• Korean Journal of Optics and Photonics
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• v.14 no.3
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• pp.266-271
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• 2003

The reflectance spectrum of optical films thicker than a few microns shows an intensity oscillation due to interference. Since the spectral period of the oscillation is inversely related to film thickness, the thickness of an optical film can be determined from the spectral frequency of the oscillation. For rapid data processing, the spectral frequency is obtained by use of a Fast Fourier Transformation technique. The conventional method of applying a Fast Fourier Transformation to the reflectance spectrum versus photon energy is modified so as to clear the ambiguity in choosing the proper effective refractive index value and to prevent the broadening of the Fourier transformed peak due to the refractive index dispersion. This technique of modified Fast Fourier Transformation is suggested by the authors for the first time to their knowledge. From the analysis of the calculated reflectance spectrum of a 30-${\mu}{\textrm}{m}$-thick dielectric film. it is shown to improve the accuracy in determining film thickness by a great amount. The improved accuracy of the modified Fast Fourier Transformation is also confirmed from the analysis of the reflectance spectra of a sample with 80-${\mu}{\textrm}{m}$-thick cover layer and 13-${\mu}{\textrm}{m}$-thick spacer layer on a PC substrate.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.625-638
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• 2018

In this paper, we derive i) a function to estimate snow cover fraction (SCF) from a MODIS satellite image that has a wide observational area and short re-visit period and ii) a function to determine snow depth from the estimated SCF map. The SCF equation is important for estimating the snow depth from optical images. The proposed SCF equation is defined using the Gaussian function. We found that the Gaussian function was a better model than the linear equation for explaining the relationship between the normalized difference snow index (NDSI) and the normalized difference vegetation index (NDVI), and SCF. An accuracy test was performed using 38 MODIS images, and the achieved root mean square error (RMSE) was improved by approximately 7.7 % compared to that of the linear equation. After the SCF maps were created using the SCF equation from the MODIS images, a relation function between in-situ snow depth and MODIS-derived SCF was defined. The RMSE of the MODIS-derived snow depth was approximately 3.55 cm when compared to the in-situ data. This is a somewhat large error range in the Republic of Korea, which generally has less than 10 cm of snowfall. Therefore, in this study, we corrected the calculated snow depth using the relationship between the measured and calculated values for each single image unit. The corrected snow depth was finally recorded and had an RMSE of approximately 2.98 cm, which was an improvement. In future, the accuracy of the algorithm can be improved by considering more varied variables at the same time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.565-577
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• 2019

In recent years, biometric authentication has been used for various applications. Since biometric features are unchangeable and cannot be revoked unlike other personal information, there is increasing concern about leakage of biometric information. Recently, Jin et al. proposed a new cancelable biometric scheme, called "Index-of-Max" (IoM) to protect fingerprint template. The authors presented two realizations, namely, Gaussian random projection-based and uniformly random permutation-based hashing schemes. They also showed that their schemes can provide high accuracy, guarantee the security against recently presented privacy attacks, and satisfy some criteria of cancelable biometrics. However, the authors did not provide experimental results for other biometric features (e.g. finger-vein, iris). In this paper, we present the results of applying Jin et al.'s scheme to iris data. To do this, we propose a new method for processing iris data into a suitable form applicable to the Jin et al.'s scheme. Our experimental results show that it can guarantee favorable accuracy performance compared to the previous schemes. We also show that our scheme satisfies cancelable biometrics criteria and robustness to security and privacy attacks demonstrated in the Jin et al.'s work.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1083-1089
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• 2018

Surgery to increase cerebral blood flow is one of the treatment methods of cerebral infarction. In order to supplement this invasive method, non-invasive devices have been introduced that use human blood pressure to pressurize the extremities to increase cerebral blood flow. However, the problem of poor speed and accuracy was raised. In this paper, the perfusion index of each arm is measured by applying pressure to both arms using Blood Oxygen Level Sensor to improve the accuracy of measurement and measurement time. The pressure applied to the arm is calculated by using the pressure value obtained from the arm. Like the existing blood pressure measuring cerebral blood flow increasing device, the blood flow can be increased by more than 20% and the measurement time can be shortened, so that it can be selectively used for the patient with cerebral infarction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.188-192
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• 2018

Surgery to increase cerebral blood flow is one of the treatment methods of cerebral infarction. However, invasive methods, such as surgery, may result in postoperative complications or side effects. In order to supplement this invasive method, non-invasive devices have been introduced that use human blood pressure to pressurize the extremities to increase cerebral blood flow. However, the problem of poor speed and accuracy was raised. In this paper, the perfusion index of each arm was measured by applying pressure to both arms using Blood Oxygen Level Sensor to improve the accuracy of measurement and measurement time. The pressure applied to the arm by 75% of the moment when it falls to the leg and the pressure calculated by using the pressure value obtained from the arm. Like the existing blood pressure measuring cerebral blood flow increasing device, the blood flow can be increased by more than 20% and the measurement time can be shortened, so that it can be selectively used for the patient with cerebral infarction.

• The Korean Journal of Nuclear Medicine Technology
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• v.27 no.1
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• pp.55-61
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• 2023

Purpose In this study, the monthly population distribution was calculated for statistical verification of 10 tests (CA19-9, CA15-3, testosterone, PTH, calcitonin, AFP, CEA, CA72-4, PSA, estradiol) with changed reagents, and the trend of change By comparing and analyzing the results, we want to verify the accuracy of the results and improve the reliability of the test. Materials and Methods From June 2021 to February 2023, for the 10 items in which the reagent was changed, the monthly percentage and standard deviation index before and after the reagent change were compared, respectively. At this time, the reference value was arbitrarily set for the comparison group in consideration of the reference value of the test item, and the groups were subdivided and compared, and the standard deviation index allowed range was -2.0 or more and 2.0 or less. Results For CA19-9, CA15-3, AFP, CEA, and calcitonin 5 test items, the change in monthly ratio in all test sections before and after the reagent change was kept constant. On the other hand, for CA72-4, PSA, testosterone, PTH, and estradiol items, the standard deviation index exceeded the acceptable standard after changing the reagent. CA72-4 test items showed an increasing trend in standard deviation index in the test section exceeding the reference value. In the case of the PSA test item, the maintenance ratio of 0.04 ng/mL was significantly decreased after changing the reagent. The testosterone test item had a standard deviation index of -2.5 in the test section exceeding 10.1 ng/mL after changing the reagent, and the standard deviation index of the PTH test item was out of the acceptable range in all test sections. It was confirmed that the estradiol test item showed an overall increase in the result value. Conclusion Through this study, the continuity and accuracy of the test results could be verified. It is considered that the stability of the test can be secured by analyzing the factors affecting the test result and solving the cause for the test item whose standard deviation index is out of the acceptable standard.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.395-432
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• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.97-101
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• 2016

In the field of clinical medicine, diagnostic accuracy studies refer to the degree of agreement between the index test and the reference standard for the discriminatory ability to identify a target disorder of interest in a patient. The receiver operating characteristic (ROC) curve offers a graphical display the trade-off between sensitivity and specificity at each cutoff for a diagnostic test and is useful in assigning the best cutoff for clinical use. In this end, the ROC curve analysis is a useful tool for estimating and comparing the accuracy of competing diagnostic tests. This paper reviews briefly the measures of diagnostic accuracy such as sensitivity, specificity, and area under the ROC curve (AUC) that is a summary measure for diagnostic accuracy across the spectrum of test results. In addition, the methods of creating an ROC curve in single diagnostic test with five-category discrete scale for disease classification from healthy individuals, meaningful interpretation of the AUC, and the applications of ROC methodology in clinical medicine to determine the optimal cutoff values have been discussed using a hypothetical example as an illustration.

• Journal of Sasang Constitutional Medicine
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• v.24 no.3
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• pp.1-16
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• 2012

Objectives For the methodological review on the accuracy study of questionnaire for Sasang constitution diagnosis, we searched the various diagnostic accuracy study of the questionnaires for Sasang constitution. Methods We searched MEDLINE, the Cochrane Library, KISS, and DBPIA. Additionally, We hand-searched the main oriental medical journals. All articles were independently reviewed and selected by two evaluators. And selected articles were assessed by "Quality Assessment of Diagnostic Accuracy Studies Tool"(QUADAS Tool) for the methodological review. Results The twenty eight studies initially identified studies were included in the methodological review. The part of "Acceptable reference standard", "Uninterpretable results reported" and "Withdrawals explained" was very weak in the risk of bias. The part of "Representative spectrum", "Acceptable delay between tests", "Incorporation avoided", "Reference standard results blinded", "Index test results blinded" was unclear in the description. Conclusions For the further study on the accuracy study of Sasang constitution diagnosis, we have to improve the aforementioned errors. Additionally, the checklist for the description of study might be needed.