• Title/Summary/Keyword: Tuning

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A Study on Web Service Performance Enhancement Using Tuning Model (튜닝 모델을 이용한 웹 서비스 성능 향상에 관한 연구)

  • Oh, Kie-Sung
    • Journal of Information Technology Services
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    • v.4 no.2
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    • pp.125-133
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    • 2005
  • Because of paradigm change to web service, numerous institutes have been suggested supporting solution about web service, and actively developed system using web service but it is hard to find out a systematic study for web service performance enhancement. Generally, there are SOAP message processing improvement and configuration optimization of server viewpoint for web service performance enhancement. Web service performance enhancement through SOAP message processing improvement have been studied related research but configuration optimization of server is hard to find out a systematic tuning model and performance criteria. In this paper, I suggested performance testing based tuning model and criteria of configuration optimization of server viewpoint. We executed practical analysis using tuning model about web service in internet. This paper show that the proposed tuning model and performance criteria is applicable to web service performance enhancement.

Design of Self-Tuning PID Controller Using GPC Method (GPC기법을 이용한 자기동조 PID제어기 설계)

  • Yoon, K.S.;Lee, M.H.
    • Journal of the Korean Society for Precision Engineering
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    • v.13 no.5
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    • pp.139-147
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    • 1996
  • PID control has been widely used for real control systems. Particularly, there are many researches on control schemes of tuning PID gains. However, to the best of our knowledge, there is no result for discrete-time systems with unknown time-delay and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown papameters and unknown time-delay system. A numerical simulation was presented to illustrate the effectiveness of this method.

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$H_{\infty}$ Self-Tuning Control of a Flexible Link Robot with Unknown Payload (미지 부하 질량을 갖는 유연 링크 로봇의 $H_{\infty}$ 자기 동조 제어)

  • Han, Ki-Bong;Lee, Shi-Bok
    • Journal of the Korean Society for Precision Engineering
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    • v.14 no.2
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    • pp.160-168
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    • 1997
  • A $H_{\infty}$self-tuning control scheme for the tip position of a flexible link robot handling unknown loads is presented here. The scheme essentially comprises a recursive least-squares identification algorithm and $H_{\infty}$self-tunning controller. The $H_{\infty}$control low is designed to be robust to uncertain parameters and the self-tunning action provides adaption to unknown parameters. Through numerical study, the performance comparison of the $H_{\infty}$self-tuning controller with a constant gain $H_{\infty}$controller as well as a LQG self-tuning controller clearly shows its superior ability in handling load changes in quiescent states.nt states.

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Prefix-tuning for Korean Natural language processing (Prefix-tuning에 기반한 한국어 자연언어 처리)

  • Min, Jinwoo;Na, Seung-Hoon;Shin, Dongwook;Kim, Seon-Hoon;Kang, Inho
    • Annual Conference on Human and Language Technology
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    • 2021.10a
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    • pp.622-624
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    • 2021
  • 현재 BERT와 같은 대용량의 코퍼스로부터 학습된 사전 학습 언어 모델을 자연어 응용 태스크에 적용하기 위해 일반적으로 널리 사용되는 방법은 Fine-tuning으로 각 응용 태스크에 적용 시 모델의 모든 파라미터를 조정하기 때문에 모든 파라미터를 조정하는데 필요한 시간적 비용과 함께 업데이트된 파라미터를 저장하기 위한 별도의 저장공간이 요구된다. 언어 모델이 커지면 커질수록 저장 공간의 비용이 증대됨에 따라 이러한 언어모델을 효율적으로 튜닝 할 수 있는 방법들이 연구되었다. 본 연구에서는 문장의 입력 임베딩에 연속적 태스크 특화 벡터인 prefix를 추가하여 해당 prefix와 관련된 파라미터만 튜닝하는 prefix-tuning을 한국어 네이버 감성 분석 데이터 셋에 적용 후 실험결과를 보인다.

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Comparing the performance of Supervised Fine-tuning, Reinforcement Learning, and Chain-of-Hindsight with Llama and OPT models (Llama, OPT 모델을 활용한 Supervised Fine Tuning, Reinforcement Learning, Chain-of-Hindsight 성능 비교)

  • Hyeon Min Lee;Seung Hoon Na;Joon Ho Lim;Tae Hyeong Kim;Hwi Jung Ryu;Du Seong Chang
    • Annual Conference on Human and Language Technology
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    • 2023.10a
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    • pp.217-221
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    • 2023
  • 최근 몇 년 동안, Large Language Model(LLM)의 발전은 인공 지능 연구 분야에서 주요 도약을 이끌어 왔다. 이러한 모델들은 복잡한 자연어처리 작업에서 뛰어난 성능을 보이고 있다. 특히 Human Alignment를 위해 Supervised Fine Tuning, Reinforcement Learning, Chain-of-Hindsight 등을 적용한 언어모델이 관심 받고 있다. 본 논문에서는 위에 언급한 3가지 지시학습 방법인 Supervised Fine Tuning, Reinforcement Learning, Chain-of-Hindsight 를 Llama, OPT 모델에 적용하여 성능을 측정 및 비교한다.

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A Study on Tuning Factor(δ) and Quality Factor(Q) Values in Design of Single-Tuned Passive Harmonic Filters (단일동조 수동고조파필터 설계시의 동조계수(δ) 및 양호도(Q)값 연구)

  • Cho, Young-Sik;Cha, Han-Ju
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.59 no.1
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    • pp.64-70
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    • 2010
  • This paper presents how to decide on tuning factor(${\delta}$) and quality factor(Q) values in design of single-tuned passive harmonic filters. Tuning factor(${\delta}$) and quality factor(Q) values have to consider before decision on circuit parameters of passive filters. A Study on these two value has not been scarcely performed and only experienced values has been used in passive harmonic filter design by far. As a experienced value, in cases of 5th and 7th filter, tuning factor(${\delta}$) is about 0.94 and 0.96 respectively and quality factor(Q) is, in all cases of, 50. If Single-tuned passive harmonic filter will be off-tuned, performance of filter will be decreased steeply and occur to parallel resonance between system reactance and filter capacitance. Therefore During the operation, In order not to off-tuning, Filter must be tuned at former order than actual tuning order. This is the same that total impedance of filter must have a reactive impedance. In this paper, Tuning factor(${\delta}$) is decided via example of real system and using the bode-plot and then performance of filters confirmed by filter current absorbtion rate. And Quality factor(Q) decided using the bode plot in example system and then performance of filters confirmed by filter current absorbtion rate also, which makes a calculated filter parameters to satisfy IEEE-519 distortion limits. Finally, Performance of the designed passive harmonic filter using the tuning factor(${\delta}$) and quality factor(Q) values, decided in this paper is verified by experiment and shows that 5th, 7th, 9th, 11th and 13th current harmonic distortions are decreased within IEEE-519 distortion limits, respectively.

Type-2 Fuzzy Self-Tuning PID Controller Design and Steering Angle Control for Mobile Robot Turning (이동로봇 선회를 위한 Type-2 Fuzzy Self-Tuning PID 제어기 설계 및 조향각 제어)

  • Park, Sang-Hyuk;Choi, Won-Hyuck;Jie, Min-Seok
    • Journal of Advanced Navigation Technology
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    • v.20 no.3
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    • pp.226-231
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    • 2016
  • Researching and developing mobile robot are quite important. Autonomous driving of mobile robot is important in various working environment. For its autonomous driving, mobile robot detects obstacles and avoids them. Purpose of this thesis is to analyze kinematics model of the mobile robot and show the efficiency of type-2 fuzzy self-tuning PID controller used for controling steering angle. Type-2 fuzzy is more flexible in verbal expression than type-1 fuzzy because it has multiple values unlike previous one. To compare these two controllers, this paper conduct a simulation by using MATLAB Simulink. The result shows the capability of type-2 fuzzy self-tuning PID is effective.

Fine-tuning Neural Network for Improving Video Classification Performance Using Vision Transformer (Vision Transformer를 활용한 비디오 분류 성능 향상을 위한 Fine-tuning 신경망)

  • Kwang-Yeob Lee;Ji-Won Lee;Tae-Ryong Park
    • Journal of IKEEE
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    • v.27 no.3
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    • pp.313-318
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    • 2023
  • This paper proposes a neural network applying fine-tuning as a way to improve the performance of Video Classification based on Vision Transformer. Recently, the need for real-time video image analysis based on deep learning has emerged. Due to the characteristics of the existing CNN model used in Image Classification, it is difficult to analyze the association of consecutive frames. We want to find and solve the optimal model by comparing and analyzing the Vision Transformer and Non-local neural network models with the Attention mechanism. In addition, we propose an optimal fine-tuning neural network model by applying various methods of fine-tuning as a transfer learning method. The experiment trained the model with the UCF101 dataset and then verified the performance of the model by applying a transfer learning method to the UTA-RLDD dataset.

A Method of Tuning Optimization for PID Controller in Nuclear Power Plants (원자력발전소 PID 공정제어기에 대한 튜닝 최적화 방법)

  • Sung, Chan Ho;Min, Moon Gi
    • Transactions of the Korean Society of Pressure Vessels and Piping
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    • v.10 no.1
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    • pp.1-6
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    • 2014
  • PID(Proportional, Integral, Derivative) controller is one of the most used process controllers in nuclear power plants. The optimized parameter setting of process controller contributes to the stable operation and efficiency in the operating nuclear power plants. PID parameter setting is tuned when new process control system is established or process control system is changed. It is a burdensome work for I&C(Instrument and Control) engineers to tune the PID controller because it requires a lot of experience and knowledge. When the plant is in operation, inadequate PID parameter setting can be the cause of the unstable process of the plant. Therefore the results of PID parameter setting should be compared, simulated, verified and finally optimized. The practical PID tuning methods used in process controller are tuning operation calculation(Ziegler-Nicholes, Minimum TIAE, Lambda, IMC), exclusive tuning program based on computer and Matlab application. This paper introduces the various tuning methods and suggests an optimized PID tuning process in the operating nuclear power plants.