• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.427-430
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• 2007

Many victims and property damages are caused in fires every year. In this paper, flame and smoke detection algorithm by using image processing technique is proposed to early alarm fires. The first decision of proposed algorithms is to check candidate of flame region with its unique color distribution distinguished from artificial lights. If it is not a flame region then we can check to candidate of smoke region by measuring difference of brightness and chroma at present frame. If we just check flame and smoke with only simple brightness and hue, we will occasionally get false alarms. Therefore we also use motion information about candidate of flame and smoke regions. Finally, to determine the flame after motion detection, activity information is used. And in order to determine the smoke, edges detection method is adopted. As a result of simulation with real CCTV video signal, it is shown that the proposed algorithm is useful for early fire recognition.

• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.516-520
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• 2009

The purpose of this study is to determine optimal filtering condition and threshold for the detection of gait-cycles for various walkway slopes as well as gait velocities. Ten young healthy subjects with accelerometer system on thigh and ankle walked on a treadmill at 9 conditions (three speeds and three slopes) for 5 minutes. Two direction signals, i.e. anterior-posterior (AP) and superior-inferior (SI) directions, of each sensor (four sensor orientations) were used to detect specific events of gait cycle. Variation of the threshold (from -1G to 1G) and lowpass cutoff frequency (fc) were applied to the event detection and their performance was evaluated according to the error index (EI), which was defined as the combination of the accuracy and false positive rate. Optimal fc and threshold were determined for each slope in terms of the EI. The optimal fc, threshold and their corresponding EI depended much on the walkway slope so that their coefficients of variation (CV) ranged 19~120%. When all data for 3 slopes were used in the identification of optimal conditions for each sensor, the best error indices for all sensor orientations were comparable ranging 1.43~1.76%, but the optimal fc and threshold depended much on the sensor position. The result indicates that the gait-cycle detection robust to walkway slope is possible by threshold method with well-defined filtering condition and threshold.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.294-297
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• 2007

Steam and advection fogs are frequently observed in the Yellow Sea located between Korea and China during the periods of March-April and June-July respectively. This study uses the remote sensing (RS) data for monitoring sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided an informative synopsis for the occurrence of steam and advection fogs through a ground truth. The RS data used in this study was GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and near-IR channel of GOES-9 and MTSAT-1R satellites was applied to estimate the extension of the sea fog. For the days examined, it was found that not only the DCD but also the texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind is used to provide a weak wind area less than threshold under stable condition of the surface wind around a fog event. The Laplacian computation for a measurement of the homogeneity was designed. A new combined method of DCD, QuikSCAT wind speed and Laplacian was applied in the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and Laplacian are -2.0 K, 8 m $s^{-1}$ and 0.1, respectively. The validation methods such as Heidke skill score, probability of detection, probability of false detection, true skill score and odds ratio show that the new combined method improves the detection of sea fog rather than DCD method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.3 s.106
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• pp.272-279
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• 2006

In this paper, we evaluate the performance of frame synchronization words of pilot bit patterns which are used for the channel estimation and frame synchronization confirmation in W-CDMA(Wide band Code Division Multiple Access) system. W-CDMA system also uses compressed mode to make measurements from another frequency without a full dual receiver terminal. It is confirmed by using computer simulation that the proposed frame synchronization words also maintain the frame synchronization property in the compressed mode by using the complementary mapping relationship of preferred pair ROC(Receiver Operating Characteristic) curves with probability of detection and false alarm are used to analized the performance of the proposed frame synchronization words by using the various detection metrics such as LLRT(Log Likelyhood Ratio Test), GLRT(Generalied Likelyhood Ratio Test), soft and hard correlation tests in AWGN and Rayleigh fading channels. It is expected that the research results fer the performance of pilot bit patterns of this paper can be useful reference for the design and implementation of frame synchronization in 3rd generation W-CDMA system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.4
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• pp.423-429
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• 2011

Cooperative spectrum sensing is proposed to overcome some problem such as multipath fading and shadowing and to improve spectrum sensing performance. There are different combining methods for cooperative spectrum sensing: hard decision method and soft decision method. In this paper, we analysis the performance of cooperative spectrum sensing with distance based weight that is kind of a soft decision rule for cognitive radio(CR) systems and CR systems sense the spectrum of the licensed user by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate(CFAR) algorithm for energy detection. The signal of licensed user is OFDM signal and the wireless channel between a licensed user and CR systems is modeled as Gaussian channel. From the simulation results, the cooperative spectrum sensing with distance based weight combining(DWC) and equal gain combing(EGC) methods shows higher spectrum sensing performance than single spectrum sensing does. And the detection probability performance with the DWC is higher than that with the EGC.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.8-16
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• 1993

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dim in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• 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)'.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.12
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• pp.1406-1411
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• 2012

An energy detection based spectrum sensing has been found to be vulnerable to the noise power uncertainty. A cooperative spectrum sensing with an energy detector has appeared as one of the solutions to alleviate this difficulty. However, its performance analysis in a fading environment has not been reported yet in the literature. Motivated by this, this paper presents the performance analysis of the scheme by extending our previous work on evaluating the performance of an energy detector in the presence of noise power uncertainty. The analysis shows that the false alarm probability and detection probability gets higher as the sensing time and/or the number of the secondary users in the OR based cooperative spectrum sensing scheme increase when the noise power uncertainty exists.

• 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)'.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.151-158
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• 2019

FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.