908 research outputs found
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets
Shapelets that discriminate time series using local features (subsequences)
are promising for time series clustering. Existing time series clustering
methods may fail to capture representative shapelets because they discover
shapelets from a large pool of uninformative subsequences, and thus result in
low clustering accuracy. This paper proposes a Semi-supervised Clustering of
Time Series Using Representative Shapelets (SE-Shapelets) method, which
utilizes a small number of labeled and propagated pseudo-labeled time series to
help discover representative shapelets, thereby improving the clustering
accuracy. In SE-Shapelets, we propose two techniques to discover representative
shapelets for the effective clustering of time series. 1) A \textit{salient
subsequence chain} () that can extract salient subsequences (as candidate
shapelets) of a labeled/pseudo-labeled time series, which helps remove massive
uninformative subsequences from the pool. 2) A \textit{linear discriminant
selection} () algorithm to identify shapelets that can capture
representative local features of time series in different classes, for
convenient clustering. Experiments on UCR time series datasets demonstrate that
SE-shapelets discovers representative shapelets and achieves higher clustering
accuracy than counterpart semi-supervised time series clustering methods
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
Anomaly detection and event mining in cold forming manufacturing processes
Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest
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