• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.19-28
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• 2009

In case of land development projects constructed, to solve induced transportation volume needs analysis of traffic demand. Trip-generation of land development projects is exactly predicted by using traffic instigating-basic-unit in each facility of land developments. But in case of a phase of trip-distribution, because a range of destinations is very enormous and it needs enormous data to reflect all of its characters, whenever trip-distribution is predicted, the method which assumes the rate of trip-distribution is same both before completion of land development projects and after is often used. But because there is no exact criterion, the method suggested above is also affected by subjective opinion. Accordingly, this study look over using trip-distribution of specific areas's DB and suggests a size of zone to predict a distribution of land development projects exactly. Also production - constrained gravity model which uses the gap between a distribution of suggested ranges and induced land development project is suggested for more exact prediction of trip-distribution. Besides accuracy of prediction is scrutinized by using Mean Squared Error.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.165-175
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• 2009

With the current move towards adopting mechanistic-empirical concepts in the design of pavement structures, state-of-the-art mechanistic analysis methodologies are needed to determine accurate pavement responses, such as stress, strain, and deformation. Previous laboratory studies of pavement foundation geomaterials, i.e., unbound granular materials used in base/subbase layers and fine-grained soils of a prepared subgrade, have shown that the resilient responses followed by nonlinear, stress-dependent behavior under repeated wheel loading. This nonlinear behavior is commonly characterized by stress-dependent resilient modulus material models that need to be incorporated into finite element (FE) based mechanistic pavement analysis methods to predict more realistically predict pavement responses for a mechanistic pavement analysis. Developed user material subroutine using aforementioned resilient model with nonlinear solution technique and convergence scheme with proven performance were successfully employed in general-purpose FE program, ABAQUS. This numerical analysis was investigated in predicted critical responses and domain selection with specific mesh generation was implemented to evaluate better prediction of pavement responses. Results obtained from both axisymmetric and three-dimensional (3D) nonlinear FE analyses were compared and remarkable findings were described for nonlinear FE analysis. The UMAT subroutine performance was also validated with the instrumented full scale pavement test section study results from the Federal Aviation Administration's National Airport Pavement Test Facility (FAA's NAPTF).

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Journal of Drive and Control
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• v.20 no.4
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• pp.115-122
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• 2023

This study utilized a discrete element method (DEM) simulation, as one of the virtual field trials, to predict the impact of tillage depth on the rotary blade shaft during rotavator tilling. The virtual field for the simulation was generated according to soil properties observed in an actual field. Following the generation of particles for the virtual field, a sequence of calibration steps followed to align the mechanical properties more closely with those of real soil. Calibration was conducted with a focus on bulk density and shear torque, resulting in calibration errors of just 0.02% for bulk density and 0.52% for shear torque. The prediction of the load on a rotary tiller's blade shaft involved a three-pronged approach, considering shaft torque, draft force, and vertical force. In terms of shaft torque, the values exhibited significant increases of 42.34% and 36.91% for every 5-centimeter increment in tillage depth. Similarly, the vertical force saw substantial growth by 40.41% and 36.08% for every 5-centimeter increment. In contrast, the variation in draft force based on tillage depth was comparatively lower at 18.49% and 0.96%, indicating that the effect of tillage depth on draft force was less pronounced than its impact on shaft torque and vertical force. From a perspective of agricultural machinery research, this study provides valuable insights into the DEM soil modeling process, accounting for changes in soil properties with varying tillage depths. These findings are expected to be instrumental in future agricultural machinery design studies.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Korean journal of applied entomology
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• v.52 no.4
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• pp.427-430
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• 2013

Recently, climate change scenarios were substituted by the Special Report on Emission Scenarios (SRES) for Representative Concentration Pathway (RCP). Using the RCP scenario, the World Meteorological Organization (WMO) produced new climate change scenarios. Further, the National Institute of Meteorological Research (NIMR) of Korea produced new climate change scenarios for the Korean Peninsula. In this study, emergence time of small brown planthopper (SBPH), Laodelphax striatellus and the number of generations a year were estimated during climatic normal year (1981-2010) with previous studies and they were predicted during 2050s (2045-2054) and 2090s (2085-2094) by means of RCP8.5 climate change scenario. In comparison with $176.0{\pm}0.97$ Julian data in the climatic normal year, the emergence time of overwintering SBPH was predicted to be $13.2{\pm}0.18$ days ($162.8{\pm}0.91$ Julian date) earlier in 2050s and $32.1{\pm}0.61$ days ($143.9{\pm}1.08$ Julian date) earlier in 2090s. The SBPH was expected to produce an additional $2.0{\pm}0.02$ generations in 2050s and $5.2{\pm}0.06$ generations in 2090s.