Improving the consensus performance via predictive mechanisms

Considering some predictive mechanisms, we show that ultrafast average-consensus can be achieved in networks of interconnected agents. More specifically, by predicting the dynamics of the network several steps ahead and using this information in the design of the consensus protocol of each agent, drastic improvements can be achieved in terms of the speed of consensus convergence, without changing the topology of the network. Moreover, using these predictive mechanisms, the range of sampling periods leading to consensus convergence is greatly expanded compared with the routine consensus protocol. This study provides a mathematical basis for the idea that some predictive mechanisms exist in widely-spread biological swarms, flocks, and networks. From the industrial engineering point of view, inclusion of an efficient predictive mechanism allows for a significant increase in the speed of consensus convergence and also a reduction of the communication energy required to achieve a predefined consensus performance.

Performance evaluation of multiplexed model predictive control for a large airliner in nominal and contingency scenarios

Model predictive control allows systematic handling of physical and operational constraints through the use of constrained optimisation. It has also been shown to successfully exploit plant redundancy to maintain a level of control in scenarios when faults are present. Unfortunately, the computational complexity of each individual iteration of the algorithm to solve the optimisation problem scales cubically with the number of plant inputs, so the computational demands are high for large MIMO plants. Multiplexed MPC only calculates changes in a subset of the plant inputs at each sampling instant, thus reducing the complexity of the optimisation. This paper demonstrates the application of multiplexed model predictive control to a large transport airliner in a nominal and a contingency scenario. The performance is compared to that obtained with a conventional synchronous model predictive controller, designed using an equivalent cost function. © 2012 AACC American Automatic Control Council).

The performance of concrete exposed to marine environments: predictive modelling and use of laboratory/on site test methods

This paper reports an approach by which laboratory based testing and numerical modelling can be combined to predict the long term performance of a range of concretes exposed to marine environments. Firstly, a critical review of the test methods for assessing the chloride penetration resistance of concrete is given. The repeatability of the different test results is also included. In addition to the test methods, a numerical simulation model is used to explore the test data further to obtain long-term chloride ingress trends. The combined use of testing and modelling is validated with the help of long-term chloride ingress data from a North Sea exposure site. In summary, the paper outlines a methodology for determining the long term performance of concrete in marine environments.

On Infinite Time Performance of Nonlinear Model Predictive Controllers

This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal performance limits of closed-loop model predictive control systems. Based on the Principle of Optimality, an upper and a lower bound of the ratio between the total performance and the finite horizon optimal cost are obtained explicitly expressed by the optimization horizon. The results also illustrate, from viewpoint of performance, how model predictive controllers approaches to infinite optimal controllers as the optimization horizon increases.

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study showsthat ifwe do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.

Social network measures for "nosduocentered" networks, their predictive power on performance

Our purpose in this article is to define a network structure which is based on two egos instead of the egocentered (one ego) or the complete network (n egos). We describe the characteristics and properties for this kind of network which we call “nosduocentered network”, comparing it with complete and egocentered networks. The key point for this kind of network is that relations exist between the two main egos and all alters, but relations among others are not observed. After that, we use new social network measures adapted to the nosduocentered network, some of which are based on measures for complete networks such as degree, betweenness, closeness centrality or density, while some others are tailormade for nosduocentered networks. We specify three regression models to predict research performance of PhD students based on these social network measures for different networks such as advice, collaboration, emotional support and trust. Data used are from Slovenian PhD students and their s

Predictive modelling of mineral scaling, corrosion and the performance of solute geothermometers in a granitoid-hosted, enhanced geothermal system

Experience is lacking with mineral scaling and corrosion in enhanced geothermal systems (EGS) in which surface water is circulated through hydraulically stimulated crystalline rocks. As an aid in designing EGS projects we have conducted multicomponent reactive-transport simulations to predict the likely characteristics of scales and corrosion that may form when exploiting heat from granitoid reservoir rocks at ∼200 °C and 5 km depth. The specifications of an EGS project at Basel, Switzerland, are used to constrain the model. The main water–rock reactions in the reservoir during hydraulic stimulation and the subsequent doublet operation were identified in a separate paper (Alt-Epping et al., 2013b). Here we use the computed composition of the reservoir fluid to (1) predict mineral scaling in the injection and production wells, (2) evaluate methods of chemical geothermometry and (3) identify geochemical indicators of incipient corrosion. The envisaged heat extraction scheme ensures that even if the reservoir fluid is in equilibrium with quartz, cooling of the fluid will not induce saturation with respect to amorphous silica, thus eliminating the risk of silica scaling. However, the ascending fluid attains saturation with respect to crystalline aluminosilicates such as albite, microcline and chlorite, and possibly with respect to amorphous aluminosilicates. If no silica-bearing minerals precipitate upon ascent, reservoir temperatures can be predicted by classical formulations of silica geothermometry. In contrast, Na/K concentration ratios in the production fluid reflect steady-state conditions in the reservoir rather than albite–microcline equilibrium. Thus, even though igneous orthoclase is abundant in the reservoir and albite precipitates as a secondary phase, Na/K geothermometers fail to yield accurate temperatures. Anhydrite, which is present in fractures in the Basel reservoir, is predicted to dissolve during operation. This may lead to precipitation of pyrite and, at high exposure of anhydrite to the circulating fluid, of hematite scaling in the geothermal installation. In general, incipient corrosion of the casing can be detected at the production wellhead through an increase in H2(aq) and the enhanced precipitation of Fe-bearing aluminosilicates. The appearance of magnetite in scales indicates high corrosion rates.

Attentional changes in pre-stimulus oscillatory activity within early visual cortex are predictive of human visual performance

Physiological and neuroimaging studies provide evidence to suggest that attentional mechanisms operating within the fronto-parietal network may exert top–down control on early visual areas, priming them for forthcoming sensory events. The believed consequence of such priming is enhanced task performance. Using the technique of magnetoencephalography (MEG), we investigated this possibility by examining whether attention-driven changes in cortical activity are correlated with performance on a line-orientation judgment task. We observed that, approximately 200 ms after a covert attentional shift towards the impending visual stimulus, the level of phase-resetting (transient neural coherence) within the calcarine significantly increased for 2–10 Hz activity. This was followed by a suppression of alpha activity (near 10 Hz) which persisted until the onset of the stimulus. The levels of phase-resetting, alpha suppression and subsequent behavioral performance varied between subjects in a systematic fashion. The magnitudes of phase-resetting and alpha-band power were negatively correlated, with high levels of coherence associated with high levels of performance. We propose that top–down attentional control mechanisms exert their initial effects within the calcarine through a phase-resetting within the 2–10 Hz band, which in turn triggers a suppression of alpha activity, priming early visual areas for incoming information and enhancing behavioral performance.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

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.

Structural and thermal performance of cold-formed steel stud wall systems under fire conditions

Cold-formed steel stud walls are a major component of Light Steel Framing (LSF) building systems used in commercial, industrial and residential buildings. In the conventional LSF stud wall systems, thin steel studs are protected from fire by placing one or two layers of plasterboard on both sides with or without cavity insulation. However, there is very limited data about the structural and thermal performance of stud wall systems while past research showed contradicting results, for example, about the benefits of cavity insulation. This research was therefore conducted to improve the knowledge and understanding of the structural and thermal performance of cold-formed steel stud wall systems (both load bearing and non-load bearing) under fire conditions and to develop new improved stud wall systems including reliable and simple methods to predict their fire resistance rating. Full scale fire tests of cold-formed steel stud wall systems formed the basis of this research. This research proposed an innovative LSF stud wall system in which a composite panel made of two plasterboards with insulation between them was used to improve the fire rating. Hence fire tests included both conventional steel stud walls with and without the use of cavity insulation and the new composite panel system. A propane fired gas furnace was specially designed and constructed first. The furnace was designed to deliver heat in accordance with the standard time temperature curve as proposed by AS 1530.4 (SA, 2005). A compression loading frame capable of loading the individual studs of a full scale steel stud wall system was also designed and built for the load-bearing tests. Fire tests included comprehensive time-temperature measurements across the thickness and along the length of all the specimens using K type thermocouples. They also included the measurements of load-deformation characteristics of stud walls until failure. The first phase of fire tests included 15 small scale fire tests of gypsum plasterboards, and composite panels using different types of insulating material of varying thickness and density. Fire performance of single and multiple layers of gypsum plasterboards was assessed including the effect of interfaces between adjacent plasterboards on the thermal performance. Effects of insulations such as glass fibre, rock fibre and cellulose fibre were also determined while the tests provided important data relating to the temperature at which the fall off of external plasterboards occurred. In the second phase, nine small scale non-load bearing wall specimens were tested to investigate the thermal performance of conventional and innovative steel stud wall systems. Effects of single and multiple layers of plasterboards with and without vertical joints were investigated. The new composite panels were seen to offer greater thermal protection to the studs in comparison to the conventional panels. In the third phase of fire tests, nine full scale load bearing wall specimens were tested to study the thermal and structural performance of the load bearing wall assemblies. A full scale test was also conducted at ambient temperature. These tests showed that the use of cavity insulation led to inferior fire performance of walls, and provided good explanations and supporting research data to overcome the incorrect industry assumptions about cavity insulation. They demonstrated that the use of insulation externally in a composite panel enhanced the thermal and structural performance of stud walls and increased their fire resistance rating significantly. Hence this research recommends the use of the new composite panel system for cold-formed LSF walls. This research also included steady state tensile tests at ambient and elevated temperatures to address the lack of reliable mechanical properties for high grade cold-formed steels at elevated temperatures. Suitable predictive equations were developed for calculating the yield strength and elastic modulus at elevated temperatures. In summary, this research has developed comprehensive experimental thermal and structural performance data for both the conventional and the proposed non-load bearing and load bearing stud wall systems under fire conditions. Idealized hot flange temperature profiles have been developed for non-insulated, cavity insulated and externally insulated load bearing wall models along with suitable equations for predicting their failure times. A graphical method has also been proposed to predict the failure times (fire rating) of non-load bearing and load bearing walls under different load ratios. The results from this research are useful to both fire researchers and engineers working in this field. Most importantly, this research has significantly improved the knowledge and understanding of cold-formed LSF walls under fire conditions, and developed an innovative LSF wall system with increased fire rating. It has clearly demonstrated the detrimental effects of using cavity insulation, and has paved the way for Australian building industries to develop new wall panels with increased fire rating for commercial applications worldwide.

Evaluation of screening tests for predicting older driver performance and safety assessed by an on-road test

A number of tests and test batteries are available for the prediction of older driver safety, but many of these have not been validated against standardized driving outcome measures. The aim of this study was to evaluate a series of previously described screening tests in terms of their ability to predict the potential for safe and unsafe driving. Participants included 79 community-dwelling older drivers (M=72.16 years, SD=5.46; range 65-88 years; 57 males and 22 females) who completed a previously validated multi-disciplinary driving assessment, a hazard perception test, a hazard change detection test and a battery of vision and cognitive tests. Participants also completed a standardized on-road driving assessment. The multi-disciplinary test battery had the highest predictive ability with a sensitivity of 80% and a specificity of 73%, followed by the hazard perception test which demonstrated a sensitivity of 75% and a specificity of 61%. These findings suggest that a relatively simple and practical battery of tests from a range of domains has the capacity to predict safe and unsafe driving in older adults.