• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.133-139
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• 2011

Maintaining adequate sealing in engine cylinder head is a crucial factor in engine design. Failure of engine operations occurs mainly owing to the leaking by decreased sealing pressure. Reliability-robustness concept is applied to the engine cylinder head system. Deterministic way to obtain engineering solution in CAE industry may not consider the effects of noises and disturbances experienced during operation. However, analytical reliability-robustness concept may make possible to reduce the sensitivity of system with noise factors. Influences of design factors including noise factors would be predicted in analytical way. Optimized design may be obtained by shrinking variability and shifting to design target. Three-dimensional finite element analyses have been performed to apply analytical reliability-robustness concept.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.201-208
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• 2006

This paper describes the control philosophy of ESP(Electronic Stability Program) which consists of the stability control the fault diagnosis and the fault tolerant control. Besides the functional performance of the stability control, robustness of control and fault diagnosis is focused to avoid the unnecessary activation of the controller. The look-up tables are mentioned to have the accurate target yaw rate of the vehicle and obtained from vehicle tests for the whole operation range of the steering wheel angle and the vehicle speed. The wheel slip control with a design goal of wheel slip invariance is implemented for the yaw compensation and the target wheel slip is determined by difference between the target yaw rate and actual yaw rate. Since the ESP has a high severity level and the robust control is required, the robustness margin for the stability control is determined according to several uncertainties and the robust fault diagnosis is performed. Both computer simulation and test results are shown in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.455-460
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• 1998

We propose robust MILP model for scheduling and design of multiproduct batch processes in this paper. Recent stochastic modeling approaches considering uncertainty have mainly focused on maximization of expected NPV. Robust model concept is applied to generate solution spectrum in which we can select the best solution based on tradeoff between robustness measure and expected NPV. Robustness measure is represented as penalty term in the objective function, which is Upper Partial Mean of NPV. We can quantify solution robustness by this penalty term and maintain model as MILP form to be computationally efficient. An example illustrates the effectiveness of the proposed model. In many cases sufficient robustness can be guaranteed through a little reduction of expected NPV.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.1
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• pp.171-179
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• 2001

The purpose of this paper is the derivation and development of the new definitions and methods for the new estimation of robustness for the systems having structured uncertainties. This proposition adopts the theoretical analysis of the Lyapunov direct methods, that is, the sign properties of the Lyapunov function derivative integrated along finite intervals of time, in place of the original method of the sign properties of the time derivative of the Lyapunov function itself. This is the new sufficient criteria to relax the stability condition and is used to generate techniques for the robust design of control systems with structured perturbations. The systems considered are assumed to be nominally linear, with time-variant, nonlinear bounded perturbations. This new techniques demonstrate the improvement of robustness bounds from the numerical results.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.862-867
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• 2021

Although Deep Neural Networks (DNNs) have shown remarkable performance in various artificial intelligence fields, it is well known that DNNs are vulnerable to adversarial attacks. Since adversarial attacks are implemented by adding perturbations onto benign examples, increasing the sparsity of DNNs minimizes the propagation of errors to high-level layers. In this paper, unlike the traditional pruning scheme removing low magnitude weights, we eliminate high magnitude weights that are usually considered high absolute values, named 'reverse pruning' to ensure robustness. By conducting both theoretical and experimental analyses, we observe that reverse pruning ensures the robustness of DNNs. Experimental results show that our reverse pruning outperforms previous work with 29.01% in Top-1 accuracy on perturbed CIFAR-10. However, reverse pruning does not guarantee benign samples. To relax this problem, we further conducted experiments by adding a regularization term for the high magnitude weights. With adding the regularization term, we also applied conventional pruning to ensure the robustness of DNNs.

• Asia pacific journal of information systems
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• v.31 no.3
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• pp.335-357
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• 2021

As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.177-178
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• 2012

• Journal of the Korean Society for Precision Engineering
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• v.24 no.1 s.190
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• pp.14-19
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• 2007

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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• pp.349-349
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• 2005