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The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor...
A new nonparametric approach for system identification has been recently proposed where the impulse response is seen as the realization of a zero-mean Gaussian process whose covariance, the so-called stable spline kernel, guarantees that the impulse response is almost surely stable. Maximum entropy properties of the stable spline kernel have been pointed out in the literature. In this paper we provide...
This paper considers the problem of modeling complex motions of pedestrians in a crowded environment. A number of methods have been proposed to predict the motion of a pedestrian or an object. However, it is still difficult to make a good prediction due to challenges, such as the complexity of pedestrian motions and outliers in a training set. This paper addresses these issues by proposing a robust...
We utilize sensors to help us monitor events in the environment around us. To save power consumption, we often prefer to use as few sensors as possible and the sensors can be on for as limited time as possible while keeping the same or similar service performance from the sensors. In this work, we propose a mechanism that can use a small subset of sensor readings and the rest of sensor readings that...
Input noise is common in situations when data either is coming from unreliable sensors or previous outputs are used as current inputs. Nevertheless, most regression algorithms do not model input noise, inducing thus bias in the regression. We present a method that corrects this bias by repeated regression estimations. In simulation extrapolation we perturb the inputs with additional input noise and...
Linear discriminant analysis that takes spatial smoothness into account has been developed and widely used in image processing society. However, two questions remain unanswered. First, which is the best way to incorporate the smoothness property of images with linear discriminant analysis? Second, which is the best representation for the smoothness property of images? To answer the first question,...
Occurrence of high imbalance in real-world domains is a direct result of rarity of interesting events, which results in skewed datasets. Without dataset rebalancing, the learning algorithm will encounter extremely low minority class samples therefore it gets biased towards the majority class in the classification tasks. Hence properly handling the imbalanced dataset is a crucial issue in the pattern...
We propose to use Gaussian process regression to remove confounds from gray matter (GM) density maps in order to improve performance in automated detection of neurodegenrative diseases. Age, total intracranial volume, sex, and acquisition site were included as design variables. Based on data from the control populations, a Gaussian process regression model was learned for each voxel. This model was...
This paper presents the forecast of the peak electricity demand (peak load) between 2014 to 2024 of the “Electricity Generating Authority of Thailand” (EGAT) by using Gaussian Process (QV), which used training data set since 2000 to 2013. The training data set composed of two important factors, including time on a monthly and the monthly of electricity peak load Moreover, it proposes a solution to...
In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We...
Pipe thickness maps are used to assess the condition in pipelines. Thickness maps are a 2.5D representation similar to elevation maps in robotics. Probabilistic frameworks, however, have barely been used in this context. This paper presents a general approach for generating probabilistic maps from heterogeneous sensor data. The key idea is to learn the spatial correlation of a sensor through Gaussian...
The ability to reason over partially observable networks of interacting states is a fundamental competency in probabilistic robotics. While the well-known factor graph and Gaussian process models provide flexible and computationally efficient solutions for this inference problem in the special cases in which all of the hidden states are either finite-dimensional parameters or real-valued functions,...
LS-based adaptation cannot fully exploit high-dimensional correlations in image signals, as linear prediction model in the input space of supports is undesirable to capture higher order statistics. This paper proposes Gaussian process regression for prediction in lossless image coding. Incorporating kernel functions, the prediction support is projected into a high-dimensional feature space to fit...
We introduce a class of methods for Gaussian process regression with functional expectation constraints. We show that the solution can be found without the need for approximations when the constraint set satisfies a representation theorem. Further, the solution is unique when the constraint set is convex. Constrained Gaussian process regression is motivated by the modeling of transposable (matrix)...
Screws and gears are a source of periodically recurring nonlinear effects in mechanical dynamical systems. Unless the state sampling frequency is much higher than the periodic effect, model-free controllers cannot always compensate these effects, and good physical models for such periodic dynamics are challenging to construct. We investigate nonparametric system identification with an explicit focus...
In this paper we introduce Gaussian Process (GP) models for music genre classification. Gaussian Processes are widely used for various regression and classification tasks, but there are relatively few studies where GPs are applied in the audio signal processing systems. The GP models are non-parametric discriminative classifiers similar to the well known SVMs in terms of usage. In contrast to SVMs,...
In this paper, the theory of Gaussian Process Regression (GPR) was introduced, and the Gaussian Process Regression model was established to predict thermal comfort index. In this model, parameters of activity level, clothing insulation, air temperature, air relative humidity, air velocity and mean radiant temperature were selected as the input vectors, and PMV index was the output vector. The calculated...
Traffic analysis using Discrete Wavelet Transform and Bayesian Regression is used to estimating the size of inhomogeneous traffic, composed of vehicles that travel in different directions without using explicit object segmentation or tracking is proposed. Using the dynamic texture motion model, here the traffic is segmented into components of homogeneous motion. From each segmented region, a set of...
This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates...
We consider mobile robot navigation in dense human crowds. In particular, we explore two questions. Can we design a navigation algorithm that encourages humans to cooperate with a robot? Would such cooperation improve navigation performance? We address the first question by developing a probabilistic predictive model of cooperative collision avoidance and goal-oriented behavior by extending the interacting...
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