A further step in the modelling of the mechanical behaviour of bagasse

A better understanding of the behaviour of prepared cane and bagasse during the crushing process is believed to be an essential prerequisite for further improvements to the crushing process. Improvements could be made, for example, in throughput, sugar extraction, and bagasse moisture. The ability to model the mechanical behaviour of bagasse as it is squeezed in a milling unit to extract juice would help identify how to improve the current process to reduce final bagasse moisture. However an adequate mechanical model for bagasse is currently not available. Previous investigations have proven with certainty that juice flow through bagasse obeys Darcy’s permeability law, that the grip of the rough surface of the grooves on the bagasse can be represented by the Mohr- Coulomb failure criterion for soils, and that the internal mechanical behaviour of the bagasse is critical state behaviour similar to that for sand and clay. Current Finite Element Models (FEM) available in commercial software have adequate permeability models. However, the same commercial software do not contain an adequate mechanical model for bagasse. Progress has been made in the last ten years towards implementing a mechanical model for bagasse in finite element software code. This paper builds on that progress and carries out a further step towards obtaining an adequate material model.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Remote monitoring of outdoor performance of low scale dye sensitized solar cells for nanosensors nodes

This is the first outdoor test of small-scale dye sensitized solar cells (DSC) powering a standalone nanosensor node. A solar cell test station (SCTS) has been developed using standard DSC to power a gas nanosensor, a radio transmitter, and the control electronics (CE) for battery charging. The station is remotely monitored through wired (Ethernet cable) or wireless connection (radio transmitter) in order to evaluate in real time the performance of the solar cells powering a nanosensor and a transmitter under different weather conditions. We analyze trends of energy conversion efficiency after 60 days of operation. The 408 cm2 active surface module produces enough energy to power a gas nanosensor and a radio transmitter during the day and part of the night. Also, by using a variable programmable load we keep the system working on the maximum power point (MPP) quantifying the total energy generated and stored in a battery. Although this technology is at an early stage of development, these experiments provide useful data for future outdoor applications such as nanosensor network nodes.

Remote monitoring of outdoor performance of low scale dye sensitized solar cells for nanosensors nodes

This is the first outdoor test of small-scale dye sensitized solar cells (DSC) powering a stand-alone nanosensor node. A solar cell test station (SCTS) has been developed using standard DSC to power a gas nanosensor, a radio transmitter, and the control electronics (CE) for battery charging. The station is remotely monitored through wired (Ethernet cable) or wireless connection (radio transmitter) in order to evaluate in real time the performance of the solar cells and devices under different weather conditions. The 408 cm2 active surface module produces enough energy to power a gas nanosensor and a radio transmitter during the day and part of the night. Also, by using a programmable load we keep the system working on the maximum power point (MPP) quantifying the total energy generated and stored in a battery. These experiments provide useful data for future outdoor applications such as nanosensor networks.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Centrifuging rotary vacuum filter mud at Tully Mill

Disposal of mud and ash, particularly in wet weather conditions, is a significant expense for mills. This paper reports on one part of a process to pelletise mud and ash, aimed at making mud and ash more attractive to growers across entire mill districts. The full process is described in a separate paper. The part described in this paper involves re-constituting mud cake from the filter station at Tully Mill and processing it in a decanter centrifuge. The material produced by re-constituting and centrifuging is drier and made up of separate particles. The material needs to mix easily with boiler ash, and the mixture needs to be fed easily into a flue gas drier to be dried to low moisture. The results achieved with the particular characteristics of Tully Mill rotary vacuum filter cake are presented. It was found that an internal rotor with a 20º beach was not adequate to process re-constituted rotary vacuum filter mud. A rotor with a 10º beach worked much more successfully. A total of four tonnes of centrifuged mud with a moisture content ranging from 60% to 65% was produced. It was found that the torque, flocculant rate and dose rate had a statistically significant effect on the moisture content. Feed rate did not have a noticeable impact on the moisture content by itself but torque had a much larger impact on the moisture content at the low feed rate than at the high feed rate. These results indicated that the moisture content of the mud can most likely be reduced with low feed rate, low flocculant rate, high dose rate and high torque. One issue that is believed to affect the operation of a decanter centrifuge was the large quantity of long bagasse fibres in the rotary vacuum filter mud. It is likely that the long fibres limited the throughput of the centrifuge and the moisture achieved.

An additional step towards modelling the mechanical behaviour of sugar cane bagasse

A better understanding of the behaviour of prepared cane and bagasse, especially the ability to model the mechanical behaviour of bagasse as it is squeezed in a milling unit to extract juice, would help identify how to improve the current milling process; for example to reduce final bagasse moisture. Previous investigations have proven with certainty that juice flow through bagasse obeys Darcy’s permeability law, that the grip of the rough surface of the grooves on the bagasse can be represented by the Mohr- Coulomb failure criterion for soils, and that the internal mechanical behaviour of the bagasse can be represented by critical state behaviour similar to that of sand and clay. Current Finite Element Models (FEM) available in commercial software have adequate permeability models. However, commercial software does not contain an adequate mechanical model for bagasse. Progress has been made in the last ten years towards implementing a mechanical model for bagasse in finite element software code. This paper builds on that progress and carries out a further step towards obtaining an adequate material model. In particular, the prediction of volume change during shearing of normally consolidated final bagasse is addressed.

Designing a capstone experience for Australian legal education

Literature suggests that universities, and law schools in particular, are not engaging final year students in a genuine capstone experience which supports the development of their professional identity and their transition out of university. Students in their final year also face significant transition issues which are just as challenging as those facing first year students entering the tertiary environment (Jervis & Hartley, 2005, 314)...

The capstone experience in law

An effective capstone experience provides closure through: Supporting students to synthesise their learning in the program by building upon the knowledge, skills and capability development that has taken place over the entirety of the curriculum; Providing enhanced opportunities for students to reflect on their personal and professional development over the course of their legal education experience and how that prepares them for their future professional and personal lives; Assisting students to attain an understanding of what it means to be a graduate of the discipline and begin to develop a professional identity.