• Journal of Digital Convergence
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• v.10 no.1
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• pp.365-369
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• 2012

We propose a vision-based method that estimates train position and movement for railway monitoring in which we use a fuzzy classifier to determine train states. The proposed method employs frame difference and background subtraction for estimating train motion and presence, respectively. These features are used as the linguistic variables of the fuzzy classifier. Experimental results show that the proposed method can correctly estimate train position and movement. Therefore the method can be used for railway monitoring systems which estimate crowd density or protect safety.

• Transactions on Electrical and Electronic Materials
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• v.14 no.4
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• pp.187-192
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• 2013

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

• The Journal of the Acoustical Society of Korea
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• v.31 no.8
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• pp.487-506
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• 2012

This paper proposes a speech enhancement algorithm to improve the speech intelligibility by suppressing both reverberation and background noise. The algorithm adopts a non-causal single-channel minimum variance distortionless response (MVDR) filter to exploit an additional information that is included in the noisy-reverberant signals in subsequent frames. The noisy-reverberant signals are decomposed into the parts of the desired signal and the interference that is not correlated to the desired signal. Then, the filter equation is derived based on the MVDR criterion to minimize the residual interference without bringing speech distortion. The estimation of the correlation parameter, which plays an important role to determine the overall performance of the system, is mathematically derived based on the general statistical reverberation model. Furthermore, the practical implementation methods to estimate sub-parameters required to estimate the correlation parameter are developed. The efficiency of the proposed enhancement algorithm is verified by performance evaluation. From the results, the proposed algorithm achieves significant performance improvement in all studied conditions and shows the superiority especially for the severely noisy and strongly reverberant environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.3
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• pp.253-262
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• 2016

Recently, UWAC (underwater acoustic communication) has been studied by many scholars and researchers. DS-CDMA, OFDM (orthogonal-frequency division multiplexing), and MIMO (multi-input multi-output), modulation and error correction, and others techniques that can transmit high-speed data are used in UWAC. In this paper, we first briefly present the theoretical background of estimating the arrival time of the first non-background segment in both signals and calculate the temporal difference. We also present the initial experimental result of estimating the arrival time.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.403-412
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• 2018

In this study, a three-dimensional variational(3DVAR) data assimilation system was developed for the operational numerical weather prediction(NWP) system at the Republic of Korea Air Force Weather Group. The Air Force NWP system utilizes the Weather Research and Forecasting(WRF) meso-scale regional model to provide weather information for the military service. Thus, the data assimilation system was developed based on the WRF model. Experiments were conducted to identify the nested model domain to assimilate observations and the period appropriate in estimating the background error covariance(BEC) in 3DVAR. The assimilation of observations in domain 2 is beneficial to improve 24-h forecasts in domain 3. The 24-h forecast performance does not change much depending on the estimation period of the BEC in 3DVAR. The results of this study provide a basis to establish the operational data assimilation system for the Republic of Korea Air Force Weather Group.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1647-1654
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• 2013

Detection performance of the vehicle on the road depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a vehicle detection system in conjunction with a robust background estimate algorithm to environment change on the road in daytime is proposed. Gaussian Mixture Model is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the vehicle for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithm were useful to detect vehicles in the road.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• Journal of Wetlands Research
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• v.25 no.1
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• pp.48-63
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• 2023

Research on the method of calculating and estimating evapotranspiration has been steadily conducted. Various models have been developed according to different backgrounds, and each of these models has different characteristics such as required input data. Therefore, this study introduces the theoretical background and characteristics of evapotranspiration models and the development process of domestic research on evapotranspiration by era. First, the origin and theoretical background of the potential evapotranspiration models are summarized in addition to classifying them by input data. Then, the characteristics of the actual evapotranspiration estimation methods are summarized. Additionally, methods based on observation and methods using the rainfall-runoff models are summarized.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1325-1328
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• 2007

When we visualize the sound field radiated from a spherical sound source, spherical acoustic holography is proper among acoustic holography methods. However, there are measurement errors due to sensor position mismatch, sensor mismatch, directivity of sensor, and background noise. These errors are amplified if one predicts the pressures close to the sources: backward prediction. The goal of this paper is to quantitatively examine the effects of the error due to sensor position mismatch on acoustic pressure estimation. This paper deals with the cases of which the measurement deviations are distributed irregularly on the hologram plane. In such cases, one can assume that the measurement is a sample of many measurement events, and the cause of the measurement error is white noise on the hologram plane. Then the bias and random error are derived mathematically. In the results, it is found that the random error is important in the backward prediction. The relationship between the random error amplification ratio and the measurement parameters is derived quantitatively in terms of their energies.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.510-514
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• 2004

This research is a preliminary one to investigate transitional problems of actual-cost-date-based contract cost estimation method and suggestions for counteracting them. As parts of it, in this paper, the brief background and methods of the new method and contract costs estimated by introducing it are presented. The results of five projects' contract costs are compared to those estimated by the conventional quantity-based method. Comparison of the both is conducted in terms of total contract costs and contract costs according to type of sub-contracts. Finally, the propositional differences of the later to the former are analysed.