• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1269-1279
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• 2021

Geostationary Ocean Color Imager II (GOCI-II), which are now operated successfully since its launch in 2020, acquires local area images with 12 Level 1B slot images that are sequentially acquired in a 3×4 grid pattern. The boundary areas between the adjacent slots are prone to discontinuity in radiance, which becomes even more clear in the following Level 2 data, and this warrants the precise analysis and correction before the distribution. This study evaluates the relative radiometric biases between the adjacent slots images, by exploiting the overlapped areas across the images. Although it is ideal to derive the statistics from humongous images, this preliminary analysis uses just the scenes acquired at a specific time to understand its general behavior in terms of bias and variance in radiance. Level 1B images of February 21st, 2021 (UTC03 = noon in local time) were selected for the analysis based on the cloud cover, and the radiance statistics were calculated only with the ocean pixels. The results showed that the relative bias is 0~1% in all bands but Band 1 (380 nm), while Band 1 exhibited a larger bias (1~2%). Except for the Band 1 in slot pairs aligned North-South, biases in all direction and in all bands turned out to have biases in the opposite direction that the sun elevation would have caused.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.34-41
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• 2021

Sidewalks are facilities used for the safe and comfortable passage of pedestrians and are paved with blocks of various materials. Currently, Korea does not have a quantitative survey method for the pavement condition of sidewalks, so it is necessary to develop an efficient survey method. Drones are being used as an efficient survey tool in various fields, but there are limited studies in which sidewalks have been investigated. This study investigates the possibility of detection by limiting the missing sidewalk blocks using a drone. This study is an initial study on the development of a method for detecting damage in sidewalk blocks. For this, sidewalk blocks were artificially removed to simulate a dropout situation, and images were acquired with 0.7-cm resolution using a drone. As a characteristic of the point cloud data acquired through image pre-processing, there was high variance of the elevation of the points in the missing area of the sidewalk block. Using these characteristics, an experiment was conducted to detect the missing parts of the sidewalk block by applying four thresholds to the variance of the elevation of points included in the grid corresponding to the sidewalk area. As a result, the detection accuracy was shown with a positive detection ratio of 70-80%, omission errors of 20-30%, and commission errors lower than 2%. It is judged that the possibility of detecting missing sidewalk blocks is high. This study focused on detecting a simulated missing sidewalk block in a limited environment. Therefore, it is expected that an efficient and quantitative method of detecting damaged sidewalk blocks can be developed in the future through additional research with considerations of the actual environment.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.275-286
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• 2019

In order to prevent local extinction of organisms and to preserve biodiversity, it is important to ensure connectivity between habitats. Even if the habitat is exposed to various disturbance factors, it is possible to avoid or respond to disturbances if they are linked to other habitats. Habitat connectivity can be assessed from a variety of perspectives, but the importance of functional connectivity based on species movement has been emphasized in recent years due to the development of computational capabilities and related software. Among them, Circuitscape, which is a connectivity evaluation tool, has an advantage it can provide detailed reference data for the city planning because it maps ecological flows on individual grid based on circuit theory. Therefore, in this study, the functional connectivity of Suwon was evaluated by applying Circuitscape and then, the ecological corridor to be conserved and supplemented was suggested based on it. The results of this study are expected to effectively complement the methodology related ecological corridor/axis, which was previously provided only in the form of a diagram, and to be effective in management of development project and urban planning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.74-82
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• 2019

The revolution of industry 4.0 is enabling us to build an intelligent connection society called smart cities. The use of renewable energy in particular solar energy is extremely important for modern society due to the growing power demand in smart cities, but its difficult to monitor and manage in each buildings since need to be deploy low energy sensors and information need to be transfer via wireless sensor network (WSN). The Internet of Things (IoT) / low-power wide-area (LPWA) is an emerging WSN technology, to collect and monitor data about environmental and physical electrical / electronics devices conditions in real time. However, providing power to IoT sensor end devices and other public electrical loads such as street lights, etc is an important challenging role because the sensor are usually battery powered and have a limited life time. In this paper, we proposes an efficient solar energy-based power management scheme for smart city based on IoT technology using LoRa wide-area network (LoRaWAN). This approach facilitates to maintain and prevent errors of solar panel based energy systems. The proposed solution maximizing output the power generated from solar panels system to distribute the power to the load and the grid. In this paper, we proved the efficiency of the proposed system with Simulink based system modeling and real-time emulation.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.103-112
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• 2019

Extraction of region of interest for satellite image classification is one of the important techniques for efficient management of the national land space. However, recent studies on satellite image classification often depend on the information of the selected image in selecting the region of interest. This study propose an effective method of selecting the area of interest using the continuous digital topographic map constructed from high resolution images. The spatial information used in this research is based on the digital topographic map from 2013 to 2017 provided by the National Geographical Information Institute and the 2015 Sejong City land cover map provided by the Ministry of Environment. To verify the accuracy of the extracted area of interest, KOMPSAT-3A satellite images were used which taken on October 28, 2018 and July 7, 2018. The baseline samples for 2015 were extracted using the unchanged area of the continuous digital topographic map for 2013-2015 and the land cover map for 2015, and also extracted the baseline samples in 2018 using the unchanged area of the continuous digital topographic map for 2015-2017 and the land cover map for 2015. The redundant areas that occurred when merging continuous digital topographic maps and land cover maps were removed to prevent confusion of data. Finally, the checkpoints are generated within the region of interest, and the accuracy of the region of interest extracted from the K3A satellite images and the error matrix in 2015 and 2018 is shown, and the accuracy is approximately 93% and 72%, respectively. The accuracy of the region of interest can be used as a region of interest, and the misclassified region can be used as a reference for change detection.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.397-418
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• 2019

It is very important to understand and analyze the interactive behaviour between ground and adjacent structures due to tunneling. With many technological advancement in modern society, numerous methods for analyzing the interactive behaviour are used in a wide range of civil engineering fields. Close range photogrammetry is mainly being used in the field of geotechnical engineering and research on measuring methods associated with GeoPIV has been currently increased. Originally, the close range photogrammetry using target points and aluminum rods for VMS (Vision Measurement System) program has been used. However, applying this has a problem that external errors can be occurred because the target points are artificially installed by hand, and if the grid between points is being wider or narrower, deficient data can be obtained. Therefore, in this study, MATLAB-based No-target program that can analyze displacement without using target was developed. Additionally, this study focused on comparison and verification with existing program through numerical analysis and laboratory model test. Three cases of Greenfield condition, Strip foundation, and Pile foundation were analyzed. From results of VMS program and No-target program, the error rate and reliability of the total displacement and the vertical displacement were analyzed. It was also compared and verified through the finite element numerical program, PLAXIS.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1124-1128
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• 2021

As regulations of emissions from ships become more stringent, electric propulsion systems have been increasingly used to solve this problem in vessels ranging from large merchant ships to small and medium-sized ships. Methods for improving the efficiency of the electric propulsion system include the improvement of power sources; the use of a system linked to environmentally friendly power sources, such as batteries, fuel cells, and solar power; and the development of hardware and control methodology for rectifiers, power conversion devices, and propulsion motors. The method using a phase-shifting transformer with diodes has been widely used for rectification. Power semiconductor devices with grid connection to an environmentally friendly power source using DC distribution, a variable speed power source, and the application of small and medium-sized electric propulsion systems have been developed. Accordingly, the demand for active front-end (AFE) rectifiers is increasing. In this study, a method using a neural network rather than a conventional proportional-integral controller was proposed to control the AFE rectifier. Tested controller data were used to design a neural network controller trained through MATLAB/Simulink. The neural network controller was applied to a rectification system designed using PSIM software. The results indicated the effectiveness of improving the waveform and power factor DC output stage according to the load variation. The proposed system can be applied as a rectification system for small and medium-sized environmentally friendly ships.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).