• Title/Summary/Keyword: Flow Detection

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Amperornetric Determination of Ascorbic Acia at a Thin Layer Flow Cell

  • Hahn, Young-Hee
    • Archives of Pharmacal Research
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    • v.11 no.1
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    • pp.56-60
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    • 1988
  • A thin layer flow cell with cell volume of $8\;{\mu}{\ell}$ was constructed. Diffusion currents of ascorbic acid was directly proportional to the 1/3 power of volume flow rates. A linear dynamic range was obtained at the concentration range between $10^{-7}\;M\;and\;10^{-4}\;M$ of ascorbic acid with a detection limit of $10^{-8}\;M$. Ascorbic acid in the multivitamin product was amperometrically determined at TLFC after simply dissolving mg range ground product in $100m{\ell}$ of pH 7.0 phosphate buffer.

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Development of a Vision-based Lane Change Assistance System for Safe Driving (안전주행을 위한 비전 기반의 차선변경보조시스템 개발)

  • Sung, Jun-Yong;Han, Min-Hong;Ro, Kwang-Hyun
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.5 s.43
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    • pp.329-336
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    • 2006
  • This paper describes a lane change assistance system for the help of safe lane change, which detects vehicles approaching from the rear side by using a computer vision algorithm and notifies the possibility of safe lane change to a driver. In case a driver tries to lane change, the proposed system can detect vehicles and keep track of them. After detecting side lane lines, region of interest for vehicle detection is decided. For detection a vehicle, optical flow technique is applied. The experimental result of the proposed algorithm and system showed that the vehicle detection rate was 91% and the embedded system would have application to a lane change assistance system being commercialized in the near future.

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An Experimental Study on Fault Detection and Diagnosis Method for a Water Chiller Using Bayes Classifier (베이즈 분류기를 이용한 수냉식 냉동기의 고장 진단 방법에 관한 실험적 연구)

  • Lee, Heung-Ju;Chang, Young-Soo;Kang, Byung-Ha
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.20 no.7
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    • pp.508-516
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    • 2008
  • Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. Failure modes in this study include refrigerant leakage, decrease in mass flow rate of the chilled water and cooling water, and sensor error of the cooling water inlet temperature. It is possible to detect and diagnose faults in this study by adopting FDD algorithm using only four parameters(compressor outlet temperature, chilled water inlet temperature, cooling water outlet temperature and compressor power consumption). Refrigerant leakage failure is detected at 20% of refrigerant leakage. When mass flow rate of the chilled and cooling water decrease more than 8% or 12%, FDD algorithm can detect the faults. The deviation of temperature sensor over $0.6^{\circ}C$ can be detected as fault.

Vision Based Traffic Data Collection in Intelligent Transportation Systems

  • Mei Yu;Kim, Yong-Deak
    • Proceedings of the IEEK Conference
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    • 2000.07b
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    • pp.773-776
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    • 2000
  • Traffic monitoring plays an important role in intelligent transportation systems. It can be used to collect real-time traffic data concerning traffic flow. Passive shadows resulted from roadside buildings or trees and active shadows caused by moving vehicles, are one of the factors that arise errors in vision based vehicle detection. In this paper, a land mark based method is proposed for vehicle detection and shadow rejection, and finally vehicle count are achieved based on the land mark detection method.

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Image Analysis of a Lateral Flow Strip Sensor for the Detection of Escherichia coli O157:H7

  • Kim, Giyoung;Moon, Ji-Hea;Park, Saet Byeol;Jang, Youn-Jung;Lim, Jongguk;Mo, Changyeun
    • Journal of Biosystems Engineering
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    • v.38 no.4
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    • pp.335-340
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    • 2013
  • Purpose: This study was performed to develop a lateral flow strip sensor for the detection of pathogenic Escherichia coli O157:H7 in various samples. Also, feasibility of using an image analysis method to improve the interpretation of the strip sensor was evaluated. Methods: The lateral flow strip sensor has been fabricated based on nitrocellulose lateral-flow membrane. Colloidal gold and E. coli O157:H7 antibodies were used as a tag and a receptor, respectively. Manually spotted E. coli O157:H7 antibody and anti-mouse antibody on nitrocellulose membrane were used as test and control dots, respectively. Feasibility of the lateral flow strip sensor to detect E. coli O157:H7 were evaluated with serially diluted E. coli O157:H7 cells in PBS or food samples. Test results of the lateral flow strip sensor were measured with an image analysis method. Results: The intensity of the test dot started to increase with higher concentration of the cells were introduced. The sensitivities of the sensor were both $10^4$ CFU/mL Escherichia coli O157:H7 spiked in PBS and in chicken meat extract, respectively. Conclusions: The lateral flow strip sensor and image analysis method could detect E. coli O157:H7 in 20 min, which is significantly quicker than conventional plate counting method.

Method of Tunnel Incidents Detection Using Background Image (배경영상을 이용한 터널 유고 검지 방법)

  • Jeong, Sung-Hwan;Ju, Young-Ho;Lee, Jong-Tae;Lee, Joon-Whoan
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.13 no.12
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    • pp.6089-6097
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    • 2012
  • This study suggested a method of detecting an incident inside tunnel by using camera that is installed within the tunnel. As for the proposed incident detection method, a static object, travel except vehicles, smoke, and contra-flow were detected by extracting the moving object through using the real-time background image differencing after receiving image from the camera, which is installed inside the tunnel. To detect the moving object within the tunnel, the positive background image was created by using the moving information of the object. The incident detection method was developed, which is strong in a change of lighting that occurs within the tunnel, and in influence of the external lighting that occurs in the entrance and exit of the tunnel. To examine the efficiency of the suggested method, the experimental images were acquired from Marae tunnel and Expo tunnel in Yeosu of Jeonnam and from Unam tunnel in Imsil of Jeonbuk. Number of images, which were used in experiment, included 20 cases for static object, 20 cases for travel except vehicles, 4 cases for smoke, and 10 cases for contra-flow. As for the detection rate, all of the static object, the travel except vehicles, and the contra-flow were detected in the experimental image. In case of smoke, 3 cases were detected. Thus, excellent performance could be confirmed. The proposed method is now under operation in Marae tunnel and Expo tunnel in Yeosu of Jeonnam and in Unam tunnel in Imsil of Jeonbuk. To examine accurate efficiency, the evaluation of performance is considered to be likely to be needed after acquiring the incident videos, which actually occur within tunnel.

Development of a lateral flow dipstick test for the detection of 4 strains of Salmonella spp. in animal products and animal production environmental samples based on loop-mediated isothermal amplification

  • Wirawan Nuchchanart;Prapasiri Pikoolkhao;Chalermkiat Saengthongpinit
    • Animal Bioscience
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    • v.36 no.4
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    • pp.654-670
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    • 2023
  • Objective: This study aimed to develop loop-mediated isothermal amplification (LAMP) combined with lateral flow dipstick (LFD) and compare it with LAMP-AGE, polymerase chain reaction (PCR), and standard Salmonella culture as reference methods for detecting Salmonella contamination in animal products and animal production environmental samples. Methods: The SalInvA01 primer, derived from the InvA gene and designed as a new probe for LFD detection, was used in developing this study. Adjusting for optimal conditions by temperature, time, and reagent concentration includes evaluating the specificity and limit of detection. The sampling of 120 animal product samples and 350 animal production environmental samples was determined by LAMP-LFD, comparing LAMP-AGE, PCR, and the culture method. Results: Salmonella was amplified using optimal conditions for the LAMP reaction and a DNA probe for LFD at 63℃ for 60 minutes. The specificity test revealed no cross-reactivity with other microorganisms. The limit of detection of LAMP-LFD in pure culture was 3×102 CFU/mL (6 CFU/reaction) and 9.01 pg/μL in genomic DNA. The limit of detection of the LAMP-LFD using artificially inoculated in minced chicken samples with 5 hours of pre-enrichment was 3.4×104 CFU/mL (680 CFU/reaction). For 120 animal product samples, Salmonella was detected by the culture method, LAMP-LFD, LAMP-AGE, and PCR in 10/120 (8.3%). In three hundred fifty animal production environmental samples, Salmonella was detected in 91/350 (26%) by the culture method, equivalent to the detection rates of LAMP-LFD and LAMP-AGE, while PCR achieved 86/350 (24.6%). When comparing sensitivity, specificity, positive predictive value, and accuracy, LAMP-LFD showed the best results at 100%, 95.7%, 86.3%, and 96.6%, respectively. For Kappa index of LAMP-LFD, indicated nearly perfect agreement with culture method. Conclusion: The LAMP-LFD Salmonella detection, which used InvA gene, was highly specific, sensitive, and convenient for identifying Salmonella. Furthermore, this method could be used for Salmonella monitoring and primary screening in animal products and animal production environmental samples.

Feature Selection Algorithms in Intrusion Detection System: A Survey

  • MAZA, Sofiane;TOUAHRIA, Mohamed
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.5079-5099
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    • 2018
  • Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

Edge Detection Using a Water Flow Model (Water Flow Model을 이용한 에지 검출)

  • Lee, Geon-Il;Kim, In-Gwon;Jeong, Dong-Uk;Song, Jeong-Hui;Gwak, Won-Gi;Park, Rae-Hong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.38 no.4
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    • pp.422-433
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    • 2001
  • In this paper, we propose a flew edge detection method based on water flow model, in which gradient image surface is considered as a 3-dimensional (3-D) geographical feature. The edges of the objects in the background can be detected by the large gradient magnitude areas and to make the edges immersed it is required to invert the gradient image. The proposed edge detector uses a water flow model based enhancement and locally adaptive thresholding technique applied to the inverted gradient image resulting in better noise performance. Computer simulations with a few synthetic and real images show that the Proposed method can extract edge contour effectively.

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Flow based Sequential Grouping System for Malicious Traffic Detection

  • Park, Jee-Tae;Baek, Ui-Jun;Lee, Min-Seong;Goo, Young-Hoon;Lee, Sung-Ho;Kim, Myung-Sup
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.10
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    • pp.3771-3792
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    • 2021
  • With the rapid development of science and technology, several high-performance networks have emerged with various new applications. Consequently, financially or socially motivated attacks on specific networks have also steadily become more complicated and sophisticated. To reduce the damage caused by such attacks, administration of network traffic flow in real-time and precise analysis of past attack traffic have become imperative. Although various traffic analysis methods have been studied recently, they continue to suffer from performance limitations and are generally too complicated to apply in existing systems. To address this problem, we propose a method to calculate the correlation between the malicious and normal flows and classify attack traffics based on the corresponding correlation values. In order to evaluate the performance of the proposed method, we conducted several experiments using examples of real malicious traffic and normal traffic. The evaluation was performed with respect to three metrics: recall, precision, and f-measure. The experimental results verified high performance of the proposed method with respect to first two metrics.