• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.141-147
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• 2011

This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

• Economic and Environmental Geology
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• v.38 no.6 s.175
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• pp.663-673
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• 2005

This study investigates the relationship between the geochemical maps and the gold-silver deposit locations. Geochemical maps of 21 elements, which are published by KIGAM, locations of gold-silver deposits, and 1:1,000,000 scale geological map of Korea are utilized far this investigation. Pixel size of the basic geochemical maps is 250m and these data are resampled in 1km spacing for the statistical analyses. Relationship between the mine location and the geochemical data are investigated using bayesian statistics and decision tree algorithms. For the bayesian statistics, each geochemical maps are reclassified by percentile divisions which divides the data by 5, 25, 50, 75, 95, and $100\%$ data groups. Number of mine locations in these divisions are counted and the probabilities are calculated. Posterior probabilities of each pixel are calculated using the probability of 21 geochemical maps and the geological map. A prediction map of the mining locations is made by plotting the posterior probability. The input parameters for the decision tree construction are 21 geochemical elements and lithology, and the output parameters are 5 types of mines (Ag/Au, Cu, Fe, Pb/Zn, W) and absence of the mine. The locations for the absence of the mine are selected by resampling the overall area by 1 km spacing and eliminating my resampled points, which is in 750m distance from mine locations. A prediction map of each mine area is produced by applying the decision tree to every pixels. The prediction by Bayesian method is slightly better than the decision tree. However both prediction maps show reasonable match with the input mine locations. We interpret that such match indicate the rules produced by both methods are reasonable and therefore the geochemical data has strong relations with the mine locations. This implies that the geochemical rules could be used as background values oi mine locations, therefore could be used for evaluation of mine contamination. Bayesian statistics indicated that the probability of Au/Ag deposit increases as CaO, Cu, MgO, MnO, Pb and Li increases, and Zr decreases.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.546-553
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• 2007

As ontologies are used widely, interest for semantic similarity search is also increasing. In this paper, we suggest a query evaluation scheme for k-nearest neighbor query, which retrieves k most similar objects to the query object. We use the best match method to calculate the semantic similarity between objects and use the signature tree to index annotation information of objects in database. The signature tree is usually used for the set similarity search. When we use the signature tree in similarity search, we are required to predict the upper-bound of similarity for a node; the highest similarity value which can be found when we traverse into the node. So we suggest a prediction function for the best match similarity function and prove the correctness of the prediction. And we modify the original signature tree structure for same signatures not to be stored redundantly. This improved structure of signature tree not only reduces the size of signature tree but also increases the efficiency of query evaluation. We use the Gene Ontology(GO) for our experiments, which provides large ontologies and large amount of annotation data. Using GO, we show that proposed method improves query efficiency and present several experimental results varying the page size and using several node-splitting methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.10-16
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• 2004

In this paper the aeroacoustic characteristics of a helicopter main rotor system is measured by using a pair of scaled rotor blades. A low noise rotor test jig is developed for noise measurement and the rotational speed, thrust and torque are measured simultaneously in order to match the aerodynamic conditions with the full scale rotor. The accuracy of the force measurement device was checked through a calibration procedure. The measured thurst and torque with a 1.2m rotor are compared to the results of analytical prediction and showed that the thrust data at various rotational speed followed the prediction relatively well, but the torque data considered less accurate. It is also found that the background noise level of the test rig is sufficiently low, and the measured noise level from the rotor can be scaled with rotor tip speed. However, the Mach number dependancy and the directivity changes depend on the noise source characteristics.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.45-51
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• 2020

Recently, many games provide data related to the users' game play, and there have been a few studies that predict opponent move by combining machine learning methods. This study predicts opponent move using match data of a real-time strategy game named ClashRoyale and a multi-label classification based on machine learning. In the initial experiment, binary card properties, binary card coordinates, and normalized time information are input, and card type and card coordinates are predicted using random forest and multi-layer perceptron. Subsequently, experiments were conducted sequentially using the next three data preprocessing methods. First, some property information of the input data were transformed. Next, input data were converted to nested form considering the consecutive card input system. Finally, input data were predicted by dividing into the early and the latter according to the normalized time information. As a result, the best preprocessing step was shown about 2.6% improvement in card type and about 1.8% improvement in card coordinates when nested data divided into the early.

• Journal of the Society of Disaster Information
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• v.3 no.1
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• pp.37-53
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• 2007

Various difficult problems occur due to insufficient bearing capacity or excessive settlements when constructing roads or large complexes. Accurate predictions on the final settlement and consolidation time can help in choosing the ground improvement method and thus enables to save time and expense of the whole project. Asaoka's method is probably the most frequently used for settlement prediction which are based on Terzaghi's one dimensional consolidation theory. Empirical formulae such as Hyperbolic method and Hoshino's method are also often used. However, it is known that the settlement predicted by these methods do not match with the actual settlements. Furthermore these methods cannot be used at design stage when there is no measured data. To find an elaborate method in predicting settlement in embankments using various test results and actual settlement data from domestic sites, Back-Propagation Neural Network(BPNN) and Recurrent Neural Network(RNN) were employed and the most suitable model structures were obtained. Predicted settlement values by the developed models were compared with the measured values as well as numerical analysis results. Analysis of the results showed that RNN yielded more compatible predictions with actual data than BPNN and predictions using cone penetration resistance were closer to actual data than predictions using SPT results. Also, it was found that the developed method were very competitive with the numerical analysis considering the number of input data, complexity and effort in modelling. It is believed that RNN using cone penetration test results can make a highly efficient tool in predicting settlements if enough field data can be obtained.

• Computers and Concrete
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• v.25 no.1
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• pp.67-73
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• 2020

Traditionally used analytical approach to predict the fatigue failure of reinforced concrete (RC) structure is generally conservative and has certain limitations. The nonlinear finite element method (FEM) offers less expensive solution for fatigue analysis with sufficient accuracy. However, the conventional implicit dynamic analysis is very expensive for high level computation. Whereas, an explicit dynamic analysis approach offers a computationally operative modelling to predict true responses of a structural element under periodic loading and might be perfectly matched to accomplish long life fatigue computations. Hence, this study simulates the fatigue behaviour of RC beams with finite element (FE) assemblage presenting a simplified explicit dynamic numerical solution to show computer aided fatigue behaviour of RC beam. A commercial FEM package, ABAQUS has been chosen for this complex modelling. The concrete has been modelled as a 8-node solid element providing competent compression hardening and tension stiffening. The steel reinforcements are simulated as two-node truss elements comprising elasto-plastic stress-strain behaviour. All the possible nonlinearities are duly incorporated. Time domain analysis has been adopted through an automatic Newmark-β time incremental technique. The program consists of twelve RC beams to visualize the real behaviour during fatigue process and to obtain the reliability of the study. Both the numerical and experimental results indicate a redistribution of stresses along the time and damage accumulation of beam which severely affect the serviceability and ultimate capacity of RC beam. The output of the FEM analysis demonstrates good match with the experimental consequences which affirm the efficacy of the computer aided model. The controlled fatigue damage evolution at service fatigue load limits makes the FE model an efficient tool in predicting high cycle fatigue behaviour of RC structures.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.59-69
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• 2021

Historical records of earthquakes are generally used as a basis to extrapolate the instrumental earthquake catalog in time and space during the probabilistic seismic hazard analysis (PSHA). However, the historical catalogs' input parameters determined through historical descriptions rather than any quantitative measurements are accompanied by considerable uncertainty in PSHA. Therefore, quantitative assessment to verify the historical earthquake parameters is essential for refining the reliability of PSHA. This study presents an approach and its application to constrain reliable ranges of the magnitude and corresponding epicenter of historical earthquakes. First, ranges rather than specific values of ground motion intensities are estimated at multiple locations with distances between each other for selected historical earthquakes by reviewing observed co-seismic natural phenomena, structural damage levels, or felt areas described in their historical records. Based on specific objective criteria, this study selects only one earthquake (July 24, 1643), which is potentially one of the largest historical earthquakes. Then, ground motion simulations are performed for sufficiently broadly distributed epicenters, with a regular grid to prevent one from relying on strong assumptions. Calculated peak ground accelerations and velocities in areas with the historical descriptions on corresponding earthquakes are converted to intensities with an empirical ground motion-intensity conversion equation to compare them with historical descriptions. For the ground motion simulation, ground motion prediction equations and a frequency-wavenumber method are used to consider the effects of possible source mechanisms and stress drop. From these quantitative calculations, reliable ranges of epicenters and magnitudes and the trade-off between them are inferred for the earthquake that can conservatively match the upper and lower boundaries of intensity values from historical descriptions.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.79-89
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• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.269-274
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• 2024

High-quality precipitation data are crucial for various industries, including disaster prevention. In South Korea, long-term high-quality data are collected through numerous ground observation stations. However, data between these stations are reprocessed into a grid format using interpolation methods, which may not perfectly match actual precipitation. A prime example of real-time observational grid data globally is the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) from National Aeronautics and Space Administration (NASA), while in South Korea, ground radar data are more commonly used. GPM and ground radar data exhibit distinct differences due to their respective processing methods. This study aims to analyze the characteristics of GPM and Constant Altitude Plan Position Indicator(CAPPI),representative real-time grid data, by comparing them with ground-observed precipitation data. The study period spans from 2021 to 2022, focusing on hourly data from Automated Synoptic Observing System (ASOS) sites in South Korea. The GPM data tend to underestimate precipitation compared to ASOS data, while CAPPI shows errors in estimating low precipitation amounts. Through this comparative analysis, the study anticipates identifying key considerations for utilizing these data in various applied fields, such as recalculating design rainfall, thereby aiding researchers in improving prediction accuracy by using appropriate data.