• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Journal of the Korean earth science society
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• v.41 no.2
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• pp.95-110
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• 2020

Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1021-1028
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• 2013

The frequency of a power system should be kept within limits to produce high-quality electricity. For a power system with a high penetration of wind generators (WGs), difficulties might arise in maintaining the frequency, because modern variable speed WGs operate based on the maximum power point tracking control scheme. On the other hand, the wind speed that arrives at a downstream WG is decreased after having passed one WG due to the wake effect. The rotor speed of each WG may be different from others. This paper proposes an algorithm for assigning the droop of each WG in a wind power plant (WPP) based on the rotor speed for the virtual inertial control considering the wake effect. It assumes that each WG in the WPP has two auxiliary loops for the virtual inertial control, i.e. the frequency deviation loop and the rate of change of frequency (ROCOF) loop. To release more kinetic energy, the proposed algorithm assigns the droop of each WG, which is the gain of the frequency deviation loop, depending on the rotor speed of each WG, while the gains for the ROCOF loop of all WGs are set to be equal. The performance of the algorithm is investigated for a model system with five synchronous generators and a WPP, which consists of 15 doubly-fed induction generators, by varying the wind direction as well as the wind speed. The results clearly indicate that the algorithm successfully reduces the frequency nadir as a WG with high wind speed releases more kinetic energy for the virtual inertial control. The algorithm might help maximize the contribution of the WPP to the frequency support.

• World Technopolis Review
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• v.8 no.1
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• pp.59-70
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• 2019

Where is a better place to live? In the coming era, this should be more than simply a livable place. It should be an adaptable place that has a flexible system adaptable to any new situation in terms of diversity. Customization and real-time operation are needed in order to realize this technologically. We expect a smart city to have a flexible system that applies technologies of self-monitoring and self-response, thereby being a promising city model towards being a better place to live. Energy demand and supply is a crucial issue concerning our expectations for the flexible system of a smart city because it is indispensable to comfortable living, especially city living. Although it may seem that energy diversification, such as the energy mix of a country, is a matter of overriding concern, the central point is the scale of place to build grids for realizing sustainable urban energy systems. A traditional hard energy path supported by huge centralized energy systems based on fossil and nuclear fuels on a national scale has already faced difficult problems, particularly in terms of energy flexibility/resilience. On the other hand, an alternative soft energy path consisting of small diversified energy systems based on renewable energy sources on a local scale has limitations regarding stability, variability, and supply potential despite the relatively light economic/technological burden that must be assumed to realize it. As another alternative, we can adopt a holonic path incorporating an alternative soft energy path with a traditional hard energy path complimentarily based on load management. This has a high affinity with the flexible system of a smart city. At a system level, the purpose of all of the paths mentioned above is not energy itself but the service it provides. If the expected energy service is fixed, the conclusive factor in choosing a more appropriate system is accessibility to the energy service. Accessibility refers to reliability and affordability; the former encompasses the level of energy self-sufficiency, and the latter encompasses the extent of energy saving. From this point of view, it seems that the small diversified energy systems of a soft energy path have a clear advantage over the huge centralized energy systems of a hard energy path. However, some insuperable limitations still remain, so it is reasonable to consider both energy systems continuing to coexist in a multiplexing energy system employing a holonic path to create and maintain reliable and affordable access to energy services that cover households'/enterprises' basic energy needs. If this is embodied in a smart city concept, this is nothing else but smart energy inclusion. In Japan, following the Fukushima nuclear accident in 2011, a trend towards small diversified energy systems of a soft energy path intensified in order to realize a nuclear-free society. As a result, the Government of Japan proclaimed in its Fifth Strategic Energy Plan that renewable energy must be the main source of power in Japan by 2050. Accordingly, Sony vowed that all the energy it uses would come from renewable sources by 2040. In this situation, it is expected that smart energy inclusion will be achieved by the Japanese version of a smart grid based on the concept of a minimum cost scheme and demand response.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.719-731
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• 2011

Based on the ICE-3G and ICE-5G ice models, we predicted the velocities of crustal uplift caused by Glacial Isostatic Adjustment (GIA) at 39 tide gauge sites operated by Korea Hydrographic and Oceanographic Administration (KHOA). We also divided the Korean peninsula in the ranges of $32-38.5^{\circ}N$ and $124-132^{\circ}E$ in $0.5^{\circ}{\times}0.5^{\circ}$ grids, and computed the GIA velocities at each grid point. We found that the average uplift rates due to GIA in South Korea were 0.33 and 1.21 mm/yr for ICE-3G and ICE-5G, respectively. Because the GIA rates were relatively high at ~1 mm/yr when the updated ice model ICE-5G was used, we concluded that the GIA effect cannot be neglected when we compute the absolute sea level (ASL) rates around the Korean peninsula. In this study, we corrected the ICE-5G GIA velocities from the relative sea level rates provided by KHOA and we computed the ASL rates at 13 tide gauge stations. As a result, we found that the average ASL velocity around the Korean peninsula was 5.04 mm/yr. However, the ASL rates near Jeju island were abnormally higher than the other areas and the average was 8.84 mm/yr.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.40-48
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• 2011

The purpose of this study is to contribute enhancing the accuracy of viewshed analysis through the explanation for an analysis method of viewshed analysis using GIS. According to previous studies, the visible area using digital terrain in viewshed analysis depends on a visible interest area, scale of terrain, spatial resolution and surface data. In this study, we used trend analysis and RMSE analysis in order to find the effect of a visible interest area, scale of terrain, etc in viewshed analysis. Results of this study are as follows. First, the result of viewshed analysis depends on a visible interest area, scale of terrain, spatial resolution, surface data such as previous studies. Second, the results in forest area are reliable than those of flat area in terms of a visible interest area. Third, the results based on raster grid data are stable than those of TIN(triangulated irregular network) in terms of input surface data. Fourth, according to the result of trend and RMSE analysis, the spatial resolution for analysis is differently applied to different scales digital terrain map in viewshed analysis. In detail, it is desirable that the spatial resolution is set less than 10m(in the case of 1/1,000 digital terrain map), 20m(in the case of 1/5,000 map), 30m(1/25,000 map).

• Journal of Korean Society of Environmental Engineers
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• v.30 no.5
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• pp.534-543
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• 2008

The experimental study with the prototype provides more acceptable data than the others. But there are so many limited conditions to perform the experimental study with the prototype. So the theoretical similitude with the scaled model and the numerical study with the CFD method have been chosen alternatively to analysis the fume movement. In this study, the ventilation was estimated from the results of the numerical study based on the experimental results as the boundary conditions. The grid A and B were same size and shape with the models which was used in the experimental study and consisted with 163,839, 122,965 cells respectively. The height of the fume layer was estimated form the mole fraction of fume components and the ventilation was determined by the velocity and temperature of the fume. The results of this study showed that the fume movements estimated from the numerical study are enough to apply to the prototype if there are proper heat loss correction factors. The numerical study is easier to change study conditions and faster to get results from the study than the experimental study. So if we find some proper heat loss correction factors, it's possible to execute the various and advanced study with the numerical study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.50-58
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• 2013

This study was carried out to evaluate the geospatial characteristics of blooming date migration in three major spring flowers across North and South Korea as influenced by climate change. A thermal time-based phenology model driven by daily maximum and minimum temperature was adjusted for the key parameters (i.e., reference temperature, chilling requirement, heating requirement) used for predicting blooming of forsythia, azaleas, and Japanese cherry. The model was run by the RCP 8.5 projected temperature outlook over the Korean Peninsula and produced the mean booming dates for the three climatological normal years in the future (2011-2040, 2041-2070, and 2071-2100) at a 12.5 km grid spacing. Comparison against the observed blooming date patterns in the baseline climate (1971-2000) showed that there will be a substantial acceleration in blooming dates of the three species, resulting in cherry booming in February and flowers of azaleas and forsythia found at the top of mountain Baikdu by the 2071-2100 period. Flowering dates of the three species in the near future (2011-2040) may be accelerated by 3-5 days at minimum and 10-11 days at maximum compared with that in the baseline period (1971-2000). Those values corresponding to the middle future (2041-2070) can be from a minimum of 9-11 days to a maximum of 23-24 days. Blooming date of Japanese cherry can be accelerated by 26 days on average for the far future (2071-2100). The acceleration seems more prominent at islands and coastal plain areas than over inland mountainous areas.