• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.263-270
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• 2009

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirected ness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

• Ocean Systems Engineering
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• v.14 no.2
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• pp.101-114
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• 2024

Seafloor sediment mapping is an essential research topic in shallow coastal waters, especially in port development, benthic habitat mapping, and underwater communications. The seafloor sediments can be interpreted by collecting sediment samples directly in the field using a grab sampler or corer. Another method is optical, especially using underwater cameras and videos. Both methods each have weaknesses in terms of area coverage (mechanic) and accurate positioning (optic). The latest technology used to overcome it is the acoustic method (echosounder) with Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning. Therefore, in this study will propose the classification of seafloor sediments in coastal waters using acoustic method that is Multibeam Echosounder (MBES) multi-frequency with five frequency (200 kHz, 250 kHz, 300 kHz, 350 kHz, and 400 kHz). In this study, the deep neural network (DNN) used the bathymetric multi frequency, bathymetric difference inters frequencies, and bathymetric features from 5 (five) frequencies as input layer and 4 (four) sediment types in 74 (seventy-four) sample sediment as output layer to make a seafloor sediment map. Results of sediment mapping using the DNN method show an overall accuracy of 71.6% (significant) and a kappa coefficient of 0.59 (moderate). The distribution of seafloor sediment in the study area is mainly silt (41.6%), followed by clayey sand (36.6%), sandy silt (14.2%), and silty sand (7.5%).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Journal of the Korean Geotechnical Society
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• v.20 no.1
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• pp.109-120
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• 2004

There has been outstanding research on the soil-water characteristic curves of unsaturated soils over the past several decades. Unfortunately, unsaturated soil mechanics has not been considered as an important factor in Korea. In this paper, laboratory test and numerical analysis(SoilVision Professional ver 3.04) were performed to investigate the prediction method of soil-water characteristic curve and unsaturated permeability coefficient in reclaimed ground. The pressure cell, desiccator, and tensiometor tests were conducted on three types of reclaimed soils(dredged soil, sand, weathered granite soil). Numerical analysis was executed to compare the results with the laboratory test results and also compared with the results of each prediction method. Based on the laboratory test, three different types of soils have shown different soil-water characteristic curves. The hysteresis fir these soils is clearly defined. As a result of numerical analysis, Fredlund & Xing's method and Fredlund & Wilson's model proved to worke out well for reclaimed ground soils in Korea. Also, predicting method based on the soil-water characteristic curves from the particle-size distributions is flirty reliable for estimating unsaturated permeability coefficient.

• Journal of Korea Water Resources Association
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• v.46 no.5
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• pp.449-463
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• 2013

This study proposes the method for determining the Muskingum channel routing model parameters based on the assumption of linear system. The proposed method was applied to the Chungju dam basin for the evaluation. Additionally, the rainfall-runoff was repeated for the Yeongchun-Chungju dam reach using seven rainfall events observed. Summarizing the results is as follows. First, the concentration time and storage coefficient of a channel reach formed by the subdivision can be expressed as the difference between the concentration times and storage coefficients of upstream and downstream basins. The storage coefficients of the channel reach estimated is equal to the storage coefficient of the Muskingum channel routing model and the weight factor can be simply estimated using the ratio between the concentration time and storage coefficient. Second, the weight factor of the Muskingum model is in inverse proportion to the Russel coefficient, which is in between 0.4166 and 0.625 when considering the Russel coefficients generally applied. Finally the application to the Yeongchun-Chungju dam reach showed that the proposed method is still valid regardless of the limitations such as the uncertainty of the observed data.

• KIEAE Journal
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• v.13 no.1
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• pp.29-37
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• 2013

This study intended to introduce the method of designing an eco-friendly building based on BIM(Building Information Modeling) and BIM-based application. The proposed application aimed to generate the environmental performance of the designed alternative automatically in real-time manner in the process of architectural design. We focused on the feasibility of BIM-based eco-friendly design process and the applicability of the developed application for the architectural design practice. In this manner, in the end of paper, we proposed the so-call EcoBIM which is the performance evaluation module for the designed alternative using BIM in the real-time manner and the new design process with it. EcoBIM generate the coefficient of overall heat transmission of wall, roof and slab of the designed alternative with their physical characteristics such as thickness, thermal resistance and so on.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.4
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• pp.163-168
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• 2005

In this paper, we propose a character-based summarization algorithm using speaker identification method from the dialog in video. First, we extract the dialog of shots containing characters' face and then, classify the scene according to actor/actress by performing speaker identification. The classifier is based on the GMM(Gaussian Mixture Model) using the 24 values of MFCC(Mel Frequency Cepstrum Coefficient). GMM is trained to recognize one actor/actress among four who are all trained by GMM. Our experiment result shows that GMM classifier obtains the error rate of 0.138 from our video data.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.119-122
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• 1994

An analytical procedure is described to estimate the mobilized tensile forces along the effective lengths of nails. Based on the horizontal focing displacements of a nailed-soil wall experiencing outward tilt about the toe with granular soil deposit, the variation of nail-soil friction coefficient is modeled. Also, the method of overall stability analysis of a nailed-soil wall is presented using the Morgenstem-Price limit-equilibrium slice method. The results predicted by the developed procedure are compared with test measurements. The comparisons show in general good agreement.

• Transactions of the Korean Society of Mechanical Engineers
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• v.7 no.1
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• pp.64-72
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• 1983

A method of numerical analysis is proposed for the pure stretch forming of A1 sheet by hemi-spherical punch. The analysis is performed by Woo's general method under the condition of variable friction and plastic yielding is based on the new anisotropic yield function proposed by Hill. A comparison of the calculated results with experiment shows good agreement for various lubrication when the initial values of the coefficient of coulomb friction at pole are less than 0.4.