• Magazine of the Korean Society of Agricultural Engineers
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• v.7 no.1
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• pp.861-876
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• 1965

During my stay in the Netherlands, I have studied the following, primarily in relation to the Mokpo Yong-san project which had been studied by the NEDECO for a feasibility report. 1. Unit hydrograph at Naju There are many ways to make unit hydrograph, but I want explain here to make unit hydrograph from the- actual run of curve at Naju. A discharge curve made from one rain storm depends on rainfall intensity per houre After finriing hydrograph every two hours, we will get two-hour unit hydrograph to devide each ordinate of the two-hour hydrograph by the rainfall intensity. I have used one storm from June 24 to June 26, 1963, recording a rainfall intensity of average 9. 4 mm per hour for 12 hours. If several rain gage stations had already been established in the catchment area. above Naju prior to this storm, I could have gathered accurate data on rainfall intensity throughout the catchment area. As it was, I used I the automatic rain gage record of the Mokpo I moteorological station to determine the rainfall lntensity. In order. to develop the unit ~Ydrograph at Naju, I subtracted the basic flow from the total runoff flow. I also tried to keed the difference between the calculated discharge amount and the measured discharge less than 1O~ The discharge period. of an unit graph depends on the length of the catchment area. 2. Determination of sluice dimension Acoording to principles of design presently used in our country, a one-day storm with a frequency of 20 years must be discharged in 8 hours. These design criteria are not adequate, and several dams have washed out in the past years. The design of the spillway and sluice dimensions must be based on the maximun peak discharge flowing into the reservoir to avoid crop and structure damages. The total flow into the reservoir is the summation of flow described by the Mokpo hydrograph, the basic flow from all the catchment areas and the rainfall on the reservoir area. To calculate the amount of water discharged through the sluiceCper half hour), the average head during that interval must be known. This can be calculated from the known water level outside the sluiceCdetermined by the tide) and from an estimated water level inside the reservoir at the end of each time interval. The total amount of water discharged through the sluice can be calculated from this average head, the time interval and the cross-sectional area of' the sluice. From the inflow into the .reservoir and the outflow through the sluice gates I calculated the change in the volume of water stored in the reservoir at half-hour intervals. From the stored volume of water and the known storage capacity of the reservoir, I was able to calculate the water level in the reservoir. The Calculated water level in the reservoir must be the same as the estimated water level. Mean stand tide will be adequate to use for determining the sluice dimension because spring tide is worse case and neap tide is best condition for the I result of the calculatio 3. Tidal computation for determination of the closure curve. During the construction of a dam, whether by building up of a succession of horizontael layers or by building in from both sides, the velocity of the water flowinii through the closing gapwill increase, because of the gradual decrease in the cross sectional area of the gap. 1 calculated the . velocities in the closing gap during flood and ebb for the first mentioned method of construction until the cross-sectional area has been reduced to about 25% of the original area, the change in tidal movement within the reservoir being negligible. Up to that point, the increase of the velocity is more or less hyperbolic. During the closing of the last 25 % of the gap, less water can flow out of the reservoir. This causes a rise of the mean water level of the reservoir. The difference in hydraulic head is then no longer negligible and must be taken into account. When, during the course of construction. the submerged weir become a free weir the critical flow occurs. The critical flow is that point, during either ebb or flood, at which the velocity reaches a maximum. When the dam is raised further. the velocity decreases because of the decrease\ulcorner in the height of the water above the weir. The calculation of the currents and velocities for a stage in the closure of the final gap is done in the following manner; Using an average tide with a neglible daily quantity, I estimated the water level on the pustream side of. the dam (inner water level). I determined the current through the gap for each hour by multiplying the storage area by the increment of the rise in water level. The velocity at a given moment can be determined from the calcalated current in m3/sec, and the cross-sectional area at that moment. At the same time from the difference between inner water level and tidal level (outer water level) the velocity can be calculated with the formula $h= \frac{V^2}{2g}$ and must be equal to the velocity detertnined from the current. If there is a difference in velocity, a new estimate of the inner water level must be made and entire procedure should be repeated. When the higher water level is equal to or more than 2/3 times the difference between the lower water level and the crest of the dam, we speak of a "free weir." The flow over the weir is then dependent upon the higher water level and not on the difference between high and low water levels. When the weir is "submerged", that is, the higher water level is less than 2/3 times the difference between the lower water and the crest of the dam, the difference between the high and low levels being decisive. The free weir normally occurs first during ebb, and is due to. the fact that mean level in the estuary is higher than the mean level of . the tide in building dams with barges the maximum velocity in the closing gap may not be more than 3m/sec. As the maximum velocities are higher than this limit we must use other construction methods in closing the gap. This can be done by dump-cars from each side or by using a cable way.e or by using a cable way.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.54-59
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• 2017

Purpose The present study aimed at assessing the effectiveness of the respiratory gating method used in the flow mode and additional localized respiratory-gated imaging, which differs from the step and go method. Materials and Methods Respiratory gated imaging was performed in the flow mode to twenty patients with lung cancer (10 patients with stable signals and 10 patients with unstable signals), who underwent PET/CT scanning of the torso using Biograph mCT Flow PET/CT at Bundang Seoul University Hospital from June 2016 to September 2016. Additional images of the lungs were obtained by using the respiratory gating method. SUVmax, SUVmean, and Tumor Volume ($cm^3$) of non-gating images, gating images, and additional lung gating images were found with Syngo,bia (Siemens, Germany). A paired t-test was performed with GraphPad Prism6, and changes in the width of the amplitude range were compared between the two types of gating images. Results The following results were obtained from all patients when the respiratory gating method was applied: $SUV_{max}=9.43{\pm}3.93$, $SUV_{mean}=1.77{\pm}0.89$, and $Tumor\;Volume=4.17{\pm}2.41$ for the non-gating images, $SUV_{max}=10.08{\pm}4.07$, $SUV_{mean}=1.75{\pm}0.81$, and $Tumor\;Volume=3.56{\pm}2.11$ for the gating images, and $SUV_{max}=10.86{\pm}4.36$, $SUV_{mean}=1.77{\pm}0.85$, $Tumor\;Volume=3.36{\pm}1.98$ for the additional lung gating images. No statistically significant difference in the values of $SUV_{mean}$ was found between the non-gating and gating images, and between the gating and lung gating images (P>0.05). A significant difference in the values of $SUV_{max}$ and Tumor Volume were found between the aforementioned groups (P<0.05). The width of the amplitude range was smaller for lung gating images than gating images for 12 from 20 patients (3 patients with stable signals, 9 patients with unstable signals). Conclusion In PET/CT scanning using the respiratory gating method in the flow mode, any lesion movements caused by respiration were adjusted; therefore, more accurate measurements of $SUV_{max}$, and Tumor Volume could be obtained from the gating images than the non-gating images in this study. In addition, the width of the amplitude range decreased according to the stability of respiration to a more significant degree in the additional lung gating images than the gating images. We found that gating images provide information that is more useful for diagnosis than the one provided by non-gating images. For patients with irregular signals, it may be helpful to perform localized scanning additionally if time allows.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.129-139
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• 2012

Given digraph network $D=(N,A),n{\in}N,a=c(u,v){\in}A$ with source s and sink t, the maximum flow from s to t is determined by cut (S, T) that splits N to $s{\in}S$ and $t{\in}T$ disjoint sets with minimum cut value. The Ford-Fulkerson (F-F) algorithm with time complexity $O(NA^2)$ has been well known to this problem. The F-F algorithm finds all possible augmenting paths from s to t with residual capacity arcs and determines bottleneck arc that has a minimum residual capacity among the paths. After completion of algorithm, you should be determine the minimum cut by combination of bottleneck arcs. This paper suggests maximum adjacency merging and compute cut value method is called by MA-merging algorithm. We start the initial value to S={s}, T={t}, Then we select the maximum capacity $_{max}c(u,v)$ in the graph and merge to adjacent set S or T. Finally, we compute cut value of S or T. This algorithm runs n-1 times. We experiment Ford-Fulkerson and MA-merging algorithm for various 8 digraph. As a results, MA-merging algorithm can be finds minimum cut during the n-1 running times with time complexity O(N).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5827-5833
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• 2013

Due to science and technology evolutions, modern systems are becoming larger and more complex. In developing complex systems, Model-Based Systems Engineering (MBSE), which is approach to reduce complexity, is being introduced and applied to various system domains. However, because of the modeling being made through a variety of languages, there is a problem with communication within the stakeholders and a lack of consistency in the models. In this paper, by investigating the rule explaining the transformation of one of the only traditional diagrams, DFD, to SysML and reusing the formerly built models, we attempt to implement by SysML. Analyzing each diagram's Metamodel and validating the connection of each component through bipartite graph especially suggest an effective transformation rule. Also, by applying to naval-combat system, we confirm efficiency of this study. Establishing the results of this study as basis for conducting further study, we will be able to transform other previous models gained from formerly built system to SysML. In this way, the stakeholder's communication can be improved and we anticipate that the application of SysML will be beneficial to the much efficient MBSE.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.63-71
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• 2019

The two-fluid model proposed by Herman and Prigogine is useful for analyzing macroscopic traffic flow in a network. The two-fluid model is used for analyzing a network through the relationship between the ratio of stopped vehicles and the average moving speed of the network, and the two-fluid model has also been applied in the urban transportation network where many signalized or unsignalized intersections existed. In general, the average travel speed and moving speed of a network decrease, and the ratio of stopped vehicles and low speed vehicles in network increase as the traffic demand increases. This study proposed the two-fluid model considering congested and uncongested traffic situations. The critical velocity and the weight factor for congested situation are calibrated by minimizing the root mean square error (RMSE). The critical speed of the Seoul network was about 34 kph, and the weight factor of the congestion on the network was about 0.61. In the proposed model, $R^2$ increased from 0.78 to 0.99 when compared to the existing model, suggesting that the proposed model can be applied in evaluating network performances or traffic signal operations.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.22-34
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• 2021

The implementation of the sunset provision of unexecuted urban parks in Seoul has been postponed; however, the mentioned parks still remain vulnerable since they can be subject to development under certain circumstances. Local governments may purchase the parks to prevent their loss but are constrained due to limited resources. The purpose of this study is to prioritize the purchase of unexecuted urban parks in Seoul based on landscape connectivity, which represents the important role of allowing the movement of wildlife and providing biodiversity in urban environments. In this study, we used four potential scenarios (PB100, PB1, PA100, PA1), which reflects the degree of land cover change resulting from the implementation of the sunset provision, and the role of Han River as a conduit or barrier for wildlife movement. Landscape connectivity was evaluated by calculating current flow betweenness centrality (CFBC). This was used to rank the importance of the unexecuted urban parks in Seoul. The results demonstrated that the implementation of the sunset provision will greatly decrease the connectivity of all parks in Seoul and particularly more so for parks in the southern part of the city. In addition, the results suggested that the low connectivity of Han river will diminish the connectivity around Bukhansan Mountain in the northern part of Seoul. Our study can be used for the prioritization of purchase, since it has the ability to evaluate the anticipated vulnerability of each park's connectivity after the sunset provision.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• Journal of Practical Agriculture & Fisheries Research
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• v.17 no.1
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• pp.57-71
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• 2015

This study was conducted to develop the fertigation system with a peristaltic hose pump and brushless DC motor. The fertigation system was consisted of sensor, main controller, motor control unit, peristaltic pump, water supply pump, control panel, and filter. The peristaltic pump discharges liquid by squeezing the tube with rollers. Rollers attached to the external circumference of the rotor compresses the flexible tube. The fluid is contained within a flexible tube fitted inside a circular pump casing. The developed fertigation system has no mixing tank but instead injects directly a concentrated nutrient solution into a water supply pipe. The revolution speed of the peristaltic pump is controlled by PWM (Pulse width modulation) method. When the revolution speed of the peristaltic pump was 300rpm, the flow rate of the 3.2, 4.8, 6.3mm diameter tube was 202, 530, 857mL/min, respectively. As increasing revolution speed, the flow rate of the peristaltic pump linearly increased. As the inner diameter of a tube larger, a slope of graph is more steep. Flow rate of three roller was more than that of four roller. Flow rate of a norprene tube with good restoring force was more than that of a pharmed tube. As EC sensor probe was installed in direct piping in comparison with bypass piping showed good performance. After starting the system, it took 16~17 seconds to stabilize EC. The maximum value of EC was 1.44~1.7dS/m at a setting value of 1.4dS/m. The developed fertigation system showed ±0.06dS/m deviation from the setting value of EC. In field test, Cucumber plants generally showed good growth. From these findings, this fertigation system can be appropriately suitable for fertigation culture for crops.

• 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.

• Radiation Oncology Journal
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• v.9 no.1
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• pp.1-6
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• 1991

The effect of 2-deoxy-d-glucose (2-DDG) on $C_3H$ mouse fibrosarcoma(FSall) was studied. Metabolic status, especially for energy metabolism, was studied using in vivo $^{31}P$-MRS, proliferative capacity was observed on flow cytometry(FC) and growth rate was measured after transplantation of $10^6$ viable tumor cells in the dorsum of foot of $C_3Hf/Sed$ mice. One gram of 2-DDG Per kg of body weight was injected intraperitoneally on 12th day of implantation. Average tumor size on 12th day of implantion was $250mm^3$. Growth rate of Fsall tumor was measured by tumor doubling time and slope on semilog plot. After 2-DDG injection, growth rate slowed down. Tumor doubling time between tumor age 5-12 days was 0.84 days with slope 0.828 and tumor doubling time between tumor age 13-28 days was 3.2 days with slope 0.218 in control group. After 2-DDG injection, tumor doubling time was elongated to 5.1 days with slope 0.136. The effect of 2-DDG studied in vivo $^{31}P$-MRS suggested that the increase of phosphomonoester (PME) and inorganic phosphate (Pi) by increasing size of tumor, slowed down after 2-DDG injection. Flow cytometry showed significantly increased S-phase and $G_2+M$ phase fraction suggesting increased proliferative capacity of tumor cells in the presence of 2-DDG. Authors observed an interesting effect of 2-DDG on FSall tumor and attempt to utilize as an adjunct for radiotherapy.