• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.153-164
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• 2022

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.625-632
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• 2022

Gas turbine generators in load-following operation in the domestic power system play a major role in maintaining the rated frequency, but often have poor frequency control. Therefore, after examining the control characteristics of the governor, which is a gas turbine speed control device, and analyzing the failure types, countermeasures were suggested for each case. In addition, it was confirmed through the governor response test that the gas turbine helps in frequency recovery depending on the speed of fuel control, but also acts as a factor impeding stable operation, such as rapid fluctuations in combustion chamber temperature and combustion vibration. Therefore, in order to maintain stable power quality, there was a need for thorough facility management as well as research on the governor control method in which the traditional PID control method and the machine learning algorithm, a core field of the 4th industry, were fused.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1739-1748
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• 2021

Real estate prices affect countries, businesses, and households, and many studies have been conducted on the real estate bubble in recent soaring real estate prices. However, if the real estate bubble prediction simply compares the real estate price, or if it does not reflect key psychological variables in real estate sales, it can be judged that the accuracy of the bubble prediction model is poor. The purpose of this study is to design a predictive model that can explain the real estate bubble situation by region using the autoencoder technique. Existing real estate bubble analysis studies failed to set various types of variables that affect prices, and most of them were conducted based on linear models. Thus, this study suggests the possibility of introducing techniques and variables that have not been used in existing real estate bubble studies.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.305-310
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• 2021

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.101-115
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• 2021

Access re-review is a valuable and important task, but it is burdensome for archivists. Thus, an access re-review automation was proposed to address this. In this situation, the National Archives of Korea actually utilized the access re-review solution in the performance of the "2020 Access Re-Review Project" and compared and analyzed it with human work. The project was, however, not a research project centered on analysis on access re-review solutions, and it has a limited result in terms of experimental use of commercial programs. Nevertheless, in the current situation where there are only macro and superficial discussions on access re-review of intelligent archives, it would be meaningful to apply the access re-review solution to archivists in real businesses and examine the results. This paper seeks to discuss the practicality that can mitigate the task of access re-review through an analysis of use cases of access re-review solutions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.90-102
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• 2022

Currently, in our society with a substantial and increasing fraction of the elderly population, transport safety for elderly drivers is becoming the center of attention. However, deficient data on vehicle crashes in South Korea limits the growth of traffic accident research pertaining to the country. So, we complemented South Korean vehicle crash data by examining USA vehicle crash data, especially the data of Ohio State, and analyzing the influential factors of elderly driver-involved crashes of the State. Subsequently, we suggested a way of improving the South Korean dataset. Notably, our study showed that the influential factors were vehicle speed, posted speed, and following other vehicles too close and provided them in the South Korean dataset.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.313-325
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• 2022

Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10^-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.133-138
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• 2023

As a side effect of recent rapid climate change and global warming, the frequency and scale of flood disasters are increasing worldwide. In Korea, the water level of the Han River is a major management target for preventing flood disasters in Seoul, the capital of Korea. In this paper, to improve the water level prediction of the Han River based on machine learning, we perform a comprehensive assessment of the quality of related dataset and propose data preprocessing methods to improve it. Specifically, we improve the dataset in terms of completeness, validity, and accuracy through missing value processing and cross-correlation analysis. In addition, we conduct a performance evaluation using random forest and LightGBM to analyze the effect of the proposed data improvement method on the water level prediction performance of the Han River.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.417-425
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• 2022

The 'Comprehensive Plan for Nurturing Digital Talents' reported at the Cabinet meeting of the Ministry of Education in August 2022 focuses on qualitative and quantitative expansion of informatics education centered on SW, AI education. With the advent of the era of artificial intelligence, data science education is also drawing attention as a field of informatics education. Data science is originally a field where various studies are fused, and advanced technologies are being used for data analysis, modeling, and machine learning. This study devised a draft of the instructional model of data science education through literature research and analysis of previous studies, and developed a final instructional model through usability test and expert validation.