Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into $K$ clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, $P$, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Managing Sensor Data On Urban Traffic

Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embarked) sensors, generating large and complex spatio-temporal series. Research efforts in handling these data range from pattern matching and data mining techniques (for forecasting and trend analysis) to work on database queries (e.g., to construct scenarios). Work on embarked sensors also considers issues on trajectories and moving objects. This paper presents a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and database procedures to query these data. The first component is geared towards supporting pattern matching, whereas the second deals with spatio-temporal database issues. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with test conducted on 1000 sensors, during 3 years, in a large French city. © 2008 Springer Berlin Heidelberg.5232 LNCS385394(2007) TheCADDYWebsite, , http://norma.mas.ecp.fr/wikimas/Caddy, CADDYScemama, G., Carles, O., Claire-SITI, Public road Transport Network Management Control: A Unified Approach (2004) 12th IEEE Int. Conf. on Road Transport Information and Control (RTICJoliveau, M., (2008) Reduction of Urban Traffic Time Series from Georeferenced Sensors, and extraction of Spatio-temporal series -in French, , Ph.D thesis, Ecole Centrale Des Arts Et Manufactures Ecole Centrale de ParisJolliffe, I., (1986) Principal Component Analysis, , Springer, New YorkJoliveau, M., Vuyst, F.D., Space-time summarization of multisensor time series. case of missing data (2007) Int. Workshop on Spatial and Spatio-temporal data mining, IEEE SSTDMDempster, A., Laird, N., Rubin, D., Maximum likelihood for incomplete data via the em algorithm (1977) Journal of the Royal Statistical Society series B, 39, pp. 1-38Hugueney, B., Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures (2006) Proc. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 542-552Hugueney, B., Joliveau, M., Jomier, G., Manouvrier, M., Naja, Y., Scemama, G., Steffan, L., Towards a data warehouse for urban traffic (in french) (2006) Revue des Nouvelles Technologies de L'Information RNTI (B2), pp. 119-137Yi, B.K., Faloutsos, C., Fast time sequence indexing for arbitrary Lp norm (2000) Proc. of the 26th VLBD Conference, pp. 385-394Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S., (2000) Dimensionality reduction for fast similarity search in large time series databases, , Journal of Knowledge and Information SystemsMariotte, L., Medeiros, C.B., Torres, R., Diagnosing Similarity of Oscillation Trends in Time Series (2007) International Workshop on spatial and spatio-temporal data mining -SSTDM, pp. 243-248Mautora, T., Naudin, E., Arcs-states models for the vehicle routing problem with time windows and related problems (2007) Computers and Operations Research, 34, pp. 1061-1084Kriegel, H.P., Kröger, P., Kunath, P., Renz, M., Schmidt, T., Proximity queries in large traffic networks (2007) Proc. ACM GIS, pp. 1-8Kim, K., Lopez, M., Leutenegger, S., Li, K., A Network-based Indexing Method for Trajectories of Moving Objects (2006) LNCS, 4243, pp. 344-353. , Yakhno, T, Neuhold, E.J, eds, ADVIS 2006, Springer, HeidelbergGuting, R., Bohlen, M., Erwig, E., Jensen, C., Lorentzos, N., Schneider, M., Vazirgianis, M., A Foundation for Representing and Querying Moving Objects (2000) ACM Transactions on Database Systems, 25 (2), pp. 1-42Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A., Porto, F., Vangenot, C., A conceptual view on trajectories (2008) Knowledge and Data Engineering, 65 (1), pp. 126-14