Hybrid hierarchical clustering: piecewise aggregate approximation, with applications

Piecewise Aggregate Approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and feature extraction on large datasets compared to previously reported and well-used feature extraction techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies on non-time-series data viz. fault detection on industrial gas turbines, and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and the APCA alternative. The method is therefore demonstrated to provide a robust readily implemented algorithm for rapid feature extraction and identification for datasets

Hybrid HC-PAA-G3K for novelty detection on industrial systems

Piecewise aggregate approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and feature extraction. A new distance-based hierarchical clustering (HC) is now proposed to adjust the PAA segment frame sizes. The proposed hybrid HC-PAA is validated by a generic clustering method ‘G3Kmeans’ (G3K). The efficacy of the hybrid HC-PAA-G3K methodology is demonstrated using an application case study based on novelty detection on industrial gas turbines. Results show the hybrid HC-PAA provides improved performance with regard to cluster separation, compared to traditional PAA. The proposed method therefore provides a robust algorithm for feature extraction and novelty detection. There are two main contributions of the paper: 1) application of HC to modify conventional PAA segment frame size; 2) introduction of ‘G3Kmeans’ to improve the performance of the traditional K-means clustering methods

Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

Piecewise Affine Registration of Biological Images for Volume Reconstruction

This manuscript tackles the reconstruction of 3D volumes via mono-modal registration of series of 2D biological images (histological sections, autoradiographs, cryosections, etc.). The process of acquiring these images typically induces composite transformations that we model as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. We use as a similarity measure an extension of the classical correlation coefficient that improves the consistency of the field. A hierarchical clustering algorithm then automatically partitions the field into a number of classes from which we extract independent pairs of sub-images. Our clustering algorithm relies on the Earth mover’s distribution metric and is additionally guided by robust least-square estimation of the transformations associated with each cluster. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach on several batches of histological data and discuss its sensitivity to parameters and noise

Symbolic-based recognition of contact states for learning assembly skills

Imitation learning is gaining more attention because it enables robots to learn skills from human demonstrations. One of the major industrial activities that can benefit from imitation learning is the learning of new assembly processes. An essential characteristic of an assembly skill is its different contact states (CS). They determine how to adjust movements in order to perform the assembly task successfully. Humans can recognise CSs through haptic feedback. They execute complex assembly tasks accordingly. Hence, CSs are generally recognised using force and torque information. This process is not straightforward due to the variations in assembly tasks, signal noise and ambiguity in interpreting force/torque (F/T) information. In this research, an investigation has been conducted to recognise the CSs during an assembly process with a geometrical variation on the mating parts. The F/T data collected from several human trials were pre-processed, segmented and represented as symbols. Those symbols were used to train a probabilistic model. Then, the trained model was validated using unseen datasets. The primary goal of the proposed approach aims to improve recognition accuracy and reduce the computational effort by employing symbolic and probabilistic approaches. The model successfully recognised CS based only on force information. This shows that such models can assist in imitation learning.</div