• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.257-268
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• 2023

Sea ice plays an important role in Earth's climate by regulating the amount of solar energy absorbed and controlling the exchange of heat and material across the air-sea interface. Its growth, drift, and melting are monitored on a regular basis by satellite observations. However, low-resolution products with passive microwave radiometer have reduced accuracy during summer to autumn when the ice surface changes rapidly. Synthetic aperture radar (SAR) observations are emerging as a powerful complementary, but previous researches have mainly focused on winter ice. In this study, sea ice drift tracking was evaluated and analyzed using SAR images and tracker with global positioning system (GPS) during late summer-early autumn period when ice surface condition changes a lot. The results showed that observational uncertainty increases compared to winter period, however, the correlation coefficient with GPS measurements was excellent at 0.98, and the performance of the ice tracking algorithm was proportional to the sea ice concentration with a correlation coefficient of 0.59 for ice concentrations above 50%.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.49-55
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• 2013

Purpose: Respiratory Gated Radiation Therapy (RGRT) has been carried out using RPM (Real-time Position Management) Respiratory Gating System (version 1.7.5, varian, USA) in Asan Medical Center. This study was to analyze and evaluate the accuracy of Respiratory Gated Radiation Therapy (RGRT) according to variation of respiration. Materials and Methods: Making variation of respiration using Motion Phantom:QUASAR Programmable Respiratory Motion Phantom (Moudus Medical Device Inc. CANADA) able to adjust respiration pattern randomly was varying period, amplitude and baseline by analyze 50 patient's respiration of lung and liver cancer. One of the variations of respiration is baseline shift gradually downward per 0.01 cm, 0.03 cm, 0.05 cm. The other variation of respiration is baseline shift accidently downward per 0.2 cm, 0.4 cm, 0.6 cm, 0.8 cm. Experiments were performed in the same way that is used RPM Respiratory Gating System (phase gating, usually 30~70% gating) in Asan Medical Center. Results: It was all exposed radiation under one of the conditions of baseline shift gradually downward per 0.01 cm, 0.03 cm, 0.05 cm. Under the other condition of baseline shift accidently downward per 0.2 cm, 0.4 cm, 0.6 cm, 0.8 cm equally radiation was exposed. Conclusion: The variations of baseline shifts didn't accurately reflect on phase gating in RPM Respiratory Gating System. This inexactitude makes serious uncertainty in Respiratory Gated Radiation Therapy. So, Must be stabilized breathing of patient before conducting Respiratory Gated Radiation Therapy. also must be monitored breathing of patient in the middle of treatment. If you observe considerable changes of breathing when conducting Respiratory Gated Radiation Therapy. Stopping treatment immediately and then must be need to recheck treatment site using fluoroscopy. If patient's respiration rechecked using fluoroscopy restabilize, it is possible to restart Respiratory Gated Radiation Therapy.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.93-102
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• 2006

Control input's saturation of active control devices for large structures under large external disturbances are often occurred. It is more difficult to obtain the exact values of mass and stiffness as structures are higher. The modelling errors between mathematical models and real structures must be also included as parameter uncertainties. Therefore, in active vibration control of civil engineering structures like buildings and bridges, the robust saturation controller design method considering both control input's saturation and parameter uncertainties of system is needed. In this paper, stabilities of linear optimal controller LQR, modified bang-bang controller, saturated sliding mode controller, and robust saturation controller among various controllers which have been studied and applied to active vibration control of buildings are investigated. Especially, unstable phenomena of the LQR, the modified bang-bang controller and the saturated sliding mode controller when the control input is saturated or parameter uncertainties exist are presented to show the necessity of the robust saturation controller. The robust stability of the robust saturation controller are shown through a numerical example of a 2DOF linear vibrating system and an experimental test of the two-story structure with an active mass damper (AMD).

• Journal of Information Technology Application
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• v.2 no.1
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• pp.1-24
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• 2000

Project management in the Construction field inherently has more uncertainty and more risks relative to ones from other area. This is the very reason for why project management is recognized as the important task to construction companies. For getting better performance in the project management, we need a system that keeps the consistencies in a automatic or semi-automatic manner through the project management stages like as project definition stage, project planning stage, project design and implementation stage. But since the early stages such as definition and planning stages has many unstructured features and also are dependent to unique expertise or experience of a specific company, we have difficulty providing systematic support for the task of these stages. This kind of problem becomes harder to solve especially in the plant construction domain that is our target domain. Therefore, in this paper, we propose and also implement a systematic approach to resolve the problem mentioned for the early project management stages in the plant construction domain. The results of our approach can be used not only for the purpose of the early project management stages but also can be used automatically as an input to commercial project management tools for the middle project management stages. Because of doing in this way, the construction project can be consistently managed from the definition to implementation stage in a seamless manner. For achieving this purpose, we adopt knowledge based inference, CBR, and neural network as major methodologies and we also applied our approach to two real world cases, power plant and drainage treatment plant cases from a leading construction company in Korea. Since these two application cases showed us very successful results, we can say our approach was validated successfully to the plant construction area. Finally, we believe our approach will contribute to many project management problems from more broader construction area.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.2
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• pp.213-218
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• 2010

A cooling system utilizing liquid helium to chill the cryopanel (800 mm $\times$ 700 mm dimensions) down to 4.2 K was designed, implemented, and tested to verify the role of the cryopanel as a heat sink for the payload of a spacecraft inside the large thermal vacuum chamber (effective dimensions : 8 m ($\Phi$) $\times$ 10 m (L)) of KARI (Korea Aerospace Research Institute). Two LHe (Liquid Helium) Dewars, one for the main supply and the other for refilling, were used to supply liquid helium or cold helium gas into this cryopanel, and flow control for the target temperature of the cryopanel within requirements was done through fine adjustment of the pressure inside the LHe Dewars. The return helium gas from the cryopanel was reused as a thermal barrier to minimize the heat influx on the core liquid helium supply pipe. The test verified a cooling time of around three hours from the ambient temperature to 40 K (combined standard uncertainty of 194 mK), the capacity for maintaining the cryopanel at intermediate temperatures, and a 1 K uniformity over the entire cryopanel surface at around 40 K with 20 W cooling power.

• The KIPS Transactions:PartD
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• v.19D no.3
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• pp.237-246
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• 2012

Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.113-124
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• 2020

Social movements to improve the performance of buildings through remodeling of aging apartment houses are being captured. To this end, the remodeling construction cost analysis, structural analysis, and political institutional review have been conducted to suggest ways to activate the remodeling. However, although the method of analyzing construction cost for remodeling apartment houses is currently being proposed for research purposes, there are limitations in practical application possibilities. Specifically, In order to be used practically, it is applicable to cases that have already been completed or in progress, but cases that will occur in the future are also used for construction cost analysis, so the sustainability of the analysis method is lacking. For the purpose of this, we would like to suggest an automated estimating method. For the sustainability of construction cost estimates, Deep-Learning was introduced in the estimating procedure. Specifically, a method for automatically finding the relationship between design elements, work types, and cost increase factors that can occur in apartment remodeling was presented. In addition, Monte Carlo Simulation was included in the estimation procedure to compensate for the lack of uncertainty, which is the inherent limitation of the Deep Learning-based estimation. In order to present higher accuracy as cases are accumulated, a method of calculating higher accuracy by comparing the estimate result with the existing accumulated data was also suggested. In order to validate the sustainability of the automated estimates proposed in this study, 13 cases of learning procedures and an additional 2 cases of cumulative procedures were performed. As a result, a new construction cost estimating procedure was automatically presented that reflects the characteristics of the two additional projects. In this study, the method of estimate estimate was used using 15 cases, If the cases are accumulated and reflected, the effect of this study is expected to increase.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.662-669
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• 2015

There has been an increased interest in the mitigation of wax deposition because wax, which usually accumulates in subsea oil-production systems, interrupts stable oil production and significantly increases the cost. To guarantee a required oil flow by mitigating wax deposition, we need to obtain a reliable estimation of the wax deposition. In this research, we perform simulations to understand the major mechanisms that lead to wax deposition, namely molecular diffusion, shear stripping reduction, and aging. While the model variables (shear reduction multiplier, wax porosity, wax thermal conductivity, and molecular diffusion multiplier) can be measured experimentally, they have high uncertainty. We perform an analysis of these variables and the amount of water and gas in the multiphase flow to determine these effects on the behavior of wax deposition. Based on the results obtained during this study for a higher wax porosity and molecular diffusion multiplier, we were able to confirm the presence of thicker wax deposits. As the shear reduction multiplier decreased, the thickness of the wax deposits increased. As the amount of water increased, there was also an increase in the amount of wax deposits until 40% water cut and decreased. As the amount of gas increased, the amount of wax deposits increased because of the loss of the light hydrocarbon component in the liquid phase. The results of this study can be utilized to estimate the wax deposition behavior by comparing the experiment (or field) and simulation data.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.136-149
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• 2011

The aim of this study is to develop future climate scenario by downscaling the regional climate model (RCM) from global climate model (GCM) based on IPCC A1B scenario. To this end, the study first resampled the KMA-RCM(Korea meteorological administration-regional climate model) from spatial resolution of 27km to 1km. Second, observed climatic data of temperature and rainfall through 1971-2000 were processed to reflect the temperature lapse rate with respect to the altitude of each meteorological observation station. To optimize the downscaled results, Co-kriging was used to calculate temperature lapse-rate; and IDW was used to calculate rainfall lapse rate. Fourth, to verify results of the study we performed correlation analysis between future climate change projection data and observation data through the years 2001-2010. In this study the past climate data (1971-2000), future climate change scenarios(A1B), KMA-RCM(Korea meteorological administration-regional climate model) results and the 1km DEM were used. The research area is entire South Korea and the study period is from 1971 to 2100. Monthly mean temperatures and rainfall with spatial resolution of 1km * 1km were produced as a result of research. Annual average temperature and precipitation had increased by $1.39^{\circ}C$ and 271.23mm during 1971 to 2100. The development of downscaling method using GIS and verification with observed data could reduce the uncertainty of future climate change projection.