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We present a new approach for humanoid gait generation based on movement primitives learned from optimal and dynamically feasible motion trajectories. As testing platform we consider the humanoid robot HRP-2, so far only in simulation. Training data is generated by solving a set of optimal control problems for a minimum-torque optimality criterion and five different step lengths. As the dynamic robot...
Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models. Currently, transferring knowledge from previous experiments to a new experiment is of particular interest because it has been shown...
In this paper, we propose a blob-based method of crowd counting across a line of interest(LOI), which can be further extended to counting inside a region of interest(ROI). Firstly, we detect moving blobs in which low-level features are extracted and grouped. Since features vary with different walking pace, blob velocity is estimated using optical flow, and principal velocity component is further extracted...
The use of machine intelligence for gamma-ray background radiation anticipation is discussed and the test results on experimentally obtained spectra are presented. In this paper, a Gaussian process is adopted for learning recent measurements and subsequently anticipating the next ones in a short ahead in time horizon. Anticipated values are compared to respective incoming measurements and a decision...
To deal with inference and reasoning problems, Gaussian process has been considered as a promising tool due to the robustness and flexibility features. Especially, solving the regression and classification, Gaussian process coupling with Bayesian learning is one of the most appropriate supervised learning approaches in terms of accuracy and tractability. Unfortunately, this combination tolerates high...
The number of application based on Apache Hadoop is increasing dramatically due to the robustness and dynamic features of this system. At the heart of Apache Hadoop, the Hadoop File System (HDFS) provides the reliability, scalability and high availability to computation by applying a static replication strategy. However, because of the characteristics of parallel operations on the application layer,...
In this paper, a generic approach is presented to constructing generative motion model from prerecorded motion data that allows the synthesis of new motions or modification of existing motions in various ways. The key idea is to decompose human motion data into a series of latent variables which decompose a number of sports from different properties, in the way motion variations are interpreted by...
In this paper we investigate the conditions that complex kernels must satisfy for proper complex-valued signals. We study the structure that complex kernels for proper complex-valued signals must have. Also, we demonstrate that complex kernels that have been previously proposed and used in adaptive filtering of complex-valued signals assume that those signals are proper, i.e, they are not correlated...
The accurate estimation of carbon and heat fluxes at global scale is paramount for future policy decisions in the context of global climate change. This paper analyzes the relative relevance of potential remote sensing and meteorological drivers of global carbon and energy fluxes over land. The study is done in an indirect way via upscaling both Gross Primary Production (GPP) and latent energy (LE)...
This paper presents a method for learning nonlinear rigid body dynamics in the special Euclidean group SE(3). The method is based on the Gaussian process dynamical model (GPDM), which combines two Gaussian processes (GPs), one for representing unknown dynamics in a space ℝd with reduced dimensionality and the other for transforming the reduced space back to the state space of the high dimensional...
Many existing control architectures assume that the main control system being designed is the only controller that governs a system's actuators. However, with the increasing availability of off-the shelf controls packages, the number of internal unadjustable control systems is increasing. Some of these control systems may behave in parasitic way by enforcing a rigid set of behaviors that could disrupt...
An electrocardiogram (ECG) signal noise caused by different source e.g. power line interference (PLI), muscle and motion artifacts etc. Eliminating signal noise from ECG signal will increase the accuracy in diagnosis disease. In this paper, we propose sinusoid kernel function on Gaussian process (GP) to eliminate power line interference (50Hz or 60Hz) for electrocardiogram (ECG) signal. Expressing...
Controlling mobile robots with complex articulated parts and hence many degrees of freedom generates high cognitive load on the operator, especially under demanding conditions such as in Urban Search & Rescue missions. We propose a solution based on reinforcement learning in order to accommodate the robot morphology automatically to the terrain and the obstacles it traverses. In this paper, we...
This paper considers the problem of approximating a kernel matrix in an autoregressive Gaussian process regression (AR-GP) in the presence of measurement noises or natural errors for modeling complex motions of pedestrians in a crowded environment. While a number of methods have been proposed to robustly predict future motions of humans, it still remains as a difficult problem in the presence of measurement...
In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electric power to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in electric power markets in a way that electricity flows from a low-cost area...
In this paper, we propose a novel regression method that can incorporate both positive and negative training data into a single regression framework. In detail, a leveraged kernel function for non-stationary Gaussian process regression is proposed. With this new kernel function, we can vary the correlation betwen two inputs in both positive and negative directions by adjusting leverage parameters...
In complex-valued signal processing, estimation algorithms require complete knowledge (or accurate estimation) of the second order statistics, this makes Gaussian processes (GP) well suited for modelling complex signals, as they are designed in terms of covariance functions. Dealing with bivariate signals using GPs require four covariance matrices, or equivalently, two complex matrices. We propose...
In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain instead of continuous-time domain are outlined. One of such advantages is that the differential entropy rate is well-defined for discrete-time stochastic processes. By formulating the maximum entropy problem for discrete-time...
Information obtained from redness grading can assist clinician for diagnosis and in making clinical decision. This research work aims to mimic human perception of fibrovascular redness using features extracted from color entropy. Gaussian process regression with the radial basis function kernel has been employed to fuse relevant features and established the model of redness perception. In this paper,...
We consider the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We first identify which relations tend to link similar individuals by means of a finite-set Gaussian Process regression model, and then efficiently propagate knowledge about individuals across their relations. Our experimental evaluation demonstrates the effectiveness of the...
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