• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.228-230
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• 2002

The relative electron density resolution was discussed by the noise power spectrum (NPS) in the heavy ion CT image. The heavy ion beam $\^$12/C accelerated up to 400MeV/u by RIMAC was used in this study. The two-dimensional (2-D) NPS in the CT image was obtained from the one-dimensional (1-D) NPS of the measured residual range distribution of water phantom for single projection, and the noise variance in the CT image was calculated from 2-D NPS. The technique used in the reconstruction was the filtered back-projection method with Shepp-Logan filter. The calculated value suggests the result of our previous works using the density resolution phantom, assuming that the relative electron density resolution is twice the standard deviation. Therefore, the estimation of the noise in CT images by 2-D NPS obtained the measured residual range distribution is the useful method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.4
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• pp.25-34
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• 2000

In this paper, we first investigate the effect of residual code phase offset on the DS/CDMA code acquisition scheme. When the code phase offset normalized to the chip duration is within the advancing step size, the sum of two successive matched filter outputs has a constant value regardless of the residual code phase offset if noise is absent. Based on this observation, we propose a new code acquisition scheme, and investigate the performance of the scheme. The Proposed code acquisition scheme is analyzed, and numerical results are given to show that the proposed scheme is more robust to the variation of the residual code phase offset and has better performance than the conventional scheme.

• Journal of Preventive Medicine and Public Health
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• v.7 no.1
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• pp.101-105
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• 1974

A study was carried out for the purpose of grasping the status of bacteriological contamination of the public wells in Iri city, during the period from 1 to 15 August, 1974. Coliform groups were detected by membrane filter method and physical conditions of the wells were checked. As results of this study, following conclusion were obtained. 1. The households which used the piped water occupied 70.8 per cent (11,907 households) out of total householdsir Iri city. 2. Temperature of the well water was 38 out of 50 samples (72.0%) with 12.1 to $16.0^{\circ}C$ and 9 wells with 10.1 to $12.0^{\circ}C$. 3. The pH values range of the well water was 6.0 to 7.9, 11 cases or 22,0 per cent less than 6.5 and 8 cases or 16.0 per cent more than 7.5. 4. The residual chlorine was found at 15 samples (30.0%) contained 0.1 to 0.5ppm, 9 samples (18.0%), 0.6 to 1.0 ppm after 24 houres of chlorination. 5. Coliform groups were found at 49 out of 50 samples (98.0%) before chlorination when 100ml well waters was tested by membrane filter technique and 15 wells(30.0%) were potable for drinking within 24 houres after chlorination. 6. Coliform groups positves were 23 out of 26 samples (88.5%) with no residual chlorine, 12 out of 16 samples (80.0%) with 0.1 to 0.5ppm and none out of 9 with 0.6 to 1.0ppm.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.56-66
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• 2001

The spectral subtraction algorithm using a criterion based on the human perception has been recently developed. The speech processed with Virag's algorithm sounds more pleasant to a human listener than those obtained by the classical methods. However, Virag's algorithm requires a robust voice activity detector (VAD). In the ESS (extended spectral subtraction) algorithm without VAD, the residual noise becomes more noticeable as the SNR decrease. In this paper we propose a new speech enhancement method, the combination of Wiener filter and spectral subtraction based on noise masking characteristics in the human auditory system. There is no need of VAD because the noise can be successively updated even during speech activity using Wiener filter. The adjustment of the subtraction parameter based on the masking threshold makes the residual noise inaudible. The proposed method has been compared with conventional spectral subtraction algorithms. Objective and subjective evaluation of the proposed system is performed with several noise types having different time-frequency distributions. The application of objective measures, the study of the speech spectrograms, as well as subjective listening tests, confirm that the enhanced speech with proposed algorithm is more pleasant to a human listener.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.195-201
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• 2015

For median filtering (MF) detection in altered digital images, this paper presents a new feature vector that is formed from autoregressive (AR) coefficients via an AR model of the gradients between the neighboring row and column lines in an image. Subsequently, the defined 10-D feature vector is trained in a support vector machine (SVM) for MF detection among forged images. The MF classification is compared to the median filter residual (MFR) scheme that had the same 10-D feature vector. In the experiment, three kinds of test items are area under receiver operating characteristic (ROC) curve (AUC), classification ratio, and minimal average decision error. The performance is excellent for unaltered (ORI) or once-altered images, such as $3{\times}3$ average filtering (AVE3), QF=90 JPEG (JPG90), 90% down, and 110% up to scale (DN0.9 and Up1.1) images, versus $3{\times}3$ and $5{\times}5$ median filtering (MF3 and MF5, respectively) and MF3 and MF5 composite images (MF35). When the forged image was post-altered with AVE3, DN0.9, UP1.1 and JPG70 after MF3, MF5 and MF35, the performance of the proposed scheme is lower than the MFR scheme. In particular, the feature vector in this paper has a superior classification ratio compared to AVE3. However, in the measured performances with unaltered, once-altered and post-altered images versus MF3, MF5 and MF35, the resultant AUC by 'sensitivity' (TP: true positive rate) and '1-specificity' (FN: false negative rate) is achieved closer to 1. Thus, it is confirmed that the grade evaluation of the proposed scheme can be rated as 'Excellent (A)'.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.376-376
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• 2007

In Liquid Crystal Display (LCD) manufacturing, the organic over coat materials over coat materials for insulation layer of color filter with acryl ate was widely used. Therefore, we approach that the organic overcoat material can use to insulation layer for color filter and liquid crystal (LC) alignment layer in this research. The LC aligning capabilities was successful stuided for the first time. The organic overcoat layer and polymer layer was coated by spin-coating. In order to characterize the LC alignment, electric optic and residual DC and atomic force microscopy (AFM) image was used. The good LCD aligning capabilities treated on the organic overcoat thin film surfaces with ion beam exposure of $45^{\circ}$ above ion beam energy density of 1200 eV can be achieved. Also the good LCD alignment capabilities treated polymer on surfaces with ion beam exposure of $45^{\circ}$ above ion beam energy density of 1800 eV can be achieved. Comparing electro-optical characteristics between the Polyimide (PI) and the overcoat, the resultant transmittance of the overcoat considerably matched that of the PI and the residual DC also exhibited similar features with the PI.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1117-1124
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• 2011

An integrated fault detection and isolation method is proposed in this paper. The main objective of this paper is development fault detection, isolation and diagnosis algorithm based on the DKF (Decentralized Kalman Filter) and the bank of IMM (Interacting Multiple Model) filters using penalty scalar for both partial and total faults and the outlier detection algorithm for preventing false alarm also included. The proposed FDI (Fault Detection and Isolation) scheme is developed in four phases. In the first phase, the outlier detection filter is designed to prevent false alarm as a pre-filter. In the second phases, two local filters and master filter are designed to detect sensor faults. In the third phases, the proposed FDI scheme checks sensor residual to isolate sensor faults and 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the last phases, four filters are designed to identify the fault type which is either the total fault or partial fault. The developed scheme can deal with not only sensor and actuator faults, but also preventing false alarm. An important feature of the proposed FDI scheme can decreases fault isolation time and figure out not only fault detection and isolation but also fault type identification. To verify the proposed FDI algorithm performance, the Simulator is also developed under the Matlab/Simulink environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.4
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• pp.469-479
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• 2015

This study have been conducted to analyze the feasibility of establishing Contamination Warning System(CWS) that is capable of monitoring early natural or intentional water quality accidents, and providing active and quick responses for domestic C_water supply system. In order to evaluate the water quality data set, pH, turbidity and free residual chlorine concentration data were collected and each statistical value(mean, variation, range) was calculated, then the seasonal variability of those were analyzed using the independent t-test. From the results of analyzing the distribution of outliers in the measurement data using a high-pass filter, it could be confirmed that a lot of lower outliers appeared due to data missing. In addition, linear filter model based on autoregressive model(AR(1) and AR(2)) was applied for the state estimation of each water quality data set. From the results of analyzing the variability of the autocorrelation coefficient structure according to the change of window size(6hours~48hours), at least the window size longer than 12hours should be necessary for estimating the state of water quality data satisfactorily.