• Journal of Oral Medicine and Pain
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• v.26 no.4
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• pp.309-318
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• 2001

The purposes of the study were (1) to evaluate the olfactory identification ability in those who have bad breath, (2) to determine the olfactory detection threshold for methyl mercaptan in normal subjects and those who have bad breath, and (3) to evaluate the effect of oral hygiene care on the olfactory detection threshold for methyl mercaptan. Sixteen male subjects with bad breath (male odor group), 9 male subjects without bad breath (male non-odor group), and 10 female subjects without bad breath (female non-odor group) were included for the study. Olfactory identification ability was assessed by administrating the Cross-Cultural Smell Identification Test (CC-SIT), and the olfactory detection threshold for methyl mercaptan was measured by two-alternative forced-choice single-staircase detection threshold procedure in a double-blinded condition. The geometric mean of the last four staircase reversal points of a total of seven reversals is used as the threshold. For the male odor group, after 1 month of intensive oral hygiene care for reducing oral volatile sulfur compounds (VSC) concentration, the olfactory detection threshold for methyl mercaptan was measured again and compared to the initial value. The ANOVA was used to test the group difference of olfactory threshold and olfactory identification ability and the paired t-test was used to test the difference of olfactory threshold between before and after reduction of oral VSC in male odor group. The results were as follows : 1. There was no significant difference in olfactory identification ability among those who have bad breath and normal male or female subjects. 2. The olfactory detection threshold for methyl mercaptan was about 8.4 ppb in normal male and female. 3. There was a tendency that male subjects with bad breath showed a higher olfactory detection threshold for methyl mercaptan when compared to those of no bad breath. 4. The olfactory detection threshold for methyl mercaptan returned to a normal level after 1 month of intensive oral hygiene care for reducing oral VSC.

• Korean Journal of Microbiology
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• v.51 no.3
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• pp.199-208
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• 2015

Biologics and medical devices manufactured with bovine-derived raw materials have the risk of viral contamination. Therefore, viral validation study is essential to ensure the safety of the products. Bovine adenovirus type-1 (BAdV-1) is one of the common bovine viral pathogens. For quantitative detection of BAdV-1 during the manufacture of biologics and medical devices, a TaqMan probe real-time PCR method was developed. Specific primers and TaqMan probe for amplifying and detecting BAdV-1 DNA were designed. Specificity, limit of detection (LOD), and robustness of the method was validated according to international guideline on the validation of nucleic acid amplification tests for the pathogen detection. The sensitivity of the assay was found to be $7.44{\times}10^1\;TCID_{50}/ml$. The real-time PCR method was reproducible, very specific to BAdV-1, and robust. Moreover, the method was successfully applied to the validation of Chinese Hamster Ovary (CHO)-K1 cells artificially infected with BAdV-1, a commercial CHO master bank, and bovine type 1 collagen. The overall results indicate that this rapid, specific, sensitive, and robust assay can be reliably used for quantitative detection of BAdV-1 contamination during the manufacture of biologics and medical devices using bovine-derived raw materials.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.7
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• pp.261-271
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• 1986

This paper describes a method for the state estimation considering current measurement component and detection of the bad data. The state values are estimated by weighted least square method in which measurement vector included bus injection current and line current. The bad data are detected using standardized variable of normal distribution and identified using sensitivity coefficients. When the bad data were occured by the bad measurement values. The results of the application to the model power system reveal the effectiveness of the presented algorithms.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.239-243
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• 1990

This paper presents a algorithm to improve accuracy and reliability in state estimation of contaminated bad data. The conventional algorithms for detection of bad data confront the problems of excessive memory requirements and long computation time. In order to overcome measurement compensation approach is proposed to reduce computation time and partitioned measurement error model has the advantage of remarkable reduction in computation time and memory requirements in estimated error computation. The proposed algorithm has been tested for IEEE sample systems, which shows its applicability to on-line power systems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.144-153
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• 1991

This paper presents an algorithm to improve accuracy and reliability in the state estimation of contaminated bad data. The conventional algorithms for detection of bad data have the problems of excessive memory requirements and long computation time. In order to overcome these problems, a measurement compensation approach is proposed to reduce computation time, and the partitioned measurement error model has the advantage of remarkable reduction in computation time and memory requirements in estimated error computation. The proposed algorithm has been tested for IEEE sample systems, which shows its applicability to on-line power systems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.93-94
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• 2016

사용자가 편리하게 사용하는 USB 인터페이스를 지원하는 컨트롤러 내부에 저장된 펌웨어를 조작하여 악의적인 행위를 수행하는 BAD USB가 출현하였다. BAD USB의 경우에는 악의적인 코드가 호스트에 존재하는 것이 아니라 장치 내부의 펌웨어에 존재하기 때문에 현재의 안티 바이러스 제품이 탐지하지 못하므로 그 대응방안이 시급하다. 이에 BAD USB를 탐지하고 대응하기 위한 연구가 활발히 진행되는 추세이지만, 아직 해결책으로는 미비한 실정이다. 따라서 본 논문에서는 사용자 인지를 기반으로 BAD USB를 탐지하는 방안을 제안한다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.270_271
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• 2009

The identification of multiple bad data, especially when mutually interacting, may be difficult to handle, since the well known procedures based on the normalized or weighted residuals may become faulty. In such a case, successive elimination of the measurement with the largest normalized residual may result in the suppression of correct measurements instead of the bad data. Then the problem of identifying bad data is considered as a combinatorial decision procedure. In this paper, binary PSO is used for the identification of multiple bad data in the power system state estimation. The proposed binary PSO based procedures behave satisfactorily in the identifying multiple bad data. The test is carried out with reference to the IEEE-14 bus system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.665-668
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• 2000

This paper presents development of a different substance monitoring system base reliable detection between the conveyer belt and hopper used lot materials transport line of steel company. Conventional detection method of a piece of iron separation system is losed the confidence, because of the place with bad surroundings of measurement so much that materials Production line are completely exposed to dust, moisture and vibration. For the solution of this problem, we developed a different substance detection system using the acoustic emittion sensor and one chip microprocessor which is available for bad surroundings and inexpensive. The reliability of the system was estimated by experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.803-808
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• 2000

This paper presents development of a different substance monitoring system base reliable detection between the conveyer belt and hopper used for materials transport line of steel company. Conventional detection method of a piece of iron separation system is losed the confidence, because of the place with bad surroundings of measurement so much that materials production line are completely exposed to dust, moisture and vibration. For the solution of this problem, we developed a different substance detection system using the acoustic emittion sensor and one chip microprocessor which is available for bad surroundings and inexpensive. The reliability of the system was estimated by experiment.