 경제적 관점에서 가치의 중심을 두었다면 4차산업혁명시대에는 서비스적 가치변환을 추구하고 있다. 시공간을 공유하는 사람 중심의 공간 서비스요, 가치의 용광로가 메타버스라고 한다면, 오징어게임과 같은 게임이론이 접목된 도시의 미래상을 고려할 필요가 있다. 4차산업 기술과 서비스가 사회로 침투되면서 도시의 공간 인프라, 동산 부동산, 객체들을 물리적공간과 가상공간에서 인공지능과 빅데이터 분석이 개입된 각종 형태와 형식의 컨텐츠가 입체적으로 생동감을 얻게 되었다. 특히, 코로나19을 맞아 메타버스기반 서비스가 대중들에게도 접근하기 시작하면서 지금까지 경험하지 못했던 메타버스 시민의 역할에 대한 필요성이 대두하게 되었다. 4차산업혁명시대의 스마트도시, 메타버스와 게임이론을 연구하였다.

• Journal of the Korean Institute of Rural Architecture
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• v.23 no.2
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• pp.9-16
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• 2021

The purpose of this study is to maintain the existing characteristics of the city by utilizing the physical decline status and floating population in small and medium cities residential areas. In addition, it intends to present the direction of flexible urban regeneration and maintenance by reflecting regional characteristics and current status. A total of three data were used in this study. Building data, floating population data, and census output area data were used. Building data and floating population data were classified into five classes. The graded data were joined to the census output area data and analyzed by overlapping the two data. As a result of analysis of 17 residential areas in 5 small and medium cities in Jeollanam-do, 4 types, 2 management models, and 4 indicators could be presented by grade and regional characteristics. This study is meaningful in that it is possible to plan regionally customized urban regeneration/maintenance management plans and projects through the typology of the current status and characteristics of the region, which is an important step in the bottom-up form.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.77-93
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• 2024

The recent development of data collection technology, which conveys various travel data in real-world such as mobile data and probe vehicle data, facilitates transportation planners identifying specified spatio-temporal travel patterns. In this study, an easily implementable travel mode classification methodology was proposed to classify inter-regional trip-modes without modeling by superimposing trajectories generated from mobile phone signaling and transportation infrastructure points into a polygon scale of a shapefile in a GIS system. Each regional mode trip was classified according to the rules such as the presence of transportation infrastructure in the trip trajectory, travel time, and the presence of access trips. An accuracy test generates Type I and Type II error results table to verify the proposed methodology. As a result, it was found that the methodology developed showed the F1-Score of the air mode 1.00, rail mode 0.95, bus mode 0.73.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.

• Industry Promotion Research
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• v.8 no.2
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• pp.1-9
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• 2023

To bridge the research gap in the area of the Fourth Industrial Revolution, we explore the correlation between the core technologies of the Fourth Industrial Revolution and the economic performance of companies. The results show that the technologies have a statistically significant positive (+) correlation with company sales. The size of the correlation is highest for 3D printing (139%), followed by big data (129%), cloud computing (127%), artificial intelligence (78%), and the internet of things (70%). We also found a statistically significant negative (-) interaction effect between the internet of things and 3D printing, cloud computing and big data, and cloud computing and 3D printing when examining the interaction effect of introducing core technologies of the Fourth Industrial Revolution on company sales. This paper represents an early attempt to examine the correlation between the core technologies of the Fourth Industrial Revolution and the economic performance of companies and may serve as a basis for further empirical research.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.478-495
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• 2016

The existing city planning was performed by internal few decision-maker, but the information that an individual could contact with through the evolution of the ICT technology increased in the smart city, and a channel diversified and came to be able to participate in a decision making process by various methods. I show that it is in the collaborative planning process to come to a mutual understanding with residents directly or indirectly if utilize big data in a process of the safe community planning of the smart city. Therefore, I compare the utilization contents between data of Matsubara-shi,Japan and data of certification city of Korea which received the certification of the WHO international safe community. In the area where this study prepares for the approval of the international safe community, it wants you to use it though you utilize the data as the supporting role of the residents participation plan.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.1-9
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• 2017

Smart Cities intelligently manage numerous infrastructures, including Smart-City IoT devices, and provide a variety of smart-city applications to citizen. In order to provide various information needed for smart-city applications, Smart Cities require a function to intelligently process large-scale streamed big data that are constantly generated from a large number of IoT devices. To provide smart services in Smart-City, the Smart-City Consortium uses stream reasoning. Our stream reasoning requires real-time processing of big data. However, there are limitations associated with real-time processing of large-scale streamed big data in Smart Cities. In this paper, we introduce one of our researches on cloud computing based real-time distributed-parallel-processing to be used in stream-reasoning of IoT big data in Smart Cities. The Smart-City Consortium introduced its previously developed smart-city middleware. In the research for this paper, we made cloud computing based real-time distributed-parallel-processing available in the cloud computing platform of the smart-city middleware developed in the previous research, so that we can perform real-time distributed-parallel-processing with them. This paper introduces a real-time distributed-parallel-processing method and system for stream reasoning with IoT big data transmitted from various sensors of Smart Cities and evaluate the performance of real-time distributed-parallel-processing of the system where the method is implemented.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.463-476
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• 2022

After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry.

• Journal of KIBIM
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• v.8 no.3
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Journal of the Korean Regional Science Association
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• v.39 no.2
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• pp.47-61
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• 2023

Satisfaction on the residential environment is a major factor influencing the choice of residence and migration, and is directly related to the quality of life in the city. As online services of real estate increases, people's evaluation on the residential environment can be easily checked and it is possible to analyze their satisfaction and its determining factors based on their evaluation. This means that a larger amount of evaluation can be used more efficiently than previously used methods such as surveys. This study analyzed the residential environment reviews of about 30,000 apartment residents collected from 'Zigbang', an online real estate service in Seoul. The apartment review of Zigbang consists of an evaluation grade on a 5-point scale and the evaluation content directly described by the dweller. At first, this study labeled apartment reviews as positive and negative based on the scores of recommended reviews that include comprehensive evaluation about apartment. Next, to classify them automatically, developed a model by using Bidirectional Encoder Representations from Transformers(BERT), a deep learning-based natural language processing model. After that, by using SHapley Additive exPlanation(SHAP), extract word tokens that play an important role in the classification of reviews, to derive determining factors of the evaluation of the residential environment. Furthermore, by analyzing related keywords using Word2Vec, priority considerations for improving satisfaction on the residential environment were suggested. This study is meaningful that suggested a model that automatically classifies satisfaction on the residential environment into positive and negative by using apartment review big data and deep learning, which are qualitative evaluation data of residents, so that it's determining factors were derived. The result of analysis can be used as elementary data for improving the satisfaction on the residential environment, and can be used in the future evaluation of the residential environment near the apartment complex, and the design and evaluation of new complexes and infrastructure.