• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.20-27
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• 1996

In this paper, we propose a coprime factor model reduction method to get a reduced order controller in $H^{\infty}$ mixed sensitivity problem with frequency weighting functions. for this purpose, the given $H^{\infty}$ mixed sensitivity problem is transformed into robust stabilization problem with coprime factor uncertainty of given plant. This method is to define frequency weighted coprime factors of plant in CSD (chain scattering description) form and reduce the coprime factors using weighted balanced truncation. then a controller is designed to the reduced order coprime factors using J-lossless coprime factorization method. Using this approach, the robust stability condition is derived and good performance is preserved in closed loop system with the given plant and the reduced order controller. Also the order of reduced controller for guaranteeing the robust stability can be determined before designing the reduced controller. The proposed method behaves well in both stable and unstable plant.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.340-343
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• 1995

In this paper, we get a reduced order controller in $H^{\infty}$ mixed sensitivity problem with weighting functions. For this purpose, we define frequency weighted coprime factor of plant in $H^{\infty}$ mixed sensitivity problem and reduce the coprime factor using the frequency weighted balanced truncation technique. The we design the controller for plant with reduced order coprime factor using J-lossless coprime factorization technique. Using this approach, we can derive the robust stability condition and achieve good performance preservation in the closed loop system with reduced order controller. And it behaves well in both stable plant and unstable plant.t.

• ETRI Journal
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• v.41 no.2
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• pp.235-241
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• 2019

In this paper, we propose a rank-weighted reconstruction feature to improve the robustness of a feed-forward deep neural network (FFDNN)-based acoustic model. In the FFDNN-based acoustic model, an input feature is constructed by vectorizing a submatrix that is created by slicing the feature vectors of frames within a context window. In this type of feature construction, the appropriate context window size is important because it determines the amount of trivial or discriminative information, such as redundancy, or temporal context of the input features. However, we ascertained whether a single parameter is sufficiently able to control the quantity of information. Therefore, we investigated the input feature construction from the perspectives of rank and nullity, and proposed a rank-weighted reconstruction feature herein, that allows for the retention of speech information components and the reduction in trivial components. The proposed method was evaluated in the TIMIT phone recognition and Wall Street Journal (WSJ) domains. The proposed method reduced the phone error rate of the TIMIT domain from 18.4% to 18.0%, and the word error rate of the WSJ domain from 4.70% to 4.43%.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.161-166
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• 1998

In this paper, an approach to the reduced order H$_{\infty}$ controller synthesis is proposed. This approach employs the frequency weighted model reduction whose frequency weights are deduced from the closed-loop system regarding the controller order reduction errors as uncertainties in a plant, while the resultant reduced order H$_{\infty}$ controller guarantees prescribed H$_{\infty}$ control performances.

• Design & Manufacturing
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• v.13 no.2
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• pp.1-5
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• 2019

In this study, micro cellular injection molding of automobile head lamp housing with uneven thickness structure was performed to obtain improvement on deformation and light-weight of the part. The thickness of the presented model was uniformly modified to control the deformation of the molded part. In order to maximize the lightweight ratio, the model having an average thickness of 2.0 mm were thinly molded to an average thickness of 1.6 mm. GFM(Gas Free Molding) and CBM(Core Back Molding) technology were applied to improve the problems of the conventional foam molding method. Equal Heat & Cool system was also applied by 3D cooling core and individual flow control system. Warpage of the molded parts with even cooling was minimized. To improve the mechanical properties of foamed products, complex resin containing nano-filler was used and variation of mechanical properties was evaluated. It was shown that the weight reduction ratio of products with light-weighted injection molding was 8.9 % and the deformation of the products was improved from the maximum of 3.6 mm to 2.0 mm by applying Equal Heat & Cool mold cooling system. Also the mechanical strength reduction of foamed product was less than 12% at maximum.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• MALSORI
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• no.67
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• pp.79-102
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• 2008

In this paper, we investigate the relation between Bayes factor and likelihood ratio test (LRT) approaches and apply the neighborhood information of Bayes factor to building an alternate hypothesis model of the LRT system. To consider the neighborhood approaches, we contemplate a distance measure between models and algorithms to be applied. We also evaluate several methods to improve performance of utterance verification using neighborhood information. Among these methods, the system which adopts anti-models built by collecting mixtures of neighborhood models obtains maximum error rate reduction of 17% compared to the baseline, linear and weighted combination of neighborhood models.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• Journal of KSNVE
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• v.4 no.3
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• pp.319-325
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• 1994

The flucturation of the engine torque appears to be the major source of the torsional vibration of the automotive driveline. The reduction of this torsional vibration has become a significant problem, due to an increase in the flucturation of the torque of recent light weighted powered engines, along with the requirements of higher performance. The torsional vibration of the automotive driveline can be reduced by soothing the fluctuation by adjusting the torsional characteristics of the clutch-disc. Computer simulation of the engine- input gear train is a useful investigative tool on studying the torsional characteristics of the clutch-disc. In this paper, a dynamic model for the automotive driveline was developed, and the engine torque and drag torque of the model were evaluated withe experimental data. By executing a simulation using the model, it has become possible to obtain the clutch-disc torsional characteristics when the engine is idling and the clucth-disc torsional characteristics for reducing the torsional vibration has been suggested.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.667-675
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• 1999

In this paper, we propose a robust controller design of RTP(Rapid Thermal Processing) system using structured uncertainty approach. Using the weighted mixed sensitivity function, we solve the robust stability problem against disturbance and temperature variation, and design a $\mu$ controller using curve fitting method against structured uncertainty. Also the reduction method should be requried because of the difficulty of implementaion with the obtained high order controller. We dal with robust stability and performance of RTP system by the design of $\mu$ controller for original model and Schur balanced reduced model. Finally the simulation results are proposed to show the validity of the proposed method.