• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.62-70
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• 2012

In this paper, we propose the gunnery detection method based on reference frame modeling and frame difference method. The frame difference method is basic method in target detection, and it's applicable to the detection of moving targets. The goal of proposed method is the detection of gunnery target which has huge variation of energy and size in the time domain. So, proposed method is based on frame difference, and it guarantee real-time processing and high detection performance. In the method of frame difference, it's important to generate reference frame. In the proposed method, reference frame is modeled and updated in real time processing using statistical values for each pixels. We performed the simulation on 73 IR video data that has gunnery targets, and the experimental results showed that the proposed method has 95.7% detection ratio under condition that false alarm is 1 per hour.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.551-557
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• 2013

In intelligent robot research, hand gesture recognition has been an important issue. And techniques that recognize simple gestures are commercialized in smart phone, smart TV for swiping screen or volume control. For gesture recognition, robust hand detection is important and necessary but it is challenging because hand shape is complex and hard to be detected in cluttered background, variant illumination. In this paper, we propose efficient hand detection algorithm for detecting pointing hand for recognition of place where user pointed. To minimize false detections, ROIs are generated within the compact search region using skin color detection result. The ROIs are verified by HOG-SVM and pointing direction is computed by both detection results of head-shoulder and hand. In experiment, it is shown that proposed method shows good performance for hand detection.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.665-674
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• 2003

In this paper, we propose an approach to correlate alerts using a clustering analysis of data mining techniques in order to support intrusion detection system. Intrusion detection techniques are still far from perfect. Current intrusion detection systems cannot fully detect novel attacks. However, intrucsion detection techniques are still far from perfect. Current intrusion detection systems cannot fully detect novel attacks or variations of known attacks without generating a large amount of false alerts. In addition, all the current intrusion detection systems focus on low-level attacks or anomalies. Consequently, the intrusion detection systems to underatand the intrusion behind the alerts and take appropriate actions. The clustering analysis groups data objects into clusters such that objects belonging to the same cluster are similar, while those belonging to different ones are dissimilar. As using clustering technique, we can analyze alert data efficiently and extract high-level knowledgy about attacks. Namely, it is possible to classify new type of alert as well as existed. And it helps to understand logical steps and strategies behind series of attacks using sequences of clusters, and can potentially be applied to predict attacks in progress.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.47-54
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• 2019

An improved method of spectrum detection algorithm for mass spectrum analysis system is proposed. In the conventional spectrum detection algorithm that utilizes the results of the linear approximation and quadratic curve fitting on the ion signal block of each mass index, it is possible to reduce the detection error in the mass spectrum detection by further improving the condition of eliminating the invalid ion signals. Also, the proposed method can reduce the estimation error of the peak value of the mass spectrum by using the result of quadratic curve fitting for the effective ion signal block in which the peak position error is corrected. To evaluate the effectiveness of the proposed method, computer simulations were carried out step by step using the measured ion signal. Also, by comparing the rate of false detection for several inputs, the proposed method showed better detection performance than the conventional method.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.22-31
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• 2013

In this paper, we propose a method that estimates the channel response from underwater communication signals with MMSE (Minimun Mean Squared Error) and detects dominant components automatically based on power of response components using CFAR (Constant False Alarm Rate). Statistical characteristics are analyzed with variation of magnitude and phase and time coherence via experimental data obtained by drifting transmitter and receiver. We show that bit error rate has small difference, 1.2 times, compared with the case using every channel information estimated within data period when estimation and equalization is performed with extracted characteristic obtained by the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.196-199
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• 2004

This paper presents a structure of the searcher using the space diversity in array antenna system operating in the DS/CDMA signal environments. The new technique exploits the fact that the In-phase and Quadrature components of interferers can respectively be viewed as independent Gaussian noise at each antenna element in most practical CDMA (Code Division Multiple Access) signal environments. The proposed PN acquisition scheme is a single dwell serial PN acquisition system consisting of two stage, that is, the searching stage and the verification stage. The searching stage correlates the received signals with the local PN oscilator for obtaining the synchronous energy at the entire uncertainty region. The verification stage compares the searching energy with the optimal threshold, which is pre-designed in the Lock-Detector, and decides whether the acquisition is successful or fail. In this paper, we analyzed the relationship of both diversity order and the mean acquisition time. In general, It is known that the mean acquisition time decreases significantly as the number of antenna elements increases. But, the enhancement of the performance is saturated in terms of PN acquisition scheme. Therefore, to decrease the mean acquisition time, we must design the optimal array antenna system by considering the operating SNR range of the receiver, the detection probability, and the false alarm probability. The performance of the proposed acquisition scheme is analyzed in frequency-selective Rayleigh fading channels. In this paper, the effect of the number of antenna elements on acquisition scheme is considered in terms of the detection probability, false alarm probability. and the mean acquisition time.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.80-88
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• 2006

In this paper we developed a novel system for automatic detection of mass type breast cancer on dense digital mammogram images. The new approaches presented in this paper are as follows: 1) we presented a method that stably decides the mass center and radius without being affected by image signal irregularity. 2) We developed a radial directional filter that is suitable to process mass image signal. 3) And we developed the multiple feature function based on mass shape spiculation, mass center homogeneity, and mass eccentricity, so as to determine mass-type breast cancer. When the proposed system is applied to dense mammographic images, the true 기arm rate is improved by 10% over a conventional system while the false alarm is increased by 1 per image.

• Parasites, Hosts and Diseases
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• v.21 no.2
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• pp.265-269
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• 1983

A comparison was made of a new serological method, thin layer immunoassay (TIA), and an established method, enzyme-linked immunosorbent assay (ELISA), in the detection and quantiacation of antibodies in clonorchiasis. Saline extract of Iyophilized Clonorchis sinensis adult worm was used as antigen, and TIA by the method of Elwing et at. (1976) and ELISA by Voller et at. (1974) were performed. Using sera from known clonorchiasis cases,100% of the sera tested were Positive by TIA and 88.35 by ELISA. TIA produced false positive results in 14 out of 36 cases, which were 10 amoebiasis cases, 16 paragonimiasis cases and 10 healthy controls. ELISA. however, produced a small number of false positives, 7 out of 55 cases. There was correlation between Immunoglobulin G level in sera and ELISA value (correlation coefficient, 0.69), whereas no correlation between Immunoglobulin G level and TIA result. The Performance of TIA and ELISA was not correlated in the results using homologous antigen.