Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

Stochastic simulation for spatial modelling of dynamic process in a living cell

One of the fundamental motivations underlying computational cell biology is to gain insight into the complicated dynamical processes taking place, for example, on the plasma membrane or in the cytosol of a cell. These processes are often so complicated that purely temporal mathematical models cannot adequately capture the complex chemical kinetics and transport processes of, for example, proteins or vesicles. On the other hand, spatial models such as Monte Carlo approaches can have very large computational overheads. This chapter gives an overview of the state of the art in the development of stochastic simulation techniques for the spatial modelling of dynamic processes in a living cell.

Emergent task-specific object semantics through distributed experience networks

Autonomous development of sensorimotor coordination enables a robot to adapt and change its action choices to interact with the world throughout its lifetime. The Experience Network is a structure that rapidly learns coordination between visual and haptic inputs and motor action. This paper presents methods which handle the high dimensionality of the network state-space which occurs due to the simultaneous detection of multiple sensory features. The methods provide no significant increase in the complexity of the underlying representations and also allow emergent, task-specific, semantic information to inform action selection. Experimental results show rapid learning in a real robot, beginning with no sensorimotor mappings, to a mobile robot capable of wall avoidance and target acquisition.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Food(ing): between human-computer and human-food-experience

This workshop is a continuation and extension to the successful past workshops exploring the intersection of food, technology, place, and people, namely 2009 OZCHI workshop, Hungry 24/7? HCI Design for Sustainable Food Culture and Sustainable Interaction with Food, Technology, and the City [1] and 2010 CHI panel Making Food, Producing Sustainability [3]. The workshop aims to bring together experts from diverse backgrounds including academia, government, industry, and non-for-profit organisations. It specifically aims to create a space for discussion and design of innovative approaches to understanding and cultivating sustainable food practices via human-computer-interaction (HCI) as well as addressing the wider opportunities for the HCI community to engage with food as a key issue for sustainability The workshop addresses environmental, health, and social domains of sustainability in particular, by looking at various conceptual and design approaches in orchestrating sustainable interaction of people and food in and through dynamic techno-social networks.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Urban Informatics Research and Insights for Libraries, Cultural Industries and Innovation Systems

Over less than a decade, we have witnessed a seismic shift in the way knowledge is produced and exchanged. This is opening up new opportunities for civic and community engagement, entrepreneurial behaviour, sustainability initiatives and creative practices. It also has the potential to create fresh challenges in areas of privacy, cyber-security and misuse of data and personal information. The field of urban informatics focuses on the use and impacts of digital media technology in urban environments. Urban informatics is a dynamic and cross-disciplinary area of inquiry that encapsulates social media, ubiquitous computing, mobile applications and location-based services. Its insights suggest the emergence of a new economic force with the potential for driving innovation, wealth and prosperity through technological advances, digital media and online networks that affect patterns of both social and economic development. Urban informatics explores the intersections between people, place and technology, and their implications for creativity, innovation and engagement. This paper examines how the key learnings from this field can be used to position creative and cultural institutions such as galleries, libraries, archives and museums (GLAM) to take advantage of the opportunities presented by these changing social and technological developments. This paper introduces the underlying principles, concepts and research areas of urban informatics, against the backdrop of modern knowledge economies. Both theoretical ideas and empirical examples are covered in this paper. The first part discusses three challenges: a. People, and the challenge of creativity: The paper explores the opportunities and challenges of urban informatics that can lead to the design and development of new tools, methods and applications fostering participation, the democratisation of knowledge, and new creative practices. b. Technology, and the challenge of innovation: The paper examines how urban informatics can be applied to support user-led innovation with a view to promoting entrepreneurial ideas and creative industries. c. Place, and the challenge of engagement: The paper discusses the potential to establish place-based applications of urban informatics, using the example of library spaces designed to deliver community and civic engagement strategies. The discussion of these challenges is illustrated by a review of projects as examples drawn from diverse fields such as urban computing, locative media, community activism, and sustainability initiatives. The second part of the paper introduces an empirically grounded case study that responds to these three challenges: The Edge, the Queensland Government’s Digital Culture Centre which is an initiative of the State Library of Queensland to explore the nexus of technology and culture in an urban environment. The paper not only explores the new role of libraries in the knowledge economy, but also how the application of urban informatics in prototype engagement spaces such as The Edge can provide transferable insights that can inform the design and development of responsive and inclusive new library spaces elsewhere. To set the scene and background, the paper begins by drawing the bigger picture and outlining some key characteristics of the knowledge economy and the role that the creative and cultural industries play in it, grasping new opportunities that can contribute to the prosperity of Australia.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Evolving genetic regulatory networks performing as stochastic switches

Recent studies have shown that small genetic regulatory networks (GRNs) can be evolved in silico displaying certain dynamics in the underlying mathematical model. It is expected that evolutionary approaches can help to gain a better understanding of biological design principles and assist in the engineering of genetic networks. To take the stochastic nature of GRNs into account, our evolutionary approach models GRNs as biochemical reaction networks based on simple enzyme kinetics and simulates them by using Gillespie’s stochastic simulation algorithm (SSA). We have already demonstrated the relevance of considering intrinsic stochasticity by evolving GRNs that show oscillatory dynamics in the SSA but not in the ODE regime. Here, we present and discuss first results in the evolution of GRNs performing as stochastic switches.

An efficient public key management regime for vehicular ad hoc networks (VANETS)

The primary goal of the Vehicular Ad Hoc Network (VANET) is to provide real-time safety-related messages to motorists to enhance road safety. Accessing and disseminating safety-related information through the use of wireless communications technology in VANETs should be secured, as motorists may make critical decisions in dealing with an emergency situation based on the received information. If security concerns are not addressed in developing VANET systems, an adversary can tamper with, or suppress, the unprotected message to mislead motorists to cause traffic accidents and hazards. Current research on secure messaging in VANETs focuses on employing the certificate-based Public Key Infrastructure (PKI) scheme to support message encryption and digital signing. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This thesis has proposed a novel public key verification and management approach for VANETs; namely, the Public Key Registry (PKR) regime. Compared to the VANET PKI scheme, this new approach can satisfy necessary security requirements with improved performance and scalability, and at a lower cost by reducing the security overheads of message transmission and eliminating digital certificate deployment and maintenance issues. The proposed PKR regime consists of the required infrastructure components, rules for public key management and verification, and a set of interactions and associated behaviours to meet these rule requirements. This is achieved through a system design as a logic process model with functional specifications. The PKR regime can be used as development guidelines for conforming implementations. An analysis and evaluation of the proposed PKR regime includes security features assessment, analysis of the security overhead of message transmission, transmission latency, processing latency, and scalability of the proposed PKR regime. Compared to certificate-based PKI approaches, the proposed PKR regime can maintain the necessary security requirements, significantly reduce the security overhead by approximately 70%, and improve the performance by 98%. Meanwhile, the result of the scalability evaluation shows that the latency of employing the proposed PKR regime stays much lower at approximately 15 milliseconds, whether operating in a huge or small environment. It is therefore believed that this research will create a new dimension to the provision of secure messaging services in VANETs.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Academic faculty’s teaching social networks : what is the extent of library faculty’s inclusion?

Collaboration between academic and library faculty is an important topic of discussion and research among academic librarians. Partnerships are vital for developing effective information literacy education. The research reported in this paper aims to develop an understanding of academic collaborators by analyzing academic faculty’s teaching social network. Academic faculty teaching social networks have not been previously described through the lens of social network analysis. A teaching social network is comprised of people and their communication channels that affect academic faculty when they design and deliver their courses. Social network analysis was the methodology used to describe the teaching social networks. The preliminary results show academic faculty were more affected by the channels of communication in how they taught (pedagogy) than what they taught (course content). This study supplements the existing research on collaboration and information literacy. It provides both academic and library faculty with added insight into their relationships.

An improved genetic algorithm and graph theory based method for optimal sectionalizer switch placement in distribution networks with DG

In this paper a new graph-theory and improved genetic algorithm based practical method is employed to solve the optimal sectionalizer switch placement problem. The proposed method determines the best locations of sectionalizer switching devices in distribution networks considering the effects of presence of distributed generation (DG) in fitness functions and other optimization constraints, providing the maximum number of costumers to be supplied by distributed generation sources in islanded distribution systems after possible faults. The proposed method is simulated and tested on several distribution test systems in both cases of with DG and non DG situations. The results of the simulations validate the proposed method for switch placement of the distribution network in the presence of distributed generation.

Bridging the representation and interaction challenges of mobile context-aware computing: designing agile ridesharing

The increasing capability of mobile devices and social networks to gather contextual and social data has led to increased interest in context-aware computing for mobile applications. This paper explores ways of reconciling two different viewpoints of context, representational and interactional, that have arisen respectively from technical and social science perspectives on context-aware computing. Through a case study in agile ridesharing, the importance of dynamic context control, historical context and broader context is discussed. We build upon earlier work that has sought to address the divide by further explicating the problem in the mobile context and expanding on the design approaches.