• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.447-453
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• 2009

This paper introduces the method for detect voices and exact end point at low SNR by maximizing voice energy. Conventional VAD (Voice Activity Detection) algorithm estimates noise level so it tends to detect the end point inaccurately. Moreover, because it uses relatively long analysis range for reflecting temporal change of noise, computing load too high for application. In this paper, the SEM-VAD (Speech Energy Maximization-Voice Activity Detection) method which uses psycho-acoustical bark scale filter banks to maximize voice energy within frames is introduced. Stable threshold values are obtained at various noise environments (SNR 15 dB, 10 dB, 5 dB, 0 dB). At the test for voice detection in car noisy environment, PHR (Pause Hit Rate) was 100%accurate at every noise environment, and FAR (False Alarm Rate) shows 0% at SNR15 dB and 10 dB, 5.6% at SNR5 dB and 9.5% at SNR0 dB.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.101-105
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• 2008

This paper proposes an analytical model to calculate VoIP (Voice of IP) capacity over wireless LANs with frequent bit errors. Since the traditional analytical models for VoIP capacity have not included the effect of bit errors, simulations ould only evaluate VoIP capacity over erroneous channels. For analytically accurate estimation of VoIP capacity over noisy channels, we extend the conventional model to include the effect of propagation errors, end-to-end delay, voice quality, the waiting time in AP(Access Point). The experiments show that our model predicts the VoIP capacity of a given network within the range from 3% to 9% difference comparing with the simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.276-283
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• 2012

In this paper, a practical quasi-optimal DOA(direction of arrival) estimator is proposed in order to develop a one-axis gimbaled ultrasonic source tracker for mobile robot applications. With help of the gimbal structure, the ultrasonic moving source tracking problem can be simply reduced to the DOA estimation. The DOA estimation is known as one of the representative long-pending nonlinear filtering problems, but the conventional nonlinear filters might be restrictive in many actual situations because it cannot guarantee the reliable performance due to the use of nonlinear signal model. This motivates us to reformulate the DOA estimation problem in the linear robust state estimation setting. Based on the assumption that the received ultrasonic signals are noisy sinusoids satisfying linear prediction property, a linear uncertain measurement model is newly derived. To avoid the DOA estimation performance degradation caused by the stochastic parameter uncertainty contained in the linear measurement model, the recently developed NCRKF (non-conservative robust Kalman filter) scheme [1] is utilized. The proposed linear DOA estimator provides excellent DOA estimation performance and it is suitable for real-time implementation for its linear recursive filter structure. The effectiveness of the suggested DOA estimation scheme is demonstrated through simulations and experiments.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.1
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• pp.13-21
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• 1992

In the previous study, car interior noise was analyzed using structural acoustic mode coupling coefficients and noise response in vehicle compartment model was simulated by the developed special purpose program. As a continued study, this paper presents a practical scheme for the interior noise reduction of a passenger car. Noisy panels on the vehicle compartment wall could be easily identified by the analysis using mode coupling coefficients. Numerical simulation for noise reduction was carried out on a simplified vehicle compartment model by using panel contribution factor and the noise reduction effect was verified by the structural modification test using Steel Skin (damping sheet).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.983-990
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• 2013

In this paper, we proposed a Hangul type chipless RFID tag, which has better legibility than the conventional chipless RFID tag not only to a computer but also to a human. We made consonant model, vowel model and whole character model by WIPL tool and checked the applicability of Hangul type chipless RFID tag. We obtain the RCS pattern of each character by simulation. Finally, We classify the character from input data in noisy environment using a variance of the data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.361-369
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• 2002

In order to extract motion features from an input image acquired by a static CCD-camera in a restricted area, we need a robust algorithm to cope with noise sensitivity and condition change. In this paper, we proposed an efficient algorithm to extract and track motion features in a noisy environment or with sudden condition changes. We extract motion features by considering a change of neighborhood pixels when moving objects exist in a current frame with an initial background. To remove noise in moving regions, we used a morphological filter and extracted a motion of each object using 8-connected component labeling. Finally, we provide experimental results and statistical analysis with various conditions and models.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.65-80
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• 1999

This paper presents an elegant method, an affine-model based approach, that can qualitatively estimate the information of camera motion. We define various types of camera motion by means of parameters of an affine-model. To get those parameters form images, we fit an affine-model to the field of instantaneous velocities, rather than raw images. We correlate consecutive images to get instantaneous velocities. The size filtering of the velocities are applied to remove noisy components, and the regression approach is employed for the fitting procedure. The fitted values of the parameters are examined to get the estimates of camera motion. The experimental results show that the suggested approach can yield the qualitative information of camera motion successfully.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.211-217
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• 2011

This paper proposes a quasi-optimal linear DOA (Direction-of-Arrival) estimator which is necessary for the development of a real-time robot auditory system tracking moving acoustic source. It is well known that the use of conventional nonlinear filtering schemes may result in the severe performance degradation of DOA estimation and not be preferable for real-time implementation. These are mainly due to the inherent nonlinearity of the acoustic signal model used for DOA estimation. This motivates us to consider a new uncertain linear acoustic signal model based on the linear prediction relation of a noisy sinusoid. Using the suggested measurement model, it is shown that the resultant DOA estimation problem is cast into the NCRKF (Non-Conservative Robust Kalman Filtering) problem [12]. NCRKF-based DOA estimator provides reliable DOA estimates of a fast moving acoustic source in spite of using the noise-corrupted measurement matrix in the filter recursion and, as well, it is suitable for real-time implementation because of its linear recursive filter structure. The computational efficiency and DOA estimation performance of the proposed method are evaluated through the computer simulations.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.