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The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
In the passive localization, the estimate error of sensor position and velocity is inevitable. By the use of the calibration, the sensor position and velocity will be improved, especially, to the Cramer-Rao lower bound (CRLB) under the ideal circumstance. The CRLB for the estimate of sensor position and velocity is derived at beginning. Then the CRLB for the estimate of source is followed. Finally,...
Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification)...
The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for...
This paper proposes a novel method to estimate relative poses for a calibrated stereo camera. Three corresponding points in 3D space are theoretically required to recover unconstraint motion which has six degrees of freedom. The proposed method solves this problem with only two 3D points by exploiting a common reference direction between poses. Two points are selected in accordance with the distance...
Pair wise learning to rank algorithms (such as Rank SVM) teach a machine how to rank objects given a collection of ordered object pairs. However, their accuracy is highly dependent on the abundance of training data. To address this limitation and reduce annotation efforts, the framework of active pair wise learning to rank was introduced recently. However, in such a framework the number of possible...
The well-known problem of estimating an unknown deterministic parameter vector over a linear system subject to additive Gaussian noise is studied from the perspective of minimizing total sensor measurement cost under a constraint on the log volume of the estimation error confidence ellipsoid. A convex optimization problem is formulated for the general case, and a closed form solution is provided when...
In classification problems feature selection has an important role for several reasons. It can reduce computational cost by simplifying the model. Also when the model is taken for practical use fewer inputs are needed which means in practice, that fewer measurements from new samples are needed. Removing insignificant features from the data set makes the model more transparent and more comprehensible...
One of the main advantages of interval type-2 fuzzy logic systems is their ability to produce prediction intervals as a by-product of the type reduction process. This is especially useful for the design of interval type-2 fuzzy logic systems, where the data are corrupted by noise, in such cases; a model that provides a granular output is more appreciable. Nevertheless, the methods have been proposed...
In this paper we present a system for indoor people tracking based on the combination of wearable sensors and a video analysis module. The sensor consists of an inertial platform, which provides attitude and acceleration data with a high rate. Data is fused by an Extended Kaiman Filtering (EKF) to reconstruct the attitude and the accelerations experienced by the wearable sensors. The information is...
To solve problems in data-driven fault prognostic study such as prediction uncertainty management, multiple fault features and on-line prognostics, an algorithm based on multivariate relevance vector machine (MRVM) is presented. It extends the existing time series iterative multi-step prediction to the application with multiple fault features by matrix partitioning technique. For on-line application,...
Airdrop systems provide a unique capability of delivering large payloads to undeveloped and inaccessible locations. Guided airdrop systems based on steerable, ram-air parafoils have been developed with the goal of improving the precision and accuracy of air-dropped payload delivery. In practice, the gliding ability of the ram-air canopies can actually create major problems for airdrop systems by making...
This work is devoted to design of interval observers for a class of Linear-Parameter-Varying (LPV) systems. Applying High Order Sliding Mode (HOSM) techniques it is possible to decrease the initial level of uncertainty in the system, which leads to improvement of set-membership estimates generated by an interval observer. In addition, it is shown that HOSM techniques may relax the applicability conditions...
Surface Plasmon Resonance has been developed into a widely-used methodology for various biosensing applications. For the most popular angular-interrogation SPR system, a convergent light beam and a photo diode array are used, where the photo diode array with a large amount of pixels is necessary for high performance, and unavoidable trivial vibration between sensing unit and photo diode array will...
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