• Quantitative Bio-Science
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• v.37 no.2
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• pp.133-141
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• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.9 s.351
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• pp.10-16
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• 2006

This paper presents a fast switching CMOS frequency synthesizer with a new coarse tuning scheme for PHS applications. The proposed coarse tuning method selects the optimal tuning capacitances of the LC-VCO to optimize the phase noise and the lock-time. The measured lock-time is about $20{\mu}s$ and the phase noise is -121dBc/Hz at 600kHz offset. This chip is fabricated with $0.25{\mu}m$ CMOS technology, and the die area is $0.7mm{\times}2.1mm$. The power consumption is 54mW at 2.7V supply voltage.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.8-17
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• 2002

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used immune algorithm in order that a PID controller can be more adaptable controlled against the external condition, including moise or disturbance of plant. Parameters P, I, D encoded in antibody randomly are allocated during selection processes to obtain an optimal gain required for plant. The result of study shows the artificial immune can effectively be used to tune, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.391-394
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• 1998

In this paper, we try to analyze two kinds of conventional neuro-fuzzy learning algorithms, which are widely used in recent fuzzy applications for tuning fuzzy rules, and give a summarization of their properties. Some of these properties show that uses of the conventional neuro-fuzzy learning algorithms are sometimes difficult or inconvenient for constructing an optimal fuzzy system model in practical fuzzy applications.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.246-249
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• 1988

In this paper, a new design technique which uses weighted least-sqare approach for the solution of the pole assignment problem is represented. This technique maybe used to assign some closed loop poles to places which reduce the large system input and output variance due to near pole-zero condition. The least-square approach is also applied to the design of servo self-tuning controller with integrator.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.487-489
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• 1997

The control of the switched reluctance motor is usually on the inductance profiles as a function of position. In this paper, a control scheme to maximize the motor torque is proposed by determining optimal turn-off angle with a self-tuning control method.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.67-70
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• 2002

This paper presents an optimal robust LQ-PID controller design method for a second order system with time-delay to meet design specifications. By LQR formulation of the second order system with time-delay, tuning parameters of PID controller are related by the weighting factors Q and R of cost function. The selection of the weighting factors Q and R are chosen to satisfy such the design specifications as overshoot and settling time.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.251-258
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• 2004

This paper presents the parameter study of multiple tuned liquid damper (MTLCD) applied to the 76-story benchmark building. A parameter study involves the effects of number of TLCD, frequency range, and central tuning frequency ratio, which are important parameters of MTLCD. The performance of MTLCD is carried out numerical analysis which reflects the nonlinear property of liquid motion. The parameters of TLCD exist different each optimal values according to mass ratio. The performance of single-TLCD (STLCD) is sensitive for tuning frequency ratio. Therefore, MTLCD is proposed to protect such the shortcoming of STLCD. The result of numerical analysis presents improved performance for robustness of MTLCD