Boolean Kalman filter and smoother under model uncertainty
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
state-space models that provide a rich framework for modeling many complex dynamical …
state-space models that provide a rich framework for modeling many complex dynamical …
An intelligent classification model for surface defects on cement concrete bridges
J Zhu, J Song - Applied sciences, 2020 - mdpi.com
This paper mainly improves the visual geometry group network-16 (VGG-16), which is a
classic convolutional neural network (CNN), to classify the surface defects on cement …
classic convolutional neural network (CNN), to classify the surface defects on cement …
Multi-agent reinforcement learning framework in sdn-iot for transient load detection and prevention
The fast emergence of IoT devices and its accompanying big and complex data has
necessitated a shift from the traditional networking architecture to software-defined networks …
necessitated a shift from the traditional networking architecture to software-defined networks …
A feature extraction method of ship-radiated noise based on fluctuation-based dispersion entropy and intrinsic time-scale decomposition
Z Li, Y Li, K Zhang - Entropy, 2019 - mdpi.com
To improve the feature extraction of ship-radiated noise in a complex ocean environment,
fluctuation-based dispersion entropy is used to extract the features of ten types of ship …
fluctuation-based dispersion entropy is used to extract the features of ten types of ship …
Hdcluster: An accurate clustering using brain-inspired high-dimensional computing
Internet of things has increased the rate of data generation. Clustering is one of the most
important tasks in this domain to find the latent correlation between data. However …
important tasks in this domain to find the latent correlation between data. However …
Bayesian optimized monte carlo planning
Online solvers for partially observable Markov decision processes have difficulty scaling to
problems with large action spaces. Monte Carlo tree search with progressive widening …
problems with large action spaces. Monte Carlo tree search with progressive widening …
Graph-based Bayesian optimization for large-scale objective-based experimental design
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Design is an inseparable part of most scientific and engineering tasks, including real and
simulation-based experimental design processes and parameter/hyperparameter …
simulation-based experimental design processes and parameter/hyperparameter …
A novel active learning reliability method combining adaptive Kriging and spherical decomposition-MCS (AK-SDMCS) for small failure probabilities
M Su, G Xue, D Wang, Y Zhang, Y Zhu - Structural and Multidisciplinary …, 2020 - Springer
Structural reliability analysis for small failure probabilities remains a challenging task,
despite the significant progress made by the active learning reliability methods (ALRMs) …
despite the significant progress made by the active learning reliability methods (ALRMs) …
A novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive …
Y Li, L Wang, X Li, X Yang - Entropy, 2019 - mdpi.com
Warships play an important role in the modern sea battlefield. Research on the line
spectrum features of warship radio noise signals is helpful to realize the classification and …
spectrum features of warship radio noise signals is helpful to realize the classification and …
Zero-to-stable driver identification: A non-intrusive and scalable driver identification scheme
Driver identification faces various challenges in real-time applications. These challenges
include high dimensional input data, moderate accuracy, scalability issues and need for …
include high dimensional input data, moderate accuracy, scalability issues and need for …