• Pediatric Infection and Vaccine
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• v.7 no.1
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• pp.113-119
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• 2000

Purpose : Rotavirus is a most common etiologic agent of pediatric gastroenteritis. The standard method to diagnose rotavirus infection was the detection of viral particles in specimens through electron microscopy. But it was complex. Enzyme immunoassay and latex agglutinin are preferred because they are relatively handy, inexpensive and take a short time, in comparison with electron microscopy. However, several reports have shown that the use of ELISA to diagnose rotavirus infection in neonates can result in false positive reactions. The main purpose of this study is to compare ELISA and RT-PCR in the diagnosis of neonatal rotavirus infection. Methods : Data presented in this study were obtained form 123 newborn babies in the nursery of the Fatima Hospital, Masan, Korea, form Jury to December, 1997. We obtained two samples of stool from each of the newborn babies and then performed the Rotazyme test and the RT-PCR. In the Rotazyme test, the results were interpreted according to visual findings. The samples were used for the RT-PCR test after at stock $-30^{\circ}C$ to identify rotavirus group A. The result of the two tests were compared. Results : The informations are divided into 73 males and females. Out of the total informations 15 were transferred from other hospitals. Their average gestational age was $38.5{\pm}1.6$ weeks. The average birth weight was $3134.8{\pm}539gm$. In the Rotazyme test, 75 samples turned out to be positive. Out of them, 55 samples(75.3%) were positive and 18 samples(24.7%) were negative in the RT-PCR. On the other hand, in the Rotazyme test, 50 samples turned out be negative. Out of them, 27 samples(54%) were positive and 23 samples(46%) were negative in the RT-PCR. Conclusion : Rotavirus infection in uncommon in neonates. The diagnosis based on visual findings using Rotazyme test has a disadvantage in the sense that it can result in false positive reactions and false negative reactions in the diagnosis of neonatal rotavirus infection.

• The Korean Journal of Nuclear Medicine
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• v.34 no.2
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• pp.119-128
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• 2000

Purpose: The aim of this study was to investigate the diagnostic role of $^{99m}Tc$-Tetrofosmin in detection of breast cancer and compared with that of $^{99m}Tc$-MIBI. Material and Methods: Forty-eight patients with a clinically palpable mass or abnormal mammographic or ultrasonographic findings had $^{99m}Tc-MIBI\;and\;^{99m}Tc$-Tetrofosmin scintimammographies after intravenous injection of 925 MBq of radiopharmaceuticals. The scintimammographs were correlated with histopathologic findings. Results: Thirty-three patients were diagnosed with breast cancer and 15 patients with benign breast diseases. The numbers of true positive, true negative, false positive, and false negative cases of $^{99m}Tc$-MIBI scintimammography were 29, 10, 5, and 4 respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of $^{99m}Tc$-MIBI scintimammographies were 87.8%, 66.7%, 85.3%, and 71.4% respectively. The numbers of true positive, true negative, false positive, and false negative cases of $^{99m}Tc$-Tetrofosmin were 31,10, 5, and 2 respectively. The sensitivity, specificity, positive predictive value, negative predictive value of $^{99m}Tc$-Tetrofosmin were 93.9%, 66.7%, 86.1%, and 73.3% respectively. One patient was false negative in both $^{99m}Tc-MIBI\;and\;^{99m}Tc$-Tetrofosmin acintimammographies and its size was 0.5 cm. Conclusion: $^{99m}Tc-Tetrofosmin\;and\;^{99m}Tc-MIBI$ were non-invasive and useful in detection of breast cancer and $^{99m}Tc$-Tetrofosmin was comparable to the $^{99m}Tc$-MIBI in detection of primary breast cancer.

• Journal of Trauma and Injury
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• v.25 no.3
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• pp.67-71
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• 2012

Purpose: Focused Assessment with Sonography for Trauma (FAST) provides an important initial screening examination in adult trauma patients. However, due to its low sensitivity, FAST is not a replacement for computed tomography (CT) in hemodynamically stable trauma patients. The aim of this study was to determine the test characteristics of FAST in adult, hemodynamically stable, blunt abdominal trauma patients by using a critical action as a reference standard. Methods: The medical records for FAST examination at a single hospital from January 2009 to February 2011 were retrospectively reviewed. The inclusion criterion was isolated, hemodynamically stable, blunt abdominal trauma. Hemodynamically unstable patients or patients with penetrating injuries were excluded. The reference standard was the presence of a critical action, which was defined as one of the following: 1) operative intervention for a finding discovered on CT, 2) interventional radiology for bleeding, 3) transfusion of 2 or more packed RBCs, or 4) death at the emergency department. Results: There were 230 patients who met the inclusion criterion. There were 20 true positive, 206 true negative, 0 false positive, and 4 false negative results. The sensitivity and the specificity were 83% and 100%, respectively. Conclusion: Despite its low sensitivity for detecting any abnormal finding discovered on CT, negative FAST could aid to exclude critical action in hemodynamically stable, blunt abdominal trauma patients.

• Journal of KIISE:Information Networking
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• v.32 no.6
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• pp.716-722
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• 2005

In the m, syntactically different URLs could represent the same resource. The URL normalization is a process that transform a URL, syntactically different and represent the same resource, into canonical form. There are on-going efforts to define standard URL normalization. The standard URL normalization designed to minimize false negative while strictly avoiding false positive. This paper considers the four URL normalization issues beyond ones specified in the standard URL normalization. The idea behind our work is that in the URL normalization we want to minimize false negatives further while allowing false positives in a limited level. Two metrics are defined to analyze the effect of each step in the URL normalization. Over 170 million URLs that were collected in the real web pages, we did an experiment, and interesting statistical results are reported in this paper.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.357-365
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• 2005

We present a B-spline fitting method based on genetic algorithm for the extraction of object contours from the complex image sequence, where objects with similar shape and intensity are adjacent each other. The proposed algorithm solves common malfitting problem of the existing B-spline fitting methods including snakes. Classical snake algorithms have not been successful in such an image sequence due to the difficulty in initialization and existence of multiple extrema. We propose a B-spline fitting method using a genetic algorithm with a new initial population generation and fitting function, that are designed to take advantage of the contour of the previous slice. The test results show that the proposed method extracts contour of individual object successfully from the complex image sequence. We validate the algorithm by false-positive/negative errors and relative amounts of agreements.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.57-66
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• 2011

In this paper, it is proposed for algorithm to detect human efficiently using a edge symmetry and gradient directional characteristics in realtime by the feature extraction in a single input image. Proposed algorithm is composed of three stages, preprocessing, region partition of human candidates, verification of candidate regions. Here, preprocessing stage is strong the image regardless of the intensity and brightness of surrounding environment, also detects a contour with characteristics of human as considering the shape features size and the condition of human for characteristic of human. And stage for region partition of human candidates has separated the region with edge symmetry for human and size in the detected contour, also divided 1st candidates region with applying the adaboost algorithm. Finally, the candidate region verification stage makes excellent the performance for the false detection by verifying the candidate region using feature vector of a gradient for divided local area and classifier. The results of the simulations, which is applying the proposed algorithm, the processing speed of the proposed algorithms is improved approximately 1.7 times, also, the FNR(False Negative Rate) is confirmed to be better 3% than the conventional algorithm which is a single structure algorithm.

• Journal of Chest Surgery
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• v.19 no.3
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• pp.374-380
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• 1986

From June 1983 to September 1984, the pleural biopsies with Cope needle were performed at department of thoracic and cardiovascular surgery, Pusan Paik Hospital, Inje college, on 78 patients for exudative pleural effusion caused by various conditions. These results were analyzed clinically and summarized as follows: 2. The accuracy of pleural biopsy was 69.2% [54 of 78 patients]. The accuracy represented by ratio for the number of biopsy was 63.5% [54 of 85 biopsies]. 3. Among 61 patients of tuberculosis or malignancy, 37 [60.7%] were confirmed by pleural biopsy. 4. Tuberculosis was diagnosed in 48 patients, in 26[54.2%] out of these by pleural biopsy alone, in 3[6.2%] by pleural biopsy and isolation of AFB, in 2[4.2%] by pleural biopsy and operation, in 4[8.3%] by isolation of AFB, in 2[4.2%] by operation, and in 11[22.9%] clinically. 5. Among 13 patients of malignancy, 4[30.8%] were diagnosed by cytology alone, 4[30.8%] by pleural biopsy and cytology alone, 4[30.8%] by pleural biopsy and cytology, 1[7.7%] by pleural biopsy alone, 1[7.7%] by pleural biopsy and operation, and remained 3 by operation, lymph node biopsy, or bronchoscopy respectively. 6. False positive of clinical diagnosis was 12.5% for tuberculosis and 28.6% for malignancy. In pathological diagnosis there was no false positive. So specificity of pleural biopsy was very high. But false negative of pleural biopsy was 29.2% for tuberculosis and 46.2% for malignancy. 7. 4 cases[5.1%] of minimal pneumothorax were in the early series.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.861-862
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• 2008

This paper presents the results of iris region localization for imprecisely taken iris images in UBIRIS ver2. UBIRIS database consisting of 500 images of 24-bit RGB typed TIFF file. They were captured by conventional digital camera in visible light environment. Experimental result showed that FP(False Positive rate) and FN(False Negative rate) were about 2.2% and 29.1%, respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.243-249
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• 2020

Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.