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Fault attacks are among the most effective techniquesto break real implementations of cryptographic algorithms. They usually require some kind of knowledge bythe attacker on the effect of the faults on the target device, which in practice turns to be a poorly reliable informationtypically affected by uncertainty. This paper is devoted toaddress this problem by softening the a-priori knowledge on the...
Noise is a prominent challenge found in many bioinformatics datasets and it refers to erroneous or missing data. The presence of noise in gene expression datasets has adverse effects on machine-learning techniques, such as supervised classification algorithms and feature selection techniques. Additionally, the identification of noise and its quantification are challenging tasks that require a proper...
Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing...
Robust Principal Component Analysis (PCA) (or robust subspace recovery) is a particularly important problem in unsupervised learning pertaining to a broad range of applications. In this paper, we analyze a randomized robust subspace recovery algorithm to show that its complexity is independent of the size of the data matrix. Exploiting the intrinsic low-dimensional geometry of the low rank matrix,...
In this paper we describe an estimator for the canonical polyadic (CP) tensor model using order statistics of the residuals. The estimator minimizes in an iterative and alternating fashion a dispersion function given by the weighted ranked absolute residuals. Specific choices of the weights lead to either equivalent or approximate versions of the least squares estimator, least absolute deviation estimator...
The statistical test rule induction method (STRIM) has been proposed as a method for effectively inducing if-then rules from a decision table. Its usefulness has been confirmed by a simulation experiment and comparison with conventional methods. However, real-world datasets often contain missing and contaminated values. This issue has been examined and addressed by various conventional methods. This...
This paper introduces a learning-based robust control algorithm that provides robust stability and performance guarantees during learning. The approach uses Gaussian process (GP) regression based on data gathered during operation to update an initial model of the system and to gradually decrease the uncertainty related to this model. Embedding this data-based update scheme in a robust control framework...
This paper introduces a novel unsupervised estimator of equivalent number of looks (ENL) that can be applied to an arbitrary image. It avoids the assumption that homogeneous speckle will dominate the investigated image that is followed by current unsupervised ENL estimators but not always valid, especially for the complex SAR scenes with high mixture and texture. Incorporating the statistical properties...
The presentation introduces robust, adaptive and output feedback-based flight control architectures applied to various aerial platforms. We begin by covering aircraft models that encompass flight dynamics, models for control design, subsystem models (including actuators, air data system and sensors), atmospheric disturbances and system uncertainty. Control design and analysis requirements for manned,...
To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust...
In this paper we examine efficacy of occlusion-free appearance learning for part based model. Appearance modeling with less accurate appearance data is problematic because it adversely affects entire learning process. We evaluate the effectiveness of excluding occluded body parts to be modeled for better appearance modeling process. To meet this end, We employ a simple but effective occlusion detection...
Spectrum sensing is the process of identifying idle spectrums and utilizing them by the secondary users such that there is no inference with primary users. Cognitive Radio Network's (CRN) primary challenge is sensing of idle spectrum and efficiently handling that spectrum by the secondary user nodes. The effective and efficient spectrum sensing can achieve by enabling cooperation between nodes to...
Vehicle pose estimation with respect to the road plays a critical role in the advances of autonomous vehicle navigation and guidance. Vision-based road lane line detection provides a feasible and low cost solution as the vehicle pose can be derived from the detection. While good progress has been made, the lane line detection has remained an open one, given challenging road appearances. In this paper,...
M-estimators are the de-facto standard method of robust estimation in robotics. They are easily incorporated into iterative non-linear least-squares estimation and provide seamless and effective handling of outliers in data. However, every M-estimator's robust loss function has one or more tuning parameters that control the influence of different data. The choice of M-estimator and the manual tuning...
The purpose of this paper is to appi}' the concept of frequency-domain model invalidation to integrity control and monitoring of induction motors. The approach is based on the concept of the generalized structured singular value. New formulations of robustness objectives in fault diagnosis are explored and simulation results applied to an induction motor demonstrate the potential of the proposed method.
A new iterative identification and control scheme is introduced which is based on active model falsification. Only very conservative a priori bounds are assumed to be known on the norm of the unmodelled dynamics and on the size of disturbances. In spite of the weak assumptions, the scheme converges close to the ‘ideal’ performance which would be achieved with perfect knowledge of the size of the unmodelled...
A polynomial time algorithm for converting time domain input-output data into worst case frequency domain set membership information with arbitrary precision is presented. Experimental data is assumed to be noise corrupted time domain input-output measurements for a single-input single-output linear shift invariant discrete time system. This method uses prior knowledge of bounds on both the output...
In this paper a tool for initial control designs for an irrigation channel is developed. The idea is that a physical model of the channel is obtained using the St. Venant equations, and a data set is generated by simulating this model. A first order nonlinear model is then estimated from the simulated data using system identification techniques, and a controller is designed based on the estimated...
Target motion analysis (TMA) for a rectilinear source movement (RSM) has been intensively studied in the last ten years. But difficulties still exist, especially when source heading or speed changes are within the same time as the conventional TMA convergence time. This paper is concerned with a new method of batch TMA for maneuvering sources using a non-linear least-squares fit between the whole...
Artificial Neural Networks (ANNs) have been adapted actively in the time series-prediction arena, but the presence of outliers that usually occur in the time series data may pollute the network training data. This is due to its ability to automatically learn any pattern without prior assumptions as well as loss of generality. In theory, the most common algorithm for training the network is the backpropagation...
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