Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary

Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary when they are far from what the algorithm has seen in the past. We propose a novel approach to kernel adaptive filtering that compares new observations against dictionary samples in terms of their unit-norm (normalised) versions, meaning that new observations that look like previous samples but have a different magnitude are not added to the dictionary. We achieve this by proposing the unit-norm Gaussian kernel and define a sparsification criterion for this novel kernel. This new methodology is validated on two real-world datasets against standard KAF in terms of the normalised mean square error and the dictionary size.Comment: Accepted at the IEEE Digital Signal Processing conference 201

Anomaly detection mechanisms to find social events using cellular traffic data

The design of new tools to detect on-the-fly traffic anomaly without scalability problems is a key point to exploit the cellular system for monitoring social activities. To this goal, the paper proposes two methods based on the wavelet analysis of the cumulative cellular traffic. The utilisation of the wavelets permits to easily filter “normal” traffic anomalies such as the periodic trends present in the cellular traffic. The two presented approaches, denoted as Spatial Analysis (SA) and Time Analysis (TA), differ on how they consider the spatial information of the traffic data. We examine the performance of the considered algorithms using cellular traffic data acquired from one the most important Italian Mobile Network Operator in the city of Milan throughout December 2013. The results highlight the weak points of TA and some important features of SA. Both approaches overcome the performance of one reference algorithm present in literature. The strategy used in the SA emerges as the most suitable for exploiting the spatial correlation when we aim at the detection of the traffic anomaly focused on the localisation of social events

Data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant

Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.Peer ReviewedPostprint (published version

Unsupervised State-Space Modeling Using Reproducing Kernels

This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1109/TSP.2015.2448527.A novel framework for the design of state-space models (SSMs) is proposed whereby the state-transition function of the model is parametrised using reproducing kernels. The nature of SSMs requires learning a latent function that resides in the state space and for which input-output sample pairs are not available, thus prohibiting the use of gradient-based supervised kernel learning. To this end, we then propose to learn the mixing weights of the kernel estimate by sampling from their posterior density using Monte Carlo methods. We first introduce an offline version of the proposed algorithm, followed by an online version which performs inference on both the parameters and the hidden state through particle filtering. The accuracy of the estimation of the state-transition function is first validated on synthetic data. Next, we show that the proposed algorithm outperforms kernel adaptive filters in the prediction of real-world time series, while also providing probabilistic estimates, a key advantage over standard methods.Felipe Tobar acknowledges financial support from EPSRC grant number EP/L000776/1

Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis

Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method