• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.927-934
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• 2017

This paper is concerned with applicability of two-degree-of-freedom controllers to the recently suggested i-PID controllers, in which unknown parts of the plant are taken into account without any modeling procedure. First, i-PID controller with two-degree-of-freedom is applied to a specific model, called Anisochronic model, to confirm the usefulness of this method. Second, using the original examples of i-PID controllers, it is confirmed that performance/robustness of system are to be improved due to two-degree-of-freedom, especially when the input changes suddenly. It is seen that as the desired robustness increases the optimal value of two-degree-of-freedom parameter ${\alpha}_A$ would be negative. It is checked and verified that if this value was limited to 1 or less as is generally known, performance would be degraded.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.66-71
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• 2017

In this paper, we study the robustness of disturbance observer based controller for DC motor in the presence of unmodeled dynamics. It is well known that the robustness property usually becomes weaker as the control gain becomes larger. On the contrary to this expectation, it is shown that the phase margin of DOB controller remains quite a large value even though the time constant of Q-filter becomes smaller. The computer simulation results show that DOB controller is able to stabilize the motor system even in the presence of unmodeled dynamics. On the contrary, the unity-feedback system fails to maintain stability when a high gain feedback is employed for the purpose of achieving better disturbance attenuation performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2142-2161
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• 2018

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.355-357
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• 2004

Java의 경우 기존의 강건성 테스팅 방법인 ballista로는 객체 타입의 인자 및 파일 시스템의 변화를 시뮬레이션 할 수 없다. 따라서 객체에 대한 특별한 접근을 필요로 한다. 본 논문에서는 객체에 폴트를 삽입하는 방식을 통하여 자바 가상 기계의 강건성(Robustness)을 테스팅하는 방법을 제안한다. 자바 디버깅 툴인 JPDA를 사용하여 자바 가상 머신에 대한 직접적인 접근 및 의도한 상태 및 환경 변경을 가능하게 하고 이를 통해 자바 가상 기계에 폴트가 심어진 객체를 수행시킬 수 있다. 객체를 수행시키고 의도한 예외상황의 발생 유무를 관찰함으로써 자바 가상 기계의 강건성을 테스트 할 수 있다.

• Earthquakes and Structures
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• v.4 no.6
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• pp.649-670
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• 2013

It is known that a base-isolated building exhibits a large response to a long-duration, long-period wave and an inter-connected system without base-isolation shows a large response to a pulse-type wave. To compensate for each deficiency, a new hybrid passive control system is investigated in which a base-isolated building is connected to another building (free wall) with oil dampers. It is demonstrated that the present hybrid passive control system is effective both for pulse-type ground motions and long-duration and long-period ground motions and has high redundancy and robustness for a broad range of disturbances.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.163-169
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• 2019

The robust controller synthesis and analysis of the water level process in the U-tube system generator (UTSG) is addressed in this paper. The parameter uncertainties of the steam generator (SG) are modeled as multiplicative perturbations which are normalized by designing suitable weighting functions. The relative errors of the nominal SG model with respect to the other operating power level models are employed to specify the weighting functions for normalizing the plant uncertainties. Then, a robust controller is designed based on ${\mu}$-synthesis and D-K iteration, and its stability robustness is verified over the whole range of power operations. A gain-scheduled controller with $H_{\infty}$-synthesis is also designed to compare its robustness with the proposed controller. The stability analysis is accomplished and compared with the previous QFT design. The ${\mu}$-analysis of the system shows that the proposed controller has a favorable stability robustness for the whole range of operating power conditions. The proposed controller response is simulated against the power level deviation in start-up and shutdown stages and compared with the other concerning controllers.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.1-7
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• 2021

In this paper, we presented the experimental basis for the theoretical background and robustness of the Convolutional Neural Network for object recognition based on artificial intelligence. An experimental result was performed to visualize the weighting filters and feature maps for each layer to determine what characteristics CNN is automatically generating. experimental results were presented on the trend of learning error and identification error rate by checking the relevance of the weight filter and feature map for learning error and identification error. The weighting filter and characteristic map are presented as experimental results. The automatically generated characteristic quantities presented the results of error rates for moving and rotating robustness to geometric changes.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.571-585
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• 2021

Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.

• The Pure and Applied Mathematics
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• v.30 no.2
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• pp.191-201
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• 2023

In this paper the control system described by Urysohn type integral equation is studied. It is assumed that control functions are integrally constrained. The trajectory of the system is defined as multivariable continuous function which satisfies the system's equation everywhere. It is shown that the set of trajectories is Lipschitz continuous with respect to the parameter which characterizes the bound of the control resource. An upper estimation for the diameter of the set of trajectories is obtained. The robustness of the trajectories with respect to the fast consumption of the remaining control resource is discussed. It is proved that every trajectory can be approximated by the trajectory obtained by full consumption of the control resource.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.83-91
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• 2023

In this paper, we propose a robustness sensitivity index (RSI) of highway networks to analyze the effect of congestion in a specific section on the entire highway. The newly proposed RSI is defined as the change in the total mileage of the transportation network per extended unit length when the length of a particular section is extended. When the RSI value is large, traffic congestion in the section has a worse effect on the entire network than in other sections. The existing network robustness index (NRI) simply observes changes in transportation networks with and without specific sections, but the RSI proposed in this study is a kind of performance indicator that allows quantitative analysis of the ripple effect of the entire network according to the degree of congestion in a specific section. While changing the degree of congestion in a particular section, it is possible to calculate how the traffic volume increases, decreases, and the size and location of the congestion section change. This analysis proves the superiority of RSI as it cannot be analyzed with NRI. Various properties of RSI are analyzed using data from the domestic highway network. In addition, using the RSI concept, it is shown that the ripple effect on other sections in which a change in the degree of congestion of a specific section occurs can be analyzed.