• Journal of Korean Medicine Rehabilitation
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• v.24 no.2
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• pp.115-130
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• 2014

Objectives The objective of this study is to interpretate Yangsaeng-Doinbub presented in "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou" in a modern kineologic approach Methods Based on the interpretation of "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou" and implementation of its kinesiology, this study presents similar kineologies and their purposes, with the reference to various documents on modern kinesiology. Results 1) Yangsaeng-Doinbub presented in "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou" is similar to stretching, active exercise and resistance exercise. 2) Exercises in Yangsaeng-Doinbub presented in "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou", which are similar to resistance exercise, can be used for isometic exercise of cervical extensor. 3) Exercises in Yangsaeng-Doinbub presented in "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou", which are similar to Stretching exercise, has its purpose to stretch quadratus Lumborum, lateral side of body, gluteus Maximus, quadriceps femoris, shoulder extensor, hamstrings, hip joint, ankle dorsi flexor, thoracic rotator,inferior shoulder joint. 4) Exercises in Yangsaeng-Doinbub presented in "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou", which are similar to active exercise, can be used for strengthen exteral oblique. 5) Doctors can make various applications of Yansaeng-Doinbub. For example, it can be used to correct improper low back and neck exercise patterns. 6) Yangsaeng-Doinbub also describes breathing methods, which help normalization of breathing exercises and increase the efficiency of spine exercises. Conclusions The modern interpretation on kinesiology of Yangsaeng-Doinbub presented in "Zhu-Bing-Yuan-Hou-Lun Yao-Bei-Bing-Zhu-Hou" leads to a conclusion that Yangsaeng-Doinbub consists of numourous exercises for various body parts. In particular, breathing methods increase efficiency of such exercises. Plus, the exercises in Yangsaeng-Doinbub can be applied to various uses by doctors.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.49.2-49.2
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• 2016

The number of discovery of asteroids with peculiar rotational states has recently increased, and hence a novel approach for lightcurve analysis is considered to be critical. In order to investigate objects such as Non-Principal Axis (NPA) rotator, we selected a NPA candidate, (5247) Kryolv as our target considering its Principal Axis Rotation (PAR) code and the visibility in early 2016. The observations of Krylov were made using Korea Microlensing Telescope Network (KMTNet) 1.6 m telescopes installed at the three southern sites with TO (Target of Opportunity) observation mode. We conducted R-band time-series photometry over a total of 51 nights from January to April 2016 with several exposures during each allocated run. The ensemble normalization photometry was employed using the AAVSO Photomtric All-Sky Survey (APASS) catalog for the standardization. We successfully confirmed its NPA spin state based on the deviation from the reduced lightcurve, and thus Krylov is recorded as the first NPA rotator of its kind in the main-belt, with its precession and rotation periods, $P{\varphi}=81.18h$ and $P_{\Psi}=67.17h$, respectively. In this paper, we present the spin direction, the 3D shape model and taxonomy of the newly confirmed NPA asteroid (5247) Krylov.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.539-548
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• 2020

Deep learning technology is currently being used and applied in many different fields. Convolution neural network (CNN) is a method of artificial neural networks in deep learning, which is commonly used for analyzing different types of images through classification. In the conventional classification of histopathology images of prostate carcinomas, the rating of cancer is classified by human subjective observation. However, this approach has produced to some misdiagnosing of cancer grading. To solve this problem, CNN based classification method is proposed in this paper, to train the histological images and classify the prostate cancer grading into two classes of the benign and malignant. The CNN architecture used in this paper is based on the VGG models, which is specialized for image classification. However, color normalization was performed based on the contrast enhancement technique, and the normalized images were used for CNN training, to compare the classification results of both original and normalized images. In all cases, accuracy was over 90%, accuracy of the original was 96%, accuracy of other cases was higher, and loss was the lowest with 9%.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.33-47
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• 2007

The requirements have been changed during development progresses, since it is impossible to define all of software requirements. These requirements change leads to mistakes because the developers cannot completely understand the software's structure and behavior, or they cannot discover all parts affected by a change. Requirement changes have to be managed and assessed to ensure that they are feasible, make economic sense and contribute to the business needs of the customer organization. We propose a feature-oriented requirements change management method to manage requirements change with value analysis and feature-oriented traceability links including intermediate catalysis using features. Our approach offers two contributions to the study of requirements change: (1) We define requirements change tree to make user requirements change request generalize by feature level. (2) We provide overall process such as change request normalization, change impact analysis, solution dealing with change request, change request implementation, change request evaluation. In addition, we especially present the results of a case study which is carried out in asset management portal system in details.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.735-742
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• 2003

In the information retrieval system, document clustering technique is to provide user convenience and visual effects by rearranging documents according to the specific topics from the retrieved ones. In this paper, we clustered documents using K-Means algorithm and present the effect of index terms weighting scheme on the document clustering. To verify the experiment, we applied Latent Semantic Indexing approach to illustrate the clustering results and analyzed the clustering results in 2-dimensional space. Experimental results showed that in case of applying local weighting, global weighting and normalization factor, the density of clustering is higher than those of similar or same weighting schemes in 2-dimensional space. Especially, the logarithm of local and global weighting is noticeable.

• Journal of The Korean Society of Clinical Toxicology
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• v.8 no.2
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• pp.79-87
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• 2010

Purpose: The previous studies on $H_1$ antihistamine overdose have generally been limited to cases of acute doxylamine succinate (DS) poisoning, yet there have been some studies on diphenhydramine (DPH) overdosing. But many clinicians consider the two drugs to be very similar and to have similar ingredients. The purpose of this study was to clarify the toxicologic characteristics and clinical outcomes between DS and DPH poisoning/overdose. Methods: We reviewed the medical and intensive care records of the patients with acute DS or DPH poisoning and who admitted to our emergency department from January 2008 and April 2010. We collected patient information regarding the features of the poisoning and the clinical and demographic characteristics. The patients were assessed for the clinical outcomes, the GCS, the PSS (Poisoning Severity Score) and the SOFA (Sequential Organ Failure Assessment). Results: Fifty seven patients (45 cases of DS poisoning and 12 cases of DPH poisoning) were enrolled. Compared with the DS group, the DPH group had higher incidences of intubation, serious mental change, QTc prolongation and ECG conduction abnormality (p=0.041, <0.001, 0.014 and 0.044, respectively). The DPH group had a higher PSS and a longer ICU stay. The peak CPK time and the CPK normalization time were longer for the patients with rhabdomyolysis due to DS poisoning. Conclusion: Two common $H_1$ antihistamines, doxylamine and diphenhydramine, are in the same ethanolamine-structural class, but the toxico-clinical outcomes are different according to many aspects. Therefore, clinicians could take a careful approach for the differential diagnosis and management between DS and DPH poisoning.

• Database Research
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• v.34 no.3
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• pp.124-136
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• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• Journal of Microbiology and Biotechnology
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• v.32 no.3
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1041-1041
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• 2001

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.