Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems

Hybrid system representations have been exploited in a number of challenging modelling situations, including situations where the original nonlinear dynamics are too complex (or too imprecisely known) to be directly filtered. Unfortunately, the question of how to best design suitable hybrid system models has not yet been fully addressed, particularly in the situations involving model uncertainty. This paper proposes a novel joint state-measurement relative entropy rate based approach for design of hybrid system filters in the presence of (parameterised) model uncertainty. We also present a design approach suitable for suboptimal hybrid system filters. The benefits of our proposed approaches are illustrated through design examples and simulation studies.

Hardness of silicon nitride thin films characterised by nanoindentation and nanoscratch deconvolution methods

Plasma enhanced chemical vapour deposition silicon nitride thin films are widely used in microelectromechanical system devices as structural materials because the mechanical properties of those films can be tailored by adjusting deposition conditions. However, accurate measurement of the mechanical properties, such as hardness, of films with thicknesses at nanometric scale is challenging. In the present study, the hardness of the silicon nitride films deposited on silicon substrate under different deposit conditions was characterised using nanoindentation and nanoscratch deconvolution methods. The hardness values obtained from the two methods were compared. The effect of substrate on the measured results was discussed.

Minimum important difference between patients with rheumatoid arthritis : the patient's perspective

OBJECTIVE: To determine the point at which differences in clinical assessment scores on physical ability, pain and overall condition are sufficiently large to correspond to a subjective perception of a meaningful difference from the perspective of the patient. METHODS: Forty patients with a diagnosis of rheumatoid arthritis participated in an evening of clinical assessment and one-on-one conversations with each other regarding their arthritic condition. The assessments included tender and swollen joint counts, clinician and patient global assessments, participant assessment of pain and the Health Assessment Questionnaire (HAQ) on physical ability. After each conversation, participants rated themselves relative to their conversational partner on physical ability, pain and overall condition. These subjective comparative ratings were compared to the differences of the individual clinical assessments. RESULTS: In total there were 120 conversations. Generally participants judged themselves as less disabled than others. They rated themselves as "somewhat better" than their conversation partner when they had a (mean) 7% better score on the HAQ, 6% less pain, and 9% better global assessment. In contrast, they rated themselves as "somewhat worse" when they had a (mean) 16% worse score on the HAQ, 16% more pain, and 29% worse global assessment. CONCLUSIONS: Patients view clinically important differences in an asymmetric manner. These results can provide guidance in interpreting results and planning clinical trials.

Cross-entropy optimization for scaling factors of a fuzzy controller : a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

A hybrid genetic algorithm for the minimum interconnection cut problem

In the real world there are many problems in network of networks (NoNs) that can be abstracted to a so-called minimum interconnection cut problem, which is fundamentally different from those classical minimum cut problems in graph theory. Thus, it is desirable to propose an efficient and effective algorithm for the minimum interconnection cut problem. In this paper we formulate the problem in graph theory, transform it into a multi-objective and multi-constraint combinatorial optimization problem, and propose a hybrid genetic algorithm (HGA) for the problem. The HGA is a penalty-based genetic algorithm (GA) that incorporates an effective heuristic procedure to locally optimize the individuals in the population of the GA. The HGA has been implemented and evaluated by experiments. Experimental results have shown that the HGA is effective and efficient.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

Clinical and radiographic outcomes of acetabular impaction grafting without cage reinforcement for revision hip replacement : a minimum ten-year follow-up study

Impaction bone grafting for reconstitution of bone stock in revision hip surgery has been used for nearly 30 years. We used this technique, in combination with a cemented acetabular component, in the acetabula of 304 hips in 292 patients revised for aseptic loosening between 1995 and 2001. The only additional supports used were stainless steel meshes placed against the medial wall or laterally around the acetabular rim to contain the graft. All Paprosky grades of defect were included. Clinical and radiographic outcomes were collected in surviving patients at a minimum of 10 years following the index operation. Mean follow-up was 12.4 years (SD 1.5; range 10.0-16.0). Kaplan-Meier survivorship with revision for aseptic loosening as the endpoint was 85.9% (95% CI 81.0 to 90.8%) at 13.5 years. Clinical scores for pain relief remained satisfactory, and there was no difference in clinical scores between cups that appeared stable and those that appeared loose radiographically.

A deconvolution method for deriving the transit time spectrum for ultrasound propagation through cancellous bone replica models

The acceptance of broadband ultrasound attenuation for the assessment of osteoporosis suffers from a limited understanding of ultrasound wave propagation through cancellous bone. It has recently been proposed that the ultrasound wave propagation can be described by a concept of parallel sonic rays. This concept approximates the detected transmission signal to be the superposition of all sonic rays that travel directly from transmitting to receiving transducer. The transit time of each ray is defined by the proportion of bone and marrow propagated. An ultrasound transit time spectrum describes the proportion of sonic rays having a particular transit time, effectively describing lateral inhomogeneity of transit times over the surface of the receiving ultrasound transducer. The aim of this study was to provide a proof of concept that a transit time spectrum may be derived from digital deconvolution of input and output ultrasound signals. We have applied the active-set method deconvolution algorithm to determine the ultrasound transit time spectra in the three orthogonal directions of four cancellous bone replica samples and have compared experimental data with the prediction from the computer simulation. The agreement between experimental and predicted ultrasound transit time spectrum analyses derived from Bland–Altman analysis ranged from 92% to 99%, thereby supporting the concept of parallel sonic rays for ultrasound propagation in cancellous bone. In addition to further validation of the parallel sonic ray concept, this technique offers the opportunity to consider quantitative characterisation of the material and structural properties of cancellous bone, not previously available utilising ultrasound.

Minimum cost of transport in human running is not ubiquitous

Purpose This study explores recent claims that humans exhibit a minimum cost of transport (CoTmin) for running which occurs at an intermediate speed, and assesses individual physiological, gait and training characteristics. Methods Twelve healthy participants with varying levels of fitness and running experience ran on a treadmill at six self-selected speeds in a discontinuous protocol over three sessions. Running speed (km[middle dot]hr-1), V[spacing dot above]O2 (mL[middle dot]kg-1[middle dot]km-1), CoT (kcal[middle dot]km-1), heart rate (beats[middle dot]min-1) and cadence (steps[middle dot]min-1) were continuously measured. V[spacing dot above]O2 max was measured on a fourth testing session. The occurrence of a CoTmin was investigated and its presence or absence examined with respect to fitness, gait and training characteristics. Results Five participants showed a clear CoTmin at an intermediate speed and a statistically significant (p < 0.05) quadratic CoT-speed function, while the other participants did not show such evidence. Participants were then categorized and compared with respect to the strength of evidence for a CoTmin (ClearCoTmin and NoCoTmin). The ClearCoTmin group displayed significantly higher correlation between speed and cadence; more endurance training and exercise sessions per week; than the NoCoTmin group; and a marginally non-significant but higher aerobic capacity. Some runners still showed a CoTmin at an intermediate speed even after subtraction of resting energy expenditure. Conclusion The findings confirm the existence of an optimal speed for human running, in some but not all participants. Those exhibiting a COTmin undertook a higher volume of running, ran with a cadence that was more consistently modulated with speed, and tended to be aerobically fitter. The ability to minimise the energetic cost of transport appears not to be ubiquitous feature of human running but may emerge in some individuals with extensive running experience.

Lower partial moment and information entropy-based dynamic electricity purchasing strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Government procurement contracts and minimum labour standards enforcement: Rhetoric, duplication and distraction?

Government contracts for services typically include terms requiring contractors to comply with minimum labour standards laws. Procurement contract clauses specify reporting procedures and sanctions for non-compliance, implying that government contracting agencies will monitor and enforce minimum labour standards within contract performance management. In this article, the case of school cleaners employed under New South Wales government contracts between 2010 and 2011 is the vehicle for exploring the effectiveness of these protective clauses. We find that the inclusion of these protective clauses in procurement contracts is unnecessary in the Australian context, and any expectations that government contracting agencies will monitor and enforce labour standards are misleading. At best, the clauses are rhetoric, and at worst, they are a distraction for parties with enforcement powers.

Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design

The total entropy utility function is considered for the dual purpose of Bayesian design for model discrimination and parameter estimation. A sequential design setting is proposed where it is shown how to efficiently estimate the total entropy utility for a wide variety of data types. Utility estimation relies on forming particle approximations to a number of intractable integrals which is afforded by the use of the sequential Monte Carlo algorithm for Bayesian inference. A number of motivating examples are considered for demonstrating the performance of total entropy in comparison to utilities for model discrimination and parameter estimation. The results suggest that the total entropy utility selects designs which are efficient under both experimental goals with little compromise in achieving either goal. As such, the total entropy utility is advocated as a general utility for Bayesian design in the presence of model uncertainty.