Network protocols and predictive control strategies for distributed real-time control applications

A trend in design and implementation of modern industrial automation systems is to integrate computing, communication and control into a unified framework at different levels of machine/factory operations and information processing. These distributed control systems are referred to as networked control systems (NCSs). They are composed of sensors, actuators, and controllers interconnected over communication networks. As most of communication networks are not designed for NCS applications, the communication requirements of NCSs may be not satisfied. For example, traditional control systems require the data to be accurate, timely and lossless. However, because of random transmission delays and packet losses, the control performance of a control system may be badly deteriorated, and the control system rendered unstable. The main challenge of NCS design is to both maintain and improve stable control performance of an NCS. To achieve this, communication and control methodologies have to be designed. In recent decades, Ethernet and 802.11 networks have been introduced in control networks and have even replaced traditional fieldbus productions in some real-time control applications, because of their high bandwidth and good interoperability. As Ethernet and 802.11 networks are not designed for distributed control applications, two aspects of NCS research need to be addressed to make these communication networks suitable for control systems in industrial environments. From the perspective of networking, communication protocols need to be designed to satisfy communication requirements for NCSs such as real-time communication and high-precision clock consistency requirements. From the perspective of control, methods to compensate for network-induced delays and packet losses are important for NCS design. To make Ethernet-based and 802.11 networks suitable for distributed control applications, this thesis develops a high-precision relative clock synchronisation protocol and an analytical model for analysing the real-time performance of 802.11 networks, and designs a new predictive compensation method. Firstly, a hybrid NCS simulation environment based on the NS-2 simulator is designed and implemented. Secondly, a high-precision relative clock synchronization protocol is designed and implemented. Thirdly, transmission delays in 802.11 networks for soft-real-time control applications are modeled by use of a Markov chain model in which real-time Quality-of- Service parameters are analysed under a periodic traffic pattern. By using a Markov chain model, we can accurately model the tradeoff between real-time performance and throughput performance. Furthermore, a cross-layer optimisation scheme, featuring application-layer flow rate adaptation, is designed to achieve the tradeoff between certain real-time and throughput performance characteristics in a typical NCS scenario with wireless local area network. Fourthly, as a co-design approach for both a network and a controller, a new predictive compensation method for variable delay and packet loss in NCSs is designed, where simultaneous end-to-end delays and packet losses during packet transmissions from sensors to actuators is tackled. The effectiveness of the proposed predictive compensation approach is demonstrated using our hybrid NCS simulation environment.

Business cycle dynamics with duration dependence and leading indicators

Durland and McCurdy [Durland, J.M., McCurdy, T.H., 1994. Duration-dependent transitions in a Markov model of US GNP growth. Journal of Business and Economic Statistics 12, 279–288] investigated the issue of duration dependence in US business cycle phases using a Markov regime-switching approach, introduced by Hamilton [Hamilton, J., 1989. A new approach to the analysis of time series and the business cycle. Econometrica 57, 357–384] and extended to the case of variable transition parameters by Filardo [Filardo, A.J., 1994. Business cycle phases and their transitional dynamics. Journal of Business and Economic Statistics 12, 299–308]. In Durland and McCurdy’s model duration alone was used as an explanatory variable of the transition probabilities. They found that recessions were duration dependent whilst expansions were not. In this paper, we explicitly incorporate the widely-accepted US business cycle phase change dates as determined by the NBER, and use a state-dependent multinomial Logit modelling framework. The model incorporates both duration and movements in two leading indexes – one designed to have a short lead (SLI) and the other designed to have a longer lead (LLI) – as potential explanatory variables. We find that doing so suggests that current duration is not only a significant determinant of transition out of recessions, but that there is some evidence that it is also weakly significant in the case of expansions. Furthermore, we find that SLI has more informational content for the termination of recessions whilst LLI does so for expansions.

The use of temporal speech and lip information for multi-modal speaker identification via multi-stream HMMs

Investigates the use of temporal lip information, in conjunction with speech information, for robust, text-dependent speaker identification. We propose that significant speaker-dependent information can be obtained from moving lips, enabling speaker recognition systems to be highly robust in the presence of noise. The fusion structure for the audio and visual information is based around the use of multi-stream hidden Markov models (MSHMM), with audio and visual features forming two independent data streams. Recent work with multi-modal MSHMMs has been performed successfully for the task of speech recognition. The use of temporal lip information for speaker identification has been performed previously (T.J. Wark et al., 1998), however this has been restricted to output fusion via single-stream HMMs. We present an extension to this previous work, and show that a MSHMM is a valid structure for multi-modal speaker identification

Demonstration of closed-­loop airborne sense-­and-­avoid using machine vision

This paper describes a vision-based airborne collision avoidance system developed by the Australian Research Centre for Aerospace Automation (ARCAA) under its Dynamic Sense-and-Act (DSA) program. We outline the system architecture and the flight testing undertaken to validate the system performance under realistic collision course scenarios. The proposed system could be implemented in either manned or unmanned aircraft, and represents a step forward in the development of a “sense-and-avoid” capability equivalent to human “see-and-avoid”.

Evaluation of the cost-effectiveness of strategies claiming to reduce the risk of surgical site infections following primary total hip arthroplasty

Background Total hip arthroplasty (THA) is a commonly performed procedure and numbers are increasing with ageing populations. One of the most serious complications in THA are surgical site infections (SSIs), caused by pathogens entering the wound during the procedure. SSIs are associated with a substantial burden for health services, increased mortality and reduced functional outcomes in patients. Numerous approaches to preventing these infections exist but there is no gold standard in practice and the cost-effectiveness of alternate strategies is largely unknown. Objectives The aim of this project was to evaluate the cost-effectiveness of strategies claiming to reduce deep surgical site infections following total hip arthroplasty in Australia. The objectives were: 1. Identification of competing strategies or combinations of strategies that are clinically relevant to the control of SSI related to hip arthroplasty 2. Evidence synthesis and pooling of results to assess the volume and quality of evidence claiming to reduce the risk of SSI following total hip arthroplasty 3. Construction of an economic decision model incorporating cost and health outcomes for each of the identified strategies 4. Quantification of the effect of uncertainty in the model 5. Assessment of the value of perfect information among model parameters to inform future data collection Methods The literature relating to SSI in THA was reviewed, in particular to establish definitions of these concepts, understand mechanisms of aetiology and microbiology, risk factors, diagnosis and consequences as well as to give an overview of existing infection prevention measures. Published economic evaluations on this topic were also reviewed and limitations for Australian decision-makers identified. A Markov state-transition model was developed for the Australian context and subsequently validated by clinicians. The model was designed to capture key events related to deep SSI occurring within the first 12 months following primary THA. Relevant infection prevention measures were selected by reviewing clinical guideline recommendations combined with expert elicitation. Strategies selected for evaluation were the routine use of pre-operative antibiotic prophylaxis (AP) versus no use of antibiotic prophylaxis (No AP) or in combination with antibiotic-impregnated cement (AP & ABC) or laminar air operating rooms (AP & LOR). The best available evidence for clinical effect size and utility parameters was harvested from the medical literature using reproducible methods. Queensland hospital data were extracted to inform patients’ transitions between model health states and related costs captured in assigned treatment codes. Costs related to infection prevention were derived from reliable hospital records and expert opinion. Uncertainty of model input parameters was explored in probabilistic sensitivity analyses and scenario analyses and the value of perfect information was estimated. Results The cost-effectiveness analysis was performed from a health services perspective using a hypothetical cohort of 30,000 THA patients aged 65 years. The baseline rate of deep SSI was 0.96% within one year of a primary THA. The routine use of antibiotic prophylaxis (AP) was highly cost-effective and resulted in cost savings of over $1.6m whilst generating an extra 163 QALYs (without consideration of uncertainty). Deterministic and probabilistic analysis (considering uncertainty) identified antibiotic prophylaxis combined with antibiotic-impregnated cement (AP & ABC) to be the most cost-effective strategy. Using AP & ABC generated the highest net monetary benefit (NMB) and an incremental $3.1m NMB compared to only using antibiotic prophylaxis. There was a very low error probability that this strategy might not have the largest NMB (<5%). Not using antibiotic prophylaxis (No AP) or using both antibiotic prophylaxis combined with laminar air operating rooms (AP & LOR) resulted in worse health outcomes and higher costs. Sensitivity analyses showed that the model was sensitive to the initial cohort starting age and the additional costs of ABC but the best strategy did not change, even for extreme values. The cost-effectiveness improved for a higher proportion of cemented primary THAs and higher baseline rates of deep SSI. The value of perfect information indicated that no additional research is required to support the model conclusions. Conclusions Preventing deep SSI with antibiotic prophylaxis and antibiotic-impregnated cement has shown to improve health outcomes among hospitalised patients, save lives and enhance resource allocation. By implementing a more beneficial infection control strategy, scarce health care resources can be used more efficiently to the benefit of all members of society. The results of this project provide Australian policy makers with key information about how to efficiently manage risks of infection in THA.

Robots move : bootstrapping the development of object representations using sensorimotor coordination

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.

Surgical site infection prevention following total hip arthroplasty in Australia : a cost-effectiveness analysis

Background: Surgical site infection (SSI) is associated with substantial costs for health services, reduced quality of life, and functional outcomes. The aim of this study was to evaluate the cost-effectiveness of strategies claiming to reduce the risk of SSI in hip arthroplasty in Australia. Methods: Baseline use of antibiotic prophylaxis (AP) was compared with no antibiotic prophylaxis (no AP), antibiotic-impregnated cement (AP þ ABC), and laminar air operating rooms (AP þ LOR). A Markov model was used to simulate long-term health and cost outcomes of a hypothetical cohort of 30,000 total hip arthroplasty patients from a health services perspective. Model parameters were informed by the best available evidence. Uncertainty was explored in probabilistic sensitivity and scenario analyses. Results: Stopping the routine use of AP resulted in over Australian dollars (AUD) $1.5 million extra costs and a loss of 163 quality-adjusted life years (QALYs). Using antibiotic cement in addition to AP (AP þ ABC)generated an extra 32 QALYs while saving over AUD $123,000. The use of laminar air operating rooms combined with routine AP (AP þ LOR) resulted in an AUD $4.59 million cost increase and 127 QALYs lost compared with the baseline comparator. Conclusion: Preventing deep SSI with antibiotic prophylaxis and antibiotic-impregnated cement has shown to improve health outcomes among hospitalized patients, save lives, and enhance resource allocation. Based on this evidence, the use of laminar air operating rooms is not recommended.

Cost-effectiveness of a telephone-delivered education programme to prevent early childhood caries in a disadvantaged area : a cohort study

Objectives Early childhood caries is a highly destructive dental disease which is compounded by the need for young children to be treated under general anaesthesia. In Australia, there are long waiting periods for treatment at public hospitals. In this paper, we examined the costs and patient outcomes of a prevention programme for early childhood caries to assess its value for government services. Design Cost-effectiveness analysis using a Markov model. Setting Public dental patients in a low socioeconomic, socially disadvantaged area in the State of Queensland, Australia. Participants Children aged 6 months to 6 years received either a telephone prevention programme or usual care. Primary and secondary outcome measures A mathematical model was used to assess caries incidence and public dental treatment costs for a cohort of children. Healthcare costs, treatment probabilities and caries incidence were modelled from 6 months to 6 years of age based on trial data from mothers and their children who received either a telephone prevention programme or usual care. Sensitivity analyses were used to assess the robustness of the findings to uncertainty in the model estimates. Results By age 6 years, the telephone intervention programme had prevented an estimated 43 carious teeth and saved £69 984 in healthcare costs per 100 children. The results were sensitive to the cost of general anaesthesia (cost-savings range £36 043–£97 298) and the incidence of caries in the prevention group (cost-savings range £59 496–£83 368) and usual care (cost-savings range £46 833–£93 328), but there were cost savings in all scenarios. Conclusions A telephone intervention that aims to prevent early childhood caries is likely to generate considerable and immediate patient benefits and cost savings to the public dental health service in disadvantaged communities.

A probabilistic framework to monitor a multi-mode outdoor robot

This paper presents an approach to autonomously monitor the behavior of a robot endowed with several navigation and locomotion modes, adapted to the terrain to traverse. The mode selection process is done in two steps: the best suited mode is firstly selected on the basis of initial information or a qualitative map built on-line by the robot. Then, the motions of the robot are monitored by various processes that update mode transition probabilities in a Markov system. The paper focuses on this latter selection process: the overall approach is depicted, and preliminary experimental results are presented

A cost-effectiveness analysis of a telephone-linked care intervention for individuals with Type 2 diabetes

AIM: To assess the cost-effectiveness of an automated telephone-linked care intervention, Australian TLC Diabetes, delivered over 6 months to patients with established Type 2 diabetes mellitus and high glycated haemoglobin level, compared to usual care. METHODS: A Markov model was designed to synthesize data from a randomized controlled trial of TLC Diabetes (n=120) and other published evidence. The 5-year model consisted of three health states related to glycaemic control: 'sub-optimal' HbA1c ≥58mmol/mol (7.5%); 'average' ≥48-57mmol/mol (6.5-7.4%) and 'optimal' <48mmol/mol (6.5%) and a fourth state 'all-cause death'. Key outcomes of the model include discounted health system costs and quality-adjusted life years (QALYS) using SF-6D utility weights. Univariate and probabilistic sensitivity analyses were undertaken. RESULTS: Annual medication costs for the intervention group were lower than usual care [Intervention: £1076 (95%CI: £947, £1206) versus usual care £1271 (95%CI: £1115, £1428) p=0.052]. The estimated mean cost for intervention group participants over five years, including the intervention cost, was £17,152 versus £17,835 for the usual care group. The corresponding mean QALYs were 3.381 (SD 0.40) for the intervention group and 3.377 (SD 0.41) for the usual care group. Results were sensitive to the model duration, utility values and medication costs. CONCLUSION: The Australian TLC Diabetes intervention was a low-cost investment for individuals with established diabetes and may result in medication cost-savings to the health system. Although QALYs were similar between groups, other benefits arising from the intervention should also be considered when determining the overall value of this strategy.

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

Economic evaluation of adolescent reproductive health education interventions in Chilinh, Vietnam

This research was an economic analysis of two novel health education interventions compared to existing practice for reproductive health among young people in northern Vietnam. The research showed that implementing an educational intervention including school-based and health facility-based components was cost effective for males and females. The findings will assist decision makers in efficient allocation of scarce resources for adolescent health promotion in Vietnam and similar socio-economic contexts in Asia.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection

Visual information in the form of lip movements of the speaker has been shown to improve the performance of speech recognition and search applications. In our previous work, we proposed cross database training of synchronous hidden Markov models (SHMMs) to make use of external large and publicly available audio databases in addition to the relatively small given audio visual database. In this work, the cross database training approach is improved by performing an additional audio adaptation step, which enables audio visual SHMMs to benefit from audio observations of the external audio models before adding visual modality to them. The proposed approach outperforms the baseline cross database training approach in clean and noisy environments in terms of phone recognition accuracy as well as spoken term detection (STD) accuracy.

Post-transcriptional regulation in the nucleus and cytoplasm: study of mean time to threshold (MTT) and narrow escape problem

Messenger RNAs (mRNAs) can be repressed and degraded by small non-coding RNA molecules. In this paper, we formulate a coarsegrained Markov-chain description of the post-transcriptional regulation of mRNAs by either small interfering RNAs (siRNAs) or microRNAs (miRNAs). We calculate the probability of an mRNA escaping from its domain before it is repressed by siRNAs/miRNAs via cal- culation of the mean time to threshold: when the number of bound siRNAs/miRNAs exceeds a certain threshold value, the mRNA is irreversibly repressed. In some cases,the analysis can be reduced to counting certain paths in a reduced Markov model. We obtain explicit expressions when the small RNA bind irreversibly to the mRNA and we also discuss the reversible binding case. We apply our models to the study of RNA interference in the nucleus, examining the probability of mRNAs escaping via small nuclear pores before being degraded by siRNAs. Using the same modelling framework, we further investigate the effect of small, decoy RNAs (decoys) on the process of post-transcriptional regulation, by studying regulation of the tumor suppressor gene, PTEN : decoys are able to block binding sites on PTEN mRNAs, thereby educing the number of sites available to siRNAs/miRNAs and helping to protect it from repression. We calculate the probability of a cytoplasmic PTEN mRNA translocating to the endoplasmic reticulum before being repressed by miRNAs. We support our results with stochastic simulations