• The Journal of Information Technology
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• v.7 no.3
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• pp.119-126
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• 2004

In multicast traffic, an input can request to connect to up to a certain number of outputs. This reviews the multicast nonblocking multistage interconnection networks. In a multistage interconnection network each stage consists of crossbars of the same size. This paper focuses on the three-stage network and its recursive extensions. Not only will this article bring the literature upto date, but it also will provide some fresh viewpoints to either clarify or simplify some issues.

• The Journal of Information Technology
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• v.4 no.2
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• pp.117-127
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• 2001

A self-structured neural network algorithm that finds optimal fuzzy membership functions and nile base to fuzzy model is proposed and a fuzzy-neural network controller is designed to get more accurate position and velocity control of wheeled mobile robot. This procedure that is composed of three steps has its own unique process at each step. The elements of output term set are increased at first step and then the rule base Is varied according to increase of the elements. The adjusted controller is in competition with controller which doesn't include any increased elements. The adjusted controller will be removed if the control-law lost. Otherwise, the controller is replaced with the adjusted system. After finished regulation of output term set and rule base, searching for input membership functions is processed with constraints and fine tuning of output membership functions is done.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.675-685
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• 2014

This paper develops a differentiated pricing strategy over each segment of expressways based on the second-best pricing method for reducing congestion. To this end, a bi-level problem is proposed, in which the upper level of the model is formulated to determine toll level of each segment for minimizing traffic congestion, whereas the lower level of the model is formulated as a variable demand assignment problem. The sensitivity analysis based algorithm is took placed to find optimal solutions of upper level model. An application of the proposed model uses the modified Sioux-Falls network. The results show that the segment-based differentiated pricing strategy performs better than the existing uniform pricing strategy in reducing traffic congestion. This study can be applied as a demand management method to relieve disutility of excessively congested segments of expressways.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• Information Systems Review
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• v.13 no.2
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• pp.1-16
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• 2011

Recently, many companies have started to manage and support CoPs formally at the organizational level because of strategic usability of CoP. These companies are also seeking ways to motivate CoP members to actively participate in their groups. Accordingly, this paper proposes one way of increasing CoP activities by rearranging CoP members. In practice, active CoP members often lead their groups. Therefore, rearranging members can, eventually, be one method to motivate more individuals to participate in CoP activities. This paper first suggests a new approach in order to improve knowledge sharing activities at the organizational level based on rearranging members of current CoPs. Second, a mathematical model is presented which maximizes total BLS (Balanced Level Score) of company A with several constraints. Then a real world problem is changed to a popular problem, VRP to solve this problem. Third, the solution program was developed to find a meaningful solution.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.3
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• pp.307-312
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• 2013

The interest in reducing the emissions and increasing the fuel economy of ICE vehicles has prompted research on hybrid vehicles, which come in the series, parallel, and power-split types. This study focuses on the series-type hybrid electric vehicle, which has a simple structure. Because each component of a series hybrid vehicle is larger than the corresponding component of the parallel type, the sizing of the vehicle is very important. This is because the performance may be greater or less than what is required. Thus, in this research, the optimal fuel economy was determined and simulated in a real-world system. The optimal sizing was achieved based on the motor, engine/generator, and battery for 13 cycles, where DP was used. The model was developed using ASCET or a Simulink-Amisim Co-simulation platform on the rapid controller prototype, ES-1000.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.389-394
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• 2017

In a conventional ship structure, stiffeners with an asymmetric section, such as inverted angles, are used widely despite the disadvantage of strength compared to the stiffeners with a symmetric section, such as a built-up T. On the other hand, T-type built-up members are attracting more attention than L-type inverted angles due to the increased size of ships. The purpose of this study was to develop an optimal design program for a built-up T, and apply an evolution strategy as an optimization technique. In the optimization process, the gross thickness concept was adopted for the design variables and objective function, and the constraints are set up based on HCSR (Harmonized Common Structural Rules). Using the developed program in this study, the optimal stiffener design was carried out for 300K VLCC and 158K COT of which the orders were obtained lately. The optimal results revealed the weight reduction effect of 144 tons and 60 tons, respectively.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.355-361
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• 2008

Stereo matching is one of the most active research areas in computer vision. In this paper, we propose a stereo matching scheme using genetic algorithm for object depth recognition. The proposed approach considers the matching environment as an optimization problem and finds the optimal solution by using an evolutionary strategy. Accordingly, genetic operators are adapted for the circumstances of stereo matching. An individual is a disparity set. Horizontal pixel line of image is considered as a chromosome. A cost function is composed of certain constraints which are commonly used in stereo matching. Since the cost function consists of intensity, similarity and disparity smoothness, the matching process is considered at the same time in each generation. The LoG(Laplacian of Gaussian) edge is extracted and used in the determination of the chromosome. We validate our approach with experimental results on stereo images.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.515-521
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• 2007

To increase the stevedore efficiency and service level at container terminal, it is essential to reduce working time of container crane which has a bottle neck in the logistic flow of container. The working speed and safety are required to be improved by controlling the movement of the trolley as quick as possible without big overshoot and any residual swing motion of container in the vicinity of target position. This paper presents optimal state feedback control using RCGAs in the case of existing constrained conditions