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Several pro-active acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, but these schemes fail with the long impulse responses inherent to public address systems. We derive a new prediction error method (PEM) based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback...
Line Spectrum Pair (LSP) decomposition is a technique developed for robust representation of the coefficients of a Linear Predictive (LP) model. It has favourable properties with respect to root loci and quantisation noise. In this article, we will explore the properties of LSP polynomials when they are used to represent quadratic models of form A2(z) and A(z)z−mA(z−1). The quadratic models show intriguing...
This paper describes a method by which a set of piece-wise constant autoregressive parameters could be obtained when the source signal is subject to a multistage analysis. It also illustrates an FIR prediction scheme with application to a speech process. It describes a method where the prediction is carried out on a sample by sample basis. This prediction scheme demonstrates the possibility of having...
We present a method to improve the performance of content-based image retrieval (CBIR) systems. The idea is based on the concept of query models [1], which generalizes the notion of similarity in multi-feature queries. In a query model features are organized in layers. Each succeeding layer has to investigate only a subset of the image set the preceding layer had to examine. For the purpose of performance...
In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for economical reasons, leads to natural ventilation as a standard tool. The nonlinear relationship between ventilation and temperature can be captured by Volterra models. These models represent the simple and logical extension of convolution models and can be successfully applied in nonlinear model-based...
This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and...
In this paper, we study the applicability of two algorithms to Smith predictor dead time compensation. The first algorithm is Iterative Feedback Tuning (IFT), a model free gradient descent controller tuning tool. Refer to [1, 2] for further details. The second algorithm is Closed-Loop Output Error (CLOE) identification. This identification algorithm minimizes the difference between the achieved closed-loop...
The bootstrap technique is a well-known method to generate multiple versions of predictors with the same structure. In this paper two different nonlinear structures are considered: neural networks and regression trees. They are both applied on real data related to the problem of predicting state bond price on the basis of the value of the previous auction and some financial indicators. Bootstrap is...
Grid systems have emerged as a means of sharing computational resources and information. Providing services for accessing, sharing and modifying large databases is a crucial task for grid management systems. This paper proposes an artificial neural network (ANN) prediction mechanism that provides an enhancement to data replication solutions within grid systems. Current replication services often exhibit...
The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology...
We present two new models that take into account the information available in user-created "favorites" lists for enhancing the quality of item recommendation. The first model uses the popularity and ratings of items in the lists to predict ratings for new items to users that have rated some items on the lists. The second model is a matrix factorization model that incorporates lists as implicit...
Air-ratio is an important engine parameter which relates closely to engine emissions, power, and brake-specific fuel consumption. Model predictive controller (MPC) is a well-known technique for air-ratio control. This paper utilizes two advanced techniques, discrete wavelet transformation (DWT) and relevance vector machine (RVM), to develop wavelet relevance vector machine model predictive controller...
Traffic forecasting is increasingly taking on an important role in many intelligent transportation systems (ITS) applications. However, prediction is typically performed for individual road segments and prediction horizons. In this study, we focus on the problem of collective prediction for multiple road segments and prediction-horizons. To this end, we develop various matrix and tensor based models...
This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H2S) in a gravity sewer system. The gaseous H2S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths...
A quasi-ARX (quasi-linear ARX) neural network (QARXNN) model is able to demonstrate its ability for identification and prediction highly nonlinear system. The model is simplified by a linear correlation between the input vector and its nonlinear coefficients. The coefficients are used to parameterize the input vector performed by an embedded system called as state dependent parameter estimation (SDPE),...
This paper aims to generate digests of tweets from live trending and ongoing topics. The primary purpose is to group the tweets by importance or usefulness so that an end user can be presented with a reasonable extract of the most important content from the Twitter stream. Summarization is accomplished using a non-parametric Bayesian model applied to Hidden Markov Models and a novel observation model...
Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their...
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based...
We propose an approach suitable to learn multiple time-varying models jointly and discuss an application in data-driven weather forecasting. The methodology relies on spectral regularization and encodes the typical multi-task learning assumption that models lie near a common low dimensional subspace. The arising optimization problem amounts to estimating a matrix from noisy linear measurements within...
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able...
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