• Clinical and Experimental Pediatrics
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• v.51 no.10
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• pp.1071-1076
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• 2008

Purpose : Respiratory syncytial virus (RSV) is the most frequent cause of lower respiratory infections in infants and young children. Early detection allows quarantining of infected inpatients to prevent nosocomial transmission and to choose a treatment. To achieve rapid reporting, to facilitate prompt antiviral therapy, and to avoid unnecessary use of antibiotics, an easy, rapid diagnostic method for RSV is needed. We evaluated a lateral flow immunochromatography (RSV Respi-Strip test) and EIA (Enzyme immuno assay) compared to RT-PCR. Methods : From April 2007 to March 2008, 112 consecutive respiratory specimens (nasopharyngeal aspirates, throat swabs, tracheal aspirates, sputum) from patients who were suffering from the clinical signs and symptoms of respiratory tract infection were enrolled in Busan. A total of 112 patients were tested with RSV Respi-Strip (Corio-BioConcept, Belgium), EIA, and RT-PCR at the same time. Results : Of the 112 specimens tested, the number of children who showed positive results at RT-PCR and Respi-Strip were 45 and 42, respectively. The Respi-Strip rapid antigen test had a sensitivity of 88% and a specificity of 94%. The positive and negative predictive values were 90% and 92%, respectively. The agreement was 83%. Conclusion : In our study, the rapid antigen test had as much sensitivity as any method for detection of RSV. The test has many advantages such as easy performance, simple interpretation, and rapid results. If the rapid antigen test is widely applied in the clinical setting, the may be useful for diagnostic and epidemiological studies of RSV infection.

• Korean Journal of Clinical Laboratory Science
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• v.43 no.1
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• pp.1-5
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• 2011

The infection rate of syphilis is still increasing in the world especially in developing countries and the infection is often seen in large amounts of clinical specimens. For the diagnosis of this disease, Rapid Plasma Reagin (RPR)/Venereal Disease Research Laboratory (VDRL) has still been used as one of major primary methods to diagnose syphilis even though the test readings are somewhat subjective with high false positive rates. Recently, the automatic ARCHITECT Syphilis TP, which is based on the detection of the TP-specific antibodies, has been introduced in many laboratories. Therefore, the clinical assessment of the method is needed to provide primary diagnosis of syphilis at the moment. We evaluated 3 different manual rapid kits and ARCHITECT Syphilis TP comparing with RPR/FTA-ABS and analysed their diagnostic properties. From February 2006 to April 2008, 203 positive and 250 negative specimens, obtained from Chungbuk National University Hospital were used for the evaluation. In the evaluation between manual rapid kits, their specificities were as high as 99.2 ~ 99.6% while their sensitivities were observed with little differences; 98.0% (199/203) for Kit A, 96.6% (196/203) for Kit B, and 97.4% (197/203) for Kit S. In the case of ARCHITECT Syphilis TP test, it showed 100% specificity (250/250) and 98.5% sensitivity (249/250). Kappa values comparing with RPR/FTA-ABS were 0.978 for Kit A, 0.964 for Kit B and Kit S, and 0.987 for ARCHITECT Syphilis TP. From our evaluation, we found out that manual rapid tests and ARCHITECT Syphilis TP have very good clinical accuracies and high kappa agreements with RPR/FTA-ABS. Due to its automation and quick simultaneous diagnosis with another serological markers, we suggest that the ARCHITECT Syphilis TP is one of best suitable method for the primary diagnosis of syphilis and that it might be able to replace RPR method in the laboratories.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• Food Science and Biotechnology
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• v.16 no.3
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• pp.337-342
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• 2007

Conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Biosensors have shown great potential for the rapid detection of foodborne pathogens. Fiber-optic biosensors have been used to rapidly detect pathogens because they can be very sensitive and are simple to operate. However, many fiber-optic biosensors rely on manual sensor handling and the sandwich assay, which require more effort and are less sensitive. To increase the simplicity of operation and detection sensitivity, a binding inhibition assay method for detecting Listeria monocytogenes in food samples was developed using an automated, fiber-optic-based immunosensor: RAPTOR (Research International, Monroe, WA, USA). For the assay, fiber-optic biosensors were developed by the immobilization of Listeria antibodies on polystyrene fiber waveguides through a biotin-avidin reaction. Developed fiber-optic biosensors were incorporated into the RAPTOR to evaluate the detection of L. monocytogenes in frankfurter samples. The binding inhibition method combined with RAPTOR was sensitive enough to detect L. monocytogenes ($5.4{\times}10^7\;CFU/mL$) in a frankfurter sample.

• The Plant Pathology Journal
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• v.40 no.1
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• pp.40-47
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• 2024

Garlic can be infected by a variety of viruses, but mixed infections with leek yellow stripe virus, onion yellow dwarf virus, and allexiviruses are the most damaging, so an easy, inexpensive on-site method to simultaneously detect at least these three viruses with a certain degree of accuracy is needed to produce virus-free plants. The most common laboratory method for diagnosis is multiplex reverse transcription polymerase chain reaction (RT-PCR). However, allexiviruses are highly diverse even within the same species, making it difficult to design universal PCR primers for all garlic-growing regions in the world. To solve this problem, we developed an inexpensive on-site detection system for the three garlic viruses that uses a commercial mobile PCR device and a compact electrophoresis system with a blue light. In this system, virus-specific bands generated by electrophoresis can be identified by eye in real time because the PCR products are labeled with a fluorescent dye, FITC. Because the electrophoresis step might eventually be replaced with a lateral flow assay (LFA), we also demonstrated that a uniplex LFA can be used for virus detection; however, multiplexing and a significant cost reduction are needed before it can be used for on-site detection.

• Journal of IKEEE
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• v.23 no.2
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• pp.448-453
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• 2019

Under the conflicting objectives of maximum user satisfaction and fast launching, there exist great needs for automated mobile app testing. In automated app testing, detection of usage-state changes is one of the most important issues for minimizing human intervention and testing of various usage scenarios. Because conventional approaches utilizing pre-collected training examples can not handle the rapid evolution of apps, we propose a novel method detecting changes in usage-state through graph-entropy. In the proposed method, widgets in a screen shot are recognized through DNNs and 'onverted graphs. We compared the performance of the proposed method with a SIFT (Scale-Invariant Feature Transform) based method on 20 real-world apps. In most cases, our method achieved superior results, but we found some situations where further improvements are required.

• Food Science of Animal Resources
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• v.41 no.2
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• pp.335-342
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• 2021

Vitamin B12 deficiency may lead to serious health issues in both infants and adults. A simple analytical method involving sample pretreatment with enzyme, followed by cyanide addition under acidic conditions; separation on an immunoaffinity column; and high-performance liquid chromatography (HPLC) was developed for the rapid detection and quantitation of vitamin B12 in powdered milk. Detection limit and powdered milk recovery were determined by quantitative analysis. The limits of detection and quantitation were 2.71 and 8.21 ㎍/L, respectively. Relative standard deviations of the intra-day and inter-day precisions varied in the ranges of 0.98%-5.31% and 2.16%-3.90%, respectively. Recovery of the analysis varied in the range of 83.41%-106.57%, suggesting that the values were acceptable. Additionally, vitamin B12 content and recovery in SRM 1849a were 54.10 ㎍/kg and 112.24%, respectively. Our results suggested that the analytical method, including the sample pretreatment step, was valid. This analytical method can be implemented in many laboratory-scale experiments that seek to save time and labor. Therefore, this study shows that immunoaffinity-HPLC/ultraviolet is an acceptable technique for constructing a reliable database on vitamin B12 in powdered milk containing starch as well as protein and/or fat in high amounts.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1726-1732
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.1
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• pp.27-32
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• 2017

This paper presents a Daubechies wavelet-based fault detection method for fault identification in transmission lines. After the Daubechies wavelet coefficients are calculated, the proposed algorithm has been implemented difference equation using C language. We have modeled a 154kV transmission line using the ATPDraw software and have acquired test data. In order to evaluate effects of DC offset, simulations carried out while varying an inception angle of the voltage $0^{\circ}$, $45^{\circ}$, $90^{\circ}$. For performance evaluation, fault distance was varied. As we can see from the off-line simulation, the proposed algorithm shows rapid and accurate fault detection. Also we can see the proposed algorithm is not affected by the fault inception angle change.

• Journal of IKEEE
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• v.23 no.1
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• pp.242-248
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• 2019

Fire flame and smoke detection algorithm studies are challenging task in computer vision due to the variety of shapes, rapid spread and colors. The performance of a typical sensor based fire detection system is largely limited by environmental factors (indoor and fire locations). To solve this problem, a deep learning method is applied. Because it extracts the feature of the object using several methods, so that if a similar shape exists in the frame, it can be detected as false postive. This study proposes a new algorithm to reduce false positives by using frame similarity before using deep learning to decrease the false detection rate. Experimental results show that the fire detection performance is maintained and the false positives are reduced by applying the proposed method. It is confirmed that the proposed method has excellent false detection performance.