• Ocean and Polar Research
• /
• v.43 no.4
• /
• pp.229-243
• /
• 2021

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.

• Progress in Medical Physics
• /
• v.17 no.4
• /
• pp.212-223
• /
• 2006

Positional accuracy of the on-board imager (OBI) isocenter with gantry rotation was presented in this paper. Three different type of automatic evaluation methods of discrepancies between therapeutic and OBI isocenter using digital image processing techniques as well as a procedure stated in the customer acceptance procedure (CAP) were applied to check OBI isocenter migration trends. Two kinds of kV x-ray image set obtained at OBI source angle of $0^{\circ},\;90^{\circ},\;180^{\circ},\;270^{\circ}$ and every $10^{\circ}$ and raw projection data for cone-beam CT reconstruction were used for each evaluation method. Efficiencies of the methods were also estimated. If a user needs to obtain an isocenter variation map with full gantry rotation, a method taking OBI image for every $10^{\circ}$ and fitting with 5th order polynomial was appropriate. However for a mere quality assurance (QA) purpose of OBI isocenter accuracy, it was adequate to use only four OBI Images taken at the OBI source angle of $0^{\circ},\;90^{\circ},\;180^{\circ}\;and\;270^{\circ}$. Maximal discrepancy was 0.44 mm which was observed between the OBI source angle of $90^{\circ}\;and\;180^{\circ}$ OBI isocenter accuracy was maintained below 0.5 mm for a year. Proposed QA program may be helpful to Implement a reasonable routine QA of the OBI isocenter accuracy without great efforts.

• The Journal of Society for e-Business Studies
• /
• v.13 no.1
• /
• pp.21-32
• /
• 2008

RFID systems provide technologies of automatic object identification through wireless communications in invisible ranges and adaptability against various circumstances. These advantages make RFID systems to be applied in various fields of industries and individual life. However, it is difficult to use tags with distinction as tags are increasingly used in life because a tag usually stores only one object identifier in common RFID applications. In addition, RFID systems often make serious violation of privacy caused by various attacks because of their weakness of radio frequency communication. Therefore, information sharing methods among applications are necessary for expansive development of RFID systems. In this paper, we propose efficient RFID scheme. At first, we design a new RFID tag structure which supports many object identifiers of different applications in a tag and allows those applications to access them simultaneously. Secondly, we propose an authentication protocol to support the proposed tag structure. The proposed protocol is designed by considering of robustness against various attacks in low cost RFID systems. Especially, the proposed protocol is focused on efficiency of authentication procedure by considering security levels of applications. In the proposed protocol, each application goes through one of different authentication procedures according to their security levels. Finally, we prove efficiency of th proposed scheme compared with the other schemes through experiments and evaluation.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.45 no.6
• /
• pp.123-131
• /
• 2008

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of one's health like the physiological and the clinicopathological changes of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition a lot. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue region from a facial image captured and classifying tongue coating are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth are similar. The proposed method includes preprocessing, over-segmenting, detecting the edge with a local minimum over a shading area from the structure of a tongue, correcting local minima or detecting the edge with the greatest color difference, selecting one edge to correspond to a tongue shape, and smoothing edges, where preprocessing consists of down-sampling to reduce computation time, histogram equalization, and edge enhancement, which produces the region of a segmented tongue. Finally, the systematic procedure separated only a tongue region from a face image with a tongue, which was obtained from a digital tongue diagnosis system. Oriental medical doctors' evaluation for the results illustrated that the segmented region excluding a non-tongue region provides important information for the accurate diagnosis. The proposed method can be used for an objective and standardized diagnosis and for an u-Healthcare system.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.29-40
• /
• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Pediatric Infection and Vaccine
• /
• v.17 no.2
• /
• pp.101-107
• /
• 2010

Purpose : In evaluation of patients, laboratory results are crucial in determination of a treatment plan. Obtaining venous blood from infants and children is a difficult procedure. Substitution of a capillary blood sample for a venous blood sample has been suggested. However, there are few studies showing mutual correlation between C-reactive protein (CRP) results in capillary and venous blood. This study was designed to determine whether the result of the capillary sample is the same as the result of the venous blood sample. Methods : After informed consent, a pair of venous and fingertip capillary blood samples were simultaneously collected from 100 children. The LC-178CRPTM was used for analysis of capillary blood and the Hitachi 7180 automatic hematology analyzer was used for analysis of venous blood. We compared CRP of both venous and capillary blood samples. Results were analyzed by crosstabulation analysis, simple regression analysis and the Bland Altman Plot method. Results : A close correlation (90.63%) was observed between capillary and venous blood analyzed by crosstabulation analysis. CRP results were similar between the two groups and showed a high coefficient correlation ($\beta$=1.3434, $R^2$=0.9888, P<0.0001) when analyzed by a simple regression model. The average value in venous blood was also higher compared to capillary blood. According to Bland Altman Plot analysis, lab results were measured at a 95% confidence interval. Conclusion : CRP results from capillary blood showed close correlation with venous blood sampling. At present, venous blood sampling is the preferred method. However, due to difficulty in venous blood sampling, capillary sampling could be considered as an alternative technique for use with children.