Results 251 to 260 of about 56,790 (283)
Some of the next articles are maybe not open access.
Gaussian Process Auto Regression for vehicle center coordinates Trajectory Prediction
TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), 2019With the increase in autonomous car technology, and advance driver assistance systems (ADAS), the demand for vehicle trajectory prediction is increasing. These systems mostly use many sensors such as lidar, radar, stereo cameras which are big and expensive to detect the location of the ego vehicles with respect to other vehicles.
Qun Lim, Kritika Johari, U-Xuan Tan
openaire +1 more source
Fault detection based on auto-regressive extreme learning machine for nonlinear dynamic processes
Applied Soft Computing, 2021Abstract Through utilizing the extreme learning machines (ELM) in modeling the nonlinear dynamic relationship of the time-series data, a novel fault detection approach based on auto-regressive ELM (ARELM) is proposed for nonlinear dynamic processes.
Yang Chen +3 more
openaire +1 more source
Dual auto-regressive modelling approach to Gaussian process identification
IEEE International Conference on Multimedia and Expo, 2001. ICME 2001., 2001By modelling sources as a multivariate auto-regressive (AR) process, we have recently presented a dual AR modelling approach to identify temporal sources in independent component analysis (ICA) (Cheung et al. 2000, Cheung and Xu 1999 & 2001). However, our proposed existing algorithms for this approach are only suitable for the case that the residual ...
openaire +1 more source
Practical Generation of Video Textures using the Auto-Regressive Process
Procedings of the British Machine Vision Conference 2002, 2002Recently, there have been several attempts at creating `video textures', that is, synthesising new (potentially infinitely long) video clips based on existing ones. One way to do this is to transform each frame of the video into an eigenspace using Principal Components Analysis so that the original sequence can be viewed as a signature through this low-
Campbell, NW +3 more
openaire +1 more source
ALARM: A logistic auto-regressive model for binary processes on networks
2013 IEEE Global Conference on Signal and Information Processing, 2013We introduce the ALARM model, a logistic autoregressive model for discrete-time binary processes on networks, and describe a technique for learning the graph structure underlying the model from observations. Using only a small number of parameters, the proposed ALARM can describe a wide range of dynamic behavior on graphs, such as the contact process ...
Ameya Agaskar, Yue M. Lu
openaire +1 more source
GLM based auto-regressive process to model Covid-19 pandemic in Turkey
Statistical Communications in Infectious Diseases, 2021Abstract Objectives: Our objective is to propose a robust approach to model daily new cases and daily new deaths due to covid-19 infection in Turkey. Methods: We consider the generalized linear model (GLM) approach for the autoregressive process (AR) with log link for modelling.
openaire +2 more sources
A weighted auto regressive LSTM based approach for chemical processes modeling
Neurocomputing, 2019Abstract Data-driven methods have been regarded as effective methods for modeling in chemical processes. However, with the increasing complexity of chemical processes in spatial domain and time domain, how to extract meaningful features and build corresponding models are keys for accurate modeling tasks. To retain temporal features of original inputs,
Xu Zhang +3 more
openaire +1 more source
On simulation of first-order auto-regressive processes with near Laplace marginals
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009The focus of this paper is the modeling of a class of stationary non-Gaussian auto-regressive processes that often find applications in statistical signal processing. We propose a general simulation procedure for constructing a time series model with a near-Laplace marginal distributions.
Mirek Pawlak, Pradeepa Yahampath
openaire +1 more source
Asymmetric heavy-tailed vector auto-regressive processes with application to financial data
Journal of Statistical Computation and Simulation, 2019Vector Auto-regressive (VAR) models are commonly used for modelling multivariate time series and the typical distributional form is to assume a multivariate normal. However, the assumption of Gaussian white noise in multivariate time series is often not reasonable in applications where there are extreme and/or skewed observations.
Mohsen Maleki +3 more
openaire +2 more sources
Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes
Communications in Nonlinear Science and Numerical Simulation, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Rong +4 more
openaire +2 more sources