• Journal of Korea Water Resources Association
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• v.40 no.4
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• pp.325-334
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• 2007

In this study, a long-term unsteady simulation model has been developed using rigid water column theory which is more accurate than Extended-period model and more efficient comparing with water-hammer simulation model. The developed model is applied to 24-hours unsteady simulation considering daily water-demand and water-hammer analysis caused by closing a valve. For the case of 24-hours daily simulation, the pressure of each node decreases as the water demand increase, and when the water demand decrease, the pressure increases. During the simulation, the amplitudes of flow and pressure variation are different in each node and the pattern of flow variation as well as water demand is quite different than that of KYPIPE2. Such discrepancy necessitates the development of unsteady flow analysis model in water distribution network system. When the model is applied to water-hammer analysis, the pressure and flow variation occurred simultaneously through the entire network system by neglecting the compressibility of water. Although water-hammer model shows the lag of travel time due to fluid elasticity, in the aspect of pressure and flow fluctuation, the trend of overall variation and quantity of the result are similar to that of water-hammer model. This model is expected for the analysis of gradual long-term unsteady flow variations providing computational accuracy and efficiency as well as identifying pollutant dispersion, pressure control, leakage reduction corresponding to flow-demand pattern, and management of long-term pipeline net work systems related with flowrate and pressure variation in pipeline network systems

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1396-1408
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• 2020

Filtered containment venting system (FCVS) is one of the severe accident mitigation systems designed to release containment pressurization to maintain its integrity. The thermal-hydraulic behaviors in FCVSs are important since they affect the operation characteristics of the FCVS. In this study, a representative FCVS was modeled by RELAP5/Mod3.3 code, and the Station BlackOut (SBO) was chosen as an accident scenario. The thermal-hydraulic behaviors of an FCVS during long-term operation with two venting strategies (open-and-close strategy, open-and-non-close strategy) and the sensitivity analysis of important parameters were investigated. The results show that the FCVS can operate up to 250 h with a periodic open-and-close strategy during an SBO. Under the combined effects of steam condensation and water evaporation, the solution inventory in the FCVS increases during the venting phase and decreases during the intermission phase, showing a periodic pattern. Under this condition, the appropriate initial water level is 3-4 m; however, it should be adjusted according to the environment temperature. The FCVS can accommodate a decay heat power of 150-260 kW and may need to feed water for a higher decay heat power or drain water for a lower decay heat power during the late phase. The FCVS can function within an opening pressure range from 450 kPa to 500 kPa and a closing pressure range between 250 kPa and 350 kPa. When the open-and-non-close strategy is adopted, the solution inventory increases quickly in the early venting phase due to steam condensation and then decreases gradually due to the evaporation of water; drying-up may occur in the late venting phase. Decreasing the venting pipe diameter and increasing the initial water level can mitigate the evaporation of the scrubbing solution. These results are expected to provide useful references for the design and engineering application of FCVSs.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.24 no.3
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• pp.203-213
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• 1991

Zooplankton samples were collected vertically from different layers with a closing net at 14 stations in the middle East Sea of Korea in February, August and September to study distribution of biological indicators for analysis of water masses. Horizontal and vertical distributions of important species of copepods and chaetognathas known as indicator species were closely related to distributions of different water masses and oceanic fronts. Pleuromamma gracilis, Calanus tenuicornis, Sagitta enflata and Sagitta minima were found to be reliable indicator species to determine warm water mass with warm core, and Calanus cristatus, Calanus tonsus and Sagitta elegans could be used as cold water species for evaluating the movement of cold current from North Korea, and Gaetanus armiger was deep sea water species. Therefore, it was found that North Korean Cold Current down to the south along the coast appeared to be significant in the surface around Chumunjin area, and from here towards the south the cold water containing S. elegans submerged under warm water with S. enflata which were about $2{\~}4^{\circ}C$ higher than that of the vicinity and reappeared near Chukpeon area in surface layer. In the layer between loom and 300m depths, distribution of Pleuromamma gracilis and Sagitta bedoti indicated that warm water mass and front zone influenced by the different water systems were formed in northwestern area off Ulreung-do. In $300{\~}500m$ layer, the proper cold water could be estimated to be present in the northwestern area off Ulreung-do throughout the survey period by the high abundance of Gaetanus armiger. In August, distributions of S. bedoti, S. enflata and S. minima were valuable index to find oceanic fronts and warm core.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.