Claus Neubauer

Learn More
Mixture of Gaussian processes models extended a single Gaussian process with ability of modeling multi-modal data and reduction of training complexity. Previous inference algorithms for these models are mostly based on Gibbs sampling, which can be very slow, particularly for large-scale data sets. We present a new generative mixture of experts model. Each(More)
We present a Bayesian framework to tackle the problem of sensor estimation, a critical step of fault diagnosis in machine condition monitoring. A Gaussian mixture model is employed to model the normal operating range of the machine. A Gaussian random vector is introduced to model the possible deviations of the observed sensor values from their corresponding(More)
This paper has three contributions to the fields of power plant monitoring. First, we differentiate out-of-range detection from fault detection. An out-of-range refers to a normal operating range of a power plant unseen in the training data. In the case of an out-of-range, instead of producing a fault alarm, the system should notify the operator to include(More)
Improving sales force productivity is a key strategic priority to drive corporate revenue growth. Sales professionals need to be able to easily identify new sales prospects, and sales executives need to ensure that the overall sales force is deployed against the best future revenue-generating sales accounts. In this paper, we describe two analytics-based(More)
Classification of corrupted images, for example due to occlusion or noise, is a challenging problem. Most existing methods tackled this problem using a two-step strategy: image reconstruction and classification of reconstructed images. However, their performances heavily relied on the accuracy of reconstruction and parameter estimation. We present a full(More)
This paper reports on the new sensor data collection, analysis and storage framework, Bridge Sensor Mart (BSM), for bridge health monitoring data. BSM defines a distributed data storage and data analytics infrastructure in order to collect, store, analyze and manage sensor data for Structural Health Monitor-
We present a robust method to identify and isolate faulty sensors among a set of correlated sensors. For each sensor, we estimate the sensor a number of times, using each of the other correlated sensors separately. We use the median of these estimates as the estimate for the sensor. When up to less than half of the sensors are faulty, this method identifies(More)