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Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and...
Neighborhood Covering Reduction extracts rules for classification through formulating the covering of data space with neighborhoods. The neighborhoods of covering are constructed based on distance measure and strictly constrained to be homogeneous. However, this strategy over focuses on boundary samples and thus makes the neighborhood covering model sensitive to noise. To tackle this problem, we construct...
In classification tasks, labeled data is a necessity but sometimes difficult or expensive to obtain. On the contrary, unlabeled data is usually abundant. Recently, different active learning algorithms are proposed to alleviate this issue by selecting the most informative data points to label. One family of active learning methods comes from Optimum Experimental Design (OED) in statistics. Instead...
This paper is to discuss optimal positioning of sampling pulse for extraction of vital information from signal at critical points using clock-less sampling by online analysis of signal. Using this concept sampling pulses are generated which are less than or equal to minimum Nyquist rate for capturing only critical points without any prior knowledge of signal. This method of sampling technique is applicable...
Defect-proneness prediction is affected by multiple aspects including sampling bias, non-metric factors, uncertainty of models etc. These aspects often contribute to prediction uncertainty and result in variance of prediction. This paper proposes two methods of data mining static code metrics to enhance defect-proneness prediction. Given little non-metric or qualitative information extracted from...
Traditional rough set of noise data, the lack of adaptability, lack of flexibility or robustness, for the engineering data can not distinguish between equivalence classes of edge region of overlap with the collection, resulting in loss of many valuable engineering information. Strong noise in the actual engineering data over-fitting due to reduced ability to distinguish the object, its limitations...
In this paper, robust adaptive output feedback control is studied for a class of discrete-time nonlinear systems in output-feedback form perturbed by a class of functional nonlinear uncertainties of Lipschitz type. To construct output feedback control, the system is transformed into the form of nonlinear-auto-regressive-moving-average (NARMA), a novel future output prediction is designed based on...
This work proposes a robust control framework to address the problem of practical tracking for a class of nonlinear systems described as hybrid automata. The framework reposes both on a suitable definition of the references to be tracked and on input-to-state stability properties of the feedback laws in order to guarantee a desired behavior of the hybrid automata in terms of robust transition between...
Neo-robust control is a new theory proposed by the authors, which utilizes both the gain and the phase information of uncertainty in robust control design. In this paper, we extend the idea of neo-robust control to a class of uncertain systems with factorized uncertainty. This class of systems is good at describing the uncertainties arising in process control systems. Conditions on robust stability,...
This paper proposes extensions to a recent control Liapunov function (CLF) based method for designing dynamical systems with trajectories that converge to the zeros of a nonlinear vector function f . Specifically, the CLF design method is extended to the case when the Jacobian of the vector function can be decomposed into a known part and a partially known part, for which certain norm bounds are known...
In this paper, a hybrid control architecture is proposed for the adaptive robust control of a class of nonlinear systems with uncertain parameter variation ranges. Specifically, the standard set-membership description of uncertainty is adopted - the bounds of the structural approximation errors associated with the parametrized models are assumed to be known but the variation ranges of model parameters...
The problem of designing H?? dynamic output-feedback controllers for polytopic Delta operator systems is considered. Given a transfer function matrix of a system with polytopic uncertainty, an appropriate, not necessarily minimal, state-space model of the system is described which permits reconstruction of all its states. To this model, a new polynomial parameter-dependent approach to state-feedback...
Selfishness detection is becoming a hot issue in mobile ad hoc networks and wireless sensor networks. We use Dempster-Shafer theory of evidence in a novel way to incorporate data-centric trust evaluation for detection of nodes' selfish forwarding behavior. Within the proposed D2S2T2 framework, trust is considered in regard to forwarding, as part of routing support, as well as in regard to recommendations,...
This paper is focused on the problem of uncertain process control by using RMPC (robust model predictive control). A relevant class of RMPC algorithms is the one characterized by the use of the LMI framework. This field started in the middle of nineties and since then several works applying LMIs in the context of RMPC have been proposed. Most of them assume a polytopic representation of the process...
We study how the maximum/minimum gain of a decentralized controller influences the maximum/minimum gain of the corresponding closed loop transfer function, and provide lower bounds on the best closed-loop performance achievable by decentralized controllers satisfying additional frequency domain restrictions. These bounds are obtained using generalizations of the structured singular value and approximated...
In this paper we study the problem of robust discrete-time H2 filtering using a Linear Matrix Inequality approach. By assuming that the number of samples available for the identification of the system is large enough, we describe the filter design problem as a semidefinite program. Afterwards, the problem of designing an input signal for the identification of the system, to improve the performance...
This paper is devoted to robust adaptive sliding mode control for a class of nonlinear systems in the Takagi-Sugeno forms with mismatched parametric uncertainties. Sufficient conditions for the existence of linear sliding surfaces are given in terms of linear matrix inequalities, by which the corresponding adaptive reaching motion controller is also designed. Simulation studies show the effectiveness...
The purpose of this paper is a parametric description (parametrization) of static output feedback stabililizing controllers for linear continuous-time systems with Markovian switching. The results are then applied to simultaneous stabilization, robust stabilization and passification problems. Based on these results some algorithms for computing of stabilizing gains are proposed. The approach is based...
In this article, a signal-compensation based approach is presented to the robust output regulation problem for a class of multi-input multi-output uncertain systems with uncertain exosystems. The use of equivalent disturbance attenuation techniques permits removal of a common restriction that the exosystem which generates the reference signals and/or the disturbance signals is accurately known. The...
We consider decision making in a Markovian setup where the reward parameters are not known in advance. Our performance criterion is the gap between the performance of the best strategy that is chosen after the true parameter realization is revealed and the performance of the strategy that is chosen before the parameter realization is revealed. We call this gap the parametric regret. We consider two...
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