Deep Online Hierarchical Unsupervised Learning for Pattern Mining from Utility Usage Data.

Non-intrusive load monitoring (NILM) has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While much work has focused on supervised algorithms, unsupervised approaches can be more interesting and of more practical use in real case scenarios. More specifically, they do not require labelled training data to be collected from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Deep Belief network (DBN) and online Latent Dirichlet Allocation (LDA). Firstly, the raw signals of the house utilities are fed into DBN to extract low-level generic features in an unsupervised fashion, and then the hierarchical Bayesian model, LDA, learns high-level features that capture the correlations between the low-level ones. Thus, the proposed method (DBN-LDA) harnesses the DBN’s ability of learning distributed hierarchies of features to extract sophisticated appliances specific features without the need of precise human-crafted input representations. The clustering power of the hierarchical Bayesian models helps further summarise the input data by extracting higher-level information representing the residents’ energy consumption patterns. Using Deep-Hierarchical models reduces the computational complexity since LDA is not directly applied to the raw data. The computational efficiency is crucial as our application involves massive data from different types of utility usages. Moreover, we develop a novel online inference algorithm to cope with this big data. Another novelty of this work is that the data is a combination of different utilities (e.g, electricity, water and gas) and some sensors measurements. Finally, we propose different methods to evaluate the results and preliminary experiments show that the DBN-LDA is promising to extract useful patterns

Asymptotic expansions for functionals of dilation of point processes

Programme 1 - Architectures paralleles, bases de donnees, reseaux et systemes distribues. Projet MistralSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1994 n.2236 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Expansion of the Fast Linac Protection System for High Duty Cycle Operation at the TESLA Test Facility

To perform a proof of principle experiment of a SASE based Free Electron Laser operating a permanent magnet undulator has been installed in the TESLA Test Facility (TTF) linac. The type of permanent magnets (NdFeB) used is known to be sensitive to irradiation. Already losses of the order of 10−6 at nominal TTF beam current can cause a degradation of the undulator magnets after a few month of operation. To protect the undulator against radiation a collimation system in front of the undulator removes the electrons with large betatron motion. To detect beam haloor dark current escaping the collimators a beam loss monitor (BLM) system based on photomultiplier has been developed. During the past two years the BLM system has been improved in its electronic components and detectors. It has become a standard tool for linac operation and is nowintegrated part of the linac protect system. In this paper the design, the operation experiences and the performance limits of the system are presented