• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.

• Membrane Journal
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• v.31 no.6
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• pp.443-455
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• 2021

In this study, polyphenylene sulfide (PPS) was used as a support and a separator was manufactured using polysulfone and inorganic additives to manufacture a separator with low membrane resistance for application of an alkali water electrolysis system, and then the effect on the thickness and porosity of the support was analyzed. The PPS felt used as a support was compressed with variables of temperature (100℃, 150℃, 200℃) and pressure (1 ton, 2 tons, 3 tons, 5 tons) to adjust the thickness. A porous separator could be manufactured by preparing a slurry with polysulfone using BaTiO₃ and ZrO₂ which have high hydrophilicity and excellent alkali resistance as inorganic particles and casting the slurry on a compressed PPS felt. Changes in morphology of the separator according to compression conditions were confirmed through an electron scanning microscope (SEM). After that, the porosity was calculated, and the thickness and porosity tended to decrease as the compression conditions increased. Various characteristics were evaluated to confirm whether it could be used as a separator for water electrolysis. As a result of measuring the mechanical strength, it was confirmed that the tensile strength gradually increased as the compression conditions (temperature and pressure) increased. Finally, it was confirmed that the porous separator manufactured through the alkali resistance test has excellent alkali resistance, and through the IV test, it was confirmed that the membranes compressed at 100℃ and 150℃ had a lower voltage and improved performance than the existing uncompressed membrane.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.305-312
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• 2023

Purpose: Although the damage caused by abnormal temperatures is extensive, blow-up or black ice is typical in concrete structures. In this study, PCM with high phase change energy was mixed with concrete to reduce temperature damage to concrete pavement. Method: In order to reduce temperature damage to low temperatures and high temperatures, capsule-type PCM with phase change temperatures of 4.5℃ and 44℃ was replaced by 10%, 30%, and 50%, and thermal performance experiments and compressive strength experiments were conducted using thermocouples and variable chambers. Result: As a result of the thermal performance experiment, it was found that the incorporation of PCM improves temperature resistance by up to 25% or more, and increases thermal resistance at all temperatures with high specific heat when substituted in large amounts. As a result of the compression strength experiment, a substitution of 30% or more resulted in a decrease in the compression strength, and a large strength difference was shown based on the phase change temperature of the PCM. Conclusion: The incorporation of PCMs has been shown to increase the thermal performance of concrete, with the greatest increase in thermal performance near the phase change temperature of PCM. In addition, a small strength reduction of 10% to 20% occurs at the highest substitution rate of 50% substitution, so there is no significant problem with usability, and additional PCM substitution is expected to improve thermal performance.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.62-76
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• 2023

Recently, machine learning (ML) techniques have been actively applied for seismic trace interpolation. However, because most research is based on training-inference strategies that treat missing trace gather data as a 2D image with a blank area, a sufficient number of fully sampled data are required for training. This study proposes trace interpolation using ML, which uses only irregularly sampled field data, both in training and inference, by modifying the training-inference strategies of trace-based interpolation techniques. In this study, we describe a method for constructing networks that vary depending on the maximum number of consecutive gaps in seismic field data and the training method. To verify the applicability of the proposed method to field data, we applied our method to time-migrated seismic data acquired from the Vincent oilfield in the Exmouth Sub-basin area of Western Australia and compared the results with those of the conventional trace interpolation method. Both methods showed high interpolation performance, as confirmed by quantitative indicators, and the interpolation performance was uniformly good at all frequencies.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.491-499
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• 2023

Scattering by Sea surface roughness occurs due to sea level roughness, communication performance deteriorates by causing frequency spread in communication signals and time variation in communication channels. In order to compare the difference in time variation of underwater acoustic communication channel according to the surface roughness, an experiment was performed in a tank owned by Hanyang University Ocean Acoustics Lab. Artificial surface roughness was created in the tank and communication signals with three bandwidths were used (8 kHz, 16 kHz, 32 kHz). The measured surface roughness was converted into a Rayleigh parameter and used as a roughness parameter, and statistical analysis was performed on the time-varying channel characteristics of the surface path using Doppler spread and correlation time. For the Doppler spread of the surface path, the Weighted Root Mean Square Doppler spread (w_fσ_ν) that corrected the effect of the carrier frequency and bandwidth of the communication signal was used. Using the correlation time of the surface path and the energy ratio of the direct path and the surface path, the correlation of total channels was simulated and compared with the measured correlation time of total channels. In this study, we propose a method for efficient communication signal design in an arbitrary marine environment by using the time-varying characteristics of the sea surface path according to the sea surface roughness.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.519-533
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• 2023

In this study, the linear cutting tests of pick cutters were carried out on a granitic rock with the average compressive strength over 100 MPa. From the tests, the correlation between the cutter acting force and the temperature measured by infrared thermal imaging camera during rock cutting was analyzed. In every experimental condition, the maximum temperature was measured at the rock surface where the chipping occurred, and the temperature generated in the rock was closely correlated with the cutter acting force. On the other hand, the temperature of a pick cutter increased up to only 36℃ above the ambient temperature, and the correlation with the cutter force was not obvious. This can be attributed to the short cutting distance under laboratory conditions and the high thermal conductivity of the tungsten carbide inserts. However, the relatively high temperature of the tungsten carbide inserts was found to be maintained. Therefore, it is recommended that a reinforcement between the insert and the head of a pick cutter or the quality improvement of silvering brazing in the production of a cutter is necessary to maintain the high cutting performance of a pick cutter.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of the Korean Wood Science and Technology
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• v.45 no.5
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• pp.580-587
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• 2017

Woodfiber insulation board can be considered as a one of the key material for low energy consumption, comfortable and safety construction of residential space because of its eco-friendly and high thermal insulation performance. This study was carried out to investigate the formaldehyde (HCHO) total volatile organic compounds (TVOC) emission properties and combustion shapes by flame test of low density fiberboards (LDFs) prepared with different adhesives. HCHO TVOC emission and combustion properties of LDFs prepared by melamine urea formaldehyde (MUF), phenol formaldehyde (PF), emulsified methylene diphenyl diisocyanate (eMDI) and latex resin adhesives were measured by desiccator method, 20 L chamber method, and flame test, respectively. As results, LDFs manufactured by MUF, eMDI and latex resin adhesives satisfied the Super $E_0$ grade of HCHO emission performance except PF resin. Furthermore, TVOC emission of all LDFs were satisfied the Korean indoor air quality standard (below $400{\mu}g/m^2{\cdot}h$). Especially, LDF with eMDI resin adhesive showed the lowest HCHO and TVOC emissivity, that $0.14mg/{\ell}$, $12{\mu}g/m^2{\cdot}h$, respectively. However, eMDI emitted the small amount ($3{\mu}g/m^2{\cdot}h$) of toluene in VOC components. In the flame test, LDF with MUF resin adhesives showed the most favorable shape after flame test compare to LDFs prepared other adhesives. Based on HCHO and TVOC emission, and combustion shapes, MUF resin adhesive may be recommended to prepare LDF for insulation purpose.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.