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Aircraft loss-of-control (LOC) is the major contributing factor to fatal accidents and is characterised by the manoeuvring of aircraft beyond the allowable flight envelopes. This paper proposes an online learning and inference based method for aircraft flight envelope estimation in order to prevent aircraft LOC. The lift and drag coefficients of the aircraft are identified online using an extended...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
A crucial component of an autonomous mine is the ability to infer rock types from mechanical measurements of a drill rig. The major difficulty lies in that there is not a clear one to one correspondence between the mechanical measurements and the rock type due to the mechanical noise as well as the variety of the rock geology. This paper proposes a novel wavelet feature space projection approach to...
Human action analysis has recently received growing interest from vision researchers. In this paper, we present a simple but effective approach for action recognition by combining multiple complementary features with Gaussian process classification. Since it is often insufficient for a single type of feature derived from action videos to characterize variations among different motions, we propose...
We improve Gaussian processes (GP) classification by reorganizing the (non-stationary and anisotropic) data to better fit to the isotropic GP kernel. First, the data is partitioned into two parts: along the feature with the highest frequency bandwidth. Secondly, for each part of the data, only the spectrally homogeneous features are chosen and used (the rest discarded) for GP classification. In this...
This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize...
We use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as well as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data we compare the frequency domain features of each dimension to estimate a rescaling...
Informative Vector Machine (IVM) is an efficient fast sparse Gaussian process's (GP) method previously suggested for active learning. It greatly reduces the computational cost of GP classification and makes the GP learning close to real time. We apply IVM for man-made structure classification (a two class problem). Our work includes the investigation of the performance of IVM with varied active data...
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