An Online RFID Localization in the Manufacturing Shopfloor

{Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the non-stationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms

Star-forming cores embedded in a massive cold clump: Fragmentation, collapse and energetic outflows

The fate of massive cold clumps, their internal structure and collapse need to be characterised to understand the initial conditions for the formation of high-mass stars, stellar systems, and the origin of associations and clusters. We explore the onset of star formation in the 75 M_sun SMM1 clump in the region ISOSS J18364-0221 using infrared and (sub-)millimetre observations including interferometry. This contracting clump has fragmented into two compact cores SMM1 North and South of 0.05 pc radius, having masses of 15 and 10 M_sun, and luminosities of 20 and 180 L_sun. SMM1 South harbours a source traced at 24 and 70um, drives an energetic molecular outflow, and appears supersonically turbulent at the core centre. SMM1 North has no infrared counterparts and shows lower levels of turbulence, but also drives an outflow. Both outflows appear collimated and parsec-scale near-infrared features probably trace the outflow-powering jets. We derived mass outflow rates of at least 4E-5 M_sun/yr and outflow timescales of less than 1E4 yr. Our HCN(1-0) modelling for SMM1 South yielded an infall velocity of 0.14 km/s and an estimated mass infall rate of 3E-5 M_sun/yr. Both cores may harbour seeds of intermediate- or high-mass stars. We compare the derived core properties with recent simulations of massive core collapse. They are consistent with the very early stages dominated by accretion luminosity.Comment: Accepted for publication in ApJ, 14 pages, 7 figure

On Learning Machines for Engine Control

The original publication is available at www.springerlink.comThe chapter deals with neural networks and learning machines for engine control applications, particularly in modeling for control. In the first section, some basics on the common features of engine control are recalled, based on a layered engine management structure. Then the use of neural networks for engine modeling, control and diagnosis is briefly described. The need for descriptive models for model-based control and the link between physical models and black box models are emphasized at the end of this section by exposing the grey box approach taken in this chapter. The second section introduces the neural models most used in engine control, namely, MultiLayer Perceptrons (MLP) and Radial Basis Function (RBF) networks. A more recent approach, known as Support Vector Regression (SVR), to build models in kernel expansion form is then presented. The third section is devoted to examples of application of these models in the context of turbocharged Spark Ignition (SI) engines with Variable Camshaft Timing (VCT). This specific context is representative of modern engine control problems. In the first example, the airpath control is studied, where open loop neural estimators are combined with a dynamical polytopic observer. The second example considers modeling the in-cylinder residual gas fraction by Linear Programming SVR (LP-SVR), based on a limited amount of experimental data and a simulator built from prior knowledge. Each example tries to show that models based on first principles and neural models must be joined together in a grey box approach to obtain efficient and acceptable results