• Natural Product Sciences
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• v.17 no.2
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• pp.147-159
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• 2011

A high-performance liquid chromatography-electrospray ionization-tandem mass spectrometric method was developed to determine simultaneously eight marker constituents of Forsythiae fructus, and subsequently applied it to classify its two botanical origins. The marker compounds of Forsythia suspensa were phillyrin, pinoresinol, phillygenin, lariciresinol and forsythiaside; those of F.viridissima were arctiin, arctigenin and matairesinol. Separation of the eight analytes was achieved on a phenyl-hexyl column (150${\times}$2.0 mm i.d., 3 ${\mu}M$) using gradient elution with the mobile phase: (A) 10% acetonitrile in 0.5% acetic acid, (B) 40% aqueous acetonitrile. A few fragment ions specific to the types of lignans, among the product ions generated by collisonally induced dissociation (CID) of molecular ion clusters, such as [M-H]$^-$ or [M+OAc]$^-$ were used not only for fingerprinting analysis but for the quantification of each epimer by using multiple-reaction monitoring mode. It was shown good linearity ($r^2{\geq}$ 0.9998) over the wide range of all analytes; intra- and inter-day precisions (RSD, %) were within 9.14% and the accuracy ranged from 84.3 to 115.1%. The analytical results of 40 drug samples, combined with multivariate statistical analyses - principal component analysis (PCA) and hierarchical cluster analysis (HCA) - clearly demonstrated the classification of the test samples according to their botanical origins. This method would provide a practical strategy for assessing the authenticity or quality of the herbal drug.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.76-82
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• 1998

Increase of greenhouse gas due to $CO_2$ and CH$_4$ gases would cause the global warming in the atmosphere. According to the global circulation model, it is pointed out in the Okhotsk Sea that the large increase of atmospheric temperature might be occurredin this region by global warming due to the doubling of greenhouse effectgases. Therefore, it is very important to monitor the sea ice extents in the Okhotsk Sea. To improve the sea ice extents and concentration with more highly accuracy, the field experiments have begun to comparewith Airborne Microwave Radiometer (AMR) and video images installed on the aircraft (Beach-200). The sea ice concentration is generally proportional to the brightness temperature and accurate retrieval of sea ice concentration from the brightness temperature is important because of the sensitivity of multi-channel data with the amount of open water in the sea ice pack. During the field experiments of airborned AMR the multi-frequency data suggest that the sea ice concentration is slightly dependending on the sea ice types since the brightness temperature is different between the thin and small piece of sea ice floes, and a large ice flow with different surface signatures. On the basis of classification of two sea ice types, it is cleary distinguished between the thin ice and the large ice floe in the scatter plot of 36.5 and 89.0GHz, but it does not become to make clear of the scatter plot of 18.7 and 36.5GHz Two algorithms that have been used for deriving sea ice concentrations from airbomed multi-channel data are compared. One is the NASA Team Algorithm and the other is the Bootstrap Algorithm. Intrercomparison on both algorithms with the airborned data and sea ice concentration derived from video images bas shown that the Bootstrap Algorithm is more consistent with the binary maps of video images.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.6
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• pp.1471-1474
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• 2005

Sasang constitution medicine is to do different treatment accordining to sasang constitution. Therefore, the constitution diagnosis in the Sasang constitution medicine is very important thing. The Process of Sasang constitution diagnosis Is difficult thing, because of consuming much time, making every effort. It is apt to be subjective tendency. So it need to make objective method. The QSCC II (Questionnaire of Sasang Constitution Classification II ) have several problems- can't do diagnosis of Taeyangin, the accuracy rate of Sasang constitution diagnosis is not high (probably 60%), and so on. So, we need the new methods for the Sasang constitution Diagnosis. We will modify the problems of QSCC II. The First is the problems of the study execution process, not-multicenter study, a low data, the absent of Taeyangin cases. So, we have to do the multicenter study. The Second is the problems of a query and the method of statistics analysis. We will modify the problems of self-report Questionnaire. That is the problems of self-report Questionnaire, the lack of objective estimation( body type, personal appearance, etc), the absent of the estimation on typical or non-typical type constitution. We modified the problems of QSCC II. Therefore we made the new self-report Questionnaire for patients. We modified the problems of self-report Questionnaire. Therefore we made the new Constituion diagnosis Questionnaire for doctors. We develop the Questionnaire of two ways for the Sasang constitution Diagnosis. The one is the new self-report Questionnaire for patients. The other is the new Constitution diagnosis Questionnaire for doctors. We have to melt down the Questionnaire of two ways for the Sasang constitution Diagnosis.

• Journal of Biosystems Engineering
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• v.38 no.4
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• pp.312-317
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• 2013

Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.6
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• pp.319-331
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• 2016

This study explores the signal characteristics for different averaging intervals and defines representative verticies for each observatory by criterion of percent rate and variance. The shorter averaging interval shows the higher frequency variation, though the lower percent rate. In the tidal currents, we could hardly find the differences between 60-minute and 20-minute averaging. The newly defined criterion improves reliability of HF-radar data compared with the present reference which deselects the half by percent rate.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.47-53
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• 2017

In past few years, high-throughput sequencing, big-data generation, cloud computing, and computational biology are revolutionary. RNA sequencing is emerging as an attractive alternative to DNA microarrays. And the methods for constructing Gene Regulatory Network (GRN) from RNA-Seq are extremely lacking and urgently required. Because GRN has obtained substantial observation from genomics and bioinformatics, an elementary requirement of the GRN has been to maximize distinguishable genes. Despite of RNA sequencing techniques to generate a big amount of data, there are few computational methods to exploit the huge amount of the big data. Therefore, we have suggested a novel gene selection algorithm combining Support Vector Machines and Intensity-dependent normalization, which uses log differential expression ratio in RNAseq. It is an extended variation of support vector machine recursive feature elimination (SVM-RFE) algorithm. This algorithm accomplishes minimum relevancy with subsets of Big-Data, such as NCBI-GEO. The proposed algorithm was compared to the existing one which uses gene expression profiling DNA microarrays. It finds that the proposed algorithm have provided as convenient and quick method than previous because it uses all functions in R package and have more improvement with regard to the classification accuracy based on gene ontology and time consuming in terms of Big-Data. The comparison was performed based on the number of genes selected in RNAseq Big-Data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.313-321
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• 2012

Korea has been continuously affected by landslides, as 70% of the land is covered by mountains and most of annual rainfall concentrates between June and September. Recently, abrupt climate change affects the increase of landslide occurrence. Gangwon region is especially suffered by landslide damages, because the most of the part is mountainous, steep, and having shallow soil. In this study, a landslide risk assessment model was developed by applying logistic regression to the various data of Duksan-ri, Inje-eup, Inje-gun, Gangwon-do, which has suffered massive landslide triggered by heavy rain in July 2006. The information collected from field investigation and aerial photos right after the landslide of study area were stored in GIS DB for analysis. Slope gradient entered in two ways-as categorical variable and as linear variable. Error matrix for each case was made, and developed model showed the classification accuracy of 81.4% and 81.9%, respectively.

• Health Policy and Management
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• v.26 no.4
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• pp.289-304
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• 2016

Background: Rehabilitations in subacute phase are different from acute treatments regarding the characteristics and required resource consumption of the treatments. Lack of accuracy and validity of the Korean Diagnosis Related Group and Korean Out-Patient Group for the acute patients as the case-mix and payment tool for rehabilitation inpatients have been problematic issues. The objective of the study was to develop the Korean Rehabilitation Patient Group (KRPG) reflecting the characteristics of rehabilitation inpatients. Methods: As a retrospective medical record survey regarding rehabilitation inpatients, 4,207 episodes were collected through 42 hospitals. Considering the opinions of clinical experts and the decision-tree analysis, the variables for the KRPG system demonstrating the characteristics of rehabilitation inpatients were derived, and the splitting standards of the relevant variables were also set. Using the derived variables, we have drawn the rehabilitation inpatient classification model reflecting the clinical situation of Korea. The performance evaluation was conducted on the KRPG system. Results: The KRPG was targeted at the inpatients with brain or spinal cord injury. The etiologic disease, functional status (cognitive function, activity of daily living, muscle strength, spasticity, level and grade of spinal cord injury), and the patient's age were the variables in the rehabilitation patients. The algorithm of KRPG system after applying the derived variables and total 204 rehabilitation patient groups were developed. The KRPG explained 11.8% of variance in charge for rehabilitation inpatients. It also explained 13.8% of variance in length of stay for them. Conclusion: The KRPG version 1.0 reflecting the clinical characteristics of rehabilitation inpatients was classified as 204 groups.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.81-91
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• 2006

Recently, G.A studies have studied and demonstrated that artificial intelligence like G.A networks, G.A PI controller. The design techniques of PI controller using G.A with the newly proposed teaming algorithm was presented, and the designed controller with AC servo motor system. The goal of this paper is to design the AC servo motor using genetic algorithm and to control drive robot. And in this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for genetic algorithm PI controller. Our experimental results show that this approach increases overall classification accuracy rate significantly. Finally, we executed for the implementation of high performance speed control system. It is used a 16-bit DSP, IMS320LF2407, which is capable of the high speed and floating point calculation.

• Journal of Biosystems Engineering
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• v.37 no.6
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• pp.404-410
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• 2012

Purpose: The objective of this research was to select high quality cucumber (cucumis sativus) seed by classifying into viable or non-viable one using Raman spectroscopy. Method: Both transmission and back-scattering Raman spectra of viable and non-viable seeds in the range from $150cm^{-1}$ to $1890cm^{-1}$ were collected with a laser illumination. Results: The Raman spectra of cucumber seed showed Raman peaks with features of polyunsaturated fatty acids. The partial least squares-discriminant analysis (PLS-DA) to predict viable seeds was developed with measured transmission and backscattering spectra with Raman spectroscopy and germination test results. Various types of spectra pretreatment were investigated to develop the classification models. The results of developed PLS-DA models using the transmission spectra with mean normalization or range normalization, and back-scattering spectra with mean normalization treatment or baseline correction showed 100% discrimination accuracy. Conclusions: These results showed that Raman spectroscopy technologies can be used to select the high quality cucumber seeds.