• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• The Journal of Engineering Geology
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• v.33 no.2
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• pp.275-291
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• 2023

Global warming has made the polar regions more accessible, leading to increased demand for the construction of new resource-development plants in oil-rich permafrost regions. The selection of locations of resource-development plants in permafrost regions should consider the surface displacement resulting from thawing and freezing of the active layer of permafrost. However, few studies have considered surface displacement in the selection of optimal locations of resource-development plants in permafrost region. In this study, Analytic Hierarchy Process (AHP) analysis using a range of geospatial information variables was performed to select optimal locations for the construction of oil-sands development plants in the permafrost region of southern Athabasca, Alberta, Canada, including consideration of surface displacement. The surface displacement velocity was estimated by applying the Small BAseline Subset Interferometric Synthetic Aperture Radar technique to time-series Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar images acquired from February 2007 to March 2011. ERA5 reanalysis data were used to generate geospatial data for air temperature, surface temperature, and soil temperature averaged for the period 2000~2010. Geospatial data for roads and railways provided by Statistics Canada and land cover maps distributed by the North American Commission for Environmental Cooperation were also used in the AHP analysis. The suitability of sites analyzed using land cover, surface displacement, and road accessibility as the three most important geospatial factors was validated using the locations of oil-sand plants built since 2010. The sensitivity of surface displacement to the determination of location suitability was found to be very high. We confirm that surface displacement should be considered in the selection of optimal locations for the construction of new resource-development plants in permafrost regions.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.9-16
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• 2024

In Korea, as disasters become larger and more complex, there is a trend of shifting from a focus on response and recovery to a focus on prevention and preparedness. In order to prevent and prepare for disasters, each local government manages disaster management resources by stockpiling them. However, although disaster management resources are stored in individual warehouses, they are managed by department rather than by warehouse, resulting in insufficient management of disaster management resources due to the heavy workload of those in charge. In order to intensively manage these disaster management resources, an integrated disaster management resource management center is established and managed at the metropolitan/provincial level. In the case of Gangwon-do, the subject of this study, a warehouse is rented and operated as an integrated disaster management resource management center. When leasing an integrated management center, there is the inconvenience of having to move the location every 1 to 2 years, so it is deemed necessary to build a dedicated facility in an available site. To select a location candidate, network analysis was used to measure access to and use of facilities along interconnected routes of networks such as roads and railways. During network analysis, the Location-Allocation method, which was widely used in the past to determine the location of multiple facilities, was applied. As a result, Hoengseong-gun in Gangwon-do was identified as a suitable candidate site. In addition, if the integrated management center uses our country's logistics system to stockpile disaster management resources, local governments can mobilize disaster management resources in 3 days, and it is said that it takes 3 days to return to normal life after a disaster occurs. Each city's disaster management resource stockpile is 3 days' worth per week, and the integrated management center stores 3 times the maximum of the city's 4-day stockpile.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.