• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.3
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• pp.98-109
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• 2000

A new PC-based Engine Control system (ECS) is developed using an object oriented programming method. This system provides more convenient environment for engine tests, easier user interface and extended functions. A Windows-based ECS software is developed with class, and the class structure is built on encapsulation and abstraction. The closed-loop engine control scheme can be easily constructed by using dynamic link library and multitasking. This means that a user can perform desired experiments without clear knowledge of the hardware structure of the ECS. Also this system allows a user to individually control the ignition and fuel injection for each cylinder in a simple manner such as through a keyboard/mouse or in a real-time operation from a closed-loop control program.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.546-549
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• 1999

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implmented.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2130-2133
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• 2003

For real-time recognizing of the load vehicles a new Neural Network algorithm is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a Processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through severa simulation experiments, real time reconstruction nonlinear image information is Processed. 1-D hardware has been composed and various experi with static and dynamic signals have implemented.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• IE interfaces
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• v.22 no.4
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• pp.359-367
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• 2009

The vehicle travel time between the demand points in downtown area is greatly influenced by complex road condition and traffic situation that change real time to various external environments. Most of research in the vehicle routing problems compose vehicle routes only considering travel distance and average vehicle speed between the demand points, however did not consider dynamic external environments such as traffic situation by service time zones. A realistic vehicle routing problem which considers traffic situation of smooth, delaying, and stagnating by three service time zones such as going to work, afternoon, and going home was suggested in this study. A mathematical programming model was suggested and it gives an optimal solution when using ILOG CPLEX. A hybrid genetic algorithm was also suggested to chooses a vehicle route considering traffic situation to minimize the total travel time. By comparing the result considering the traffic situation, the suggested algorithm gives better solution than existing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.139-148
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• 2011

This paper presents the minimum-cost network selection scheme which determines the transmission instance in the multi-band maritime communication system, so that the shipment-related real-time information can be transmitted within the maximum allowed period. The transmission instances and the corresponding network selection process are modeled by a Markov Decision Process (MDP), for the channel model in the 2-state Markov chain, which can be solved by stochastic dynamic programming. It derives the minimum-cost network selection rule, which can reduce the network cost significantly as compared with the straight-forward scheme with a periodic transmission.

• IE interfaces
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• v.13 no.2
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• pp.211-216
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• 2000

A methodology that determines the optimal shutdown time of a nuclear power plant is suggested. The shutdown time is decided considering the trade off between the cost of accident and the loss of profit due to the early shutdown. We adopt the bayesian approach in manipulating the model parameter that predicts the accidents. We build decision tree models and apply dynamic programming approach to decide whether to shutdown immediately or operate one more period. The branch parameters in decision trees are updated by bayesian approach. We apply real data to this model and provide the cost of accidents that guarantees the immediate shutdown.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.311-311
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• 2000

We propose a DP-based formulation for matching line patterns by defining a robust and stable geometric representation that is based on the conceptual organizations. Usually, the endpoint proximity and collinearity of image lines, as two main conceptual organization groups, are useful cues to match the model shape in the scene. As the endpoint proximity, we detect junctions from image lines. We then search for junction groups by using geometric constraint between the junctions. A junction chain similar to the model chain is searched in the scene, based on a local comparison. A Dynamic Programming-based search algorithm reduces the time complexity for the search of the model chain in the scene. Our system can find a reasonable matching, although there exist severely distorted objects in the scene. We demonstrate the feasibility of the DP-based matching method using both synthetic and real images.

• Water for future
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• v.19 no.4
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• pp.339-346
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• 1986

A real-time single reservoir operation model using the Min-max Dynamic Programming for the flood control of Soyanggang Dam and Choongju Dam is developed. The objective function is to minimize the maximum release from each dam and the constraints are those from ther reservoir and channel characteristics. Control and utilization efficiencies are used to measure the performance of the reservoir operation method (ROM). In comparison with those of simulation models(such as the Rigid ROM, the Technical ROM and the Linear Decision Rule), the efficiencies of the optimization model are superior for all return periods.