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Analyzing the stochastic characteristics of electric consumption series in many countries shows the presence of atypical observations or outliers. Outliers are deviant data points that do not follow the model of the majority of observations. They significantly degrade the accuracy of conventional day-ahead estimation and forecasting methods even if they are present in a very low fraction. Thus, both...
Numerical substructures adopt assumed hysteretic model in traditional substructure tests. The difference between the assumed model and true model might bring the extra large test error. To diminish the negative effort caused by the inaccuracy of the restoring model of numerical substructures, the hysteretic model of experimental substructures is identified on line and model of the part in numerical...
In this paper, we propose a method for detection and tracking of multiple planes in sequences of Time of Flight (ToF) depth images. Our approach extends the recent J-linkage algorithm for estimation of multiple model instances in noisy data to tracking. Instead of randomly selecting plane hypotheses in every image, we propagate plane hypotheses through the sequence of images, resulting in a significant...
A robust foreground object segmentation technique is proposed, capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds. The method employs contextual spatial information by analysing each image on an overlapping patch-by-patch basis and obtaining a low-dimensional texture descriptor for each patch. Each descriptor is passed through an adaptive multi-stage...
Moving objects detection in dynamic scenes is a challenging task in many computer vision applications. Traditional background modeling methods do not work well in these situations since they assume a nearly static background. In this paper, a novel operator named spatial extended center-symmetric local binary pattern (SCS-LBP) for background modeling is proposed. It extracts spatial and temporal information...
The Huberized LASSO model, a robust version of the popular LASSO, yields robust model selection in sparse linear regression. Though its superior performance was empirically demonstrated for large variance noise, currently no theoretical asymptotic analysis has been derived for the Huberized LASSO estimator. Here we prove that the Huberized LASSO estimator is consistent and asymptotically normal distributed...
The recently proposed integrated direct/indirect adaptive robust controller (DIARC) for a class of nonlinear systems with unknown input dead-zones is combined with desired trajectory compensation to achieve asymptotic stability with excellent tracking performance. The algorithm is tested on a linear motor drive system preceded by a simulated non-symmetric dead-zone which is practically supposed to...
In many filter applications the exact steering vector is not know, and thus, robust beamforming methods have to be used. In this contribution, an algorithm which achieves robust beamforming via target tracking is proposed. In contrast to existing approaches, the algorithm works on sparse signals with arbitrary steering vector shapes, and the parameters of the algorithm are adapted in an optimal way...
In the context of linear regression, the least absolute shrinkage and selection operator (LASSO) is probably the most popular supervised-learning technique proposed to recover sparse signals from high-dimensional measurements. Prior literature has mainly concerned itself with independent, identically distributed noise with moderate variance. In many real applications, however, the measurement errors...
Perceptual watermarking should take full advantage of the results from human visual system (HVS) studies. Just noticeable distortion (JND), which refers to the maximum distortion that the HVS does not perceive, gives us a way to model the HVS accurately. In this paper, another very important aspect affecting human perception, visual saliency, is introduced to modulate JND profile. Based upon visual...
It is well known that an origin of MPC is the controller in adaptive control scheme. In other words, MPC is popularly employed as the control law in adaptive control. In primitive adaptive MPC, constraints are not taken into account. However, after this adaptive MPC, tremendous progress has been made in MPC theory independently in a way that various constraints on the state and input variables are...
This paper presents a new strategy for controlling induction motors with unknown parameters. Using a simple linearized model of induction motors, we design robust adaptive controllers and unknown parameters update laws. The control design and parameters estimators are proved to have global stable performance against sudden load variations. All closed loop signals are guaranteed to be bounded. Simulations...
This paper presents an integrated direct/indirect adaptive robust control (DIARC) scheme for a class of nonlinear systems preceded by unknown non-symmetric, non-equal slope dead-zone nonlinearity. Due to the inherent nonlinear parametrizaton nature of the unknown dead-zone nonlinearity, existing robust adaptive control methods have been focusing on using various linearly parametrized models with on-line...
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground...
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by non-parametric kernel density estimation. The major contribution is a novel bidirectional learning framework for discrimination between the object and background. It has the following advantages: 1) it embeds background information, unlike...
This electronic propagation is the most important spatial characteristics of bioelectrical signals, whereas time delay estimation (TDE) is the most effective quantitative method in the spatial propagation feature extraction of biomedical signals. A new robust TDE algorithm using the variable step-size affine projection ( RVSAPA) and its application experiments are presented in this paper. Due to it...
In this paper, a new simple, adaptive, high-capacity steganographic algorithm for 3D models using contour theory in spatial domain is proposed. Every vertex of a 3D model can adaptively embed variable 3sigma (sigmages1) bits using contour space subdivision and multi quantization index modulation (MQIM) with low distortion. We first use vertex weight estimation which is used to estimate the degree...
This paper proposes a robust NLMS algorithm with a novel noise modeling based on stationary/nonstationary noise decomposition. The ambient noise including the near-end signal is modeled as a weighted sum of the stationary and the nonstationary components. These components are independently estimated with an appropriate time constant for better accuracy. The estimates are weighted by the stationary/...
In this paper, we propose a novel approach which integrates adaptive appearance model and hierarchical estimation mechanism composed of global estimation and local estimation. Hierarchical estimation runs in two phases: In first phase, global estimation coarsely predicts a region in where true state may be present, and then local estimation tries to find out the true state inside the region at second...
This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as "radical ICA", "SDD ICA", "Erica" and "Evd" for separation purposes. This comparative study...
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