• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.1
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• pp.54-65
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• 2000

We propose a parallel acquisition scheme using antenna arrays for initial acquisition of direct-sequence spread-spectrum (DS-SS) signals. The proposed parallel scheme can lower substantially the range of detectable signal-to-noise ratio (SNR) as compared to the conventional parallel scheme with a single antenna. The proposed scheme uses the sum of the independent decision samples from antenna arrays corresponding to an identical subsequence of the pseudonoise (PN) code as a decision variable. We derive the probabilities of detection, missing, and false alarm under an additive white Gaussian noise (AWGN) channel and a flat Rayleigh fading channel. Using these, we get the mean acquisition time of the proposed scheme. From numerical results, we see that the acquisition performance becomes improved continually as the number of antennas increases.

• Aerospace Engineering and Technology
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• v.3 no.2
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• pp.229-237
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• 2004

In this paper, the design method and algorithms of the signal processor for a multipurpose radar system are analyzed. The signal processor, operating at the two modes-collision avoidance mode and weather mode, has 4 steps of ADC, NCI, STC, CFAR. Several algorithms of NCI and CFAR are analyzed and the optimal design is proposed to the system. CVI and CMLD algorithm have good performance in decreasing the false alarm rate and increasing detection probability, Regarding processor computational capacity, K=12 for CVI, M=16~20, Ko=M-4 for CMLD is suggested. CVI processing needs much time, two or more processors need to be allocated to CVI. So, for the system with four processors, two processors should be allocated to VID of NCI with ADC input and CFAR with STC, and two processors are should be allocated to CVI.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1447-1452
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• 2012

Aim: To evaluate the differential diagnostic potential of lesion stiffness assessed by the sonoelastographic strain index ratio (SR) and elastographic color scoring system (UE) for breast lesions. Materials and Methods: Three hundred and forty two breast masses (158 benign and 184 malignant) from 325 consecutive patients (mean age 44.2 years; range 16-81)who had been scheduled for a sonographically guided core biopsy were examined proposed by Itoh et al, with scoring 1-3=benign and 4-5=malignant. Strain and area ratios of each lesion were calculated within the same machine. Histological diagnosis was used as the reference standard. The area under the curve (AUC) and cut-off point were obtained by receiver operating curve and the cross table Fischer Test was carried out for assessing diagnostic value. Sensitivity, specificity, PPV, NPV, accuracy and false-discovery rates were compared. Results: The mean strain ratios for benign and malignant lesions were 1.87 and 7.9 respectively. (P<0.0001). When a cutoff point of 3.54 was used, SR had a sensitivity of 94.6%, a specificity 94.3%, a PPV of 95.1%, an NPV of 93.7% and an accuracy of 94.4%. The AUC values were 0.90 for the 5 point scoring system (UE) and 0.96 for the strain index ratio. The overall diagnostic performance was SR method was better (P<0.05). Conclusions: Strain ratio measurement could be another effective predictor in elastography imaging besides 5 the point scoring system for differential diagnosis of breast lesions.

• 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 Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3229-3232
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• 2015

Background: Triple-negative breast cancer (TNBC) is known to be associated with aggressive biologic features and a poor clinical outcome. Therefore, early detection of TNBC without missed diagnosis is a requirement to improve prognosis. Preoperative ultrasound features of TNBC may potentially assist in early diagnosis as characteristics of disease. Purpose: To retrospectively evaluate the sonographic features of TNBC compared to ER (+) cancers which include HER(-) and HER2 (+), and HER2 (+) cancers which are ER (-). Materials and Methods: From June 2012 through June 2014, sonographic features of 321 surgically confirmed ER (+) cancers (n=214), HER2 (+) cancers (n=66), and TNBC (n=41) were retrospectively reviewed by two ultrasound specialists in consensus. The preoperative ultrasound and clinicopathological features were compared between the three subtypes. In addition, all cases were analyzed using morphologic criteria of the ACR BI-RADS lexicon. Results: Ultrasonographically, TNBC presented as microlobulated nodules without microcalcification (p=0.034). A lower incidence of ductal carcinoma in situ (p<0.001), invasive tumor size that is>2 cm (p=0.011) and BI-RADS category 4 (p<0.001) were significantly associated with TNBC. With regard to morphologic features of 41 TNBC cases, ultrasonographically were most likely to be masses with irregular (70.7%) microlobulated shape (48.8%), be circumscribed (17.1%) or have indistinct margins (17.1%) and parallel orientation (68.9%). Especially TNBC microlobulated mass margins were more more frequent than with ER (+) (2.0%) and HER2 (+) (4.8%) cancers. Conclusions: TNBC have specific characteristic in sonograms. Ultrasonography may be useful to avoid missed diagnosis and false-negative cases of TNBC.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.523-530
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• 2009

Despite the growing need for customized operating system kernels for embedded devices, kernel development continues to suffer from insufficient reliability and high testing cost for several reasons such as the high complexity of the kernel code. To alleviate these difficulties, this study proposes the MOdel-based KERnel Testing (MOKERT) framework for detection of concurrency bugs in the kernel. MOKERT translates a given C program into a corresponding Promela model, and then tries to find a counter example with regard to a given requirement property, If found, MOKERT executes that counter example on the real kernel code to check whether the counter example is a false alarm or not, The MOKERT framework was applied to the Linux proc file system and confirmed that the bug reported in a ChangeLog actually caused a data race problem, In addition, a new data race bug in the Linux proc file system was found, which causes kernel panic.

• Biomedical Science Letters
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• v.17 no.2
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• pp.151-155
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• 2011

Short tandem repeats (STRs) loci are the genetic markers used for forensic human identity test. With multiplex polymerase chain reaction (PCR) assays, STRs are examined and measured PCR product length relative to sequenced allelic ladders. In the repeat region and the flanking region of the commonly-used STR may have DNA sequence variation. A mismatch due to sequence variation in the DNA template may cause allele drop-out (i.e., a "null" or "silent" allele) when it falls within PCR primer binding sites. The STR markers were co-amplified in a single reaction by using commercial PowerPlex$^{(R)}$ 16 system and AmpFlSTR$^{(R)}$ Identifiler$^{(R)}$ PCR amplification kits. Separation of the PCR products and fluorescence detection were performed by ABI PRISM$^{(R)}$ 3100 Genetic Analyzer with capillary electrophoresis. The GeneMapper$^{TM}$ ID software were used for size calling and analysis of STR profiles. Here, this study described a forensic human identity test in which allelic drop-out occurred in the STR system D18S51. During the course of human identity test, two samples with a homozygous (16, 16 and 21, 21) genotype at D18S51 locus were discovered using the PowerPlex$^{(R)}$ 16 system. The loss of alleles was confirmed when the samples were amplified using AmpFlSTR$^{(R)}$ Identifiler$^{(R)}$ PCR amplification kit and resulted in a heterozygous (16, 20 and 20, 21) genotype at this locus each other. This discrepancy results suggest that appropriate measures should be taken for database comparisons and that allele should be further investigated by sequence analysis and be reported to the forensic community.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• The Journal of Korean Society of Virology
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• v.30 no.2
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• pp.151-159
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• 2000

Hepatitis C virus (HCV) is one of the important human pathogen that can cause acute and chronic hepatitis, liver cirrhosis and hepatocellular carcinoma. Recently, the third generation radiation immuno assay (RIA) method has been developed as a very sensitive test to detect anti-HCV antibody. However, false positive is the problem with RIA test. To solve this the RIA results were compared to those of 5-antigen recombinant immunoblot assay (5-RIBA) and reverse transcription-polymerase chain reaction (RT-PCR). Among 12,767 serum samples tested from clinic visitors, total 275 (2.2%) samples were antibody positive by RIA. RIBA was performed with 148 RIA positives cases but among them was shown eighty five was antibody positive and sixty three (42.6%) was negative result. However, nested RT-PCR test was shown also carried out with 43 positive, 6 intermediates and 25 negatives of RIBA. As a result of the nested RT-PCR results, HCV antigen were detected in RIBA positive, 33.3% (2/6) RIBA intermediate and 12% (3/25). Clinical syndrome of all 148 patients as a with chronic active hepatitis (46.0%), cirrhosis (18.9%), hepatocellular carcinoma (8.1%) and others (27.0%) and they were positive in reaction by RIA test. But RIBA positive patients with 34.9% of chronic active hepatitis, 18.6% of cirrhosis, 4.6% of hepatocellular carcinoma and 41.9% of others were detected to be positive case by nested RT-PCR.