• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1203-1214
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• 2016

Obesity is a risk factor for various diseases as well as itself a disease and associated with socioeconomic factors. The obesity proportion has been increasing in Korea over about 15 years so that investigation of the socioeconomic factors related with obesity is important in terms of preventation of obesity. In particular, the association between obesity and socioeconomic status varies with gender and has spatial dependency. In the paper, we estimate the effects of socioeconomic factors on obesity proportion by gender, considering the spatial correlation. Here, a conditional autoregressive model under the Bayesian framework is used in order to take into account the spatial dependency. For the real applicaiton, we use the obestiy proportion dataset at 25 districts of Seoul in 2010. We compare the proposed spatial model with a non-spatial model in terms of the goodness-of-fit and prediction measures so the spatial model performs well.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.49-55
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• 2022

In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.2
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• pp.176-183
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• 2021

Objectives : The purpose of this study was to find out how demographic factors, suicide attempt patterns, psychiatric history and management of suicide attempters affect the completion of emergency department (ED) based case management program. Methods : Among the patients who attempted suicide and visited the emergency department of Chung-Ang University Hospital from June 1, 2018 to May 31, 2021, 661 patients who agreed to case management were studied. After being discharged from the emergency department, subjects were registered for an eight-week follow-up service program. Hierarchical logistic regression analysis was conducted with demographic factors, suicide attempt patterns, psychiatric history and management as independent variables, and completion of case management program as dependent variables. Results : Suicide attempt pattern had the most significant influence on the completion of case management program, followed by demographic factors, psychiatric history and management. Those who completed the case management program were significantly more likely to have suicide plans in the future, more authentic in suicide attempts, and had higher proportion of past suicide attempts than those who did not complete the program. Conclusions : To ensure that the subjects complete the follow-up project program and get connected to community services, an individualized approach with consideration of suicide attempt patterns, demographic factors, and psychiatric history is needed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Korea Water Resources Association
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• v.57 no.6
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• pp.409-419
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• 2024

Climate change is already impacting sustainable water resource management. The influence of climate change on water supply from reservoirs has been generally assessed using climate change scenarios generated based on global climate models. However, inherent uncertainties exist due to the limitations of estimating climate change by assuming IPCC carbon emission scenarios. The decision scaling approach was applied to mitigate these issues in this study focusing on four reservoir watersheds: Chungju, Yongdam, Hapcheon, and Seomjingang reservoirs. The reservoir water supply reliablity was analyzed by combining the rainfall-runoff model (IHACRES) and the reservoir operation model based on HEC-ResSim. Water supply reliability analysis was aimed at ensuring the stable operation of dams, and its results ccould be utilized to develop either structural or non-structural water supply plans. Therefore, in this study, we aimed to assess potential risks that might arise during the operation of reserviors under various climate conditions. Using observed precipitation and temperature from 1995 to 2014, 49 climate stress scenarios were developed (7 precipitation scenarios based on quantiles and 7 temperature scenarios ranging from 0℃ to 6℃ at 1℃ intervals). Our study demonstrated that despite an increase in flood season precipitation leading to an increase in reservoir discharge, it had a greater impact on sustainable water management compared to the increase in non-flood season precipitation. Furthermore, in scenarios combining rainfall and temperature, the reliability of reservoir water supply showed greater variations than the sum of individual reliability changes in rainfall and temperature scenarios. This difference was attributed to the opposing effects of decreased and increased precipitation, each causing limitations in water and energy-limited evapotranspiration. These results were expected to enhance the efficiency of reservoir operation.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.131-145
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• 2019

Korea has set up a radiation emergency planning zone based on the 「Act on Physical Protection and Radiological Emergency」 to protect residents living near nuclear power plants in the event of nuclear disasters. Little research has been conducted on the appropriateness of existing nuclear evacuation facilities because of a general lack of interest in nuclear accidents. This research addresses this gap by analyzing the location adequacy of evacuation facilities in Busan's emergency protection planning area based on vulnerable populations and accessibility analyses. The Gijang-gun which has the greatest risk, shows that only 4.05% of the total urban area was included in the evacuation service area within 5 minutes while only 36.93% of Geumjeong-gu and 37.23% of Haeundae-gu were included in the evacuation-enabled area. In addition, evaluation facilities in the elderly population hotspots were lacking, and there was a wide gap between dongs within the same Gu. Thus, additional evacuation facilities need to be designated and installed considering the spatial equity between areas and safety of both the public and vulnerable populations.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Archives of design research
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• v.18 no.3 s.61
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• pp.117-128
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• 2005

The French Belle Epoque is a period where the literary and artistic movement was very activated. The birth of the cubism reflects this atmosphere of the times and the change of paradigm in all fields. The Bergsonism is often designated as one of the important backgrounds of cubism. The problem consists in knowing if Bergsonian ideas gave real influence on the cubist movement and up to what point. Our analysis will show that it is not homogenous and very variable according to painters. In the case of Picasso and Braques it seems be a simple inspiration of Zeitgeist. But the influence upon Metzinger and Gleizes is explicit. The text of 1912, Du cubism, prove their attachment to his thought. The key concept of cubist theory, influenced by Bergsonian philosophy, is the concept of simultaneity. Cubist simultaneity is in one hand a reflection of an artist's psychological experience and the other hand a synthesis of multiple views for grasping the object in itself by the way of conceptual representation. The temporal simultaneity could be identified with the notion of memory, which is a temporal continuity connecting the past to dynamic present. The spatial simultaneity is a juxtaposition of multiple views obtained by the movement around the object. But the dose reading of Bergson's text shows that there is a divergence between the notion of cubist simultaneity and his ideas. The biased interpretation is often, as well as the strict understanding, like the history shows us well, a great source of inspiration and creativity. The cubist mouvement is not far from this case.