• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.517-524
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• 2022

This paper analyzes wave measurement using X-band navigation (ship) radar, changes in radar signal due to snowfall and precipitation, and factors that obstruct wave measurement. Data obtained from the radar installed at Sokcho Beach were used, and data from the Korea Meteorological Administration and the Korea Hydrographic and Oceanographic Agency were used for the meteorological data needed for comparative verification. Data from the Korea Meteorological Administration are measured at Sokcho Meteorological Observatory, which is about 7km away from the radar, and data from the Korea Hydrographic and Oceanographic Agency are measured at a buoy about 3km away from the radar. To this point, changes in radar signals due to rainfall or snowfall have been transmitted empirically, and there is no case of an analysis comparing the results to actual weather data. Therefore, in this paper, precipitation, snowfall data, CCTV, and radar signals from the Korea Meteorological Administration were comprehensively analyzed in time series. As a result, it was confirmed that the wave height measured by the radar according to snowfall and rainfall was reduced compared to the actual wave height, and a decrease in the radar signal strength according to the distance was also confirmed. This paper is meaningful in that it comprehensively analyzes the decrease in the signal strength of radar according to snowfall and rainfall.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.337-342
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• 2021

The purpose of this study is to analyze the environment and strategic behaviors of cultural contents companies with a focus on Iconix, and to derive strategic recommendations for Iconix to pursue in order to create a sustainable competitive advantage. As a result of the analysis, Iconix is a vertically integrated development-business system from content planning to business in line with their mission to develop into an all-weather entertainment content provider that can confidently compete with the major players in the US and Europe that are already leading the global market. It is building a strong global business network covering both domestic and overseas markets in stages, taking a high-level global strategy. However, depending on Pororo's success or due to various problems within the organizational structure, it is facing limitations. Therefore, if the various strategic suggestions presented in this study are implemented based on the One Source Multi Channel/Multi Use strategy that can maximize the added value of contents through the participation and business linkage of leading companies in each sector of the entertainment industry, the total entertainment will be stabilized. It will establish itself as a leader in the contents industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.30-41
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• 2022

With the advent of autonomous driving, personal mobility, drones, and smart roads, it is necessary to respond to changes in the road traffic environment in the road guidance system. However, the use of road signs to guide the road is decreasing compared to the past due to the advent of devices such as navigation and smartphones. Therefore, in this study, a large-scale survey was conducted to derive road sign issues and usage plans to respond to future changes. Based on this, this study presented a strategy to diversify road sign functions by analyzing the factors affecting the use of road signs by citizens. As a result, first, it is necessary to provide real-time variable road guidance information that reflects user needs such as traffic, weather, and local events. Second, it is necessary to informatize digital road signs such as reflecting maps with precision. Third, it is necessary to demonstrate road guidance in a virtual environment that reflects various future mobility and road environments.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.3
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• pp.206-214
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• 2023

Fish school monitoring technology is utilized for various purposes, such as boat fishing and resource surveys. With advancements in information and communication technology, this technology has expanded its application to remote areas. Its significance has grown in fishing sites, particularly for improving the efficiency and cost-effectiveness of set-net fishing. Set-net fishing gears are not limited to coastal areas, but are also installed in inland and remote sea regions. Consequently, fishermen require technology that allows them to quickly transmit information about approaching fish schools and enables them to perform long-term monitoring. The development of remote monitoring technology for set-net fish schools must consider crucial design factors such as communication range, transmission speed, power consumption of information modules, and operational expenses. In this study, we developed a low-power remote monitoring module for set-net fish school based on WCDMA. The module was specifically designed to minimize power consumption, allowing for communication over long distances and extended operation times in set-net fishing applications. Furthermore, we developed a web server software application that enables remote access to fish schools and provides real-time weather information. The performance of the developed module was evaluated through set-net fishing site application and experiments with moving ships on the sea. The experimental results demonstrated that the remote monitoring system, consisting of the developed low-power remote monitoring module for set-net fish school based on WCDMA and a fish finder, had an average power consumption of 4.6 W, a maximum communication range of 22.84 km, and a data transmission and reception rate of 98.79%. The maximum fish school information transmission and reception rate was 97.26%

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.4
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• pp.307-315
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• 2023

Korea controls the departure of vessels based on the Maritime Safety Act such that only ships with seaworthiness can navigate in bad weather, but scientific evaluation results and quantitative basis for the designation of ships subject to control are insufficient. Opinions for improvement are being raised for a reasonable departure control operation. The purpose of this study is to evaluate the adequacy of the current departure control standards through actual measurement of tugboats, which are the type of vessels subject to control when a wind and wave advisory is effective, and to present quantitative grounds for improvement of controls. A sensor was installed on the tugboat to measure the ship's three-axis motion and hull acceleration, and the hull motion performance was measured by operating in the sea area with a significant wave height of 3 m. The measured values were compared and analyzed based on seaworthiness evaluation factors and limit value standards. The actual ship was excluded from the current control standard according to tonnage, but as a result of the analysis, the pitch value exceeded the operation standard, and a risk to navigation safety existed. The results of this study suggest the need for additional actual measurement studies that can represent various ship types and specifications and review ship departure control targets.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.4
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• pp.121-132
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• 2023

Marine heatwaves (MHWs), referring to anomalously high sea surface temperatures, have drawn significant attention from marine scientists due to their broad impacts on the surface marine ecosystem, fisheries, weather patterns, and various human activities. In this study, we examined the impact of the distribution of Changjiang diluted water (CDW), a significant factor causing oceanic property changes in the East China Sea (ECS) during the summer, on MHWs. The surface salinity distribution in the ECS indicates that from June to August, the eastern extension of the CDW influences areas as far as Jeju Island and the Korea Strait. In September, however, the CDW tends to reside in the Changjiang estuary. Through the Empirical Orthogonal Function analysis of the cumulative intensity of MHWs during the summer, we extracted the loading vector of the first mode and its principal component time series to conduct a correlation analysis with the distribution of the CDW. The results revealed a strong negative spatial correlation between areas of the CDW and regions with high cumulative intensity of MHWs, indicating that the reinforcement of stratification due to low-salinity water can increase the intensity and duration of MHWs. This study suggests that the CDW may still influence the spatial distribution of MHWs in the region, highlighting the importance of oceanic environmental factors in the occurrence of MHWs in the waters surrounding the Korean Peninsula.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.