• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.236-242
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• 2019

In this study, we developed the system that can collect and store environmental disaster data into the database and use it for environmental disaster management by converting structured and unstructured documents such as images into electronic documents. In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. Environmental disaster information is one of important elements of disaster cycle. Environment disaster information management refers to the act of managing and processing electronic data about disaster cycle. However, these information are mainly managed in the structured and unstructured form of reports. It is necessary to manage unstructured data for disaster information. In this paper, the intelligent generation approach is used to convert handout into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored into the disaster database system. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle. The expected effect of this research will be able to apply it to smart environmental disaster management and decision making by combining artificial intelligence technologies and historical big data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Journal of architectural history
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• v.3 no.1
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• pp.57-82
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• 1994

This paper is the result of the researches and the field surveys of the villages and the dwellings of Korean immigrants in Yien-Pien area, north-eastern China. This study aims to persue both of the origin and the process of development of their settlements and dwelling types from late 19C to the present. Their processes are too complex to analysis by single view-point. I have eyes to interprete them from three pionts; 1)correspondences between the dwelling types and the econo-political history of their region, 2)cultural assimilation with the native dwelling types, and 3)the direction of their modernization with the economical development of modern China. Three village types have been pioneered; 1)the villages of indivisual immigration, 2)the villages of planned group immigration, and 3)the villages of socilistic reform villages of 1) were composed of organic village patterns and various shaped dwelling lots on the sloped site; villages both of 2) and 3), gird patterns and uniformed lots on open fields. Historically, villages of 1) were pioneered before 1931; villages of 2), 1936-1945; villages of 3), from 1945. Each of dwelling types had strong relations with the village types to which it belonged. Before 1931, dwellings were built up based on so called "Ham-buk dwelling type" which was dominent in north-eastern Korea. In the era of gruop-immigration, various dwelling types were flew into Yen-Pien from southern Korea. In modern China, their southern types were changed into Yen-Pien type as similar as Ham-book type. After 1945, with the Great leap Forward and the Cultural Revolution, as communization of indivisual properties and reorganization of rural communities, each of dwellings became smaller and simpler in aspects of scales as well as functions. There are two types in Yen-Pien dwellings, those are 'single-file' and 'double-file' type. Three sub-types of latter arc 'six-bays', 'eight-bays', and rarely 'ten-bays'. The most common element of all types is Chong-ju-k'an; which is large room with heated floor, openig to kitchen. Now, modern dwellings of Korean immigrants are changing their spatial compositions, materials, and structures. With cultural assimilation as well as modernization, especially in urban areas, they are compelled to accept the elements of Chinese dwellings. But the spatial element of "Chong-ju-k'an", which is the core element of Yen-Pien dwelling type, never fade away nor is changed.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.215-229
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• 2020

In response to the recent 4th industrial revolution, the demand for 3D object models in the latest fields of digital twin, autonomous driving, and VR/AR, as well as the existing fields such as city, construction, transportation, and energy has increased significantly. It is expected that the demand for 3D object models with various precision from LOD1 to LOD4 will increase more and more in various industry fields. However, the Ministry of Land, Infrastructure and Transport, and the local government and the private sector have partially built 3D object models of different precisions for some specific regions because of the huge cost. Therefore, this study proposes a feasible plan that can solve the cost problem in generating 3D object models for the whole territory. For our purpose, we first analyzed usage, demand, generation technology and generation cost for 3D object models. Afterwards, we proposed LOD3 model generation plan for all territory using automatic 3D object model generation technology based on image matching. Additionally, we supplemented the proposed plan by using LOD4 generation plan for landmarks and LOD2 generation plan non-urban area. In the near future, we expect this would be a great help in establishing a feasible and effective 3D object model generation plan for the whole country.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.172-177
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• 2021

As part of the digital era, a digital twin that simulates the weak part of a product by performing a stress test that reduces the lifespan of some expensive equipment that cannot be done in reality by accurately moving the real world to virtual reality is being actively used in the manufacturing industry. Due to the development of IoT, the digital twin, which accurately collects data collected from the real world and makes it the same in the virtual space, is mutually beneficial through accurate prediction of urban life problems such as traffic, disaster, housing, quarantine, energy, environment, and aging. Based on its action, it is positioned as a necessary tool for smart city construction. Although digital twin is widely applied to the manufacturing field, this study proposes a smart city model suitable for the 4th industrial revolution era by using it to smart cities and increasing citizens' safety, welfare, and convenience through the proposed model. In addition, when a digital twin is applied to a smart city, it is expected that more accurate prediction and analysis will be possible by real-time synchronization between the real and virtual by maintaining realism and immediacy through real-time interaction.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.116-127
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• 2022

Recently, the smart city market based on the 4th industrial revolution is rapidly expanding worldwide and is being promoted in various ways. Korea has promoted various smart city public and private partnership projects, but there were limits to the activation of smart city public and private partnership projects due to insufficient enactment and revision of laws, public-oriented ordering method, and lack of private execution capacity. Therefore, this study intends to suggest key success factors for each stage of smart city public and private partnership projects through the analysis of the order status of the smart city national pilot city and the analysis of previous research. Through this, it is expected that it will be possible to eliminate various types of risks that may occur in the domestic smart city public and private partnership projects and contribute to revitalizing the smart city public and private partnership projects.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.107-122
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• 2023

The demand for spatial information in the era of the 4th Industrial Revolution is expanding Additionally, interest in attribute data related to geography or location is increasing. In the field of spatial information, spatial information policies and services tailored to the public can be provided through linkage and integration with new attribute data, and these data are resources for this purpose. In order to meet this expanding and diverse demand for spatial information utilization, it is necessary to develop technologies for linking and utilizing various attribute information such as public data. In this study, we aim to present a technology development strategy for linking and integrating attribute data and spatial information through a review of theories related to data linkage and integration, the current status of data on public data portals, and existing prior research. As a result, it was suggested that the data identifier of the attribute data to be linked should be used to develop linkage technology between spatial information and attribute data, and an attribute data linkage process that can be used when designing a prototype for technology development was presented.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.