• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2367-2371
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• 2003

A new instrument for early detection of Alzheimer's disease is constructed from the investigative items with both the investigation of living environment, and the functional tests of the sense, the physiology, and the left and right brains. This paper describes the indexes obtained from the results of test using a new instrument for early detection of Alzheimer's disease. The indexes for early detection of Alzheimer's disease were obtained from the investigations of the living environment and the social adaptability, the functional tests of the sight and the hearing in the five senses, and the functional tests of left hemispheres in brain.

• Asian Oncology Nursing
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• v.12 no.1
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• pp.52-60
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• 2012

Purpose: This study is to determine knowledge about early detection and risk perception of cancer according to taking cancer screening tests in the general population. Methods: The participants were 151 people aged 40 years or older. A questionnaire consisted of knowledge about early detection (warning signs, cancer screening methods, general knowledge for early detection), cancer risk perception and history of cancer screening during past 2 years. Results: The percentages of correct answers were 64.7% in knowledge about warning signs, 73.7% in knowledge of cancer screening tests and 80.1% in general knowledge for early detection. Participants had the highest knowledge about screening methods for stomach cancer and the lowest for liver and colon cancer. The level of risk perception was medium. The participants who participated in cancer screening showed lower risk perception than those who did not. There was no significant relationship between knowledge and performance of cancer screening. The primary reason for not participating in cancer screening was patient's perception of their own health. Conclusion: These results suggest that cancer risk perception can affect the performance of cancer screening and we need to study how to handle this problem. Additionally screening programs should focus on liver cancer and colon cancer.

• Nutritional Sciences
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• v.2 no.2
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• pp.119-124
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• 1999

Food allergies play an important role in Atopic Dermatitis (AD). Dietary manipulation is essential in the management of AD. However, there has been a paucity of data reporting the prevalence of food allergies in AD patients in Korea. In this study, the Food Open Challenge Tests (FOCT) were conducted to investigate food allergies in AD patients. The skin-prick test and the detection of specific IgE, as well as allergy history of patient were used to detect food allergies in all AD patients. Elimination diet was conducted for two weeks prior to FOCTs. The prevalence of food allergies by FOCT is as follows: milk (67.3%); chicken (64.2%); pork (62.8%); eggs (61.0%); beef(55.4%) ; wheat (52.0%) and soybean (45.2%). Allergenic food items in Korean AD patients were different from people in other foreign countries. The seven major foods those tested positively by FOCTs were completely eliminated from the replaced diets for two weeks, and were subsequently reintroduced one at a time. Results from FOCTs were not comparable with allergy history or skin-prick tests or specific IgE detection. The sensitivity and specificity of skin-prick tests and specific IgE detection were lower than FOCTs. Allergy history, skin-prick tests, and specific IgE detection are useful for the identification of food allergen but its clinical significance differed according to food items. Therefore, we conclude that even though a 10-day delay was necessary after food challenge, FOCT is a useful and valid method to confirm food allergies and may be essential for the effective control of food allergies for treatment of AD.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.9-19
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• 2024

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

• Journal of Food Hygiene and Safety
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• v.11 no.2
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• pp.129-132
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• 1996

Recently, as concern about the residual antibiotics in milk increase, the detection methods of residual antibiotics used extensevely at the present time were investigated and compared to their properties and the detection limits of variable antibiotics. At first, comparactive tests of the detectable sensitivity of 4 test organisms, B. cereus, B. subtilis, M.luteus, B.stearothermophilus C-953, were performed by disc assay. As a result, B.stearothermophilus was the most sensitive strain of all other strains and showe the detect limit of 5-50 ppb for penlicillins (PCs). And also, B.subitilis was showed the more effective detection limit, 200-400 ppb, for aminoglycosides (AGs) and M.luteus was showed predominant sensitivity , 50-500 ppb for macrolides(MLs) and B.cereus was the most sensitive strain for tetracyclines (TCs) and showed the detection limit of 100-400 ppb. Therefore, each test strains were showed a different sensitivity in the detection of the different antibiotic families. When the detection limit of disc assay and other methods were compared, TTCmethod was less sensitive than other methods showing 5-50 ppb detectable lebel for PCs. Also, for the detection of other antibiotic families TTC method was showed the worst sensitivity and Delvo and Charm Farm tests were similar to the detectable properties of AGs and MLs. Although disc assay was showed the similar detection limit for PCs with Delvo and Charm Farm, it was more widely effective for the detection of kanamycin, erythromycin, chlortetracycline, doxycycline, verginiamycin and so on than Delvo or Charm Farm. CharmII test was showed the best sensitivity for the most of antibiotics except neomycin and gentamycin. But it was necessary that different tests must be performed to each antibiotic family and so it was regarded that the effectiveness of that method was low.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.1021-1024
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• 2000

In this paper we describe a nonparametric Wilcoxon test and a parametric Z test based on statistical hypothesis testing for the detection of edges. We use the threshold determined by specifying significance level $\alpha$, while Bovik, Huang and Munson[4] consider the range of possible values of test statistics for the threshold. From the experimental results of edge detection, the Z method performs sensitively to the noisy image, while the Wilcoxon method is robust over both noisy nd noise-free images. Comparison with our statistical tests and Sobel operator shows that our tests perform more effectively in both noisy and noise-free images.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.11a
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• pp.42-71
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• 2001

Leakage detection system can possibly locate leak point without laboratory analysis. Several different types of sensors provide these benefits. But the use of these technologies is not widespread, mainly because of cost. Each of the leakage detection systems available has different advantages and disadvantages. The ideal system would be affordable, durable enough to last through the life of the landfill, automated, and applicable to all types of landfills. The laboratory tests were performed to investigate the relationship between electrical resistivity and the unsaturated subsurface condition and to evaluate the contamination due to leachate based on measuring electrical resistivity. The results of experiment show that the electrical resistivity of soil decreases as moisture density increases. The electrical resistivity of soil decreases as the concentration of leachate in pore fluid increases. These facts indicate that electrical resistivity method can be a promising tool in detecting of leachate. Also, the field model tests were conducted to verify that detection of leachate leak point on detection system using electrical characteristics is accurate. Field model test results of leakage detection system imply that the leakage detection system using electrical characteristics have the great potential of detecting exactly the leak point of leachate.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.69-75
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• 2017

PURPOSES : The purpose of this study is to evaluate different types of Ground Penetrating Radar (GPR) testing for characterizing the road cavity detection. The impulse and step-frequency-type GPR tests were conducted on a full-scale testbed with an artificial void installation. After analyzing the response signals of GPR tests for detecting the road cavity, the characteristics of each GPR response was evaluated for a suitable selection of GPR tests. METHODS : Two different types of GPR tests were performed to estimate the limitation and accuracy for detecting the cavities underneath the asphalt pavement. The GPR signal responses were obtained from the testbed with different cavity sizes and depths. The detection limitation was identified by a signal penetration depth at a given cavity for impulse and step-frequency-type GPR testing. The unique signal characteristics was also observed at cavity sections. RESULTS : The impulse-type GPR detected the 500-mm length of cavity at a depth of 1.0 m, and the step-frequency-type GPR detected the cavity up to 1.5 m. This indicates that the detection capacity of the step-frequency type is better than the impulse type. The step-frequency GPR testing also can reflect the howling phenomena that can more accurately determine the cavity. CONCLUSIONS :It is found from this study that the step-frequency GPR testing is more suitable for the road cavity detection of asphalt pavement. The use of step-frequency GPR testing shows a distinct image at the cavity occurrences.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1175-1182
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• 2013

The lock-in mode infrared thermography (IRT) technique has been developed to improve the detection capability of defects in materials with high thermal conductivity, and it has been shown to provide better detection capability than conventional active IRT. Therefore, to investigate the application of this technique to nuclear piping components, lock-in mode IRT tests were conducted on pipe specimens containing simulated wall-thinning defects. Phase images of the wall-thinning defects were obtained from the tests, and they were compared with thermal images obtained from conventional active IRT tests. It showed that the ability to size the detected wall-thinning defects in piping components was improved by using lock-in mode IRT. The improvement was especially apparent when detecting short and narrow defects and defects with slanted edges. However, the detection capability for shallow wall-thinning defects did not improve much when using lock-in mode IRT.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.399-408
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• 2016

There is a need to detect heteroscedasticity in a regression analysis; however, it invalidates the standard inference procedure. The diagnostics on heteroscedasticity may be distorted when both outliers and heteroscedasticity exist. Available heteroscedasticity detection methods in the presence of outliers usually use robust estimators or separating outliers from the data. Several approaches have been suggested to identify outliers in the heteroscedasticity problem. In this article conventional tests on heteroscedasticity are modified by using a sequential outlier detection methods to separate outliers from contaminated data. The performance of the proposed method is compared with original tests by a Monte Carlo study and examples.