• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1392-1395
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• 1996

Nonlinear PID controllers have increasingly used in current industrial practice because it is robust and is easy to operate. Little guideline and tuning method, however, has been recommended for the nonlinear PID controllers while a lot of result is available for the linear PID controllers. Application guideline and tuning formulae are presented for error square type nonlinear controllers, which are most popular currently, are presented.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.203-206
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• 1998

This paper describes the procedure of getting information about auto-tuning of PID regulator by the injection of high step input, called limited input, during a transient time of control. The key point is that system identification and control could be continuously executed. This means that the system information obtained by limited input despite of system uncertainty can be continuously applied to the PI regulator. Simulation and experiment result of brushless DC motor system having monotone increasing step response demonstrate the usefulness of proposed auto-tuning algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.194-199
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• 1989

In this paper, using the direct drive arm for plant, the controller is developed to track the desired trajectory in high speed and precision. For the purpose of this, through extending self-tuning pole-placement algorithm, we developed self-tuning pole-shift algorithm which is fast in response and good tracking for the reference tracking change. Developed controller is applied a three-link direct drive arm with the varing payload to track the desired tracking. And, through the computer simulation, the performance of developed controller is compared with the performance of the computed torque method and the self-tuning pole placement algorith.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.469-470
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• 2008

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.401.2-401
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• 2002

An unconstrained tuning fork with a 3-D model has been numerically analyzed by Finite Element Method (FEM) and Boundary Element Method (BEM). The first three natural frequencies were calculated by the FEM modal analysis. Then the change of the modal frequencies was examined with the variation of the tuning fork length and width. (omitted)

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1173-1177
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• 2006

Using an inner P controller, a tuning rule useful for processes with wide ratios of time delay over time constant is proposed. Internal model control method and pole assignment method are utilized. It can be used for processes with wide range of the ratio of time delay to time constant without incovenience to choose different tuning rules.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.478-484
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• 1990

An automatic tuning method of a PID controller which is used for single input single output processes is proposed. In the proposed tuning method, the frequency response data model is adopted along with the performance index which is an integral of time weighted square error between reference model and process frequency response data model for tuning. This method is easier to retune when either the process dynamics is changed or the reference model is changed. Finally, an example is provided to show the usefulness of the method.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.37-43
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• 2018

Science DMZ is a network architecture that considers complicated network components such as dedicated network, DTN, and minimum security policy to maximize transfer efficiency. And DTN tuning is an essential component to take full advantage of Science DMZ's available bandwidth. In addition, tuning of network system should be performed concurrently to minimize packet loss due to network bottleneck. In this paper, we propose a tuning method of data transfer node and network system for maximizing transfer efficiency in Science DMZ network architecture. As a result of the performance measurement using the KREONET, the network performance after the DTN tuning shows 180% improvement than that of existing method without DTN tuning. In addition, performance of 9.4Gb/s was shown without loss of performance measurement after tuning network system applying shaping policy.

• Journal of Information Technology Services
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• v.19 no.5
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• pp.83-91
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• 2020

Language Models such as BERT has been an important factor of deep learning-based natural language processing. Pre-training the transformer-based language models would be computationally expensive since they are consist of deep and broad architecture and layers using an attention mechanism and also require huge amount of data to train. Hence, it became mandatory to do fine-tuning large pre-trained language models which are trained by Google or some companies can afford the resources and cost. There are various techniques for fine tuning the language models and this paper examines three techniques, which are data augmentation, tuning the hyper paramters and partly re-constructing the neural networks. For data augmentation, we use no-answer augmentation and back-translation method. Also, some useful combinations of hyper parameters are observed by conducting a number of experiments. Finally, we have GRU, LSTM networks to boost our model performance with adding those networks to BERT pre-trained model. We do fine-tuning the pre-trained korean-based language model through the methods mentioned above and push the F1 score from baseline up to 89.66. Moreover, some failure attempts give us important lessons and tell us the further direction in a good way.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.39-43
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• 2016

In this paper, we propose PID gain auto tuning method in steering type mobile robot. PID controller gain select method are various methods. Ziegler-Nichols step tuning method is one method tuning in PID controller. Use step tuning method find a the first gain and did experiment in steering mobile robot. and Make a new the second gains from the first gains. After appling the second gain in PID controller, Where perform observe for convergence time and stabilization error. Experiments result the second gain are useful in real steering mobile robot system.