• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.729-738
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• 2015

In this paper, we compare the robustness to the assumption of normality of the single control charts to control the mean and variance simultaneously. The charts examined were semicircle control chart, max chart and MSE chart with Shewhart individuals control charts. Their in-control and out-of-control performance were studied by simulation combined with computation. We calculated false alarm rate to compare among single charts by changing subgroup size and shifting mean of quality characteristics. It turns out that max chart is more robust than any of the others if the process is in-control. In some cases max chart and MSE chart are more robust than others if the process is out-of-control.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.397-405
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• 2012

This research proposes a framework of buffer management for multi-project systems in the critical chain project management (CCPM) method, expressed in the form of max-plus linear (MPL) representation. Since time buffers are inserted in the projects for absorbing uncertainties in task durations and protecting the completion times, the proposed method provides a procedure for frequently surveying the rates of consumed buffers and the rate of elapsed times. Their relation expresses the performance of the projects which is plotted on a chart through the completed processes. The chart presents the current performance of the projects and their interaction, which alerts managers to make necessary decisions at the right time for managing each project and the entire multi-project system. The proposed framework can analyze the complex system readily, and it enables managers to make an effective decision on scheduling. The effectiveness of the framework is demonstrated through a numerical example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.264-267
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• 1998

We are dealing with the preliminary diagnosis from the information of headache interview chart. We quantify the qualitative information based on the interview chart by dual scaling. Prototype of fuzzy diagnostic sets and the neural linear regression methods are established with these quantified data, These new methods can be used to classify new patient's tone of diseases with certain degrees of belief and its concerned symptoms. We call these procedures as neural Fuzzy Differential Diagnosis of Headache (NFDDH-1). Also we investigate three measures to medical diagnosis, where relations between symptoms and diseases are described by intutionistic fuzzy set (IFS) data. Two measures are described by nin-max and max-min IFS operators, respectively. Another measure is the similarity degree, i.e., IFS distance between patient's symptoms and prototypes of diseases. We consider some reasonable criteria for three measures in order to determine the label of headache, We will establish hree measures in NFDDH-2 and combine two packages as NFDDH

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.6
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• pp.59-70
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• 2005

A series of resonant column test was performed to investigate the dynamic deformation characteristics of silty sand soils mixed with polypropylene fibrillated type fiber. Results show that optimum mixing ratios were $0.2\%$ for 19mm of cut fiber for shear modulus and $0.1\%$ for 60mm cut fiber fur damping ratio. As shear strain was increased, normalized values of shear modulus (G(Reinforced)/ G(Unreinforced)) of fiber reinforced soil were increased up to $10^{-3}\%\~10^{-1}\%$ ranges. However, normalized damping ratio (D(Reinforced/D(Unreinforced)) was diminished with an increase in strain beyond $10^{-3}\%\~10^{-1}\%$ for the damping capacity of soils mixed with fiber. Normalized shear modulus $(G/G_{max})$ obtained from the test was plotted in the chart suggested by Seed and Idriss. The shear modulus of silty sand was located between sand and gravel curves.

• The Journal of the Petrological Society of Korea
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• v.18 no.4
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• pp.315-336
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• 2009

We have studied the orientational characteristics of microcrack frequency, it's length and density in Tertiary crystalline tuff from the northeastern part of the Gyeongsang Basin. 134 sets of microcracks on horizontal surfaces of 3 rock samples from Heunghae-eup were distinguished by enlarged photomicrographs of the thin sections. The variability in patterns among microcrack length-frequency histograms for three rock samples from different altitudes were derived. The pattern of histograms changes progressively from negative exponential form to log-normal form in proportion to altitude. The distribution pattern for rock sample no.1 from lower altitude shows the broad length distribution characterized by higher mean and median, and higher standard deviation. Meanwhile, this distribution pattern corresponds with characteristics of joint length distribution in sedimentary rocks of the lower part of the Gyeongsang Supergroup. The occurrence frequency of shorter microcracks increases toward both NW and NE directions from the $N0{\sim}10^{\circ}W$, with the dominant direction of $N80{\sim}90^{\circ}W$ and $N80{\sim}90^{\circ}E$, respectively. This distribution pattern represents the relative differences in formation timing among microcrack sets and the result of the new initiation of shorter microcracks. Meanwhile, the longest microcracks within $N60{\sim}70^{\circ}W$($L_{max}$:1.18 mm) and $N0{\sim}10^{\circ}W$($L_{max}$:0.80 mm) directions are seen, but this kind of microcracks are very limited in number. Whole domain of the directional angle($\theta$)-frequency(N), length(L) and density($\rho$) chart can be divided into five sections in terms of phases of the distribution of related curves. From the distribution chart, density curve shows five distinct peaks in the WNW-ESE($N70{\sim}80^{\circ}W$), NS~NNE-SSW($N0{\sim}10^{\circ}W$, $N10{\sim}20^{\circ}E$), ENE-WSW($N50{\sim}60^{\circ}E$), and nearly EW($N80{\sim}90^{\circ}E$) directions, respectively. Especially, main directions of faults correspond with the directional angle showing high density. Consequently, these distribution patterns of density curve reflect the representative maximum principal stress orientations suggested in previous studies.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.156-161
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• 2012

Purpose : IRMA method for the experimental reagents, as set out in the manual settings for doing and reporting the test results should, in principle. But many of the reagents allows for the setting of the plot does not have a lot of information. Depending on the angle setting of the table make a difference in test results and what settings are best suited to investigate. Materials and Methods : The hospital has Boramae DREAM10, Cobra, SR-300's using the Immunotech TSH reagents have been compared with the measurements. Since then there using EXCEL calculation of the equipment compared with the measurement. Results : In the comparison between each piece of equipment 1.SR-300: DREAM 10 y = 1.1376x - 0.046 (LOG-LOGIT), 2.SR-300: COBRA y = 1.0985x + 0.042 (LOG-LOG) 3.DREAM10: COBRA y = 0.965x + 0.0887 (LOG-LOG) was the result. In all charts the same X, Y axis, X values were good in general when you have. Linear-Logit value of Cobra Dream10 and Excel tends to match the calculated values provided. Conclusion : Is to guide the B / max, B / Total Logit Y axis of the chart is set to draw a look at the value equation when the X-axis LOG a high concentration are disadvantages rising urgently toward the slope. Linear-Logit plots close to the straight line has a curve. If you have a chart to guide on setting AS setting and therefore, set to guide the absence of information on the need to set up the experiment are thought to pass through.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of the Society of Naval Architects of Korea
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• v.28 no.2
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• pp.52-68
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• 1991

A new propeller series is developed using the newly developed blade section(KH18 section) which behaves better cavitation characteristics and higher lift-drag ratio at wide range of angle-of-attack. The pitch and camber distributions are disigned in order to have the same radial and chordwise loading distribution with the selected circumferentially averaged wake input. Since the geometries of the series propeller, such as chord length, thickness, skew and rate distribations, are selected by regression of the recent full scale propeller geometric data, the performance prediction of a propeller at preliminary design stage can be mure realistic. Number of blades of the series propellers is 4 and the expanded blade area ratios are 0.3, 0.45, 0.6 and 0.75. Mean pitch ratios are selected as 0.5, 0.65, 0.8, 0.75 and 1.1 for each expanded area ratio. The new propeller series is composed of 20 propellers and is named as KD(KRISO-DAEWOO) propeller series. Propeller open water tests are performed at the experimental towing tank, and the cavitation observation tests and fluctuating pressure measurements are carried out at the cavitation tunnel of KRISO. $B_{P}-\delta$ curves, which can be used to select the optimum propeller diameter at the preliminary design stage, are derived from a regression analysis of the propeller often water test results. The KD-cavitation chart is derived from the cavitation observation test results by choosing the local maximum lift coefficient and the local cavitation number as parameters. The caviy extent of a propeller can be predicted more accurately by using the KD-cavitation chart at a preliminary design stage, since it is derived from the results of the cavitation observation tests in the selected ship's wake, whereas the existing cavitation charts, such as the Burrill's cavitation chart, are derived from the test results in uniform flow.

• Magazine of the Korean Society of Agricultural Engineers
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• v.19 no.1
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• pp.4296-4311
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• 1977

This study was conducted to derive an Instantaneous Unit Hydrograph for the accurate and reliable unitgraph which can be used to the estimation and control of flood for the development of agricultural water resources and rational design of hydraulic structures. Eight small watersheds were selected as studying basins from Han, Geum, Nakdong, Yeongsan and Inchon River systems which may be considered as a main river systems in Korea. The area of small watersheds are within the range of 85 to 470$\textrm{km}^2$. It is to derive an accurate Instantaneous Unit Hydrograph under the condition of having a short duration of heavy rain and uniform rainfall intensity with the basic and reliable data of rainfall records, pluviographs, records of river stages and of the main river systems mentioned above. Investigation was carried out for the relations between measurable unitgraph and watershed characteristics such as watershed area, A, river length L, and centroid distance of the watershed area, Lca. Especially, this study laid emphasis on the derivation and application of Instantaneous Unit Hydrograph (IUH) by applying Nash's conceptual model and by using an electronic computer. I U H by Nash's conceptual model and I U H by flood routing which can be applied to the ungaged small watersheds were derived and compared with each other to the observed unitgraph. 1 U H for each small watersheds can be solved by using an electronic computer. The results summarized for these studies are as follows; 1. Distribution of uniform rainfall intensity appears in the analysis for the temporal rainfall pattern of selected heavy rainfall event. 2. Mean value of recession constants, Kl, is 0.931 in all watersheds observed. 3. Time to peak discharge, Tp, occurs at the position of 0.02 Tb, base length of hlrdrograph with an indication of lower value than that in larger watersheds. 4. Peak discharge, Qp, in relation to the watershed area, A, and effective rainfall, R, is found to be {{{{ { Q}_{ p} = { 0.895} over { { A}^{0.145 } } }}}} AR having high significance of correlation coefficient, 0.927, between peak discharge, Qp, and effective rainfall, R. Design chart for the peak discharge (refer to Fig. 15) with watershed area and effective rainfall was established by the author. 5. The mean slopes of main streams within the range of 1.46 meters per kilometer to 13.6 meter per kilometer. These indicate higher slopes in the small watersheds than those in larger watersheds. Lengths of main streams are within the range of 9.4 kilometer to 41.75 kilometer, which can be regarded as a short distance. It is remarkable thing that the time of flood concentration was more rapid in the small watersheds than that in the other larger watersheds. 6. Length of main stream, L, in relation to the watershed area, A, is found to be L=2.044A0.48 having a high significance of correlation coefficient, 0.968. 7. Watershed lag, Lg, in hrs in relation to the watershed area, A, and length of main stream, L, was derived as Lg=3.228 A0.904 L-1.293 with a high significance. On the other hand, It was found that watershed lag, Lg, could also be expressed as {{{{Lg=0.247 { ( { LLca} over { SQRT { S} } )}^{ 0.604} }}}} in connection with the product of main stream length and the centroid length of the basin of the watershed area, LLca which could be expressed as a measure of the shape and the size of the watershed with the slopes except watershed area, A. But the latter showed a lower correlation than that of the former in the significance test. Therefore, it can be concluded that watershed lag, Lg, is more closely related with the such watersheds characteristics as watershed area and length of main stream in the small watersheds. Empirical formula for the peak discharge per unit area, qp, ㎥/sec/$\textrm{km}^2$, was derived as qp=10-0.389-0.0424Lg with a high significance, r=0.91. This indicates that the peak discharge per unit area of the unitgraph is in inverse proportion to the watershed lag time. 8. The base length of the unitgraph, Tb, in connection with the watershed lag, Lg, was extra.essed as {{{{ { T}_{ b} =1.14+0.564( { Lg} over {24 } )}}}} which has defined with a high significance. 9. For the derivation of IUH by applying linear conceptual model, the storage constant, K, with the length of main stream, L, and slopes, S, was adopted as {{{{K=0.1197( {L } over { SQRT {S } } )}}}} with a highly significant correlation coefficient, 0.90. Gamma function argument, N, derived with such watershed characteristics as watershed area, A, river length, L, centroid distance of the basin of the watershed area, Lca, and slopes, S, was found to be N=49.2 A1.481L-2.202 Lca-1.297 S-0.112 with a high significance having the F value, 4.83, through analysis of variance. 10. According to the linear conceptual model, Formular established in relation to the time distribution, Peak discharge and time to peak discharge for instantaneous Unit Hydrograph when unit effective rainfall of unitgraph and dimension of watershed area are applied as 10mm, and $\textrm{km}^2$ respectively are as follows; Time distribution of IUH {{{{u(0, t)= { 2.78A} over {K GAMMA (N) } { e}^{-t/k } { (t.K)}^{N-1 } }}}} (㎥/sec) Peak discharge of IUH {{{{ {u(0, t) }_{max } = { 2.78A} over {K GAMMA (N) } { e}^{-(N-1) } { (N-1)}^{N-1 } }}}} (㎥/sec) Time to peak discharge of IUH tp=(N-1)K (hrs) 11. Through mathematical analysis in the recession curve of Hydrograph, It was confirmed that empirical formula of Gamma function argument, N, had connection with recession constant, Kl, peak discharge, QP, and time to peak discharge, tp, as {{{{{ K'} over { { t}_{ p} } = { 1} over {N-1 } - { ln { t} over { { t}_{p } } } over {ln { Q} over { { Q}_{p } } } }}}} where {{{{K'= { 1} over { { lnK}_{1 } } }}}} 12. Linking the two, empirical formulars for storage constant, K, and Gamma function argument, N, into closer relations with each other, derivation of unit hydrograph for the ungaged small watersheds can be established by having formulars for the time distribution and peak discharge of IUH as follows. Time distribution of IUH u(0, t)=23.2 A L-1S1/2 F(N, K, t) (㎥/sec) where {{{{F(N, K, t)= { { e}^{-t/k } { (t/K)}^{N-1 } } over { GAMMA (N) } }}}} Peak discharge of IUH) u(0, t)max=23.2 A L-1S1/2 F(N) (㎥/sec) where {{{{F(N)= { { e}^{-(N-1) } { (N-1)}^{N-1 } } over { GAMMA (N) } }}}} 13. The base length of the Time-Area Diagram for the IUH was given by {{{{C=0.778 { ( { LLca} over { SQRT { S} } )}^{0.423 } }}}} with correlation coefficient, 0.85, which has an indication of the relations to the length of main stream, L, centroid distance of the basin of the watershed area, Lca, and slopes, S. 14. Relative errors in the peak discharge of the IUH by using linear conceptual model and IUH by routing showed to be 2.5 and 16.9 percent respectively to the peak of observed unitgraph. Therefore, it confirmed that the accuracy of IUH using linear conceptual model was approaching more closely to the observed unitgraph than that of the flood routing in the small watersheds.