Robust bucket position tracking for a large hydraulic excavator

The device we study is the excavation arm of a large hydraulic mining shovel having a multi-loop kinematic form. We describe an iterative algorithm that allows the position of the bucket to be tracked from measurements of the linear actuator extensions. The important characteristic of this algorithm is that it is numerically well-behaved when the linkage is close to singular configurations. While we focus on a specific device, the algorithm is easy to adapt to other multi-loop linkages. (C) 2004 Elsevier Ltd. All rights reserved.

Tracking the shift to 'Postgenomics'

Current knowledge about the variety and complexity of the processes that allow regulated gene expression in living organisms calls for a new understanding of genes. A 'postgenomic' understanding of genes as entities constituted during genome expression is outlined and illustrated with specific examples that formed part of a survey research instrument developed by two of the authors for an ongoing empirical study of conceptual change in contemporary biology. Copyright (C) 2006 S. Karger AG, Basel.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

Fast-tracking middle schooling reform: A model for the integrated curriculum

To participate effectively in the post-industrial information societies and knowledge/service economies of the 21st century, individuals must be better-informed, have greater thinking and problem-solving abilities, be self-motivated; have a capacity for cooperative interaction; possess varied and specialised skills; and be more resourceful and adaptable than ever before. This paper reports on one outcome from a national project funded by the Ministerial Council on Education, Employment Training and Youth Affairs, which investigated what practices, processes, strategies and structures best promote lifelong learning and the development of lifelong learners in the middle years of schooling. The investigation linked lifelong learning with middle schooling because there were indications that middle schooling reform practices also lead to the development of lifelong learning attributes, which is regarded as a desirable outcome of schooling in Australia. While this larger project provides depth around these questions, this paper specifically reports on the development of a three-phase model that can guide the sequence in which schools undertaking middle schooling reform attend to particular core component changes. The model is developed from the extensive analysis of 25 innovative schools around the nation, and provides a unique insight into the desirable sequences and time spent achieving reforms, along with typical pitfalls that lead to a regression in the reform process. Importantly, the model confirms that schooling reform takes much more time than planners typically expect or allocate, and there are predictable and identifiable inhibitors to achieving it.

Particle tracking in a salinity gradient: A method for measuring sinking rate of individual phytoplankton in the laboratory

This paper presents a new method to measure the sinking rates of individual phytoplankton “particles” (cells, chains, colonies, and aggregates) in the laboratory. Conventional particle tracking and high resolution video imaging were used to measure particle sinking rates and particle size. The stabilizing force of a very mild linear salinity gradient (1 ppt over 15 cm) prevented the formation of convection currents in the laboratory settling chamber. Whereas bulk settling methods such as SETCOL provide a single value of sinking rate for a population, this method allows the measurement of sinking rate and particle size for a large number of individual particles or phytoplankton within a population. The method has applications where sinking rates vary within a population, or where sinking rate-size relationships are important. Preliminary data from experiments with both laboratory and field samples of marine phytoplankton are presented here to illustrate the use of the technique, its applications, and limitations. Whereas this paper deals only with sinking phytoplankton, the method is equally valid for positively buoyant species, as well as nonbiological particles.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.