Adjusting building thermostats for environmental gains : understanding the issues

There has been increasing reliance on mechanical heating, ventilation and air-conditioning (HVAC) systems to achieve thermal comfort in office buildings. The use of universal standards for thermal comfort adopted in air-conditioned spaces often results in a large disparity between mean daily external summer temperatures and temperatures experienced indoors. The extensive overuse of air-conditioning in warm climates not only isolates us from the vagaries of the external environment, but is generally dependent on non-renewable energy. A pilot study conducted at the Queensland University of Technology (QUT) involved altering the thermostat set-points to two or three degrees above the normal summer setting in two air-conditioned buildings during the subtropical summer. This paper presents the findings of the research that led to the formulation of the test study. The findings of the test study are printed in the companion paper DES 72: Adjusting Building Thermastats for Environmental Gains – a Pilot Study.

Animal Control - What constitutes a reliable cue to stop animal movement ?

Controlling free-ranging livestock requires low-stress cues to alter animal behaviour. Recently modulated sound and electric shock were demonstrated to be effective in controlling free-ranging cattle. In this study the behaviour of 60, 300 kg Belmont Red heifers were observed for behavioural changes when presented cues designed to impede their movement through an alley. The heifers were given an overnight drylot shrink off feed but not drinking water prior to being tested. Individual cattle were allowed to move down a 6.5 m wide alley towards a pen of peers and feed located 71 m from their point of release. Each animal was allowed to move through the alley unimpeded five times to establish a basal behavioural pattern. Animals were then randomly assigned to treatments consisting of sound plus shock, vibration plus shock, a visual cue plus shock, shock by itself and a control. The time each animal required to reach the pen of peers and feed was recorded. If the animal was prevented from reaching the pen of peers and feed by not penetrating through the cue barrier at set points along the alley for at least 60 sec the test was stopped and the animal was returned to peers located behind the release pen. Cues and shock were manually applied from a laptop while animals were observed from a 3.5 m tower located outside the alley. Electric shock, sound, vibration and Global Position System (GPS) hardware were housed in a neck collar. Results and implications will be discussed.

Energy efficient home with price sensitive stochastically programmable TCAs

Introduction of dynamic pricing in present retail market, considerably affects customers with an increased cost of energy consumption. Therefore, customers are enforced to control their loads according to price variation. This paper proposes a new technique of Home Energy Management, which helps customers to minimize their cost of energy consumption by appropriately controlling their loads. Thermostatically Controllable Appliances (TCAs) such as air conditioner and water heater are focused in this study, as they consume more than 50% of the total household energy consumption. The control process includes stochastic dynamic programming, which incorporated uncertainties in price and demand variation. It leads to an accurate selection of appliance settings. It is followed by a real time control of selected appliances with its optimal settings. Temperature set points of TCAs are adjusted based on price droop which is a reflection of actual cost of energy consumption. Customer satisfaction is maintained within limits using constraint optimization. It is showed that considerable energy savings is achieved.

Improved image set classification via joint sparse approximated nearest subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).

An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.

Brain and cognitive development

'The Millennial Adolescent' offers contemporary, stimulating insights for those currently teaching as well as those preparing to teach. This book investigates the characteristics of Generation Y, using students own voices, generational theory and case studies. The text is structured around the principle that effective teachers need to know who they are teaching as well as what to teach, how to teach it, and how to assess the outcome. Using generational theory, 'The Millennial Adolescent' investigates the characteristics of Generation Y, or the Millennial Generation, and points out what all teachers need to know about working with this current generation of students who are described in a number of ways digital natives, team oriented, confident, multi-taskers, high achievers, and a generation unlike any other. The book contains well-known frameworks for developing understandings about adolescents, blended and contrasted with a contemporary socio-cultural construction of adolescence, set in our particular time, era and society. This book reflects the uniqueness of Australian contexts, while connecting with international trends and global patterns. Engaging and full of insights, this book is essential reading for all professionals dealing with adolescents.

Robot navigation in sensor space

This thesis investigates the problem of robot navigation using only landmark bearings. The proposed system allows a robot to move to a ground target location specified by the sensor values observed at this ground target posi- tion. The control actions are computed based on the difference between the current landmark bearings and the target landmark bearings. No Cartesian coordinates with respect to the ground are computed by the control system. The robot navigates using solely information from the bearing sensor space. Most existing robot navigation systems require a ground frame (2D Cartesian coordinate system) in order to navigate from a ground point A to a ground point B. The commonly used sensors such as laser range scanner, sonar, infrared, and vision do not directly provide the 2D ground coordi- nates of the robot. The existing systems use the sensor measurements to localise the robot with respect to a map, a set of 2D coordinates of the objects of interest. It is more natural to navigate between the points in the sensor space corresponding to A and B without requiring the Cartesian map and the localisation process. Research on animals has revealed how insects are able to exploit very limited computational and memory resources to successfully navigate to a desired destination without computing Cartesian positions. For example, a honeybee balances the left and right optical flows to navigate in a nar- row corridor. Unlike many other ants, Cataglyphis bicolor does not secrete pheromone trails in order to find its way home but instead uses the sun as a compass to keep track of its home direction vector. The home vector can be inaccurate, so the ant also uses landmark recognition. More precisely, it takes snapshots and compass headings of some landmarks. To return home, the ant tries to line up the landmarks exactly as they were before it started wandering. This thesis introduces a navigation method based on reflex actions in sensor space. The sensor vector is made of the bearings of some landmarks, and the reflex action is a gradient descent with respect to the distance in sensor space between the current sensor vector and the target sensor vec- tor. Our theoretical analysis shows that except for some fully characterized pathological cases, any point is reachable from any other point by reflex action in the bearing sensor space provided the environment contains three landmarks and is free of obstacles. The trajectories of a robot using reflex navigation, like other image- based visual control strategies, do not correspond necessarily to the shortest paths on the ground, because the sensor error is minimized, not the moving distance on the ground. However, we show that the use of a sequence of waypoints in sensor space can address this problem. In order to identify relevant waypoints, we train a Self Organising Map (SOM) from a set of observations uniformly distributed with respect to the ground. This SOM provides a sense of location to the robot, and allows a form of path planning in sensor space. The navigation proposed system is analysed theoretically, and evaluated both in simulation and with experiments on a real robot.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Dense Correspondence Extraction in Difficult Uncalibrated Scenarios

The relationship between multiple cameras viewing the same scene may be discovered automatically by finding corresponding points in the two views and then solving for the camera geometry. In camera networks with sparsely placed cameras, low resolution cameras or in scenes with few distinguishable features it may be difficult to find a sufficient number of reliable correspondences from which to compute geometry. This paper presents a method for extracting a larger number of correspondences from an initial set of putative correspondences without any knowledge of the scene or camera geometry. The method may be used to increase the number of correspondences and make geometry computations possible in cases where existing methods have produced insufficient correspondences.