• Journal of the Korean Society of Safety
• /
• v.28 no.3
• /
• pp.23-28
• /
• 2013

This study focuses on finding ways to derive train's optimal ECO driving pattern, which can improve the ride quality and reduce driving energy consumption with keeping the time interval between the stations. As research method, we compared difference of currently operating train's ATO and MCS driving patterns, and concentrated upon the things need to consider in simulation in order to improve the existing pattern of ATO driving pattern's issues with securing the train operation safety. Determining driving pattern minimizing energy consumption by controlling powering within speed limit and controlling switching to coasting at appropriate point considering the track conditions for each section, and determining braking control starting time considering ride comfort and precise stopping is considered to be most important.

• KSBB Journal
• /
• v.22 no.4
• /
• pp.197-200
• /
• 2007

This study examined the conditions of filtration and concentration of methanol extract from biomass. Filtration efficiency was improved by adding diatomaceous earth as a filter aid. The optimal amount of diatomaceous earth was 6% (w/w) to reduce the filtration time. The filtration time was reduced by 4.2% in first extraction, 30.0% in second extraction, 22.8% in third extraction, and 19.0% in fourth extraction, respectively. The optimal temperature of water bath was below 50$^{\circ}C$ for preventing paclitaxel degradation during concentration of methanol extract using a rotary evaporator. The temperature of concentrated solution in rotary evaporator was relatively low compared to bath temperature because of latent heat of evaporation. The stopping point of concentration in rotary evaporator for the following step was at a specific gravity of 0.96 of the concentrated solution in terms of the purity and yield of paclitaxel. This information is very useful for mass extraction of biomass for the recovery of paclitaxel from plant cell culture.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.14 no.4
• /
• pp.66-73
• /
• 2006

This study focuses on the economies of launching regular air services between capital cities of South Korea and North Korea. The demand for traveling between these two cities is too small to justify scheduled air service. However, it may be possible to provide regular air service by utilizing via flight of incumbent Seoul-Beijing flights. There are numerous flight services between Seoul and Beijing and we may allow some of the Seoul-Beijing flights to stop by Pyung Yang for commercial traffic handling. This study tries to find the optimal discount rate which passengers traveling between Seoul and Beijing via Pyung Yang, the idea is reasonable considering the inconvenience of stopping at Pyung Yang. The Stated Preference Techniques are applied for the study. The required data were collected through interviews of passengers traveling from Seoul to Beijing. The major variables that are considered in flight choice are air fare, flying time, and flight frequency. The relative importance of these major variables is estimated by the logit models calibrated with stated preference data.

• Korea Trade Review
• /
• v.44 no.1
• /
• pp.31-44
• /
• 2019

This study examines the normalization of Korean GM management between the Korean government and GM in terms of external negotiation game and internal negotiation game using Putnam's Two-Level Games. In addition, GM's Win-set change and negotiation strategy were analyzed. This analysis suggested implications for the optimal negotiation strategy for mutual cooperation between multinational corporations and local governments in the global business environment. First, the negotiation strategy for Korea's normalization of GM management in Korea can be shifted to both the concession theory and the opposition theory depending on the situation change and the government policy centered on the cautious theory. Second, GM will maximize its bargaining power through 'brink-end tactics' by utilizing the fact that the labor market is stabilized, which is the biggest weakness of the Korean government, while maintaining a typical Win-set reduction strategy. GM will be able to restructure at any time in terms of global management strategy, and if the financial support of the Korean government is provided, it will maintain the local factory but withdraw the local plant at the moment of stopping the support. In negotiations on the normalization of GM management in Korea, it is necessary to prepare a problem and countermeasures for various scenarios and to maintain a balance so that the policy does not deviate to any one side.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.5
• /
• pp.26-37
• /
• 2009

This paper suggests a sensor's placement method for detecting the dilemma zone problem when real-time driver's safety service is provided at signalized intersections by multiple pointed traffic sensors using USN environments. For detecting the dangerous situations from vehicles accelerating through yellow intervals, red-light running and stopping abruptly like as dilemma zone problem, VISSIM(microscopic, behavior-based multi-purpose traffic simulation program) is used to perform a real-time multiple detection situation by changing the input data like as various inflow-volume, design speed change, driver perception and response time. As a result, the optimal interval of traffic sensors is 20~27m, and the initialized sensor location from stop-line is different according to road design speed. Moreover, the pattern of detection about dilemma zone is also different according to inflow-volumes. This paper shows that the method is useful to evaluate the sensor's placement problem based on micro-simulation and the results can be used as the basic research for USN services.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.337-342
• /
• 2015

SMT is an equipment that picks up electronic components and does precise placing onto PCBs. In order to do this, it stops in front of a camera installed in the middle to go over vision inspection. And after that it is move for placing. There are 16 different types of routes in this process. This paper presents the fastest algorithm to place (sLa-Camera-pLb) among all these routes. In order to do this, instead of stopping in front of camera the object should move on while going over the vision inspection. Among all possible tracks, this thesis will provide algorithm to find out the fastest tracks to do vision inspection and placing. And as a result, this thesis have demonstrated that this method can save about 16% of time compared to going over inspection while the object is standing still through simulation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.11B
• /
• pp.1030-1037
• /
• 2008

This paper presents an algorithm for the path-finding problem in unknown environments with cooperative and commutative multi-robots. To verify the algorithm, we investigate the problem of escaping through the exit of a randomly generated maze by muti-robots. For the purpose, we adopt the so called frontier cells and cell utility functions, which were used in the exploration problem for the multi-robots. For the wireless communications among the mobile robots, we modify and utilize the so called the random basket routing, a kind of hop-by-hop opportunistic routing. A mobile robot, once it finds the exit, will choose its next action, either escape immediately or stay-and-relay the exit information for the others, where the robot takes one action based on a given probability. We investigate the optimal probability that minimizes the average escaping time (out of the maze to the exit) of a mobile robot.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.241-254
• /
• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.