1,012 research outputs found
Classification with Costly Features using Deep Reinforcement Learning
We study a classification problem where each feature can be acquired for a
cost and the goal is to optimize a trade-off between the expected
classification error and the feature cost. We revisit a former approach that
has framed the problem as a sequential decision-making problem and solved it by
Q-learning with a linear approximation, where individual actions are either
requests for feature values or terminate the episode by providing a
classification decision. On a set of eight problems, we demonstrate that by
replacing the linear approximation with neural networks the approach becomes
comparable to the state-of-the-art algorithms developed specifically for this
problem. The approach is flexible, as it can be improved with any new
reinforcement learning enhancement, it allows inclusion of pre-trained
high-performance classifier, and unlike prior art, its performance is robust
across all evaluated datasets.Comment: AAAI 201
A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management
Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems
Deep Classification of Epileptic Signals
Electrophysiological observation plays a major role in epilepsy evaluation.
However, human interpretation of brain signals is subjective and prone to
misdiagnosis. Automating this process, especially seizure detection relying on
scalp-based Electroencephalography (EEG) and intracranial EEG, has been the
focus of research over recent decades. Nevertheless, its numerous challenges
have inhibited a definitive solution. Inspired by recent advances in deep
learning, we propose a new classification approach for EEG time series based on
Recurrent Neural Networks (RNNs) via the use of Long-Short Term Memory (LSTM)
networks. The proposed deep network effectively learns and models
discriminative temporal patterns from EEG sequential data. Especially, the
features are automatically discovered from the raw EEG data without any
pre-processing step, eliminating humans from laborious feature design task. We
also show that, in the epilepsy scenario, simple architectures can achieve
competitive performance. Using simple architectures significantly benefits in
the practical scenario considering their low computation complexity and reduced
requirement for large training datasets. Using a public dataset, a multi-fold
cross-validation scheme exhibited an average validation accuracy of 95.54\% and
an average AUC of 0.9582 of the ROC curve among all sets defined in the
experiment. This work reinforces the benefits of deep learning to be further
attended in clinical applications and neuroscientific research.Comment: 4 pages, 3 figure
- …