• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.5
• /
• pp.213-224
• /
• 2009

This paper presents a new algorithm for estimating a driver's physical condition using real-time vision system and performs experimentation for real facial image data. The system relies on a face recognition to robustly track the center points and sizes of person's two pupils, and two side edge points of the mouth. The face recognition constitutes the color statistics by YUV color space together with geometrical model of a typical face. The system can classify the rotation in all viewing directions, to detect eye/mouth occlusion, eye blinking and eye closure, and to recover the three dimensional gaze of the eyes. These are utilized to determine the carelessness and drowsiness of the driver. Finally, experimental results have demonstrated the validity and the applicability of the proposed method for the estimation of a driver's physical condition.

• Journal of Biomedical Engineering Research
• /
• v.37 no.1
• /
• pp.21-30
• /
• 2016

Weibull distribution with two parameters, shape (k) and scale (s) parameters are used to model the fatigue failure analysis due to periodic vortex shedding of the synovial fluid in knee joints. In order to determine the later parameter, a suitable statistical model is required for velocity distribution of synovial fluid flow. Hence, wide applicability of Weibull distribution in life testing and reliability analysis can be applied to describe the probability distribution of synovial fluid flow velocity. In this work, comparisons of three most widely used methods for estimating Weibull parameters are carried out; i.e. the least square estimation method (LSEM), maximum likelihood estimator (MLE) and the method of moment (MOM), to study fatigue failure of bone joint due to periodic vortex shedding of synovial fluid. The performances of these methods are compared through the analysis of computer generated synovial fluidflow velocity distribution in the physiological range. Significant values for the (k) and (s) parameters are obtained by comparing these methods. The criterions such as root mean square error (RMSE), coefficient of determination ($R^2$), maximum error between the cumulative distribution functions (CDFs) or Kolmogorov-Smirnov (K-S) and the chi square tests are used for the comparison of the suitability of these methods. The results show that maximum likelihood method performs well for most of the cases studied and hence recommended.

• Journal of Advanced Research in Ocean Engineering
• /
• v.3 no.1
• /
• pp.32-40
• /
• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• Journal of Korea Water Resources Association
• /
• v.30 no.6
• /
• pp.621-628
• /
• 1997

Excavation works on stream beds have been done for various reasons including aggregate collection, sediment dredging, bridge constructions, or laying pipes under the ground. These activities may cause significant loadings of SS (suspended solids) resulting in water pollution and other detrimental effects to the surrounding environment. This research investigates application potential of a fickian diffustion model, derived from two dimensional advection-diffusion equation through some simplifying assumptions, as a planning tool for the estimation of SS loadings from excavation works and evaluation fo pollution prevention measures in case that sophisticated numerical simulation models are not applicable due to various practical reasons. Through a case study of the Juncheon stream in the Donghae City on the Kangwondo Province, this study demonstrates applicability of the fickian diffustion model as a practical method for the preliminary estimation of Ss loadings from excavation works and evaluation of performance of fabrics made of synthetic fiber for the reduction of downstream SS concentration with deficient field data.

• Journal of Korea Water Resources Association
• /
• v.52 no.12
• /
• pp.963-973
• /
• 2019

This study reviews a urban flooding risk criteria estimation model to predict risk criteria in areas where flood risk criteria are not precalculated by using watershed characteristic data and limit rainfall based on damage history. The risk criteria estimation model was designed using Support Vector Machine, one of the machine learning algorithms. The learning data consisted of regional limit rainfall and watershed characteristic. The learning data were applied to the SVM algorithm after normalization. We calculated the mean absolute error and standard deviation using Leave-One-Out and K-fold cross-validation algorithms and evaluated the performance of the model. In Leave-One-Out, models with small standard deviation were selected as the optimal model, and models with less folds were selected in the K-fold. The average accuracy of the selected models by rainfall duration is over 80%, suggesting that SVM can be used to estimate flooding risk criteria.

• Journal of Navigation and Port Research
• /
• v.39 no.3
• /
• pp.179-184
• /
• 2015

The purpose of this study is to provide appropriate methodology to ensure the safety and integrity of the production riser in offshore structure. In order to select integrity estimation methodology for production riser, level I and II Non-destructive Damage Evaluation (NDE) methods that were applied to existing structures are classified and reviewed. Numerical analysis is performed to verify the applicability and capability on damage detection of reviewed methods. As a result, the damage detection methodology using modal strain energy is more sensitive in detection of the damage than other methods. In practice, the number of sensors is limited due to the environmental and financial conditions. The impact on damage detection performance by reducing the number of sensors is systematically investigated through a series of numerical analyses and the results are discussed. The optimal number of sensor for the integrity estimation of production riser is recommended.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.8
• /
• pp.663-673
• /
• 2013

The drastic growth of mobile communication and spreading of smart phone make the significant attention on Location Based Service. The one of most important things for vitalization of LBS is the accurate estimating position for mobile object. Focusing on IMU deployed in smart phone, we develop a hybrid positioning estimation framework with a combination of WPS. The developed approaches can strengthen the advantages of independent indoor applicability of IMU. The estimation of IMU is efficiently compensated by radio fingerprint based Wi-Fi Positioning System. We put a focus especially on the hybrid algorithmic framework. Compared on the existing approaches of WPS or IMU, we achieve the comparable higher performance on both of average error of estimation and deviation of errors. Furthermore test-bed based on smart phone platform is practically developed and all data have been harvested from the actual measurement of test indoor area. This can approve the practical usefulness of proposed framework.

• Journal of Communications and Networks
• /
• v.5 no.2
• /
• pp.157-166
• /
• 2003

This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

• Geomechanics and Engineering
• /
• v.12 no.6
• /
• pp.1003-1020
• /
• 2017

Estimation of groundwater inflow into underground opening is of critical importance for the design and construction of underground structures. Groundwater inflow into a pilot underground storage facility in China was estimated using analytical equations, numerical modeling and field measurement. The applicability of analytical and numerical methods was examined by comparing the estimated and measured results. Field geological investigation indicated that in local scale the high groundwater inflows are associated with the appearance of open joints, fractured zone or dykes induced by shear and/or tensile tectonic stresses. It was found that 8 groundwater inflow spots with high inflow rates account for about 82% of the total rate for the 9 caverns. On the prediction of the magnitude of groundwater inflow rate, it was found that could both (Finite Element Method) FEM and (Discrete Element Method) DEM perform better than analytical equations, due to the fact that in analytical equations simplified assumptions were adopted. However, on the prediction of the spatial distribution estimation of groundwater inflow, both analytical and numerical methods failed to predict at the present state. Nevertheless, numerical simulations would prevail over analytical methods to predict the distribution if more details in the simulations were taken into consideration.

• Journal of computational fluids engineering
• /
• v.22 no.1
• /
• pp.37-42
• /
• 2017

Infrared signals are frequently used to detect objects exposed to wide variety of environmental conditions. Detection by infrared signature is accomplished by distinguishing objects by using the IR radiant contrast between objects and the background. There are several methods of estimating the IR radiant contrast. The inverse distance weighting method, which is one of the IR radiant contrast estimation method using the effect of distance from objects, is known to be an effective way to analyze radiant contrast for complex backgrounds. However this method has a disadvantage of requiring a long calculation time. In this study we propose a statistical method of estimating the IR radiant contrast by using randomly selected pixels of arbitrary number among background pixels to reduce calculation time. Some measured IR images in MWIR and LWIR regions are used to test the applicability of the method proposed and we found that the proposed method is very effective in determining the IR radiant contrast showing very rapid estimation with minar accuracy loss.