• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.323-329
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• 2009

This paper presents an automatic LCD gamma control system using gamma curve optimization. It controls automatically gamma adjustment registers in mobile LCD driver IC to reduce gamma correction error and adjusting time. The proposed gamma system contains Module-Under-Test (MUT, LCD module), PC installed with program, multimedia display tester for measuring luminance, and control board for interface between PC and LCD module. Proposed algorithm and program are applicable for most of the LCD modules. It is realized to calibrate gamma values of 1.8, 2.0, 2.2 and 3.0. The control board is designed with DSP and FPGA, and it supports various interfaces such as RGB and CPU. Developed automatic gamma control system showed significantly reduced gamma adjusting time of 240 sec. and much less average gamma error of 11% than 42h and 27% with conventional manual method. We believe that the proposed system is very useful to provide high-quality LCD and to improve production process.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.5
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• pp.93-102
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• 2003

An optimal design method for hybrid structural control system of building structures subject to earthquake excitation is presented in this paper. Designing a hybrid structural control system may be defined as a process that optimizes the capacities and configuration of passive and active control systems as well as structural members. The optimal design proceeds by formulating the optimization problem via a multi-stage goal programming technique and, then, by finding reasonable solution to the optimization problem by means of a goal-updating genetic algorithm. In the multi-stage goal programming, design targets(or goals) are at first selected too correspond too several stages and the objective function is th n defined as the sum of the normalized distances between these design goals and each of the physical values, that is, the inter-story drifts and the capacities of the control system. Finally, the goal-updating genetic algorithm searches for optimal solutions satisfying each stage of design goals and, if a solution exists, the levels of design goals are consecutively updated to approach the global optimal solution closest too the higher level of desired goals. The process of the integrated optimization design is illustrated by a numerical simulation of a nine-story building structure subject to earthquake excitation. The effectiveness of the proposed method is demonstrated by comparing the optimally designed results with those of a hybrid structural control system where structural members, passive and active control systems are uniformly distributed.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.105-110
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• 2015

This study proposes the more improved mathematical model than conventional that for the 3-DOF Helicopter System in Quanser Inc., and checks the validity about the proposed model by performance comparison between the controller based on the conventional model and that based on the proposed model. Research process is next : First, analyze the dynamics for the 3-DOF helicopter system and establish the linear mathematical model. Second, check the eliminated nonlinear-elements in linearization process for establishing the linear mathematical model. And establish the improved mathematical model including the parameters corresponding to the eliminated nonlinear-elements. At that time, it is used for modeling that Particle Swarm Optimization algorithm the meta-heuristic global optimization method. Finally, design the controller based on the proposed model, and verify the validity of the proposed model by comparison about the experimental results between the designed controller and the controller based on the conventional model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.137-140
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• 1997

In general, stereolithography parts is not suitable for master pattern. Because of its bad surface roughness. Therefore, To reduce roughness it requires post-process that is depending on user skill and takes long time to do. This study aims to develop an expert system which can select an optimal build orientation, reduce roughness and shorten post-processing time. Genetic Algorithm was introduced for optimization. A simplified computation model was developed for real-time response. For accurate roughness estimation, mterpolation of experimental data was implemented.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.105-114
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• 1999

By using the conceptual impedance and the elasticity of a serial chain of spring-damper system, a real-time collision-free trajectory generation algorithm is proposed. The reference points on a trajectory connected by the spring-damper system have a mechanism for self-Position adjustment to avoid a collision by the impedance, and the local adjustment of each reference point is propagated through the elasticity to a real robot at the end of the spring-damper system. As a result, the overall trajectory consisting of the reference points becomes free of collision with environmental obstacles and efficient having the shortest distance as possible. In this process, the reference points connected by the spring-damper system take role of virtual robot as global guidance for a real robot, and a cooperative optimization is carried out by the system of virtual robots. A control algorithm is proposed to implement the impedance for a car-like mobile robot.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.284-292
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• 2019

For the continuous operation of a nuclear reactor, burnt fuel needs to be replaced with fresh fuel, where appropriate (ex-vessel) fuel handling is required. Particularly for the Sodium-cooled Fast Reactor (SFR) refueling, its process has unique challenges due to liquid sodium coolant. The ex-vessel spent fuel transportation should concern several design features such as the radiation shielding, decay-heat removal, and inert space separated from air. This paper proposes a new design optimization methodology of cask shielding to transport the spent fuel assembly in a prototype SFR for the first time. The Particle Swarm Optimization (PSO) algorithm had been applied to design trade-offs between shielding and cask weight. The cask is designed as a double-cylinder structure to block an inert sodium region from the air-cooling space. The PSO process yielded the optimum shielding thickness of 26 cm, considering the weight as well. To confirm the shielding performance, the radiation dose of spent fuel removed at its peak burnup and after 1-year cooling was calculated. Two different fuel positions located during transportation were also investigated to consider a functional disorder in a cask drive system. This study concludes the current cask design in normal operations is satisfactory in accordance with regulatory rules.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.75-80
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• 1999

This paper presents a new method to find a optimal solution for stochastic system. This method uses Genetic Algorithm(GA) and simulation. GA is used to search for new alternative and simulation is used to evaluate alternative. The stochastic system has one or more random variables as inputs. Random inputs lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of they system. These estimates could greatly differ from the corresponding real characteristics for the system. We need multiple replications to get reliable information on the system. And we have to analyze output data to get a optimal solution. It requires too much computation to be practical. We address the problem of reducing computation. The procedure on this paper use GA character, an iterative process, to reduce the number of replications. The same chromosomes could exit in post and present generation. Computation can be reduced by using the information of the same chromosomes which exist in post and present current generation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.23-30
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• 2003

This study develops an efficient heuristic which is based on genetic approach for AGVs flow path layout problem. The suggested solution approach uses a algorithm to replace two 0-1 integer programming models and a branch-and-bound search algorithm. Genetic algorithms are a class of heuristic and optimization techniques that imitate the natural selection and evolutionary process. The solution is to determine the flow direction of line in network AGVs. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. Genetic algorithm procedure is suggested, and a simple illustrative example is shown to explain the procedure.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.82-91
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• 1996

In this study, we put our object to carry out adaptive modeling of cutting process in turning system, and to find out the optimal cutting conditions to maximize material removal rate under some constraints. We used a back-propagation neural network to model the cutting process adaptively and a genetic algorithm to find out optimal cutting conditions. The experimental results show that a back-propagation neural network could model the cutting process effciently, and optimized cutting conditions for maximizing the material removal rate were obtained through the adaptive process model and genetic algorithms. Therefore, the proposed approach can be applied to the real machining system.