• Title, Summary, Keyword: detection

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Determination of Aflatoxins Using High-Performance Liquid Chromatography and Fluorescence or UV Absorbence Detection (HPLC에 의한 aflatoxin 분석법에 관한 연구 형광 및 자외선 흡광 검출의 비교)

  • 김종규;강회양;민경진
    • Journal of Environmental Health Sciences
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    • v.22 no.1
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    • pp.36-44
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    • 1996
  • A comparison was made of two detection methods(UV absorbence detection and fluorescence detection with pre-column derivatization, with trifluoroacetic acid) coupled with HPLC for the simultaneous determination of aflatoxin $B_1, B_2, G_1$ and $G_2$. A good separation of the four aflatoxins was achieved on a reversed-phase $C_{18}$ column (30 cm x 3.9 mm) with methanol-acetonitrile-water(20+20+60) for absorbence detection or acetonitrile-water(25+75) for fluorescence detection at the flow rate of 1.0 ml/min. The calibration graphs were linear over the ranges 100 ppb-1 ppm for $B_1/G_1$ and 30~300 ppb for $B_2/G_1$ with absorbence detection, and 1~500 ppb for $B_1/G_1$ and 0.3~150 ppb for $B_2/G_2$ with fluorescence detection. The correlation coefficients were greater than 0.94 and 0.99 for absorbance detection and for fluorescence detection, respectively. The detection limit was 100 ng for $B_1/G_1$ and 30 ng for $B_2/G_2$ with absorbence detection, and 1 ng for $B_1/G_1$ and 0.3 ng for $B_2/G_2$ with fluorescence detection. Recovery rates of aflatoxin $B_1, B_2, G_1$ and $G_2$ added to yeast-extract sucrose broth medium were 66.6%, 59.4%, 67.5% and 59.2%, respectively, for absorbence detection and 82.9%, 71.5%, 80.0% and 69.3%, respectively, for fluorescence detection. The four aflatoxins in culture medium were quantitatively detected by the two methods. The aflatoxins in the rice sample were not detected the absorbence detection method, but were below 10 ppb using the fluorescence detection method. Analysis of aflatoxins by both the absorbence and fluorescence methods coupled with HPLC showed acceptable linearity and good recovery. The absorbence detection was less timeconsuming and safer for treatment. The fluorescence detection was more elective and sensitive though elevated $B_1$ and $G_1$ contents were determined from the TFA-induced conversion of $B_1$ to $B_{2a}$ and $G_1$ to $G_{2a}$.

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Improved Fusion Method of Detection Features in SAR ATR System (SAR 자동표적인식 시스템에서의 탐지특징 결합 방법 개선 방안)

  • Cha, Min-Jun;Kim, Hyung-Myung
    • Journal of the Korea Institute of Military Science and Technology
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    • v.13 no.3
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    • pp.461-469
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    • 2010
  • In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.

Driver's Face Detection Using Space-time Restrained Adaboost Method

  • Liu, Tong;Xie, Jianbin;Yan, Wei;Li, Peiqin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.9
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    • pp.2341-2350
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    • 2012
  • Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver's face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver's face can be detected rapidly and precisely.

FPGA-Based Real-Time Multi-Scale Infrared Target Detection on Sky Background

  • Kim, Hun-Ki;Jang, Kyung-Hyun
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.11
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    • pp.31-38
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    • 2016
  • In this paper, we propose multi-scale infrared target detection algorithm with varied filter size using integral image. Filter based target detection is widely used for small target detection, but it doesn't suit for large target detection depending on the filter size. When there are multi-scale targets on the sky background, detection filter with small filter size can not detect the whole shape of the large targe. In contrast, detection filter with large filter size doesn't suit for small target detection, but also it requires a large amount of processing time. The proposed algorithm integrates the filtering results of varied filter size for the detection of small and large targets. The proposed algorithm has good performance for both small and large target detection. Furthermore, the proposed algorithm requires a less processing time, since it use the integral image to make the mean images with different filter sizes for subtraction between the original image and the respective mean image. In addition, we propose the implementation of real-time embedded system using FPGA.

Change Detection using KOMPSAT EOC Images

  • Jeong Jae-joon;Kim Younsoo
    • Proceedings of the KSRS Conference
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    • pp.518-521
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    • 2004
  • Change detection is one of the common research topics in remote sensing. In general, global change detection methods using image difference method, etc, are used in low resolution images and local change detection methods using floating windows, etc, are used in high resolution images. But, these methods have disadvantages in practical use. If changed area images are automatically produced, these images will be used in public area such as regional planning, regional development managements. In this research, we developed new change detection method applicable KOMPSAT EOC images. This method automatically produces subset images in changed area.

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AUTOMATIC MOTION DETECTION USING FALSE BACKGROUND ELIMINATION

  • Seo, Jin Keun;Lee, Sukho
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.17 no.1
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    • pp.47-54
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    • 2013
  • This work deals with automatic motion detection for with surveillance tracking that aims to provide high-lighting movable objects which is discriminated from moving backgrounds such as moving trees, etc. For this aim, we perform a false background region detection together with an initial foreground detection. The false background detection detects the moving backgrounds, which become eliminated from the initial foreground detection. This false background detection is done by performing the bimodal segmentation on a deformed image, which is constructed using the information of the dominant colors in the background.

A Vision-Based Method to Find Fingertips in a Closed Hand

  • Chaudhary, Ankit;Vatwani, Kapil;Agrawal, Tushar;Raheja, J.L.
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.399-408
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    • 2012
  • Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

Design Of Intrusion Detection System Using Background Machine Learning

  • Kim, Hyung-Hoon;Cho, Jeong-Ran
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.5
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    • pp.149-156
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    • 2019
  • The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

An Aggregate Detection of Event Correlation using Fuzzy Control (퍼지제어를 이용한 관련성 통합탐지)

  • 김용민
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.13 no.3
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    • pp.135-144
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    • 2003
  • An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

Robust Speech Detection Based on Useful Bands for Continuous Digit Speech over Telephone Networks

  • Ji, Mi-Kyongi;Suh, Young-Joo;Kim, Hoi-Rin;Kim, Sang-Hun
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.3E
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    • pp.113-123
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    • 2003
  • One of the most important problems in speech recognition is to detect the presence of speech in adverse environments. In other words, the accurate detection of speech boundary is critical to the performance of speech recognition. Furthermore the speech detection problem becomes severer when recognition systems are used over the telephone network, especially wireless network and noisy environment. Therefore this paper describes various speech detection algorithms for continuous digit recognition system used over wire/wireless telephone networks and we propose a algorithm in order to improve the robustness of speech detection using useful band selection under noisy telephone networks. In this paper, we compare some speech detection algorithms with the proposed one, and present experimental results done with various SNRs. The results show that the new algorithm outperforms the other speech detection methods.