• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of Korean Society of Forest Science
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• v.89 no.5
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• pp.655-665
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• 2000

Taking account of the structural variation on species composition by topography, the successional trends were comparatively analyzed for the three topographic positions (valley, mid-slope, and ridge) in the natural deciduous forest of Mt. Chumbong area. The analysis was based upon the subsequent process of generation replacement by understory saplings and seedlings over the overstory trees which will be eventually fallen down. This study adopted the plot sampling method, establishing twenty $20m{\times}20m$ quadrats and collecting vegetation and site data on each different topographic position. The transition matrix model, which was modified from the mathematical theory of Markov chain, was employed to analyze the successional trends and thereafter to predict the overstory species composition in the future for each different topographic position. In valley, the simulation indicated the remarkable decrease in the proportion of species composition of present dominants Quercus mongolica and Fraxinus mandshurica from current 23% and 21% to around 4% of each at the steady state, which is predicted to take less than 200 years. On the other hand, the proportion of such species as Abies holophylla, Acer mono, Tilia amurensis, and Ulmus laciniata will increase at the steady state. In mid-slope, the result showed the remarkable decrease in the proportion of Juglans mandshurica, Kalopanax pictus, and Tilia amurensis from current 15%, 8%, and 15% to 2%, 1%, and 5%, respectively, at steady state predicted to take more than 250 years. In ridge, the current dominant Quercus mongolica was predicted to be decreased dramatically from 58% to 8% at steady state which could be achieved about 200 years. On the contrary, the proportion of Acer mono and Tilia amurensis will be increased from current 4% and 3% to more than 20% and 40%, respectively, at the steady state. Overall results suggested that the study forest is more likely seral rather than climax community. Even though a lot of variation is inevitable due to various kinds of site and vegetation development, the study forest is considered to be more than 200 years away from the steady state or climax in terms of overstory species composition.

• Progress in Medical Physics
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• v.20 no.3
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• pp.139-144
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• 2009

Recently, the mathematical analysis of stent simulation has been improved, with the help of development of various tool which measure mechanical property and location of stent in artery. The most crucial part of the stent modeling is how to design ideal stent and to evaluate the interaction between stent and artery. While there has been great deal of researches on the evaluation of the expansion, stress distribution, deformation of the stent in terms of the various parameters, few verification through computer simulation has been performed about deformation and stress distribution of the stent. In this study, we have produced the corresponding results between experimental test using Universal Testing Machine and computer simulation for the ideal model of stent. Also, we have analyzed and compared stress distribution of stent in the cases of that with membrane and that without membrane. The results of this study would provide minimum change of plan and good quality for ideal stent replacing damaged artery through the analysis using computer simulation in the early stage of stent design.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.273-280
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• 2003

The purpose of this study is to identify and evaluate the competitiveness of ports in ASEAN(Association of Southeast Asian Nations), which plays a leading role in basing the hub of international logistics strategies as a countermeasure in changes of international logistics environments. This region represents most severe competition among Mega hub ports in the world in terms of container cargo throughput at the onset of the 21 st century. The research method in this study accounted for overlapping between attributes, and introduced the HFP method that can perform mathematical operations. The scope of this study was strictly confined to the ports of ASEAN. which cover the top 100 of 350 container ports that were presented in Containerization International Yearbook 2002 with reference to container throughput. The results of this study show Singapore in the number one position. Even compared with major ports in Korea (after getting comparative ratings and applying the same data and evaluation structure), the number one position still goes to Singapore and then Busan(2) and Manila(2), followed by Port Klang(4), Tanjugn Priok(5), Tanjung Perak(6), Bangkok(7), Inchon(8), Laem Chabang(9) and Penang(9). In terms of the main contributions of this study, it is the first empirical study to apply the combined attributes of detailed and representative attributes into the advanced HFP model which was enhanced by the KJ method to evaluate the port competitiveness in ASEAN. Up-to-now, none have comprehensively conducted researches with sophisticated port methodology that has discussed a variety of changes in port development and terminal transfers of major shipping lines. Moreover, through the comparative evaluation between major ports in Korea and ASEAN, the presentation of comparative competitiveness for Korea ports is a great achievement in this study. In order to reinforce this study, it needs further compensative research, including cost factors which could not be applied to modeling the subject ports by lack of consistently qualified in ASEAN.