• Journal of Korean Society for Atmospheric Environment
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• v.16 no.3
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• pp.265-275
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• 2000

By using thermal desorption/gas chromatograph/flame ionization detector(TD/GC/FID), this study was carried out to evalute an accuracy and a precision on Benzene(B), Toluene(T), o-Xylene(X) analysis in an industrial hygiene laboratory. Limits of detection of TD/GC/FID on B, T, X were showed 13.75ng/sample or less. For the accuracy of the method by concentration levels, overall bias was showed 7.7% as an absolute value, and the pooled coefficient of variation showed 3.51%. For the precision on repeatability of peak area and retention time between within-run and between-run of analytical system, it is showed the results of within-run gave better than those of between-run. Also the accuracy by sorbents(Tenax TA and Chromosorb 106)was evaluated, and the precision on reproducibility between MDHS72 and this study was compared. It is showed it is possible for TD/GC/FID to evaluate accurately B, T, X concentration levels of less than 1ppm at indoor or outdoor of workplaces in Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.203-209
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• 2021

With the recent development of satellite sensor technology, high-spatial-resolution imagery of various spectral wavelength bands have become possible. Worldview-3 satellite sensor provides panchromatic images with high-spatial-resolution and VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) bands with low-spatial-resolution, so it can be used in various fields such as defense, environment, and surveying. In this study, mineral detection was performed using Worldview-3 satellite imagery. In order to effectively utilize the VNIR and SWIR bands of the Worldview-3 satellite image, the sharpening technique was applied to the spatial resolution of the panchromatic image. To confirm the utility of SWIR bands for mineral detection, mineral detection using only VNIR bands was performed and comparatively evaluated. As the mineral detection technique, SAM (Spectral Angle Mapper), a representative similarity technique, was applied, and the pixels detected as minerals were selected by applying an empirical threshold to the analysis result. Quantitative evaluation was performed using reference data on the results of similarity analysis to evaluate the accuracy of mineral detection. As a result of the accuracy evaluation, the detection rate and false detection rate of mineral detecting using SWIR bands were calculated to be 0.882 and 0.011, respectively, and the results using only VNIR bands were 0.891 and 0.037, respectively. It was found that the detection rate when the SWIR bands were additionally used was lower than that when only the VNIR bands were used. However, it was found that the false detection rate was significantly reduced, and through this, it was possible to confirm the applicability of SWIR bands in mineral detection.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.378-383
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• 1998

Needless to say, the importance of the quality of observed values shall be emphasized in the field of 'TQM', because, the first step of 'TQM' should be some data - observed values. Usually, meaning of the quality of observed values should be, a) accuracy (trueness and precision), b) detection limit, c) cost and so on. However, the authors will describe mainly on b), in this paper. The definitions of technical terms related to 'Detection Limit' are defined in ISO l1843-1 Capability of detection - Part1:Terms and definitions (1998). The most important terms extracted from the above standard are shown in the following table. The application of the 'Detection Limit' to the actual measurement is discussed in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.17-21
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• 2011

This paper presents a real- time detection of on-road succeeding vehicles based on low level edge features and a boosted cascade of Haar-like features. At first, the candidate vehicle location in an image is found by low level horizontal edge and symmetry characteristic of vehicle. Then a boosted cascade of the Haar-like features is applied to the initial hypothesized vehicle location to extract the refined vehicle location. The initial hypothesis generation using simple edge features speeds up the whole detection process and the application of a trained cascade on the hypothesized location increases the accuracy of the detection process. Experimental results on real world road scenario with processing speed of up to 27 frames per second for $720{\times}480$ pixel images are presented.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.558-570
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1155-1172
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• 2015

In this paper, we propose a novel saliency detection framework based on illumination invariant features to improve the accuracy of the saliency detection under the different light conditions. The proposed algorithm is divided into three steps. First, we extract the illuminant invariant features to reduce the effect of the illumination based on the local sensitive histograms. Second, a preliminary saliency map is obtained in the CIE Lab color space. Last, we use the region growing method to fuse the illuminant invariant features and the preliminary saliency map into a new framework. In addition, we integrate the information of spatial distinctness since the saliency objects are usually compact. The experiments on the benchmark dataset show that the proposed saliency detection framework outperforms the state-of-the-art algorithms in terms of different illuminants in the images.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.265-270
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• 2016

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1774-1779
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• 2016

This paper proposes a portable cable fault detection system with automatic fault distinction and distance measurement using time-frequency correlation and reference signal elimination method and automatic fault classification algorithm in order to have more accurate fault determination and location detection than conventional time domain refelectometry (TDR) system despite increased signal attenuation due to the long distance to cable fault location. The performance of the developed system method was validated via an experiment in the test field constructed for the standardized performance test of power cable fault location equipments. The performance evaluation showed that accuracy of the developed system is less than 1.34%. Also, an error of automatic fault type and location by detection of phase and peak value through elimination of the reference signal and normalization of correlation coefficient and automatic fault classification algorithm not occurred.