• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.456-460
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• 2020

In this research, a radiation detection backpack to be used discreetly or by a wide range of users was developed using array silicon-photomultiplier (SiPM) and CsI (Tl), and its characteristics were evaluated. The R-squared value, which indicates the responsiveness of a detector based on the signal intensity, was determined to be 0.981, indicating a good linear responsivity. The energy resolutions for gamma radiation energies of Co-57 (122 keV), Ba-133 (356 keV), Cs-137 (662 keV), and Co-60 (1332 keV) were found to be 13.40, 10.50, 6.77, and 3.16%, respectively. These results confirm good energy resolution characteristics. Furthermore, in the case of mixed sources, the gamma radiation peaks were readily distinguishable, and the R-squared value for energy linearity was calculated to be 0.999, demonstrating an exceptional energy linearity. Further research based on the results of this study would enable the commercialization of lightweight SiPM-based wireless radiation detection backpacks that can be used for longer durations by replacing the photomultiplier tube, which is mainly used as the optical sensor in existing radiation detection backpacks.

• Journal of Power Electronics
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• v.7 no.1
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• pp.64-71
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• 2007

This paper proposes a novel active frequency drift (AFD) method to improve the islanding detection performance with minimum current harmonics. To detect the islanding phenomenon of grid-connected photovoltaic (PV) inverters concerning the safety hazards and possible damage to other electric equipment, anti-islanding methods have been described. The AFD method that uses chopping fraction (cf) enables the islanding detection to drift up (or down) the frequency of the voltage during the islanding situation. However, the performance of the conventional AFD method is inefficient and causes difficulty in designing the appropriate cf value to meet the limit of harmonics. In this paper, the periodic chopping fraction based on a novel AFD method is proposed. This proposed method shows the analytical design value of cf to meet the test procedure of IEEE Std. 929-2000 with power quality and islanding detection time. To verify the validation of the proposed method, the islanding test results are presented. It is confirmed that the proposed method has not only less harmonic distortion but also better performance of islanding detection compared with the conventional AFD method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.107-117
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• 2016

STD method using a mean and standard deviation is widely used in various inspection systems. The result of detection using the STD method is very dependent on the threshold value. This paper proposes an adaptive defect detection algorithm to with a precise detection of an ultimate defect. The proposed method is determined threshold value adaptively using a skewness that indicates a similarity of intensity and normal distribution of image. In the experiment, we used a various TFT-LCD images for a quantitative evaluation of defect detection performance evaluation result to prove the performance of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.229-237
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• 2017

In this paper we propose an auto tonal detection method which exploits short term stationary when targets located in a detection beam area and then additional methods are proposed in order to reduce the computational complexity of the proposed method. The proposed method is adaptive to input signals and robust against interference caused by multiple targets because it compares an expected value of input signals with a threshold value which are estimated from a single beam while signals are keep stationary. The performances of the proposed methods are evaluated using by simulated data and acquired data from real ocean. The proposed method has shown better performance than conventional CFAR (Constant False Alarm Rate) methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.736-743
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• 2013

In IRST(infrared search and track) system, the small target detection is very difficult because the IR(infrared) image have various clutter and sensor noise. The noise and clutter similar to the target intensity value produce many false alarms. In this paper. We propose the adaptive detection method which obtains optimal target detection using the image intensity information and the prior information of target. In order to enhance the target, we apply the human visual system. we determine the adaptive threshold value using image intensity and distance measure in target enhancement image. The experimental results indicate that the proposed method can efficiently extract target region in various IR images.

• The Journal of Information Systems
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• v.15 no.4
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• pp.211-224
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• 2006

This study investigates the application of data mining techniques such as artificial neural networks, rough sets, and induction teaming to the intrusion detection systems. To maximize the effectiveness of data mining for intrusion detection systems, we introduced the asymmetric costs with false positive errors and false negative errors. And we present a method for intrusion detection systems to utilize the asymmetric costs of errors in data mining. The results of our empirical experiment show our intrusion detection model provides high accuracy in intrusion detection. In addition the approach using the asymmetric costs of errors in rough sets and neural networks is effective according to the change of threshold value. We found the threshold has most important role of intrusion detection model for decreasing the costs, which result from false negative errors.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.259-268
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• 2007

A knock detection circuit that is based on the signal of an accelerometer installed on the engine block of a spark ignition automotive engine has a band-pass filter with a certain frequency as a parameter to be calibrated. A new statistical method for the determination of the frequency which is the most suitable for the knock detection in real-time applications is proposed. The method uses both the cylinder pressure and block vibration signals and is divided into two steps. In both steps, a new recursive trigonometric interpolation method that calculates the frequency contents of the signals is applied. The new trigonometric interpolation method developed in this paper improves the performance of the Discrete Fourier Transformation, allowing a flexible choice of the size of the moving window. In the first step, the frequency contents of the cylinder pressure signal are calculated. The knock is detected in the cylinder of the engine cycle for which at least one value of the maximal amplitudes calculated via the trigonometric interpolation method exceeds a threshold value indicating a considerable amount of oscillations in the pressure signal; this cycle is selected as a knocking cycle. In the second step, the frequency analysis is performed on the block vibration signal for the cycles selected in the previous step. The knock detectability, which is an individual cylinder attribute at a certain frequency, is verified via a statistical hypothesis test for testing the equality of two mean values, i.e. mean values of the amplitudes for knocking and non-knocking cycles. Signal-to-noise ratio is associated in this paper with the value of t-statistic. The frequency with the largest signal-to-noise ratio (the value of t-statistic) is chosen for implementation in the engine knock detection circuit.

• Journal of the Korean GEO-environmental Society
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• v.19 no.1
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• pp.5-14
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• 2018

All kinds of monitoring data in construction site could have outlier created from diverse cause. In this study generation technique of synthesis value, its regression, final outlier detection and assessment are conducted to distinct outlier data included in extensive time series dataset. Synthesis value having weight factor of correlation between a number of datasets consist of many monitoring data enable to detect outlier by increasing its correlation. Standard artificial dataset in which intentional outliers are inserted has been used for assessment of synthesis value technique. These results showed increase of detection accuracy for outlier and general tendency in case of having different time series models in common. Accuracy of outlier detection increased in case of using more dataset and showing similar time series pattern.

• Korean Journal of Veterinary Research
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• v.45 no.4
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• pp.569-574
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• 2005

Enzyme-linked immunosorbent assay (ELISA) for detection of antibodies to reticuloendotheliosis virus (REV) at single serum dilution was standardized. REV HI, one of the Korean field isolates, was inoculated into chicken embryo fibroblast (CEF) cells and was harvested from the culture fluids and cells after 10 to 12 days. Viruses were purified by centrifugation at the $107,000{\times}g$ for 12 hours on 20, 30, 45% (W/V) sucrose gradient. Virus specific fraction was collected and used as ELISA antigen. To standardize ELISA, the optimal concentration of coating antigen ($1{\mu}g/well$) and conjugate (1/1000) was determined by corrected OD (OD value of positive serum-OD value of negative serum) and P/N ratio (OD value of positive serum/OD value of negative serum). To calculate ELISA titer by measuring absorbance at 1/400 single serum dilution, serum titrations were carried out for various sample sera together with standard positive and negative sera. The observed titers of serum samples were plotted against sample/positive (s/p) ratios at 1/400 serum dilution. From the above data, the ELISA titers could be calculated by the equation of $log_{10}$ ELISA titer = 2.2763 ($log_{10}$ s/p) + 3.482 (r = 0.93). For evaluating the sensitivity, the standardized method were compared with conventional agar gel immunodiffusion (AGID) test method using serum samples collected from REV infected field chicken flocks. Fifty seven of 60 samples (95%) were positive for REV by ELISA, whereas only 11 (18.3%) samples were positive by AGID test. This results suggested that the ELISA tests developed in this study could be used for detection of antibodies to REV with high sensitivity.

• 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)'.