• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.915-923
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• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.

• Journal of KIISE
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• v.44 no.8
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• pp.822-831
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• 2017

In this paper, we propose a Korean dependency parsing model using multi-task learning based pointer networks. Multi-task learning is a method that can be used to improve the performance by learning two or more problems at the same time. In this paper, we perform dependency parsing by using pointer networks based on this method and simultaneously obtaining the dependency relation and dependency label information of the words. We define five input criteria to perform pointer networks based on multi-task learning of morpheme in dependency parsing of a word. We apply a fine-tuning method to further improve the performance of the dependency parsing proposed in this paper. The results of our experiment show that the proposed model has better UAS 91.79% and LAS 89.48% than conventional Korean dependency parsing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.195-198
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• 2000

Sliding mode control method is popularly used for robustness to distrurbance and variance of systems internal parameter. However, one of the serious problem of this method is Chattering which occurs in neighborhood of sliding manifold. Another problem is that we cannot expect robustness before system starts sliding mode. A new tuning method of sliding manifold which changes the parameter of sliding manifold dynamically using Wavelet Neural Network is proposed in this paper. We can expect the better performance in sliding mode control by the wavelet neural networks excellent property of approximating arbitrary function for multi-resolution analysis and decrease chattering drastically.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.72-77
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• 2017

This paper proposes the explicit tuning rule of multi-loop PID controller for brushless direct current motors to predict the system behaviors in time and frequency domains, using properties of the convex combination method. The convex set of the proposed controllers formulates the envelope to satisfy the performances in time and frequency domains. The final control parameters are determined by solving the convex optimization problem subject to the constraints which are represented as convex set of time domain performances. The effectiveness of the proposed control method is shown in the numerical simulation, in which controller tuning algorithm and dynamics of brushless DC motor are well taken into account.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.45-49
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• 2014

In this work, a phase shifter radio-frequency integrated chip (RFIC) using a simple all-pass network is presented. As a tuning element of the phase shifter RFIC, tunable capacitors with a multi-layer dielectric of a para-/ferro-/para-electrics using a high tunable BST ferroelectric and a low-loss BZN paraelectric thin film were utilized. In order to evaluate and analyze the fabricated phase shifter RFIC, the same elements such as an inductor and capacitor integrated into it are also fabricated and tested. The designed phase shifter RFIC was fabricated on a quartz substrate in the size of $1.16{\times}1.21mm^2$. As the test results, the maximum phase difference of $350^{\circ}$ is obtained at 15 V and its tuning frequency bandwidth is 90 MHz from 2.72 to 2.81GHz.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.505-508
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• 2006

This paper presents a new analytical approach for designing multiloop PI controllers for disturbance rejection in multivariable processes with time delay. The proposed method is based on IMC-PID design approach. To overcome a sluggish load response by dominant pole in the process, the IMC filter is modified to compensate the dominant pole effect. Based on the modified IMC filter, an analytical tuning rule for multiloop PI controller is driven by extending the generalized IMC-PID method for single input/single output (SISO) systems [1] to multi input/multi output (MIMO) systems. Simulation results show that the proposed method gives a satisfactory load performance as well as servo performance in the multiloop system.

• Journal of Power Electronics
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• v.12 no.6
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• pp.851-858
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• 2012

This paper presents resonant inverter tuning for current source parallel resonant induction heating systems based on a new self oscillating switching technique. The phase error is suppressed in a wide range of operating frequencies in comparison with Phase Locked Loop (PLL) techniques. The proposed switching method has the capability of tuning under fast changes in the resonant frequency. According to this switching method, a multi-frequency induction heating (IH) system is proposed by using a single inverter. In comparison with multi-level inverter based IH systems, the advantages of this technique are its simple structure, better transients and wide range of operating frequencies. A laboratory prototype was built with an operating frequency of 35 kHz to 55 kHz and 300 W of output power. The performance of the IH system shows the validity of the new switching technique.

• ETRI Journal
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• v.46 no.1
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• pp.71-81
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• 2024

Recent advances in deep learning for speech and visual recognition have accelerated the development of multimodal speech recognition, yielding many innovative results. We introduce a Korean audiovisual speech recognition corpus. This dataset comprises approximately 150 h of manually transcribed and annotated audiovisual data supplemented with additional 2000 h of untranscribed videos collected from YouTube under the Creative Commons License. The dataset is intended to be freely accessible for unrestricted research purposes. Along with the corpus, we propose an open-source framework for automatic speech recognition (ASR) and audiovisual speech recognition (AVSR). We validate the effectiveness of the corpus with evaluations using state-of-the-art ASR and AVSR techniques, capitalizing on both pretrained models and fine-tuning processes. After fine-tuning, ASR and AVSR achieve character error rates of 11.1% and 18.9%, respectively. This error difference highlights the need for improvement in AVSR techniques. We expect that our corpus will be an instrumental resource to support improvements in AVSR.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.5
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• pp.241-249
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• 2003

This paper describes a novel approach for performing eigenvalue analysis and frequency domain analysis of multi-machine power system. The salient feature of this approach is a direct approach for constructing the state matrix equations of linearized power systems about its operating point using modular technique. These state matrix equations are then used to obtain eigenvalues and mode shapes of the system, and frequency response, or Bode, plots of selected transfer functions. The proposed program provides a flexible tool for systematic analyses of tuning PSS's parameters. The paper also presents its application to the analyses of a single-machine infinite bus system and two-area system with 4 machines.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.