• 호남수학학술지
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• 제42권4호
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• pp.645-651
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• 2020

The distinguishing number (index) D(G) (D'(G)) of a graph G is the least integer d such that G has an vertex labeling (edge labeling) with d labels that is preserved only by a trivial automorphism. The strong product G ☒ H of two graphs G and H is the graph with vertex set V (G) × V (H) and edge set {{(x1, x2),(y1, y2)}|xiyi ∈ E(Gi) or xi = yi for each 1 ≤ i ≤ 2.}. In this paper we study the distinguishing number and the distinguishing index of strong product of two graphs. We prove that for every k ≥ 2, the k-th strong power of a connected S-thin graph G has distinguishing index equal two.

• Communications for Statistical Applications and Methods
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• 제22권3호
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• pp.305-312
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• 2015

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.

• 대한한의진단학회지
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• 제9권1호
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• pp.69-83
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• 2005

Background and purpose: The purpose of this study is to review the recent achievements on ryodoraku and suggest new ryodoraku studies. Methods: The study papers related with ryodoraku, published in foreign countries and Korea up to the present, were collected first by internet search & journal. And then the collected papers were classified and summarized. Results and Conclusion: There are three study trends. One is finding some effect and analysis for some symptoms, another is finding a distinguishing mark and a diagnostic index, and the other is raising several points & providing solution & studying about relativity with another diagnosis. Some studies finding some effect and analysis for some symptoms are to be classified into four items(1. effect of drugs medication. 2. effect of ryodoraku therapy. 3. effect of other treatments. 4. effect of other treatments with ryodoraku therapy). Other studies finding a distinguishing mark and a diagnostic index are to be classified into two items(1. diagnostic index of symptoms. 2. distinguishing mark of disease). The other studies are to be classified into three items(1. ryodoraku introduction and raising several points at issue. 2. improvement machinery and tools. 3. studying about relativity with another diagnosis). Finally we need solving the ryodoraku problems(the condition of measurement and reproducibility, relation with Kyung-rak(經絡) and Ryodoraku, the reason of Ryodoraku points producing and etc.).

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.281-284
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• 2008

Due to the remarkable development of the GIS and spatial information technology, the information on the national land and scientific management are disseminated. According to the result of research for an efficient analysis of forest site, it presents distinguishing of satellite image and methodology of TPI (Topographic Position Index). The prediction of forest site distribution through this research, specified Gyeongju-si area, gives an effect to distinguishing honor system through Quickbird image with the resolution 0.6m. Furthermore it was carried out through TPI grid that is abstracted by DEM, slope of study area and type of topography, as well as it put its operation on analysis and verification of relativity between the result of prediction on forest site distribution and the field survey report. It distinguishes distribution of country rock that importantly effects to producing of soil, using 1: 5000 forest maps and grasping distribution type of soil using satellite image and TPI, it is supposed to provide a foundation of the result on prediction of forest site. With the GIS techniques of analysis, inclination of discussion, altitude, etc, and using high resolution satellite image and TPI, it is considered to be capable to provide more exact basis information of forest resources, management of forest management both in rational and efficient.

• 대한물리의학회지
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• 제19권3호
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• pp.55-63
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• 2024

PURPOSE: This study examined the test-retest reliability and clinical utility of the Modified Trunk Impairment Scale (mTIS), Trunk Control Test (TCT), and Postural Assessment Scale for Stroke - Trunk Control (PASS-TC) in patients with chronic stroke. METHODS: Thirty-eight stroke patients were reassessed using the mTIS, TCT, and PASS-TC with a seven-day interval between assessments. The test-retest reliability was evaluated using the intraclass correlation coefficient (ICC_2,1), the standard error of measurement (SEM), the minimal detectable change (MDC), and MDC%, as well as Bland-Altman analysis. The relationship between the mTIS, TCT, PASS-TC scores, and the Barthel Index (BI) was also investigated. RESULTS: The test-retest reliability for the mTIS, TCT, and PASS-TC was high, with ICC values ranging from .91 to .94 (95% confidence interval: .83-.97). The MDCs for the mTIS and TCT were 2.35 and 13.9, respectively, while the MDC for the P ASS-TC was 2.54, all below 20% of the maximum possible score, indicating reliable measurement. The optimal mTIS cut-off score for distinguishing between mild (75-95 points) and severe (50-74 points) dependence on the BI was ≥ 9.5, with an accuracy of 79%. Patients with an mTIS score ≥ 9.5 (out of 15) showed an 18-fold higher likelihood of achieving a mild level of functional independence than those with a score < 9.5. CONCLUSION: The mTIS, TCT, and PASS-TC showed high test-retest reliability and no systematic errors in chronic stroke patients. The MDC values were reliable, indicating meaningful change. Among these, the mTIS is a sensitive and a useful tool for predicting functional independence in clinical practice and is straightforward to apply.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.23-26
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• 2000

This paper descrtbcs our prlmary study for a new mncthod of recogninng materials. which is need for precision work system. This IS a study of dynarnlc characteristics of sensor. new melhod ($R_{SAI}$) has thc sensing ability of distinguishing materials. Experiment and annlysis are executed for proper dynamic scnslng condition. First. we developed advanced smart sensor Second, we develop new methods that have a sensing ability of distinguish matarialsAccording to frequency changing. mtluence of smart sensor are evaluated through new recognition Index ($R_{SAI}$) that ratioof sensing ability index. Disungush of object is cxucuted wllh RsA, method relalivcly according to liequency changing. Wecan use the RsAl for finding materids. Applfciltionr of thls method are linding abnormal condition of obicct (automanufacturing).keling ofobject (medlcal product). tobolics, safety diagnosis of structure, etc

• 대한핵의학회지
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• 제17권2호
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• pp.19-24
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• 1983

The purpose of this study was to develop a method by which the sensitivity of radionuclide liver imaging for detection of liver cirrhosis could be enhanced. Dynamic blood flow scan were performed 21 cases of liver cirrhosis patients by using computerized gamma camera named arterialization index. The results were as follows: 1) Arterialization index were higher in liver cirrhosis comparing to normal value 0.33 and its mean is 2. 02. 2) Comparing to static liver scan, higher sensitivity in dynamic hepatic blood flow scan for detection of liver cirrhosis. Sensitivity for detection of liver cirrhosis is over 90%. 3) There are no correlation between arterialization index and serum albumin level. The use of hepatic dynamic blood flow scan proved effective in detection of liver cirrhosis. However, the test may be used as an aid in distinguishing between normal and pathologic livers.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• 한국공작기계학회논문집
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• 제15권3호
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• pp.73-81
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• 2006

This paper describes our primary study for a new method of recognizing materials, which is need for precision work system. This is a study of dynamic characteristics of smart sensors, new method$(R_{SAI})$ has the sensing ability of distinguishing materials. Experiment and analysis are executed for finding the proper dynamic sensing condition. First, we developed advanced smart sensor. We made smart sensors for experiment. The type of smart sensor is HH type. The smart sensor was developed for recognition of material. Second, we develop new estimation methods that have a sensing ability of distinguish materials. Dynamic characteristics of sensor are evaluated through new recognition index$(R_{SAI})$ that ratio of sensing ability index. Distinguish of object is executed with $R_{SAI}$ method relatively. We can use the $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object (auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.