• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.471-476
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• 1994

In this paper a new controller is proposed which gives the resultant system the appointed input-output properties, low sensitivity and robust stability. The proposed controller consists of a reference model and a robust compensator. The reference model determines the input-output properties of the total system and is constructed by using the nominal model of the plant. We can design the reference model by applying design techniques which pay attention to steady robustness and no attention to sensitivity and robust stability, and need all state variables of the plant. The robust compensator is obtained as a solution of the mixed sensitivity problem in H infinity control theory. Therefore, low sensitivity and robust stability are guaranteed in the resultant system. The simulation experiments show that the proposed controller is effective and useful.

• Bulletin of the Korean Mathematical Society
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• v.56 no.6
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• pp.1467-1483
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• 2019

This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI's surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a risky-asset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.373-384
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• 2002

This paper presents a robust PID controller design technique using the concept of model matching method in the frequency domain. To design the robust PID controller satisfying disturbance attenuation and robust tracking property for a reference input, first an H$\infty$ controller satisfying given performance is designed using the H$\infty$ control method. And then, the parameters(proportional, integral, and derivative gains) of the robust PID controller are determined using the model matching at frequency domain. The proposed technique is applied to a position controller design of the web. The simulation results show that the proposed robust PID controller satisfies disturbance attenuation and tracking property.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.1
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• pp.75-82
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• 2002

In this paper, the robust PID(Proportional-Integral-Derivative) controller was designed for speed control of BLDC motors using the frequency region model matching method. It was designed the robust PID controller satisfying disturbance attenuation and robust tracking performance using an H$\infty$ control method. The robust PID controller gains with the performances of the designed H$\infty$ controller are determined using the model matching method at frequency domain. Consequently, simulation results show that the proposed PID speed controller satisfies load torque disturbance attenuation and robust tracking performance, and this study has usefulness and applicability for the speed control system design of BLDC motors.

• Journal of Power System Engineering
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• v.4 no.3
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• pp.72-80
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• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identification. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identification are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identity the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2690-2692
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• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identifcation. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identifcation are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identify the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.9-16
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• 1997

The method of designing robust two degree of freedom(2 DOF) controllers for linear systems with parameter uncertainties and unmodeled dynamics is presented in this paper. Robust performance condition that accounts for robust model matching of closed loop system and disturbance rejection is derived. Using the robust performance condition, the feedback controller is designed to meet robust stability and disturbance rejection specifications, while prefilter is used to improve the robust model matching properties. The $H^{\infty}$ and $\mu$ controller for six degree of freedom vehicle with parameter variations are designed and compared. Simulations for hydrodynamic parameter variations and disturbance are presented to demonstrate the achievement of good robust performance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.26-26
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• 2000

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using robust H$_2$ filter. We suggest an uncertainty model for INS and apply the suggested discrete robust H$_2$ filter to the uncertainty model compared with kalman filter, the discrete robust H$_2$ filter is shown by simulation to have good performance of alignment time and accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.