• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.6
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• pp.445-454
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• 1998

In order to establish a technical concept for the detection of defects in weldments in thin steel plate, an experimental and theoretical investigation was carried out for artificial defects in a steel plate having a thickness of 2.4mm by using the guided wave technique. In particular the goal was to find the most effective testing parameters paying attention to the relationship between the excitation frequency by a tone burst system and various incident angles. It was found that the test conditions that worked best was for a frequency of 840kHz and an incident angle of 30 or 85 degrees, most of the defects were detected with these conditions. Also, it was clear that a guided wave mode generated under an incident angle of 30 degrees was a symmetric mode, So, and that of 85 degrees corresponded to an antisymmetric mode, Ao. By using the two modes, most of all of the defects could be detected. Furthermore, it was shown that the antisymmetric mode was more sensitive to defects near the surface than the symmetric mode. Theoretical predictions confirmed this sensitivity improvement with Ao for surface defects because of wave structure variation and energy concentration near the surface.

• Computers and Concrete
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• v.20 no.6
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• pp.635-643
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• 2017

The rehabilitation and strengthening of concrete structures using Fiber-Reinforced Polymer (FRP) materials have been widely investigated. As a priority issue, however, the effect of curing conditions on the bonding behavior between FRP and concrete structures is still elusive. This study aims at developing a prediction model to accurately capture the mode-II interfacial debonding between FRP strips and concrete under different curing conditions. Single shear debonding experiments were conducted on FRP-concrete samples with respect to different curing time t and temperatures T. The J-integral formulation and constrained least square minimization are carried out to calibrate the parameters, i.e., the maximum slip $\bar{s}$ and stretch factor n. The prediction model is developed based on the cohesive model and Arrhenius relationship. The experimental data are then analyzed using the proposed model to predict the debonding between FRP and concrete, i.e., the interfacial shear stress-slip relationship. A Finite Element (FE) model is developed to validate the theoretical predictions. Satisfactory agreements are obtained. The prediction model can be used to accurately capture the bonding performance of FRP-concrete structures.

• Wind and Structures
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• v.13 no.6
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• pp.557-580
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• 2010

A new inflow turbulence generation method and a combined dynamic SGS model recently developed by the authors were applied to evaluate the wind effects on 508 m high Taipei 101 Tower. Unlike the majority of the past studies on large eddy simulation (LES) of wind effects on tall buildings, the present numerical simulations were conducted for the full-scale tall building with Reynolds number greater than $10^8$. The inflow turbulent flow field was generated based on the new method called discretizing and synthesizing of random flow generation technique (DSRFG) with a prominent feature that the generated wind velocity fluctuations satisfy any target spectrum and target profiles of turbulence intensity and turbulence integral length scale. The new dynamic SGS model takes both advantages of one-equation SGS model and a dynamic production term without test-filtering operation, which is particular suitable to relative coarse grid situations and high Reynolds number flows. The results of comparative investigations with and without generation of inflow turbulence show that: (1) proper simulation of an inflow turbulent field is essential in accurate evaluation of dynamic wind loads on a tall building and the prescribed inflow turbulence characteristics can be adequately imposed on the inflow boundary by the DSRFG method; (2) the DSRFG can generate a large number of random vortex-like patterns in oncoming flow, leading to good agreements of both mean and dynamic forces with wind tunnel test results; (3) The dynamic mechanism of the adopted SGS model behaves adequately in the present LES and its integration with the DSRFG technique can provide satisfactory predictions of the wind effects on the super-tall building.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.38.1-38.1
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• 2021

Dust polarization depends on the physical and mechanical properties of dust, as well as the properties of local environments. To understand how dust polarization varies with grain mechanical properties and the local environment, in this paper, we model the wavelength-dependence polarization of starlight and polarized dust emission by aligned grains by simultaneously taking into account grain alignment and rotational disruption by radiative torques (RATs). We explore a wide range of the local radiation field and grain mechanical properties characterized by tensile strength. We find that the maximum polarization and the peak wavelength shift to shorter wavelengths as the radiation strength U increases due to the enhanced alignment of small grains. Grain rotational disruption by RATs tends to decrease the optical-near infrared polarization but increases the ultraviolet polarization of starlight due to the conversion of large grains into smaller ones. In particular, we find that the submillimeter (submm) polarization degree at 850㎛(P850) does not increase monotonically with the radiation strength or grain temperature (Td), but it depends on the tensile strength of grain materials. Our physical model of dust polarization can be tested with observations toward star-forming regions or molecular clouds irradiated by a nearby star, which have higher radiation intensity than the average interstellar radiation field. Finally, we compare our predictions of the P850-Td relationship with Planck data and find that the observed decrease of P850 with Td can be explained when grain disruption by RATs is accounted for, suggesting that interstellar grains unlikely to have a compact structure but perhaps a composite one. The variation of the submm polarization with U (or Td)can provide a valuable constraint on the internal structures of cosmic dust

• Archives of design research
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• v.19 no.5 s.67
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• pp.323-334
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• 2006

The continuous development of digital technology has actualized ubiquitous computing, and the environment of human life has come to face changes. Prediction of the future so that human life may be prepared can be very important. A number of predictions of ubiquitous space are presented, ranging from related professional research to mass media, However, proper analyses and understanding are required to understand ubiquitous space and apply it to design. This study seeks to understand ubiquitous space through analysis of the trend in digital technology from a new perspective, to research cases, and predict the future of ubiquitous space. Human, object, and environment have been set as basic factors as the subjects smoothly exchanges various types of information in the physical space, and the trend in digital technology is analyzed. From a human-oriented perspective, the background and development trend of digital technology has been analyzed under the theme of interaction and interlace. From an object-oriented perspective, an analysis was unfolded under the theme of products' evolvement from radios to robots. From an environment-focused perspective, an analysis has been carried out under the theme of situation recognition, intrinsic factors, and integration and connectivity. By applying the analytic results, the types of studies that predict the future of ubiquitous space and generate concepts have been classified and analyzed into three different types of studies for experiment, industry, and public. In this manner, ubiquitous space has been forecasted. This study seeks a systematic analysis of the understanding of the trends in digital technology and employs a case study of ubiquitous space based on systematic analysis. In consideration of all these, this study is expected to contribute to concept generation and development by designers.

• Horticultural Science & Technology
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• v.28 no.6
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• pp.937-941
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• 2010

This study investigated the effect of global warming on full bloom date (FBD) of 'Niitaka' pear ($Pyrus$ $pyrifolia$ Nakai) tree by calculating the development stage index by hourly temperatures recorded at Pear Research Station, estimating the distribution of average FBD and the change of FBD according to temperature rising by integrating development rate at 67 locations in Korea Meteorological Administration site. Development stage index of 'Niitaka' pear tree was 0.9593 at Naju location. Differences between full bloom dates observed at Cheonan region and predictions by development stage index were 0-7 days, and matched year was 35.3%. FBDs of 67 locations were distributed from April 4 to May 28. When yearly temperature was raised 1, 2, 3, 4, and $5^{\circ}C$ at 67 locations, predicted FBD was accelerated at most of the locations. However, FBD decelerated at south coast locations from $3^{\circ}C$ rise and did not bloom at 'Gosan', 'Seogwipo', and 'Jeju' locations from $4^{\circ}C$ rise. When monthly temperature was raised 1, 3, and $5^{\circ}C$ at 67 locations, predicted FBD was the most accelerated at March temperature rise, and followed by April, February, January and December. Therefore, global warming will cause acceleration of the full bloom date at pear production areas in Korea.

• Ocean Systems Engineering
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• v.13 no.2
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• pp.173-193
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• 2023

Reliable prediction of the motion of FOWT (floating offshore wind turbine) and associated mooring line tension is important in both design and operation/monitoring processes. In the present study, a 5MW OC4 semisubmersible wind turbine is numerically modeled, simulated, and analyzed by the open-source numerical tool, OpenFAST and in-house numerical tool, Charm3D-FAST. Another commercial-level program FASTv8-OrcaFlex is also introduced for comparison for selected cases. The three simulation programs solve the same turbine-floater-mooring coupled dynamics in time domain while there exist minor differences in the details of the program. Both the motions and mooring-line tensions are calculated and compared with the DeepCWind 1/50 scale model-testing results. The system identification between the numerical and physical models is checked through the static-offset test and free-decay test. Then the system motions and mooring tensions are systematically compared among the simulated results and measured values. Reasonably good agreements between the simulation and measurement are demonstrated for (i) white-noise random waves, (ii) typical random waves, and (iii) typical random waves with steady wind. Based on the comparison between numerical results and experimental data, the relative importance and role of the differences in the numerical methodologies of those three programs can be observed and interpreted. These comparative-study results may provide a certain confidence level and some insight of potential variability in motion and tension predictions for future FOWT designs and applications.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.