• Journal of the Korean Regional Science Association
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• v.14 no.2
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• pp.51-64
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• 1998

This paper investigates how the factor inputs of firms are affected by the expectation about land-price increase in the future. We develope a two-factor (land and labor) model, in which expectation about land-price increase plays a key role in determining the "optimal" input level of labor and land. Expecting capital gains from input of the land when land price increases, firms input land up to the point where the marginal productivity of land falls short of the marginal cost of purchasing the land, in order to maximize the "joint-profit". That is, firms have an incentive to use more land than they do when capital gains are not expected. We mean joint-profit by profit in the standard sense plus capital gains. Once the land is input "excessively", the productivity of labor increase and labor is also input more, since land and labor are assumed as complementary in production. This mechanism works in the opposite direction when land price decrease. This paper suggests that land price fluctuation is a major destabilizer of an economy.or destabilizer of an economy.

• English Language & Literature Teaching
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• v.18 no.3
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• pp.93-120
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• 2012

This paper aims to address the gains and losses of the CMC environment in Language learning. Data were attained from twelve middle school ESL students who took English as a second language class and twelve pre-service teachers taking ESL foundation course. This exploration describes the role of CMC focusing on its' advantages and disadvantages which language teachers need to consider. The findings revealed that the teachers, tasks, and other elements involved in the CMC environment provided several gains and losses for many areas of learning. This implies that CMC alone does not provide an optimal learning environment, but rather it is used as an essential tool in providing opportunities to enhance language learning. Several suggestions are made for teachers and pre-service teacher education how CMC instruction might be better designed. The paper concludes with some practical considerations for future research in the area of CMC.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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• pp.696-705
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• 1991

This paper describes the procedures for pre-tuning and re-tuning the gains of PI controller based on output patterns -output error integral- of the unknown process which may not have any information, for example, system order, deadtime, time constant, etc. The key ideas of the proposed adaptive scheme are as follows. The scheme determines the initial gains by using ZNM (Ziegler-Nichols Method) with relay feedback, and then the adaptive algorithms by pattern recognition are introduced for re-runing the PI gains with on-line scheme whenever control conditions are changed. Because, among the various auto-tuning procedures, ANM with relay feedback has the difficulty in re-tuning with on-line and Bristol method has no comment on initial settings and has variables to pre-determine, which makes the algorithm comples, the proposed methods have the combined scheme with above two procedures to recover those problems. And this paper proposes a simple way to determine adaptive constant in Bristol method. To show the validity of the proposed method, an example is illustrated by computer simulation and a laboratory process, heat exchanger, is experimented.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1256-1260
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• 2006

This paper presents an enhanced ${\alpha}-{\beta}$ tracking filter whose ${\alpha}-{\beta}$ gains are updated by an approximation method at every scan to account for the transition of measurement dependent observation error variance in two-dimensional Cartesian coordinates. The approximate ${\alpha}-{\beta}$ gains are calculated from the amount of the change in the tracking index and the partial derivatives of the ${\alpha}-{\beta}$ gains with respect to a nominal tracking index. It is shown via simulation that the proposed tracker provides improved performance compared to the conventional ${\alpha}-{\beta}$ tracking filter.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1014-1021
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• 1999

PID controllers with constant gains have been widely used in various control systems. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a variable PID controller for robot manipulators. We divide total workspace of manipulators into several subspaces. PID controllers in each subspace are optimized using evolution strategy which is a kind of global search algorithm. In real operation, the desired trajectories may cross several subspaces and we select the corresponding gains in each subspace. The gains may have large difference on the boundary of subspaces, which may cause oscillatory motion. So we use artificial neural network to have continuous smooth gain curves to reduce the oscillatory motion. From the experimental results, although the proposed variable PID controller for robots should pay for some computational burden, we have found that the controller is more superior to the conventional constant gain PID controller.

• Journal of Power Electronics
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• v.10 no.4
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• pp.382-387
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• 2010

This paper presents a novel design method for determining the PID gains of a speed controller for a servo system compensating variations in bandwidth at a low speed. The variations in bandwidth of a speed controller are measured at a low speed and the relationship between the bandwidth and the damping ratio are verified by determining the location of the closed loop pole. The proposed algorithm uses the z-transform of a plant and speed controller and applies the time-varying sampling method for determining the PID gains of the speed controller at low speed. The magnitude and the phase condition are considered for finding a suitable control gain. The usefulness and effectiveness of the proposed method is demonstrated through experimental results such as low speed control and robust disturbance responses.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.3
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• pp.134-141
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• 2004

Recently, neural network techniques are widely used in adaptive and learning control schemes for production systems. However, in general it takes up a lot of time to learn in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult for the PID gains suitably, lots of researches have been reported with respect of turning schemes of PID gains. A neural network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed neural network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accidents.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.717-719
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• 2003

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.89-91
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• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.1
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• pp.180-186
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• 2001

PID control is widely used in industrial areas, but it is not easy to tune PID gains for an optimal control. The proposed learning method is to tune PID gains using the gradient approach. We use two estimation functions in this method : one is an error function for tuning of PID gains, and the other is a performance measuring function for a completion of learning. This paper shows that optimal PID controllers can be acquired when this learning method is applied to 10 systems with different natural frequencies and damping ratios.