• The Korean Journal of Applied Statistics
• /
• v.26 no.5
• /
• pp.835-845
• /
• 2013

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.16 no.1
• /
• pp.75-89
• /
• 2014

In recent years, utilization of underground space has been increasing in various parts of the world. In particular, open-cut method is usually applied to the shallow depth excavation. However some problems such as extreme traffic congestion and unstability of adjacent structures etc. might occur. In order to cope with these problems, the M-CAM (Modified Cellular Arch Method) method was proposed to excavate soil tunnels at shallow depth with secured enough stability and minimized construction period. In this study, sensitivity analysis was performed to predict the influence of the size of CPW(Continuous Pile Wall) and ground conditions on the behavior of the tunnel. First of all, embedded depth and diameter (or thickness) of CPW, coefficient of lateral earth pressure, and ground conditions were selected as parameters that could affect tunnel stability. Meanwhile, FLAC 2D based on finite difference method was used for numerical analysis. As a result of this study, it was checked out that embedded depth among sizes of CPW had a greatest influence on the stability of a tunnel.

• Current Optics and Photonics
• /
• v.4 no.1
• /
• pp.31-43
• /
• 2020

The feasibility of the application of terahertz electromagnetic waves in the diagnosis of prostate cancer was examined. Four samples of incomplete cancerous prostatic paraffin-embedded tissues were examined using terahertz spectral imaging (TPI) system and the results obtained by comparing the absorption coefficient and refractive index of prostate tumor, normal prostate tissue and smooth muscle from one of the paraffin tissue masses examined were reported. Three hundred and sixty cases of absorption coefficients from one of the paraffin tissues examined were used as raw data to classify these three tissues using the Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LS-SVM). An excellent classification with an accuracy of 92.22% in the prediction set was achieved. Using the distribution information of THz reflection signal intensity from sample surface and absorption coefficient of the sample, an attempt was made to use the TPI system to identify the boundaries of the different tissues involved (prostate tumors, normal and smooth muscles). The location of three identified regions in the terahertz images (frequency domain slice absorption coefficient imaging, 1.2 THz) were compared with those obtained from the histopathologic examination. The tissue tumor region had a distinctively visible color and could well be distinguished from other tissue regions in terahertz images. Results indicate that a THz spectroscopy imaging system can be efficiently used in conjunction with the proposed advanced computer-based mathematical analysis method to identify tumor regions in the paraffin tissue mass of prostate cancer.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.23 no.6
• /
• pp.474-481
• /
• 2011

The discharge coefficient and spatial velocity distribution were clarified by carrying out a physical experiment to assess the performance of sluice for tidal power generation. The physical experiment was performed by manufacturing 10 sluce models whose scale is 1/70 of the prototype and installing it in the planar open channel, which has apron sections in front of and behind the sluice models. In particular, it was attempted to reasonably determine the locations and method of measuring water levels that may affect estimation of the discharge coefficient. Based on the experimental results for various conditions of discharges and tidal levels, the discharge coefficient of the sluice in the experiment was estimated as 1.3 to 1.4. Meanwhile, it was found that velocities were 2~3% faster at the sluices near the central region whereas 4~5% slower at the sluices on both sides, in comparison to the average value of the mean velocities of the ten sluices.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.37 no.1
• /
• pp.9-18
• /
• 2013

Two-phase vapor-liquid flows exist in many shell and tube heat exchangers such as condensers, evaporators, and nuclear steam generators. To understand the fluid dynamic forces acting on a structure subjected to a two-phase flow, it is essential to obtain detailed information about the characteristics of a two-phase flow. The characteristics of a two-phase flow and the flow parameters were introduced, and then, an experiment was performed to evaluate the pressure loss in the tube bundles and the fluid-dynamic force acting on the cylinder owing to the pressure distribution. A two-phase flow was pre-mixed at the entrance of the test section, and the experiments were undertaken using a normal triangular array of cylinders subjected to a two-phase cross-flow. The pressure loss along the flow direction in the tube bundles was measured to calculate the two-phase friction multiplier, and the multiplier was compared with the analytical value. Furthermore, the circular distributions of the pressure on the cylinders were measured. Based on the distribution and the fundamental theory of two-phase flow, the effects of the void fraction and mass flux per unit area on the pressure coefficient and the drag coefficient were evaluated. The drag coefficient was calculated by integrating the measured pressure on the tube by a numerical method. It was found that for low mass fluxes, the measured two-phase friction multipliers agree well with the analytical results, and good agreement for the effect of the void fraction on the drag coefficients, as calculated by the measured pressure distributions, is shown qualitatively, as compared to the existing experimental results.

• Journal of Cadastre & Land InformatiX
• /
• v.50 no.1
• /
• pp.201-214
• /
• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.5
• /
• pp.2402-2409
• /
• 2011

The voice has distinguishable acoustic properties of speaker as well as transmitting information. The speaker recognition is the method to figures out who speaks the words through acoustic differences between speakers. The speaker recognition is roughly divided two kinds of categories: speaker verification and identification. The speaker verification is the method which verifies speaker himself based on only one's voice. Otherwise, the speaker identification is the method to find speaker by searching most similar model in the database previously consisted of multiple subordinate sentences. This paper composes feature vector from extracting MFCC coefficients and uses the dynamic time warping algorithm to compare the similarity between features. In order to describe common characteristic based on phonological features of spoken words, two subordinate sentences for each speaker are used as the training data. Thus, it is possible to identify the speaker who didn't say the same word which is previously stored in the database.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.20 no.11
• /
• pp.1009-1017
• /
• 2010

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.21 no.4
• /
• pp.335-345
• /
• 2008

In this study, we suggest a stochastic finite element scheme for the probabilistic analysis of the composite laminated plates, which have been applied to variety of mechanical structures due to their high strength to weight ratios. The applied concept in the formulation is the weighted integral method, which has been shown to give the most accurate results among others. We take into account the elastic modulus and in-plane shear modulus as random. For individual random parameters, independent stochastic field functions are assumed, and the effect of these random parameters on the response are estimated based on the exponentially varying auto- and cross-correlation functions. Based on example analyses, we suggest that composite plates show a less coefficient of variation than plates of isotropic and orthotropic materials. For the validation of the proposed scheme, Monte Carlo analysis is also performed, and the results are compared with each other.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.2
• /
• pp.1-10
• /
• 2003

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.