Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

Heritability of the network architecture of intrinsic brain functional connectivity

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23±2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h2 (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤15%), and when global signal regression was implemented, heritability estimates decreased substantially h2 (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

Communication Architecture Design for Real-Time Networked Control Systems

Networked control over data networks has received increasing attention in recent years. Among many problems in networked control systems (NCSs) is the need to reduce control latency and jitter and to deal with packet dropouts. This paper introduces our recent progress on a queuing communication architecture for real-time NCS applications, and simple strategies for dealing with packet dropouts. Case studies for a middle-scale process or multiple small-scale processes are presented for TCP/IP based real-time NCSs. Variations of network architecture design are modelled, simulated, and analysed for evaluation of control latency and jitter performance. It is shown that a simple bandwidth upgrade or adding hierarchy does not necessarily bring benefits for performance improvement of control latency and jitter. A co-design of network and control is necessary to maximise the real-time control performance of NCSs

Real-time image streaming over a low-bandwidth wireless camera network

In this paper we describe the recent development of a low-bandwidth wireless camera sensor network. We propose a simple, yet effective, network architecture which allows multiple cameras to be connected to the network and synchronize their communication schedules. Image compression of greater than 90% is performed at each node running on a local DSP coprocessor, resulting in nodes using 1/8th the energy compared to streaming uncompressed images. We briefly introduce the Fleck wireless node and the DSP/camera sensor, and then outline the network architecture and compression algorithm. The system is able to stream color QVGA images over the network to a base station at up to 2 frames per second. © 2007 IEEE.

Effects of the architecture of tissue engineering scaffolds on cell seeding and culturing

The advance of rapid prototyping techniques has significantly improved control over the pore network architecture of tissue engineering scaffolds. In this work we assessed the influence of scaffold pore architecture on cell seeding and static culturing, by comparing a computer‐designed gyroid architecture fabricated by stereolithography to a random‐pore architecture resulting from salt‐leaching. The scaffold types showed comparable porosity and pore size values, but the gyroid type showed a more than tenfold higher permeability due to the absence of size‐limiting pore interconnections. The higher permeability significantly improved the wetting properties of the hydrophobic scaffolds, and increased the settling speed of cells upon static seeding of immortalised mesenchymal stem cells. After dynamic seeding followed by 5 days of static culture, gyroid scaffolds showed large cell populations in the centre of the scaffold, while salt‐leached scaffolds were covered with a cell‐sheet on the outside and no cells were found in the scaffold centre. It was shown that interconnectivity of the pores and permeability of the scaffold prolongs the time of static culture before overgrowth of cells at the scaffold periphery occurs. Furthermore, novel scaffold designs are proposed to further improve the transport of oxygen and nutrients throughout the scaffolds, and to create tissue engineering grafts with designed, pre‐fabricated vasculature.

An intelligent network control system for plug-in electric vehicles and the associate communication mechanism

The development of an intelligent plug-in electric vehicle (PEV) network is an important research topic in the smart grid environment. An intelligent PEV network enables a flexible control of PEV charging and discharging activities and hence PEVs can be utilized as ancillary service providers in the power system concerned. Given this background, an intelligent PEV network architecture is first developed, and followed by detailed designs of its application layers, including the charging and discharging controlling system, mobility and roaming management, as well as communication mechanisms associated. The presented architecture leverages the philosophy in mobile communication network buildup

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Architecture for service profiling

Service oriented architecture is gaining momentum. However, in order to be successful, the proper and up-to-date description of services is required. Such a description may be provided by service profiling mechanisms, such as one presented in this article. Service profile can be defined as an up-to-date description of a subset of non-functional properties of a service. It allows for service comparison on the basis of non-functional parameters, and choosing the service which is most suited to the needs of a user. In this article the notion of a service profile along with service profiling mechanism is presented as well as the architecture of a profiling system. © 2006 IEEE.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

A large-scale computing infrastructure for design education

Most departmental computing infrastructure reflects the state of networking technology and available funds at the time of construction, which converge in a preconceived notion of homogeneity of network architecture and usage patterns. The DMAN (Digital Media Access Network) project, a large-scale server and network foundation for the Hong Kong Polytechnic University's School of Design was created as a platform that would support a highly complex academic environment while giving maximum freedom to students, faculty and researchers through simplicity and ease of use. As a centralized multi-user computation backbone, DMAN faces an extremely hetrogeneous user and application profile, exceeding implementation and maintenance challenges of typical enterprise, and even most academic server set-ups. This paper sumarizes the specification, implementation and application of the system while describing its significance for design education in a computational context.

Designed biodegradable hydrogel structures prepared by stereolithography using poly(ethylene glycol)/poly(D,L-lactide)-based resins

Designed three-dimensional biodegradable poly(ethylene glycol)/poly(D,L-lactide) hydrogel structures were prepared for the first time by stereolithography at high resolutions. A photopolymerisable aqueous resin comprising PDLLA-PEG-PDLLA-based macromer, visible light photo-initiator, dye and inhibitor in DMSO/water was used to build the structures. Porous and non-porous hydrogels with well-defined architectures and good mechanical properties were prepared. Porous hydrogel structures with a gyroid pore network architecture showed narrow pore size distributions, excellent pore interconnectivity and good mechanical properties. The structures showed good cell seeding characteristics, and human mesenchymal stem cells adhered and proliferated well on these materials.

Properties of photo-cured poly(D,L-lactide) networks

Poly(D,L-lactide) is a degradable polymer with a long history of use in medical applications. It is strong and stiff and degrades over the course of months into lactic acid, a body-own substance. In the field of tissue engineering it is commonly used to fabricate scaffolds. Stereolithography is a high resolution rapid prototyping technique by which designed 3D objects can be built using photo-initiated radical polymerisations. Poly(D,Llactide) (PDLLA) networks can be obtained by photopolymerisation of oligomers functionalised with unsaturated groups. In this work, PDLLA oligomers of varying architectures (arm lengths, numbers of arms) were synthesised and end-functionalised with methacrylate groups. These macromers were photo-crosslinked in solution to yield PDLLA networks of different architectures. The influence of the network architecture on its physical properties was studied.

Designed poly(ethylene glycol)/poly(D,L-lactide)-based hydrogel structures prepared by stereolithography

Three-dimensional biodegradable poly(ethylene glycol)/poly(D,L-lactide) hydrogel structures were prepared by stereolithography. A photo-polymerisable liquid resin comprising PDLLA-PEG-PDLLA-based macromer, visible light photo-initiator, dye and inhibitor in DMSO/water was used to build the structures. Hydrogels with welldefined architectures and good mechanical properties were prepared. Hydrogel structures with a gyroid pore network architecture showed narrow pore size distributions, excellent pore interconnectivity and good mechanical properties. The structures showed good cell seeding characteristics, and human mesenchymal stem cells adhered and proliferated on these materials.

Characterisation of microvasulature changes after closed soft tissue trauma by contrast enhanced micro-CT imaging

INTRODUCTION It is known that the vascular morphology and functionality are changed following closed soft tissue trauma (CSTT) [1], and bone fractures [2]. The disruption of blood vessels may lead to hypoxia and necrosis. Currently, most clinical methods for the diagnosis and monitoring of CSTT with or without bone fractures are primarily based on qualitative measures or practical experience, making the diagnosis subjective and inaccurate. There is evidence that CSTT and early vascular changes following the injury delay the soft tissue tissue and bone healing [3]. However, a precise qualitative and quantitative morphological assessment of vasculature changes after trauma is currently missing. In this research, we aim to establish a diagnostic framework to assess the 3D vascular morphological changes after standardized CSTT in a rat model qualitatively and quantitatively using contrast-enhanced micro-CT imaging. METHODS An impact device was used for the application of a controlled reproducible CSTT to the left thigh (Biceps Femoris) of anaesthetized male Wistar rats. After euthanizing the animals at 6 hours, 24 hours, 3 days, 7 days, or 14 days after trauma, CSTT was qualitatively evaluated by macroscopic visual observation of the skin and muscles. For visualization of the vasculature, the blood vessels of sacrificed rats were flushed with heparinised saline and then perfused with a radio-opaque contrast agent (Microfil, MV 122, Flowtech, USA) using an infusion pump. After allowing the contrast agent to polymerize overnight, both hind-limbs were dissected, and then the whole injured and contra-lateral control limbs were imaged using a micro-CT scanner (µCT 40, Scanco Medical, Switzerland) to evaluate the vascular morphological changes. Correlated biopsy samples were also taken from the CSTT region of both injured and control legs. The morphological parameters such as the vessel volume ratio (VV/TV), vessel diameter (V.D), spacing (V.Sp), number (V.N), connectivity (V.Conn) and the degree of anisotropy (DA) were then quantified by evaluating the scans of biopsy samples using the micro-CT imaging system. RESULTS AND DISCUSSION A qualitative evaluation of the CSTT has shown that the developed impact protocols were capable of producing a defined and reproducible injury within the region of interest (ROI), resulting in a large hematoma and moderate swelling in both lateral and medial sides of the injured legs. Also, the visualization of the vascular network using 3D images confirmed the ability to perfuse the large vessels and a majority of the microvasculature consistently (Figure 1). Quantification of the vascular morphology obtained from correlated biopsy samples has demonstrated that V.D and V.N and V.Sp were significantly higher in the injured legs 24 hours after impact in comparison with the control legs (p<0.05). The evaluation of the other time points is currently progressing. CONCLUSIONS The findings of this research will contribute to a better understanding of the changes to the vascular network architecture following traumatic injuries and during healing process. When interpreted in context of functional changes, such as tissue oxygenation, this will allow for objective diagnosis and monitoring of CSTT and serve as validation for future non-invasive clinical assessment modalities.