• Journal of the Korean Institute of Navigation
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• v.3 no.1
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• pp.47-77
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• 1979

A microwave Navigational Aid system is suggested suitable for fishing boats too small to be equipped with Radar or Radio-Direction-Finder. The system proposed here is similar to that of Talking-Beacon developed in Japan, but the distinctive modification proposed is that an increase of sixteen times in peak transmitting power, thus an accompanying increase of coverage, is achieved with the same mean transmitting power as that of Japan without sacrificing the clearness of azimuth information, by adopting a pulse repetition modulation instead of pulse width modulation as in Japan system. An experimental land station transmitter of transmitting frequency of 9, 370MHz and of peak power of 35kw with a microwave beam of 1 degree in horizontal width and 7 degrees in vertical width rotating once every three minutes, and also an experimental receiver of 20-dB in sensitivity and of an assumed cost of 100 dollars, operated by a 12 volts battery source are made, and the sail test results are reported showing that a bearing infromation of an accuracy of within two degrees can be obtainable every three minutes at a distance of as far as 24 miles from the transmitter if the transmitter is located as high as 100 meters above sea-level.

• Psychiatry investigation
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• v.15 no.11
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• pp.1030-1036
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• 2018

Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.643-654
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• 2019

Purpose: The purpose of this study was to develop and psychometrically test the Korean Geriatric Loneliness Scale (KGLS). Methods: The initial items were based on in-depth interviews with 10 older adults. Psychometric testing was then conducted with 322 community-dwelling older adults aged 65 or older. Content, construct, and criterion-related validity, classification in cutoff point, internal consistency reliability, and test-retest reliability were used for the analysis. Results: Exploratory factor analysis showed three factors, including 15 items explaining 91.6% of the total variance. The three distinct factors were loneliness associated with family relationships (34.3%), social loneliness (32.4%), and a lack of belonging (24.9%). As a result of confirmatory factor analysis, 14 items in the three-factor structure were validated. Receiver operating characteristic analysis demonstrated that the KGLS' cutoff point of 32 was associated with a sensitivity of 71.0%, specificity of 80.2%, and area under the curve of .83. Reliability, as verified by the test-retest intraclass correlation coefficient, was .89, and Cronbach's ${\alpha}$ was .90. Conclusion: As its validity and reliability have been verified through various methods, the KGLS can contribute to assessing loneliness in South Korean older adults.

• Journal of Korean Academy of Nursing
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• v.50 no.6
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• pp.787-798
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• 2020

Purpose: This study aimed to assign weights for subscales and items of the Post-Intensive Care Syndrome questionnaire and suggest optimal cut-off values for screening unplanned hospital readmissions of critical care survivors. Methods: Seventeen experts participated in an analytic hierarchy process for weight assignment. Participants for cut-off analysis were 240 survivors who had been admitted to intensive care units for more than 48 hours in three cities in Korea. We assessed participants using the 18-item Post-Intensive Care Syndrome questionnaire, generated receiver operating characteristic curves, and analysed cut-off values for unplanned readmission based on sensitivity, specificity, and positive likelihood ratios. Results: Cognitive, physical, and mental subscale weights were 1.13, 0.95, and 0.92, respectively. Incidence of unplanned readmission was 25.4%. Optimal cut-off values were 23.00 for raw scores and 23.73 for weighted scores (total score 54.00), with an area of under the curve (AUC) of .933 and .929, respectively. There was no significant difference in accuracy for original and weighted scores. Conclusion: The optimal cut-off value accuracy is excellent for screening of unplanned readmissions. We recommend that nurses use the Post-Intensive Care Syndrome Questionnaire to screen for readmission risk or evaluating relevant interventions for critical care survivors.

• Journal of Veterinary Science
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• v.19 no.6
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• pp.840-845
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• 2018

The purpose of this study was to propose a standard for differentiation between normal dogs and patients with hyperadrenocorticism (HAC) by measuring skin thickness via ultrasonography in small breed dogs. Significant changes in skin thickness of patients treated with prednisolone (PDS) or patients with HAC treated with trilostane were evaluated. Skin thickness was retrospectively measured on three abdominal digital images obtained from small breed dogs weighing < 15 kg that underwent abdominal ultrasonography. Mean skin thickness of normal dogs was $1.03{\pm}0.25mm$ (mean ${\pm}$ SD). Both the HAC and PDS groups showed significantly thinner skin than that in the normal group. Seven of the 10 HAC patients treated with trilostane had increased skin thickness. The area under the curve value of 0.807 was based on the receiver operating characteristics (ROC) curve for differentiating normal dogs from HAC patients. Sensitivity was 76% and specificity was 73% when skin thickness was less than the 0.83 mm cutoff value. In conclusion, measurement of skin thickness in small breed dogs by using ultrasonography is likely to provide clinical information useful in differentiating HAC patients from normal dogs. However, exposure to PDS, trilostane, and other conditions may have a significant effect on skin thickness.

• Current Optics and Photonics
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• v.5 no.3
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• pp.213-219
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• 2021

We describe a single-channel rubidium (Rb) radio-frequency atomic magnetometer (RFAM) as a receiver that takes magnetic signal resonating with Zeeman splitting of the ground state of Rb. We optimize the performance of the RFAM by recording the response signal and signal-to-noise ratio (SNR) in various parameters and obtain a noise level of 159 $fT{\sqrt{Hz}}$ around 30 kHz. When a resonant radiofrequency magnetic field with a peak amplitude of 8.0 nT is applied, the bandwidth and signal-to-noise ratio are about 650 Hz and 88 dB, respectively. It is a good agreement that RFAM using alkali atoms is suitable for receiving signals in the very low frequency (VLF) carrier band, ranging from 3 kHz to 30 kHz. This study shows the new capabilities of the RFAM in communications applications based on magnetic signals with the VLF carrier band. Such communication can be expected to expand the communication space by overcoming obstacles through the high magnetic sensitive RFAM.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.156-162
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• 2021

Objective: The aim of the present study was to evaluate the predictive capability of fasting-state measurements of glucose and insulin levels alone for abnormal glucose tolerance in women with polycystic ovary syndrome (PCOS). Methods: In total, 153 Korean women with PCOS were included in this study. The correlations between the 2-hour postload glucose (2-hr PG) level during the 75-g oral glucose tolerance test (OGTT) and other parameters were evaluated using Pearson correlation coefficients and linear regression analysis. The predictive accuracy of fasting glucose and insulin levels and other fasting-state indices for assessing insulin sensitivity derived from glucose and insulin levels for abnormal glucose tolerance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Significant correlations were observed between the 2-hr PG level and most fasting-state parameters in women with PCOS. However, the area under the ROC curve values for each fasting-state parameter for predicting abnormal glucose tolerance were all between 0.5 and 0.7 in the study participants, which falls into the "less accurate" category for prediction. Conclusion: Fasting-state measurements of glucose and insulin alone are not enough to predict abnormal glucose tolerance in women with PCOS. A standard OGTT is needed to screen for impaired glucose tolerance and type 2 diabetes mellitus in women with PCOS.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.