• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.175-178
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• 2002

A number of controlled selection methods, which have some advantages for practical surveys in considering controls beyond stratification, have developed throughout the last half-century. With respect to the optimization of sampling plan, it is obvious that we may use optimal controlled selection in preference to satisfactory controlled selection. However, there are currently certain restrictions on the employment of optimal controlled selection. We present further research to improve an algorithm for optimal controlled selection and to develop standard software.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.151-159
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• 1997

In sample surveys, we expect preferred samples that reduce the survey cost and increase the precision of estimators will be selected. Goodman and Kish (1950) introduced controlled selection as a method of sample selection that increases the probability of drawing preferred samples, while decreases the probability of drawing nonpreferred samples. In this paper, we obtain the controlled plans using the maximum entropy principle, and when the order of nonpreferred samples is considered, we propose the algorithm to obtain a controlled plan.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Earthquakes and Structures
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• v.6 no.6
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• pp.647-664
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• 2014

A reduction of the response of irregular structures subjected to earthquake excitation by control devices equipped by suitable control algorithm is proposed in this paper. The control algorithm, which is used, is the pole placement one. A requirement of successful application of pole placement algorithm is a definition-selection of suitable poles (eigen-values) of controlled irregular structures. Based on these poles, the required action is calculated and applied to the irregular structure by means of control devices. The selection of poles of controlled irregular structure, is a critical issue for the success of the algorithm. The calculation of suitable poles of controlled irregular structure is proposed herein by the following procedure: a fictitious symmetrical structure is considered from the irregular structure, adding vertical elements, such as columns or shear walls, at any location where is necessary. Then, the eigen-values of symmetrical structure are calculated, and are forced to be the poles of irregular controlled structure. Based on these poles and additional damping, the new poles of the controlled irregular structure are calculated. By pole placement algorithm, the feedback matrix is obtained. Using this feedback matrix, control forces are calculated at any time during the earthquake, and are applied to the irregular structure by the control devices. This procedure results in making the controlled irregular structure to behave like a symmetrical one. This control strategy can be applied to one storey or to multi-storey irregular buildings. Furthermore, the numerical results were shown that with small amount of control force, a sufficient reduction of the response of irregular buildings is achieved.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.3
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• pp.299-303
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• 2012

Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.696-706
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• 1992

In sheet metal fabrication using NCT(numerically controlled turret), the automatic tool selection for the NCT operation is the major problem to be solved first to improve its production performance. However, the punching tool selection has been done by human experts either manually or semi-automatically. In this paper, we have introduced the shape-index-set to handle the shape of sheet metal parts and developed an algorithm through which one can find easily the successive matching curves between two curve lists, one from the punching tool and the other from the boundaries of the sheet metal part. Based on this algorithm, we have also devised the method that can select automatically the tools to punch out the boundaries of sheet metal parts. The result of several computational experiments shows the successful tool selection without any fail.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1828-1831
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• 1997

this paper, to represent the robot motion approximately in space, delas with algorithm for position recognition of space robot, target and obstacle with vision system in 2-D. And also there are algorithms for precise distance-measuring and calibration usign laser displacement system, and for trajectory selection for optimizing moving to object, and for robot locomtion with air-thrust valve. And the software synthesizing of these algorithms hleps operator to realize the situation certainly and perform the job without any difficulty.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2237-2242
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• 2005

The original RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected states, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. It is generally known that the performance of RRTs can be improved depending on the selection of the metrics in choosing the nearest vertex and bias techniques in choosing random states. We designed a path planning algorithm based on the RRT method for a remote-controlled mobile robot. First, we considered a bias technique that is goal-biased Gaussian random distribution along the command directions. Secondly, we selected the metric based on a weighted Euclidean distance of random states and a weighted distance from the goal region. It can save the effort to explore the unnecessary regions and help the mobile robot to find a feasible trajectory as fast as possible. Finally, the constraints of the actuator should be considered to apply the algorithm to physical mobile robots, so we select control inputs distributed with commanded inputs and constrained by the maximum rate of input change instead of random inputs. Simulation results demonstrate that the proposed algorithm is significantly more efficient for planning than a basic RRT planner. It reduces the computational time needed to find a feasible trajectory and can be practically implemented in a remote-controlled mobile robot.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.2
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• pp.51-60
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• 2005

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented by adding a fuzzy precompensator to an existing TCSC-PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Nonlinear simulation results show that the proposed control technique is superior to conventional TCSC-PSS in dynamic responses over the wide range of operating conditions and in convinced robust and reliable in view of structure.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1177-1186
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• 2017

This paper presents a torque ripple reduction method of direct torque control (DTC) using fuzzy controller with optimal selection strategy of voltage vectors in a five-phase induction motor. The conventional DTC method has some drawbacks. First, switching frequency changes according to the hysteresis bands and motor's speed. Second, the torque ripple is rapidly increased in long control period. In order to solve these problems, some/most papers have proposed torque ripple reduction methods by using the optimal duty ratio of the non-zero voltage vector. However, these methods are complicated in accordance with the parameter. If this drawback is eliminated, the torque ripple can be reduced compared with conventional method. In addition, the DTC can be simply controlled without the use of the parameter. Therefore, the proposed algorithm is changing the voltage vector insertion time by using the designed fuzzy controller. Also, the optimized voltage vector selection method is used in accordance with the torque error. Simulation and experimental results show effectiveness of the proposed control algorithm.