• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.16 no.1
• /
• pp.11-21
• /
• 2020

This study aims to develop and advance the evaluation technology for assessing PHWR safety. For this purpose, the complete loss of AC power or station blackout (SBO) was selected as a target accident scenario and the analysis model to evaluate the plant responses was envisioned into the MARS-KS input model. The model includes the main features of the primary heat transport system with a simplified model for the horizontal fuel channels, the secondary heat transport system including the shell side of steam generators, feedwater and main steam line, and moderator system. A steady state condition was achieved successfully by running the present model to check out the stable convergence of the key parameters. Subsequently, through the SBO transient analyses two cases with and without the coolant leakage via the PHTS pumps were simulated and the behaviors of the major parameters were compared. The sensitivity analysis on the amount of the coolant leakage by varying its flow area was also performed to investigate the effect on the system responses. It is expected that the results of the present study will contribute to upgrading the evaluation technology of the detailed thermal hydraulic analysis on the SBO transient of the operating PHWRs.

• Journal of Advanced Navigation Technology
• /
• v.22 no.6
• /
• pp.491-499
• /
• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.5
• /
• pp.621-626
• /
• 2019

The driver's steering wheel maneuver is a typical disturbance that causes excessive body motion and traveling instability of a vehicle. Abrupt and extreme operation can cause rollover depending on the geometric and dynamic characteristics, e.g., SUV vehicles. In this study, to cope with the performance limitation of conventional cars, fundamental research on the structurization of a control system was performed as follows. Mathematical modeling of the lateral behavior induced by driver input was carried out. A controller was designed to reduce the body motion based on this model. An algorithm was applied to secure robust control performance against modeling errors due to parameter uncertainty, $H_{\infty}$. Using the decoupled 1/4 car, a dynamic load simulating model considering the body moment was suggested. The simulation result showed the validity of the load-simulating model. The framework for a lateral behavior control system is proposed, including an experimental 1/4 vehicle unit, load simulating module, suspension control module, and hardware-in-the-loop simulation technology.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.3
• /
• pp.466-472
• /
• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• The Journal of Fisheries Business Administration
• /
• v.41 no.3
• /
• pp.129-151
• /
• 2010

This study is to analyze the efficiency of Korean fishing ports using DEA. First, the study calculated the efficiency scores based on a CCR-BCC framework and hence technical, pure technical, and scale efficiency scores are seperated for the 38 fishing ports under study. The Average of technical, pure technical, and scale efficiency are turned out to be 0.6834, 0.8582, and 0.7774 respectively. The 15 fishing ports are fully efficient under the constant returns to scale while 21 fishing ports under the variable returns to scale. Second, the super efficiency scores are also calculated under the radial model without the consideration of slacks. The highest score is turned out to be 4.4984 for the P16 fishing port with the average score of 0.9652 for the entire fishing ports. Nevertheless, P16 fishing port has showed up only once as a reference set. On the other hand, P34 fishing port has showed up 11 times as a reference set, which scored the second highest score of 2.9815. Finally the super efficiency scores are calculated under the non-radial model with the explicit consideration of slacks. Now the P34 fishing port scored the highest score of 2.3424 with even 15 times referred to a bench-mark. Therefore the importance of P34 fishing port is emphasized once again on the field of bench-marking for the efficiency of fishing ports. When the targets for the input factors to improve the efficiency of each DMU are calculated the area of fishing port needs the most adjustment to be reduced for 40.36% on the average, while the cosignment sales area does the least adjustment for 13.70%.

• Journal of the Society of Naval Architects of Korea
• /
• v.31 no.1
• /
• pp.14-21
• /
• 1994

The studios on the stability of damaged ships have been carried out continuously to prevent frequent damages or sinkings which cause large loss of life and fortunes. For dry cargo ships, continuing losses have resulted in new legislation of the probabilistic damage stability. IMO has developed requirements for the subdivison and damage stability of dry cargo ships based on probabilistic concepts. The calculation of the probabilistc damage stability is a complicated and iterative job hence development of computer programs is indispensable. In this research, programming of the probabilistic damage stability according to new requirements has been done and the results were compared with those carried out by the other foreign packages. New algorithm using a grid model in a transversal section was introduced to reduce efforts in preparing input data for damage scenarios and as a result, has brought significant improvement in efficiency and performance.

• Asia-Pacific Journal of Business
• /
• v.13 no.4
• /
• pp.45-59
• /
• 2022

Purpose - This study investigates the performance of investment strategies incorporating estimated stock market cycle based on a lead-lag relationship between business cycle and stock market cycle, thereby deriving empirical implications on risk management. Design/methodology/approach - The data period ranges from June 1953 to September 2022 and de-trended short rate, term spread, credit spread, stock market volatility are considered as major input variables to estimate business cycle and stock market cycle by applying probit model. Based on the estimated stock market cycle, two types of strategies are constructed and their performance relative to the benchmark is empirically examined. Findings Two types of strategies based on stock market cycle are considered: The first strategy is to long(short) on stocks when stock market stage is expected to be an expansion(a recession), and the second one is to long on stocks(bonds) when expecting an expansion(a recession). The empirical results show that the strategies based on stock market cycle outperforms a simple buy and hold strategy in both in-sample and out-of-sample investigation. Also the out-of-sample evidence suggests that the second strategy which is in line with asset allocation is more profitable than the first one. Research implications or Originality The strategies considered in this study are based on the estimated stock market cycle which only depends on a few easily available financial variables, thereby making easier to establish such a strategy. It implies that investors enhance investment performance by constructing a relatively simple trading strategies if they set their position on stocks or choose which asset class to buy conditioning on stock market cycle.

• Journal of Navigation and Port Research
• /
• v.35 no.5
• /
• pp.415-421
• /
• 2011

The purposes of this study were to identify the economic impacts of hosting a marine leisure event and thus provide fundamental information that helps maximize the economic value of the event. In order to accomplish such purposes, this study employed both an economic impact analysis(EIA) using regional input output model and a benefit and cost ratio analysis(BCR). In specific, this study utilized a survey method with a total of 300 event visitors and 70 foreign players and thus collected expenditure data from 110 valid out of town visitors and 58 foreign players. In addition, investment expenditure data were collected from the host city official. Accordingly, EIA and BCR indicated following findings. First, the total direct impact from both visitors and players was 387 million Won and this direct impact resulted in output multiplier effect(OME) of 591 million Won, value added multiplier effect(VAME) of 306 million Won and income multiplier effect(IME) of 252 million Won. Second, the host city's investment expenditure created OME, VAME and IME of 825, 432 and 366 million Won, respectively. In conclusion, these findings suggest that in order to effectively boost potential economic benefits, more marketing efforts development policies should be implemented for increasing the number of out of town visitors and the amount of spendings from them.

• Journal of Energy Engineering
• /
• v.21 no.2
• /
• pp.158-167
• /
• 2012

The purpose of this paper is to examine the factors that encouraged the industrial growth of Koran city gas industry during 1995-2009, by carrying out input-output structural decomposition analysis(IO-SDA) using Syrquin's model. The results show that the main factors which contributed to the growth of the Korean city gas industry are final domestic demand(48.4%) and technological change(38.6%). By examining the results for the three periods of 1995-2000, 2000-2005, and 2005-2009, the tendency of changes between the two main factors is drawn. In contrast to the drastic decreasing tendency of the final domestic demand's contribution to the growth, 84.5%, 18.9%, and 15.4%, respectively for each period, there is an increasing tendency for technological change as seen by the results of 7.4%, 70.0%, and 42.2%, respectively. These findings may be a result from the fact that the rate of gas supply in the residential sector has been saturated recently. They are also reflective of the energy consumption trend of industrial activities as there has been a shift in the approach for supplying energy, from the traditional approach which use fossil fuels to the newer approach which uses environmentally friendly energy sources. For the continued growth of the city gas industry, policymakers sould consider greater investment in the expansion of city gas supply infrastructure for industrial activities rather than for the residential sector.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.3
• /
• pp.639-647
• /
• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.