• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.305-314
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• 2016

In this study, a quantitative assessment was carried out in order to identify the agricultural drought in time and space using the Terra MODIS remote sensing data for the agricultural drought. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were selected by MOD13A3 image which shows the changes in vegetation conditions. The land cover classification was made to show only vegetation excluding water and urbanized areas in order to collect the land information efficiently by Type1 of MCD12Q1 images. NDVI and EVI index calculated using land cover classification indicates the strong seasonal tendency. Therefore, standardized Vegetation Stress Index Anomaly (VSIA) of EVI were used to estimated the medium-scale regions in Korea during the extreme drought year 2001. In addition, the agricultural drought damages were investigated in the country's past, and it was calculated based on the Standardized Precipitation Index (SPI) using the data of the ground stations. The VSIA were compared with SPI based on historical drought in Korea and application for drought assessment was made by temporal and spatial correlation analysis to diagnose the properties of agricultural droughts in Korea.

• Journal of IKEEE
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• v.21 no.4
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• pp.397-403
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• 2017

In the modern society, psychological diseases and impulsive crimes due to stress are occurring. In order to reduce the stress, the existing treatment methods consisted of continuous visit counseling to determine the psychological state and prescribe medication or psychotherapy. Although this face-to-face counseling method is effective, it takes much time to determine the state of the patient, and there is a problem of treatment efficiency that is difficult to be continuously managed depending on the individual situation. In this paper, we propose an artificial intelligence emotion management system that emotions of user monitor in real time and induced to a table state. The system measures multiple bio-signals based on the PPG and the GSR sensors, preprocesses the data into appropriate data types, and classifies four typical emotional states such as pleasure, relax, sadness, and horror through the SVM algorithm. We verify that the emotion of the user is guided to a stable state by providing a real-time emotion management service when the classification result is judged to be a negative state such as sadness or fear through experiments.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.3
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• pp.53-60
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• 1997

Generally, a safety estimation based on the buckling strength is carried out to evaluate the strength of plate members in the design process of ship hull structures and more accurate and efficient tool for the buckling strength estimation of enormous plate members of ship structure is naturally demended for saving design process. While, in the reason that the design codes of classification societies do not consider the various effects or include some effects roughly, considerate safe side results are suggested occasionally. In this study, advanced buckling strength estimation system prepared various classification buckling evaluation codes and new evaluation code considering the effects of in-plane tension, plate boundary condition, lateral load & residual stress is developed using the window management system of engineering workstation. Additionally maximum deflection estimation formula is equipped for the increase of fabrication reliability of thin plate ship structure. From this evaluation system, more reliable buckling safety of plate panel will be guaranteed in the ship hull design stage. In order to expand the use of this system pc version system will be developed sooner or later.

• 모자간호학회지
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• v.4 no.1
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• pp.68-79
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• 1994

This study measured variables influencing the breast feeding patterns of lactating mothers over a 40 day period In 1993 in the Jeonla area. The Methodology used was a questionnaire covering 92 items based on statistical discriminant analysis. The results were as follows : The successful group was measured against the unsuccessful group over a 4month lactation period ; The successful group was measured over a 4month lactation period ; the unsuccessful less than 4month lactation period. Principal factor analysis was used to generate comparative data factors which were ; 1) nonunderstanding of mother's breast feeding, 2) physical and psychological stress, 3) insufficient milk supply, 4) mother's negative acceptance of baby, 5) lack of spousal support, 6) sore nipple and breast pain, 7) baby's negative acceptance, 8) lack of familial support, 9) baby's diarrhea and watery milk. Discriminant statistical analysis of sever factors included ; 1) insufficient milk supply 2) sore nipple and breast pain, 3) pre-natal planning of breast feeding method, 4) mother's occupation 5) breast feeding method of previous infant, 6) nipple type, and 7) infant birth order. This analysis predicted a 78.9% successful breast feeding. Criterion correlation analysis revealed ; D=-1.780+.165$\times$(Fac3)+.135$\times$(Fac6)+.927$\times$(prenatal planning of breast feeding method)+.900$\times$(mother's occupation)+.675$\times$ (breast feeding method of previous infant)+1.0l4$\times$(nipple type)+.378$\times$(infant birth order). We classified the unsuccessful group as more than .63937 and the successful group less than -.82742 of the D value obtained from the above criterion correlation in order to check the success or the non-success of breast feeding mothers. The rate of correct classification of the grouped cases employing a statistical discriminant analysis was significantly improved to 78.9% when these cases were compared with the actual grouped classification.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.3
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• pp.7-22
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• 1997

Korean Library Association brought out the fourth revised and enlarged edition of KDC last year. Compared with the former edition It Is a marked improvement. Neverthless, it leaves much room for improvement. In order to examine and evaluate the edition more effectively, I confined my study to the class 'Philosophy'. In my judgment the problem resolves itself into following three points: 1) Each regions, blanches of philosophy is not properly balanced. As is generally known KDC was originally derived from DDC. As a result KDC and DBC are similiar with regard to their stress on the philosophical tradition of the West. In consequence, it is lacking in universality. 2) The classifiers neglected on several occassions the logical regulations of classification. The vertical and horizontal relations between the subjects are not strictly respected. 3) The persons concerned were not well informed of philosophical conceptions and genealogies. There are some misused conceptions and disorganized genealogies of philosophy. To my knowledge these problems originate in the lack of professional understanding of philosophy necessary to make the work satisfactory As a result of the examination I came to the conclusion that it is inevitable for the classifiers, to ask to specialists in philosophy for mutual cooperation. Without their professional advices the classifiers will find difficulty in solving the problems and in improving the classification

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Animal Bioscience
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• v.35 no.6
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• pp.791-803
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• 2022

Genome/gene-editing (GE) techniques, characterized by a low technological barrier, high efficiency, and broad application among organisms, are now being employed not only in medical science but also in agriculture/veterinary science. Different engineered CRISPR/Cas9s have been identified to expand the application of this technology. In pig production, GE is a precise new breeding technology (NBT), and promising outcomes in improving economic traits, such as growth, lean or healthy meat production, animal welfare, and disease resistance, have already been documented and reviewed. These promising achievements in porcine gene editing, including the Myostatin gene knockout (KO) in indigenous breeds to improve lean meat production, the uncoupling protein 1 (UCP1) gene knock-in to enhance piglet thermogenesis and survival under cold stress, the generation of GGTA1 and CMP-N-glycolylneuraminic acid hydroxylase (CMAH) gene double KO (dKO) pigs to produce healthy red meat, and the KO or deletion of exon 7 of the CD163 gene to confer resistance to porcine reproductive and respiratory syndrome virus infection, are described in the present article. Other related approaches for such purposes are also discussed. The current trend of global regulations or legislation for GE organisms is that they are exempted from classification as genetically modified organisms (GMOs) if no exogenes are integrated into the genome, according to product-based and not process-based methods. Moreover, an updated case study in the EU showed that current GMO legislation is not fit for purpose in term of NBTs, which contribute to the objectives of the EU's Green Deal and biodiversity strategies and even meet the United Nations' sustainable development goals for a more resilient and sustainable agri-food system. The GE pigs generated via NBT will be exempted from classification as GMOs, and their global valorization and commercialization can be foreseen.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• Journal of the Society of Naval Architects of Korea
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• v.28 no.1
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• pp.150-168
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• 1991

A practical procedure for the ultimate compressive strength-based safety and reliability assessment of the double skin upper deck structure is described. The external compressive stress acting on the upper deck structure which is due to the still water and wave-induced sagging moment is approximately estimated by using the existing rule of classification society. The ultimate compressive stress of double skin structure under the action of sagging moment is analyzed by using idealized structural unit method. Here an idealized plate element subjected to uniaxial load is formulated by idealizing the nonlinear behaviour of the actual element taking account of the initial imperfections in the form of initial deflection and welding residual stress. The interaction effect between the local and global failure in the structure is also taken into consideration. The accuracy of the present method is verified comparing with the present solution and the existing numerical and experimental results for unit member and welded box columns. The safety of the structure is evaluated using the concept of conventional central safety factor and the reliability assessment is made by using Cornel's MVFOSM method. The present procedure is then applied to upper deck structure of double skin product oil carrier. The influence of the initial imperfections and the yield stress of the material on the safety and reliability of the structure is investigated.

• Journal of Korean Academy of Nursing
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• v.34 no.8
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• pp.1388-1394
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• 2004

Purpose. Korean women are likely to experience symptoms of depression, possibly due to socially fixed limitations on the roles that Korean women are expected to perform. Also if a Korean woman experinces negative relationship problem or stress in her family, she would feel responsible, which will worsen her depression. Nonetheless, much of the research on depression among Korean women has focused on menopausal women. This study aims to understand the depression of Korean women to provide fundamental data to develop nursing intervention method for promoting women's health. Methods. The present investigation assessed the prevalence and correlates of depression in a large sample of Korean women, aged 18 or older, from the general population. With a probability sample of 3312 women drawn from two areas in Korea, a survey, which contains the Center for Epidemiological Studies Depression Scale (CES-D) and background, was completed. Results. According to CES-D classification criteria, $36.5\%$ of the women in the sample displayed either no depression or mild depression, $55.6\%$ exhibited moderate depression, and $7.8\%$ manifested severe depression. Significant bivariate relationships were observed between depression and each measured background variable except alcohol use. Logistic regression analysis indicated that the strongest combination of predictors of depression included income, menopausal, and marital status. Conclusion. The data support the premise that Korean women disproportionately experience elevated levels of depression. Consistent with the theory, depression may be related to social pressures to conform to the traditional roles. The study suggests the need for further research, primary prevention activities, and increased access to treatment.