• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.557-563
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• 2017

When large-scale gene expression data are analyzed using lasso regression, the estimation of regression coefficients may be unstable due to the highly correlated expression values between associated genes. This irregularity, in which the coefficients are reduced by L1 regularization, causes difficulty in variable selection. To address this problem, we propose a regression model which exploits the repetitive bootstrapping of gene expression values prior to lasso regression. The genes selected with high frequency were used to build each regression model. Our experimental results show that several genes were consistently selected in all regression models and we verified that these genes were not false positives. We also identified that the sign distribution of the regression coefficients of the selected genes from each model was correlated to the real dependent variables.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.259-271
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• 2008

We propose a protein function finding algorithm that is able to predict specific molecular function for unannotated proteins through domain analysis from protein-protein network. To do this, we first construct protein-protein interaction(PPI) network in Saccharomyces cerevisiae from MIPS databases. The PPI network(proteins; 3,637, interactions; 10,391) shows the characteristics of a scale-free network and a hierarchical network that proteins with a number of interactions occur in small and the inherent modularity of protein clusters. Protein-protein interaction databases obtained from a Y2H(Yeast Two Hybrid) screen or a composite data set include random false positives. To filter the database, we reconstruct the PPI networks based on the cellular localization. And then we analyze Hub proteins and the network structure in the reconstructed network and define structural modules from the network. We analyze protein domains from the structural modules and derive functional modules from them. From the derived functional modules with high certainty, we find tentative functions for unannotated proteins.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.89-95
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• 2020

Recently, studies are being conducted to recognize a sketch image of a GUI (Graphical User Interface) based on a deep learning and to make it into a code implemented in an application. UI / UX designers can communicate with developers through storyboards when developing mobile applications. However, UI / UX designers can create different widgets for ambiguous widgets. In this paper, we propose an automatic UI detection method using symbol markers to improve the accuracy of DNN (Deep Neural Network) based UI identification. In order to evaluate the performance with or without the symbol markers, their accuracy is compared. In order to improve the accuracy according to of the symbol marker, the results are analyzed when the shape is a circle or a parenthesis. The use of symbol markers will reduce feedback between developer and designer, time and cost, and reduce sketch image UI false positives and improve accuracy.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.119-128
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• 2014

In this paper, a fast human detection method using adaptive UV-histogram and template matching is proposed. The proposed method improves the detection rate in the scene of complex environment. The method firstly generates U-histogram to extract human candidates and adaptively generates V-histogram for each labled U-histogram, thus it could extract humans correctly, which was impossible in the previous method. The method tries to match the human candidates with the adaptively sized omega shape template to the focal length and distance in order to improve the detection accuracy. It also detects false positives by rematching the template with accumulated foreground images and hence is robust to the occlusion. Experimental results showed that the proposed method has superior performance to the Bae's method in the complex environment with about 15% improvement in precision and 80% in recall and has 20 times faster processing time than Xia's method.

• Journal of Chest Surgery
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• v.50 no.2
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• pp.126-129
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• 2017

The identification of circulating tumor cells (CTCs) is clinically important for diagnosing cancer. We have previously developed a size-based filtration platform followed by epithelial cell adhesion molecule immunofluorescence staining for detecting CTCs. To characterize CTCs independently of cell surface protein expression, we incorporated a chromosomal fluorescence in situ hybridization (FISH) assay to detect abnormal copy numbers of chromosomes in cells collected from peripheral blood samples by the size-based filtration platform. Aneuploid cells were detected in the peripheral blood of patients with lung cancer. Unexpectedly, aneuploid cells were also detected in the control group, which consisted of peripheral blood samples from patients with benign lung diseases, such as empyema necessitatis and non-tuberculous mycobacterial lung disease. These findings suggest that chromosomal abnormalities are observed not only in tumor cells, but also in benign infectious diseases. Thus, our findings present new considerations and bring into light the possibility of false positives when using FISH for cancer diagnosis.

• Journal of Food Hygiene and Safety
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• v.26 no.4
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• pp.302-307
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• 2011

One of the most important objectives of post-marketing monitoring of dietary supplements is the early detection of unknown and unexpected adverse events (AEs). Several causality algorithms, such as the Naranjo scale, the RUCAM scale, and the M & V scale are available for the estimation of the likelihood of causation between a product and an AE. Based on the existing algorithms, the Korea Food & Drug Administration has developed a new algorithm tool to reflect the characteristics of dietary supplements in the causality analysis. However, additional work will be required to confirm if the newly developed algorithm tool has reasonable sensitivity and not to generate an unacceptable number of false positives signals.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.682-685
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• 2005

Large White, an introduced European pig breed, and Meishan, a Chinese indigenous pig breed, were hybridized directly and reciprocally and a total of 260 pigs, including purebreds, Large White and Meishan, and their hybrids, White${\times}$Meishan (LM) and Meishan${\times}$Large White (ML) pigs, were bred in our laboratory. The mRNA differential display PCR (DD-PCR) was used to detect the age-dependent changes of differential gene expression in backfat tissue between hybrids and parents. Some measures were taken to reduce the false positives in our experiment. Among the total of 2,686 bands obtained, 1,952 bands (about 72.67%) were reproducible and eight patterns (fifteen kinds) of gene expression were observed. The percentage of differentially expressed genes between hybrids and parents is 56.86% at the age of four months and 57.71% at the age of six months. This indicated that the differences of gene expression between hybrids and their parents were very obvious. U-test was used to compare the patterns of gene expression between the age of four and six months, and results showed that bands occurring in only one hybrid and bands displayed in one hybrid and one parent were significantly different at p<0.05, and bands visualized in only two hybrids were significantly different at p<0.01. These indicated that differential gene expression between hybrids and parents changed at different ages.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2171-2174
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• 2012

Background: Pancreatic cancer is a distressing disease with a miserable prospects and early recognition remains a challenge due to ubiquitous symptomatic presentation, deep anatomical location, and aggressive etiology. False positives and problems in distinguishing pancreatitis from adenocarcinoma limit the use of CA 19-9 as both disorders can present with similar symptoms and share radiographic physiognomies. This study aimed to assess the relative increase in accuracy of diagnosing the patients with chronic pancreatitis, benign neoplasm of pancreas and adenocarcinomas with CA 19-9, haptoglobin, and serum amyloid A in comparison to CA 19-9 alone. Materials and Methods: This hospital based case control study was carried out in the Departments of Medicine and Biochemistry of Manipal Teaching Hospital, Pokhara, Nepal, between $1^{st}$ January 2010 and $31^{st}$ December 2011. The variables assessed were age, gender, serum CA19-9, serum haptoglobulin, serum Amyloid A. The data were analyzed using Excel 2003, R 2.8.0 Statistical Package for the Social Sciences (SPSS) for Windows Version 16.0 (SPSS Inc; Chicago, IL, USA) and the EPI Info 3.5.1 Windows Version. Results: Out of 197 cases of pancreatic disease, maximum number of assumed cases were of adenocarcinoma of pancreas (95). Number of males (59) were more than females (36) in assumed cases of adenocarcinoma of pancreas. The mean values of CA19-9 raised considerably in cases of chronic pancreatitis, benign neoplasm and adenocarcinoma of pancreas when compared to controls. The highest augmention in CA19-9 values were in cases of adenocarcinoma of pancreas. The p-value indicates that in cases of chronic pancreatitis, there was not significant increase in precision of diagnosis. Conclusions: These statistics established that haptoglobin and SAA are useful in discriminating cancer from benign conditions as well as healthy controls.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.452-461
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• 2008

Binary diffing is a method to find differences in similar binary executables such as two different versions of security patches. Previous diffing methods using flow information can detect control flow changes, but they cannot track constant value changes. Biffing methods using assembly instructions can detect constant value changes, but they give false positives which are due to compiling methods such as instruction reordering. We present a binary diffing method and its implementation named SCV which utilizes both structure and value information. SCV summarizes structure and constant value information from disassembled code, and matches the summaries to find differences. By analyzing a Microsoft Windows security patches, we showed that SCV found necessary differences caused by constant value changes which the state-of-the-art binary diffing tool BinDiff failed to find.