• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.464-470
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• 2016

In this paper, we propose an improved face detection algorithm and determination method for drowsiness status of driver from the opening and closing frequency of the detected eye. For this purpose, face, eyes, nose, and mouth are detected based on conventional Viola-Jones face detection algorithm and spatial correlation of face. Here the spatial correlation of face is performed by DFP(Detect Face Part) based on seven characteristics. The experimental results on Caltect face image database revealed that the detection rates of noise particularly showed the improved performance of 13.78% in comparison to that of the previous Viola-Jones algorithm. Furthermore, we analyze the driver's drowsiness determination cumulative value of the eye closed state as a function of time based on SVM (Support Vector Machine) and PERCLOS(Percentage Closure of Eyes). The experimental results confirmed the usefulness of the proposed method by obtaining a driver's drowsiness determination rate of 93.28%.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.255-261
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• 2003

Tunnelling in poor and heterogeneous ground is a difficult task. Even with a good geological investigation, uncertainties with respect to the local rock mass structure will remain. Especially for such conditions, a reliable short-term prediction of the conditions ahead and outside the tunnel profile are of paramount importance for the choice of appropriate excavation and support methods. The information contained in the absolute displacement monitoring data allows a comprehensive evaluation of the displacements and the determination of the behaviour and influence of an anisotropic rock mass. Case histories and with numerical simulations show, that changes in the displacement vector orientation can indicate changing rock mass conditions ahead of the tunnel face (Schubert & Budil 1995, Steindorfer & Schubert 1997). Further research has been conducted to quantify the influence of weak zones on stresses and displacements (Grossauer 2001). Sellner (2000) developed software, which allows predicting displacements (GeoFit$\circledR$). The function parameters describe the time and advance dependent deformation of a tunnel. Routinely applying this method at each measuring section allows determining trends of those parameters. It shows, that the trends of parameter sets indicate changes in the stiffness of the rock mass outside the tunnel in a similar way, as the displacement vector orientation does. Three-dimensional Finite Element simulations of different weakness zone properties, thicknesses, and orientations relative to the tunnel axis were carried out and the function parameters evaluated from the results. The results are compared to monitoring results from alpine tunnels in heterogeneous rock. The good qualitative correlation between trends observed on site and numerical results gives hope that by a routine determination of the function parameters during excavation the prediction of rock mass conditions ahead of the tunnel face can be improved. Implementing the rules developed from experience and simulations into the monitoring data evaluation program allows to automatically issuing information on the expected rock mass quality ahead of the tunnel.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.194-200
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• 2012

In this paper we analyze the code sequence generating algorism defined on $GF(2^n)$ proposed by S.Bostas and V.Kumar[7] and derive the implementation functions of code sequence generator using Boolean functions which can map the vector space $F_2^n$ of all binary vectors of length n, to the finite field with two elements $F_2$. We find the code sequence generating boolean functions based on two kinds of the primitive polynomials of degree, n=5 and n=7 from trace function. We then design and implement the code sequence generators using these functions, and produce two code sequence groups. The two groups have the period 31 and 127 and the magnitudes of out of phase(${\tau}{\neq}0$) autocorrelation and crosscorrelation functions {-9, -1, 7} and {-17, -1, 15}, satisfying the period $L=2^n-1$ and the correlation functions $R_{ij}({\tau})=\{-2^{(n+1)/2}-1,-1,2^{(n+l)/2}-1\}$ respectively. Through these results, we confirm that the code sequence generators using boolean functions are designed and implemented correctly.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.127-135
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• 2020

This study aims to measure the effects of real interest rate (RIR), gross domestic savings (GDS), and net exports (EN) shocks on Indonesia's economic growth (EG). The focus on Indonesia is unique due to the abundant resources available in the nation, but they are unsuccessful in boosting economic growth. This study applied a quantitative method to comprehensively analyze the correlation between variables by employing Vector Autoregression Model (VAR) combined with Vector Error Correction Model (VECM). Various procedures are preformed: Augmented Dickey-Fuller test (ADF), Optimum Lag Test, Johansen Cointegration Test, Granger Causality Test, as well as Impulse Response Function (IRF) and Error Variance Decomposition Analysis (FEVD). The data were collected from the World Bank and the Asian Development Bank from 1986 to 2017. The findings of the study indicated that economic growth responded positively to real interest rate shocks, which implies that when the real interest rate experiences a shock (increase), the economy will be inclined to growth. While, economic growth responded negatively to gross domestic savings and net export shocks. Policymakers are expected to consider several matters, particularly the economic conditions at the time of formulating policy, so that the prediction effectiveness of a policy can be appropriately assessed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.5 no.1
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• pp.3-16
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• 1995

In this paper, we deal with the cryptographic properties of Boolean functions generated by recursively extended methods from the points of balancedness, nonlinearity and correlation properties. First, we propose a new concept 'Strict Uncorrelated Criterion(SUC)' for two Boolean functions as a necessary condition for constructing Boolean functions of S-box which can be guaranteed to be resistant against Differential cryptanalysis, then we show that the recurively extended Boolean functions with particular form preserve the SUC. We also examine the correlation properties of Boolean functions using Walsh-Hadamard transformations and apply them to discuss nonlinearity, correlation properties and SUC of semi-bent function which is defined over odd dimensional vector space. Finally, we compare semi-bent function with Boolean functions which are generated by other similar recursive methods.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3
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• pp.123-128
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• 2007

This Paper Presents new transient signal classification algorithms for underwater transient signals. In general. the ambient noise has small spectral deviation and energy variation. while a transient signal has large fluctuation. Hence to detect the transient signal, we use the spectral deviation and power variation. To classify the detected transient signal. the feature Parameters are obtained by using the Wigner-Ville distribution based eigenvalue decomposition. The correlation is then calculated between the feature vector of the detected signal and all the feature vectors of the reference templates frame-by-frame basis, and the detected transient signal is classified by the frame mapping rate among the class database.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.23 no.1
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• pp.1-11
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• 1995

Thermospheric neutral winds and temperatures have been collected from the ground-based Fabry-Perot interferometer (FPI) at Thule Air Base ($76.5^{\circ}N{\;}69.0^{\circ}W$), Greenland since 1985. The thermospheric observations are obtained by determining the Doppler characteristics f the [OI] 6300 ${\AA}$ emissions of atomic oxygen. The FPI operates routinely during the winter season, with a limitation in the observation by the existence of clouds. For this study, data acquired from 1985 to 1991 were analyzed. The neutral wind measurements from these long-term measurements are used to investigate the influence of solar cycle variation on the high-latitude thermospheric dynamics. These data provide experimental results of the geomagnetic polar cap are also compared with the predictions of two semiempirical models : the vector spherical harmonics (VSH) model of Killeen et al. (1987) and the horizontal wind model (HWM) of Hedin et al. (1991). The experimental results show a good positive correlation between solar activity and thermospheric wind speed over the geomagnetic polar cap. The calculated correlation coefficient indicates that an increase of 100 in F10.7 index corresponds to an increase in wind speed of about 100 m/s. The model predictions reveal similar trends of wind speed variation as a function of solar activity, with the VSH and HWM models tending to overestimate and underestimate the wind speed, respectively.