• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.57-63
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• 1997

In this paper, we study the efficient feature vector extraction method and front-end processing to improve the performance of the speech recognition system using KT(Korea Telecommunication) database collected through various telephone channels. First of all, we compare the recognition performances of the feature vectors known to be robust to noise and environmental variation and verify the performance enhancement of the recognition system using weighted cepstral distance measure methods. The experiment result shows that the recognition rate is increasedby using both PLP(Perceptual Linear Prediction) and MFCC(Mel Frequency Cepstral Coefficient) in comparison with LPC cepstrum used in KT recognition system. In cepstral distance measure, the weighted cepstral distance measure functions such as RPS(Root Power Sums) and BPL(Band-Pass Lifter) help the recognition enhancement. The application of the spectral subtraction method decrease the recognition rate because of the effect of distortion. However, RASTA(RelAtive SpecTrAl) processing, CMS(Cepstral Mean Subtraction) and SBR(Signal Bias Removal) enhance the recognition performance. Especially, the CMS method is simple but shows high recognition enhancement. Finally, the performances of the modified methods for the real-time implementation of CMS are compared and the improved method is suggested to prevent the performance degradation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.8
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• pp.747-757
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• 2010

The ice accreted on the airfoil is one of the critical drivers that causes the degradation of aerodynamic performance as well as aircraft accidents. Hence, an efficient numerical code to predict the accreted ice shape is crucial for the successful design of de-icing and anti-icing devices. To this end, a numerical code has been developed for the prediction of glaze ice accretion shape on 2D airfoil. Constant Source-Doublet method is used for the purpose of computational efficiency and heat transfer in the icing process is accounted for by Messinger model. The computational results are thoroughly compared against available experiments and other computation codes such as LEWICE and TRAJICE. The direction and thickness of ice horn are shown to yield similar results compared to the experiments and other codes. In addition, the effects of various parameters - temperature, free-stream velocity, liquid water contents, and droplet diameter - on the ice shape are systematically analyzed through parametric studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1B
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• pp.39-49
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• 2012

With group mobility, minimizing routing overhead among intra-group nodes can be achieved by making longer the routing update period, which may lead to degradation of inter-group's routing performance and consequently the inter-group packet delivery success ratio will be decreased. To reduce the inter-group routing overhead without lowering the inter-group packet delivery success ratio, a mobility-adaptive routing update(MARU) scheme is proposed in this paper. Main idea of the proposed scheme is group-based mobility prediction and updating routing paths by selecting intermediate common relay groups.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.55-60
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• 2020

In this paper, we propose a fast block matching algorithm of motion estimation using early detection of optimal candidate with high priority and a threshold. Even though so many fast algorithms for motion estimation have been published to reduce computational reduction full search algorithm, still so many works to improve performance of motion estimation are being reported. The proposed algorithm calculates block matching error for each candidate with high priority from previous partial matching error. The proposed algorithm can be applied additionally to most of conventional fast block matching algorithms for more speed up. By doing that, we can find the minimum error point early and get speed up by reducing unnecessary computations of impossible candidates. The proposed algorithm uses smaller computation than conventional fast full search algorithms with the same prediction quality as the full search algorithm. Experimental results shows that the proposed algorithm reduces 30~70% compared with the computation of the PDE and full search algorithms without any degradation of prediction quality and further reduces it with other fast lossy algorithms.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.476-492
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• 2018

In this study, various laboratory experiments were conducted on tuff, basalt and diorite specimens, which were obtained in the southern part of Korean Peninsula. Experiments were performed under dry and water saturated conditions. Results showed that strength degradation and change of deformation characteristics were remarkable although the specimens had small porosity. Based on the results, regression models that are capable of predicting important mechanical rock properties, such as uniaxial compressive strength, Young's modulus, Brazilian tensile strength were proposed. P-wave velocity and Shore hardness were selected as independent variables and the results showed satisfactory prediction performance for the experimental data collected in this study.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.197-209
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• 2009

Stereoscopic video delivers depth perception to users contrary to 2D video. Therefore, we need to develop a new video quality assessment model for stereoscopic video. In this paper, we propose a new method for objective assessment of stereoscopic video. The proposed method detects blocking artifacts and degradation in edge regions such as in conventional video quality assessment model. And it detects video quality difference between views using depth information for efficient quality prediction. We performed subjective assessment of stereoscopic video to check the performance of the proposed method, and we confirmed that the proposed algorithm is superior to the existing method in PSNR in respect to correlation with results of the subjective assessment.

• Structural Engineering and Mechanics
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• v.64 no.1
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• pp.11-21
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• 2017

It has been observed in the past that, the reinforced concrete (RC) bridge columns are very often subjected to torsional moment in addition to flexure and shear during seismic vibration. Ignoring torsion in the design can trigger unexpected shear failure of the columns (Farhey et al. 1993). Performance based seismic design is a popular design philosophy which calls for accurate prediction of the hysteresis behavior of structural elements to ensure safe and economical design under earthquake loading. However, very few investigations in the past focused on the development of analytical models to accurately predict the response of RC members under cyclic torsion. Previously developed hysteresis models are not readily applicable for torsional loading owing to significant pinching and stiffness degradation associated with torsion (Wang et al. 2014). The present study proposes an improved polygonal hysteresis model which can accurately predict the hysteretic behavior of RC circular and square columns under torsion. The primary curve is obtained from mechanics based softened truss model for torsion. The proposed model is validated with test data of two circular and two square columns. A good correlation is observed between the predicted and measured torque-twist behavior and dissipated energy.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.156-161
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• 2001

Currently, agricultural facilities are evaluated using either basic inspections or detailed analysis. However, conventional analyses as well as methods based on fuzzy logic and rule of thumb have not been very successful in providing a clear relationship between rating and real state of agricultural facilities, because they can't provide exactly acceptable reliability of degraded structures with manager or supervisor. Therefore, in this stage, we must define probabilistic variables for representing degradation of structures being given damages during a survival time. This paper describes the application of neural network system in developing the relation between subjective ratings and parameters of agricultural reservoir as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on several parameters. The specific application problem for agricultural reservoir in the rural area of Korea is presented and database is constructed to maintain training data set, the information of inspection and facilities. This study showed that a successful training of a neural network could be useful, especially if the input data set for target problem contains parameters with a diverse combination of inter-correlation coefficients. And the networks had a prediction rating of about $^{\ast}^{\ast}^{\ast}%$. The neural network system is expected to show high performance fairly in estimate than statistical method to use equation that is consisted of very lowly interrelated variables.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.165-173
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• 2004

H.264 is the newest international video coding standard that provides high coding efficiency. A macroblock in H.264 has 7 different motion-compensation block sizes in the Inter mode, and several different prediction directions in the Intra mode. In order to achieve as highest coding efficiency as possible, H.264 reference model employs complex mode decision technique based on rate-distortion (RD) optimization which requires high computational complexity. In this paper, we propose two techniques -'early SKIP mode decision' and 'selective intra mode decision' - which can further reduce the computational complexity. Simulation results show that without considerable performance degradation, the proposed methods reduce encoding time by 30% on average and save the number of computing rate-distortion cost by 72%.