• 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.

• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.4
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• pp.157-164
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• 2022

The TMD has a simpler structure than other vibration control devices and shows excellent control performance for the standardized vibration occurring in the structure. However, when the vibration cycle of the structure coincides with the vibration cycle of the TMD due to the sudden external loads, the off-tuning occurs, which threatens the structure while increasing the vibration width of the TMD. Therefore, Electromagnetic Tuned Mass Damper (ETMD) was developed as a semi-active TMD that prevents off-tuning while exhibiting excellent control performance like TMD. To verify the control performance of the developed ETMD, the bending behavior control performance evaluation experiment using a simple beam bridge was performed. The experimental method compared the mutual control power by experimenting with the existing TMD method and the developed ETMD under nine excitation frequency conditions. As a result, it was confirmed that the control effect of ETMD was about 4.85% higher than that of TMD at 3.02Hz, which generates the maximum displacement in the simple beam bridge. Also, the off-tuning occurred in some excitation conditions when using TMD, although the off-tuning did not occur when using ETMD. Therefore, the excellent control performance of the ETMD developed in this study was verified.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.05a
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• pp.249-252
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• 2003

Acoustic behavior in combustion chamber with acoustic cavity is numerically investigated by adopting linear acoustic analysis. Helmholtz-type resonator is employed as a cavity model to suppress acoustic instability. The tuning frequency of acoustic cavity is adjusted by varying the sound speed in acoustic cavity. Acoustic pressure responses of chamber to acoustic oscillating excitation are shown md acoustic damping effect of acoustic cavity is quantified by damping factor. As the tuning frequency approaches the target frequency of the resonant mode, mode split from the original resonant mode to lower and upper modes appears and thereby damping effect is degraded. Considering mode split and damping effect as a function of tuning frequency, it is desirable to make acoustic cavity tuned to maximum frequency of those of the possible splitted upper modes.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.401-411
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• 2003

Important results about two-degree-of-freedom PID controllers are surveyed for the tutorial purpose, including equivalent transformations, various explanations about the effect of the two-degree-of-freedom structure, relation to the preceded-derivative PID and the I-PD controllers, and an optimal tuning method.

• Proceedings of the Korean Magnetic Resonance Society Conference
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• 2001.02a
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• pp.9-9
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• 2001

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.246-252
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• 2012

Recently, concern over climate change and global politics associated with traditional fossil fuel energy sources has driven significant increase in wind energy utilization over the past decade around world. Generally, life-time of wind turbine system should be guaranteed for twenty years. Therefore, performance deterioration of wind turbine system occurs owing to aging effects for long term operation. In this work, a new type of auto tuning algorithm for overcoming the problem of performance deterioration is proposed. Furthermore, various simulations are carried out to verify the feasibility of the proposed scheme.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.143-149
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• 2018

It has become ever harder to design reliable circuits with each nanometer technology node; under normal operation conditions, a transistor device can be affected by various aging effects resulting in performance degradation and eventually design failure. The reliability (aging) effect has traditionally been the area of process engineers. However, in the future, even the smallest of variations can slow down a transistor's switching speed, and an aging device may not perform adequately at a very low voltage. Therefore, circuit designers need to consider these reliability effects in the early stages of design to make sure there are enough margins for circuits to function correctly over their entire lifetime. However, such an approach excessively increases the size and power dissipation of a system. As the impact of reliability, new techniques in designing aging-resilient circuits are necessary to reduce the impact of the aging stresses on performance, power, and yield or to predict the failure of a system. Therefore, in this paper, a novel low power on-chip self-tuning circuit considering the aging effects has been proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.14-20
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• 1996

This paper presents a self tuning method of a velocity type PID controller for minimum or non-minimum phase systems with time delays. The velocity type PID control structure is determined in the process of minimizing the variance of the auxilliary output, and self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design parameter. This method is simple and effective compared with other existing methods[1,2]. Numerical examples are included to illustrate the procedure and to show the performance of the control system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.281-286
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• 2005

The main objective of the research work presented in this article is to present a systematic approach for designing a multilayer feed-forward artificial neural network based self-tuning power system stabilizer (ST-ANNPSS). In order to suggest an approach for selecting the number of neurons in the hidden layer, the dynamic performance of the system with ST-ANNPSS is studied and hence compared with that of conventional PSS. Finally the effect of variation of loading condition and equivalent reactance, Xe is investigated on dynamic performance of the system with ST-ANNPSS. Investigations reveal that ANN with one hidden layer comprising nine neurons is adequate and sufficient for ST-ANNPSS. Studies show that the dynamic performance of STANNPSS is quite superior to that of conventional PSS for the loading condition different from the nominal. Also it is revealed that the performance of ST-ANNPSS is quite robust to a wide variation in loading condition.