• Journal of Korean Institute of Industrial Engineers
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• v.35 no.3
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• pp.203-212
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• 2009

The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.284-292
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• 2005

This paper presents a new framework of kinematic tolerance synthesis and describes the implemented algorithm for planar mechanical systems comprised of higher kinematic pairs. Input to the synthesis algorithm is a parametric model of the mechanical system with allowed parameter ranges (tolerance ranges). The model is specified as the part profiles consisting of line and arc segments and the motion axes along which each part moves. The algorithm analyzes tolerance in generalized configuration space, called contact zones bounding the worst-case variations, and identifies bad system variations. The bad system variations then are removed out of the parameter ranges by adjusting the nominal parameter values if possible and then shrinking the ranges otherwise. This cycle is repeated until no more bad variations we found. I show the effectiveness of the algorithm by case studies on several mechanisms.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.261-270
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• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered ore combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke bums. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant fer eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the duality and productivity was as good as with human operators.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.261-270
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• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered or combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke burns. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant for eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the quality and productivity was as good as with human operators.

• Journal of KSNVE
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• v.11 no.2
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• pp.226-233
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• 2001

It is essential to analyze structural vibrations due to turbulent wall pressure fluctuations over a body surface which moves through a fluid, because the vibrations can be a severe source of noise affecting to passengers in airplanes and SONAR performance. Generally, this kind of problems have been solved for very simplified models, e.g. plates, which can be applied to the wavenumber domain analysis. In this paper, a finite element modeling of the walt pressure fluctuations is investigated, which can be applied to those over arbitrary smooth surfaces. It is found that the modeled wall pressure fluctuation at nodes becomes uncorrelated at higher frequencies and at lower flow speeds, and the response is over-estimated due to the aliased power. Then the frequency range available for uncorrelated loading model and two power correction schemes are presented.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.159-163
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• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.25.3-25
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• 2001

This paper describes a control method of a robot cooperating with a human. A task in which a robot and a human move an object cooperatively is considered. To develop the force controller of the robot, the characteristics of human arm are investigated. The arm is forced to move along a trajectory in the experiment and the exerted force and the displacement are analyzed, It is found the force characteristics of the human arm is regarded as an optimal damper with minimizing a cost function. Then, the model is implemented to a robot and the cooperation of the robot and a human operator is examined. The effectiveness of the derived model is investigated and the experimental results show that the human moves the object supported by the robot with a minimum jerk trajectory.

• Journal of Power System Engineering
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• v.17 no.1
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• pp.85-90
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• 2013

Dry CVT consists of a driving pulley and a driven pulley joined by rubber V-belt. Each pulley consists of a fixed flange and a movable flange. The movable flange moves toward a fixed flange under the actuation of a centrifugal roller, as the driving pulley speed increases. The important claim which have an influence on the performance of the Dry CVT is the wear of the centrifugal roller. In this study ball type is proposed instead of roller type of movable flange to resolve the claim. Simulation is carried out for new model to verificate performance, experiments are carried out for new model to evaluate performances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3480-3500
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• 2017

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.817-824
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• 2000

It is essential to analyze structural vibrations due to turbulent wall pressure fluctuations over a body surface which moves through a fluid, because the vibrations can be a severe source of noise affecting to passengers in airplanes and SONAR performance. Generally, this kind of problems have been solved for very simplified models, e.g. plates, which can be applied to the wavenumber domain analysis. In this paper, a finite element modeling of the wall pressure fluctuations over arbitrary smooth surfaces is investigated. It is found that the modeled wall pressure fluctuation at nodes becomes uncorrelated at higher frequencies and at lower flow speeds, and the response is over-estimated due to the aliased power. Finally, the frequency range available for uncorrelated loading model and two power correction schemes are presented.