• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.89-101
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• 2023

In order to prevent damages caused by cyber-attacks on nations, businesses, and other entities, anomaly detection techniques for early detection of attackers have been consistently researched. Real-time reduction and false positive reduction are essential to promptly prevent external or internal intrusion attacks. In this study, we hypothesized that the type and frequency of attack events would influence the improvement of anomaly detection true positive rates and reduction of false positive rates. To validate this hypothesis, we utilized the 2015 login log dataset from the Los Alamos National Laboratory. Applying the preprocessed data to representative anomaly detection algorithms, we confirmed that using characteristics that simultaneously consider the type and frequency of attack events is highly effective in reducing false positives and execution time for anomaly detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.

• Korean Journal of Clinical Laboratory Science
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• v.45 no.4
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• pp.149-153
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• 2013

Candidiasis is a fungal infection of the most common causes; generally, opportunistic infections occur often in patients with weakened immune systems. Because of high rates in fungal infection patients and increasing frequency of being isolated from clinical materials, quickly identifying of Candida albicans is critical. By identifying 404 yeast cell strains of referred samples via API 20C kits, NGL and PRO tests and Germ tube (GT) test were conducted and compared. In the 3.0 McFarland yeast cells, 0.1% ${\rho}-nitrophenyl-N-acetyl-{\beta}-D-galactosaminide$ (NGL) and 0.04% ${\small{L}}$-proline ${\beta}$-naphtylamide (PRO) were each put in test tubes and incubated at $35^{\circ}C$ for 15, 30, 60 and 90 minutes. Afterwards, 1 drop of 2% NaOH was applied, and if the color turned yellow; it was positive for NGL test. Afterwards, 1% ${\rho}$-dimethylaminocinnamaldehyde was applied, and if the upper layer turned pink or red, it was positive for PRO test. NGL and PRO tests were conducted for all C. albicans and identified accurately within 30 minutes. In NGL, PRO test, false-positive, negative were not seen, whereas, GT test showed false-positive in 1 strain and false-negative in 3 strains. Therefore, sensitivity and specificity of NGL, PRO tests were 100% and 99.5%, respectively, and positive and negative predictive rate were 99.5% and 100%, respectively. However, GT test sensitivity and specificity were 98.5% and 99.5%, respectively, and positive and negative predictive rates were 99.5% and 98.5%, respectively. In conclusion, NGL, PRO tests are better than GT tests for sensitivity and specificity, therefore, these reliable tests will be useful in clinical laboratories.

• Journal of Radiation Protection and Research
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• v.44 no.2
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• pp.64-71
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• 2019

Background: It is very difficult to distinguish between a radioactive contamination source and background radiation from natural radionuclides in the marine environment by means of online monitoring system. The objective of this study was to investigate a statistical process for triggering abnormal level of count rate data measured from our on-line seawater radioactivity monitoring. Materials and Methods: Count rate data sets in time series were collected from 9 monitoring posts. All of the count rate data were measured every 15 minutes from the region of interest (ROI) for $^{137}Cs$ ($E_{\gamma}=661.6keV$) on the gamma-ray energy spectrum. The Shewhart ($3{\sigma}$), CUSUM, and Bayesian S-R control chart methods were evaluated and the comparative analysis of determination methods for count rate data was carried out in terms of the false positive incidence rate. All statistical algorithms were developed using R Programming by the authors. Results and Discussion: The $3{\sigma}$, CUSUM, and S-R analyses resulted in the average false positive incidence rate of $0.164{\pm}0.047%$, $0.064{\pm}0.0367%$, and $0.030{\pm}0.018%$, respectively. The S-R method has a lower value than that of the $3{\sigma}$ and CUSUM method, because the Bayesian S-R method use the information to evaluate a posterior distribution, even though the CUSUM control chart accumulate information from recent data points. As the result of comparison between net count rate and gross count rate measured in time series all the year at a monitoring post using the $3{\sigma}$ control charts, the two methods resulted in the false positive incidence rate of 0.142% and 0.219%, respectively. Conclusion: Bayesian S-R and CUSUM control charts are better suited for on-line seawater radioactivity monitoring with an count rate data in time series than $3{\sigma}$ control chart. However, it requires a continuous increasing trend to differentiate between a false positive and actual radioactive contamination. For the determination of count rate, the net count method is better than the gross count method because of relatively a small variation in the data points.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7381-7384
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• 2015

This study was conducted to 1206 women who had cervical cancer screening at Chonburi Cancer Hospital. The spilt-sample study aimed to compare the efficacy of abnormal cervical cells detection between liquid-based cytology (LBC) and conventional cytology (CC). The collection of cervical cells was performed by broom and directly smeared on a glass slide for CC then the rest of specimen was prepared for LBC. All slides were evaluated and classified by The Bethesda System. The results of the two cytological tests were compared to the gold standard. The LBC smear significantly decreased inflammatory cell and thick smear on slides. These two techniques were not difference in detection rate of abnormal cytology and had high cytological diagnostic agreement of 95.7%. The histologic diagnosis of cervical tissue was used as the gold standard in 103 cases. Sensitivity, specificity, positive predictive value, negative predictive value, false positive, false negative and accuracy of LBC at ASC-US cut off were 81.4, 75.0, 70.0, 84.9, 25.0, 18.6 and 77.7%, respectively. CC had higher false positive and false negative than LBC. LBC had shown higher sensitivity, specificity, PPV, NPV and accuracy than CC but no statistical significance. In conclusion, LBC method can improve specimen quality, more sensitive, specific and accurate at ASC-US cut off and as effective as CC in detecting cervical epithelial cell abnormalities.

• Journal of Gastric Cancer
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• v.6 no.4
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• pp.291-294
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• 2006

F-18 FDG-PET/CT could be used to evaluate the surveillance of recurrent stomach cancer, but some cases reported as false-positives. The authors found an activated charcoal granuloma from intraperitoneal chemotherapy by using a curative resection and mitomycin C for stomach cancer. A mass behind the right colon that showed on CT 6 months after an operation in a 46-year-old male patient had no progression in size, but 36 months after the operation, an increase was seen on F-18 FDG-PET/CT, and a metastatic tumor was suspected. The tumor was resected by an explorative laparotomy and was diagnosed as being an activated charcoal granuloma based on the histologic finding. Based on this case, we should be reminded of the possibility of a false-positive on analysis of F-18 FDG-PET/CT caused by an activated charcoal granuloma in a patient who has intraperitoneal chemotherapy.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• Analytical Science and Technology
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• v.35 no.6
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• pp.242-248
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• 2022

The effect of ethylenediamine (EDA) on the reaction of luminol or Bluestar with blood and bleaches was studied. For this purpose, blood, chlorine bleach, and oxygen bleach were applied to filter paper, treated with EDA-containing luminol or Bluestar, and the changes in chemiluminescence intensity were observed. It was found that the chemiluminescence intensity of the luminol (or Bluestar)-blood reaction did not change with the increasing concentration of EDA. However, the chemiluminescence intensity of the luminol (or Bluestar)-chlorine bleach reaction decreased and the chemiluminescence intensity of the luminol (or Bluestar)-oxygen bleach reaction increased, with increasing EDA concentration. Thus, it was found that when EDA was added to luminol (or Bluestar), which is a blood-sensitive reagent, EDA suppressed the false-positive reaction induced by chlorine bleach and induced a false-positive reaction with oxygen bleach. Consequently, the addition of EDA is not recommended for enhancing bloodstains with luminol or Bluestar.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.117-123
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• 2022

Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.442-449
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• 2020

Objective: To compare the diagnostic performance of contrast-enhanced spectral mammography (CESM) versus ultrasonography (US) in symptomatic patients with dense breasts, while using histology as the gold standard. Materials and Methods: After obtaining approval from the local ethics board, this prospective study collected data from patients with symptomatic breasts who underwent CESM and US examinations from May 1, 2017 to September 30, 2017. We then selected those with dense breasts and pathological results as our sample population. Both CESM and US results were classified by a radiologist through the Breast Imaging Reporting and Data System, and the results were compared with their corresponding histological results. The chi-square test was conducted to compare the diagnostic performance of CESM and US, and the receiver operating characteristic curves for the two imaging modalities were obtained. Results: A total of 131 lesions from 115 patients with dense breasts were included in this study. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 93.8%, 88.1%, 88.2%, 93.7%, and 90.8% for CESM, and 90.6%, 82.1%, 82.9%, 90.2%, and 86.3% for US, respectively. The p values for sensitivity, specificity, PPV, NPV, and accuracy were 0.687, 0.388, 0.370, 0.702, and 0.238, respectively. The area under the curve of CESM (0.917) was comparable with that of US (0.884); however, the differences between CESM and US were not statistically significant (p = 0.225). Eight false-positive cases and 4 false-negative cases for breast cancer were found in CESM, while 12 false-positive cases and 6 false-negative cases were found in US. Conclusion: The diagnostic performances of CESM and US are comparable in symptomatic women with dense breasts; however, the routine use of additional US imaging is questionable for lesions that can be detected by CESM.