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In order to improve the robustness of Adaptive Matched Field Processing(AMFP), a Conditional Probability Constraint Matched Field Processing (MFP-CPC) is proposed. The algorithm derives the posterior probability density of the source locations from Bayesian Criterion, then the main lobe of AMFP is protected and the side lobe is restricted by the posterior probability density, so MFP-CPC not only has...
Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection...
Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate...
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning...
In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including 1) computational infeasibility of handling an entire dataset at once, 2) existence of heterogeneously...
We propose a robust method for estimating the orientation and displacement of an inertial measurement unit undergoing planar periodic motion. Such movements is a common approximation to human gait and running. We formulate the problem introducing a sparse vector of outlier errors and l1-regularization. The problem thus becomes robust to outliers in the data. The problem can be rewritten as a quadratic...
The visual and automatic classification of vehicles plays an important role in the Transport Area. Besides of security issues, the monitoring of the type of traffic in streets and highways, as well the traffic dynamics over time, allows the optimization of use and of resources related to such public infrastructure. In this work we propose a novel method, called 2D-DBM, for robust and efficient automatic...
Fast and robust 3D reconstruction of facial geometric structure from a single image is a challenging task with numerous applications, but there exist two problems when applied "in the wild": the 3D estimates are unstable for different photos of the same subject; the 3D estimates are over-regularized and generic. In response, a robust method for regressing discriminative 3D morphable face...
The traditional affine iterative closest point (ICP) algorithm is fast and accurate for affine registration between two point sets, but it is easy to fall into local minimum. This paper proposes a robust Affine ICP algorithm based on corner points. First, an objective function is established under the guidance of corner points, where the corner points as the shape control point guides the affine registration...
1H-MRSI (proton Magnetic Resonance Spectroscopic Imaging) is now widely used to assist physicists to analyze and quantify brain metabolites in a noninvasive way. In case of glioma, the brain tissue metabolite composition is not widely different but metabolites concentration are varying depending on the grade of the tumor. In the higher stage of the tumor, new metabolites could be detected such as...
There has been a phenomenal increase in the utility of text classification (TC) in applications like targeted advertisement and sentiment analysis. Most applications demand that the model be efficient and robust, yet produce accurate categorizations. This is quite challenging as their is a dearth of labelled training data because it requires assigning labels after reading the whole document. Secondly,...
This paper investigates the problem of network-based fault-tolerant controller design for networked control systems (NCSs) in the presence of random delays and data packet dropouts. A novel actuator fault model which is more general and practical than the conventional actuator fault models is developed. Considering this new fault model, the NCSs are firstly modeled as a Markovian jump system (MJS)...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications. Even though RPCA has been shown to be very successful in solving many rank minimisation problems, there are still cases where degenerate or suboptimal solutions are obtained. This...
The problem of spatial sensor location under parametric uncertainty of the repetitive distributed-parameter process is discussed. The idea is to reduce the uncertainty of the model used for the design of the iterative learning control, thus increasing the system performance. Particularly, an iterative scheme for estimation of the system parameter distributions is proposed based on the sequential experimental...
In this paper, we propose an optimization model for planning a robust path against changes in traffic volume. Robustness is based on the form of the travel time function. The proposed model can be applied not only when traffic volume increases but also when it decreases. In addition, the proposed model can set the ratio of consideration by a parameter depending on whether the traffic volume is increasing...
In this fast developing world, the number of motor vehicles is increasing rapidly but road resources remain limited, causing severe congestion problem of city traffic. In order to predict short-term traffic condition accurately, we propose a short-term traffic condition prediction method for urban road network based on improved support vector machine. As outliers inevitably exist in collected traffic...
This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel...
Tensor-based analysis of brain imaging data, in particular functional Magnetic Resonance Imaging (fMRI), has proved to be quite effective in exploiting their inherently multidimensional nature. It commonly relies on a trilinear model generating the analyzed data. This assumption, however, may prove to be quite strict in practice; for example, due to the natural intra-subject and inter-subject variability...
Monitoring mental fatigue has become important for improving cognitive performance and health outcomes especially for older adults. Previous models using eye-tracking data allow inference of fatigue during cognitive tasks, such as driving, but they require us to engage in specific cognitive tasks. A model capable of inferring fatigue in natural-viewing situations when individuals are not performing...
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