• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.77-81
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• 2022

최근 사전학습 언어모델에 내재된 지식을 최대한으로 활용하고자 태스크에 대한 설명을 입력으로 주는 manual prompt tuning 방법과 자연어 대신 학습가능한 파라미터로 태스크에 대한 이해를 돕는 soft prompt tuning 방법론이 자연어처리 분야에서 활발히 연구가 진행되고 있다. 이에 본 연구에서는 페르소나 대화 생성 태스크에서 encoder-decoder 구조 기반의 사전학습 언어모델 BART를 활용하여 manual prompt tuning 및 soft prompt tuning 방법을 고안하고, 파인튜닝과의 성능을 비교한다. 전체 학습 데이터에 대한 실험 뿐 아니라, few-shot 세팅에서의 성능을 확인한다.

• Journal of Drive and Control
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• v.13 no.3
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• pp.15-23
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• 2016

The direct-drive servo valve(DDV) is a kind of one-stage valve because the main spool valve is directly driven by the dc motor. Since the DDV structure is simple, it is less expensive, more reliable, and offers a reduced internal leakage and a reduced sensitivity to fluid contamination. The control system of the DDV is highly nonlinear due to a current limiter, a voltage limiter, and the flow-force effect on the spool motion. The shape of the step response of the DDV-control system varies considerably according to the magnitudes of the step input and the load pressure. The system-design requirements mean that the overshoots should be less than 20%, and the errors at 0.02s should be less than 2%, regardless of the reference-step input sizes of 1V and 5V and the load-pressure magnitudes of 0MPa and 20.7MPa. To satisfy the system-design requirements, the PID-controller parameters of $K_c$, $T_i$ and $T_d$, and the derivative-feedback gain of $K_{der}$ are designed using the root locus and manual tuning.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.176-178
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• 1998

Recently, Control method of induction Motor is applied in full circuit model, as circular diagram. In Elevetor moderization problem, there is no circuit information. Nothing but, Motor terminal voltage and HP of motor. So, in this study, using KS induction Motor table, try to solve initial valve problem and make some implementations of manual tuning for use of automatic tuning of induction motor parameter.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.20-25
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• 2021

This paper is the result of a research on hybrid tuning technology developed to improve the actual fuel efficiency and reduce emissions of in-use light-duty diesel trucks. In this study, a hybrid powertrain was constructed by inserting an electric motor between the diesel engine and manual transmission of an internal combustion engine vehicle and installing a battery. To verify the safety, a test was conducted based on the Korean tuning regulations. In particular, since there has been no case of tuning an internal combustion engine vehicle into a hybrid vehicle in Korea, it was necessary to carry out all procedures to receive tuning approval. The approval process consists of a technical review, safety verification test, and application for tuning approval. As a result, the test vehicle was approved for tuning because both the technical review and vehicle test results were suitable. Therefore, this study confirmed the safety of diesel hybrid tuning technology, and laid the foundation for the research and development of technologies to tune into an eco-friendly vehicle as well as the activation of related industries.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.10
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• pp.888-893
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• 2000

This paper deals with the development of an automatic design software for DC servo motor control, which provides good performance with rapid response and velocity control accuracy. In the proposed method, the design is automatically executed using Matlab, and iterative learning control algorithms are used in the design process. We applied this method to 50W, 100W, 200W, 300W, 500W, 750W, 1.8kW and 4.5kW DC servo motors which are widely used in the industry. We compare the results of the manual tuning design method with that of the automatic design method presented in this paper. From the experimental results, we can find that the performance of the proposed method is better than that of the manual tuning design method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.621-624
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• 2002

The purpose of this paper is a study on getting proper gain set of PID controller which satisfies multi-performance specifications of the control system. The multi-objective optimization method is introduced to evaluate specifications, and the genetic algorithm is used as an optimal problem solver. To enhance the performance of genetic algorithm itself, adaptive technique is included. According to the proposed method in this paper, finding suitable gain set can be more easily accomplishable than manual gain seeking and tuning.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.914-922
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• 1999

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.575-591
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• 2006

In a previous research, we presented the Grid Method and confirmed it as a systematic and efficient problem formulation method for the task-oriented design of robot kinematics. However, our previous research was limited in two ways. First, it gave only a local optimum due to its use of a local optimization technique. Second, it used constant weights for a cost function chosen by the manual weights tuning algorithm, thereby showing low efficiency in finding an optimal solution. To overcome these two limitations, therefore, this paper presents a global optimization technique and an adaptive weights tuning algorithm to solve a formulated problem using the Grid Method. The efficiencies of the proposed algorithms have been confirmed through the kinematic design examples of various robot manipulators.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.137-143
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• 2019

As the performance of computers increases, the use of deep learning, which has faced technical limitations in the past, is becoming more diverse. In many fields, deep learning has contributed to the creation of added value and used on the bases of more data as the application become more divers. The process for obtaining a better performance model will require a longer time than before, and therefore it will be necessary to find an optimal model that shows the best performance more quickly. In the artificial neural network modeling a tuning process that changes various elements of the neural network model is used to improve the model performance. Except Gride Search and Manual Search, which are widely used as tuning methods, most methodologies have been developed focusing on heuristic algorithms. The heuristic algorithm can get the results in a short time, but the results are likely to be the local optimal solution. Obtaining a global optimal solution eliminates the possibility of a local optimal solution. Although the Brute Force Method is commonly used to find the global optimal solution, it is not applicable because of an infinite number of hyper parameter combinations. In this paper, we use a statistical technique to reduce the number of possible cases, so that we can find the global optimal solution.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.443-448
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• 2009

We introduce an interactive image cartooning system based on personal subjectivity. To effectively tune various parameters needed to adjust image style, our system uses interactive genetic algorithm. By selecting several pre-stylized image samples using simple user interface, the user can easily achieve the desired result without having any signal-processing knowledge. Our system reduces the parameter tuning time drastically compared to the conventional system, which involves manual parameter setting.