Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

Evaluating the performance of weed containment programmes

Aim: To develop approaches to the evaluation of programmes whose strategic objectives are to halt or slow weed spread. Location: Australia. Methods: Key aspects in the evaluation of weed containment programmes are considered. These include the relevance of models that predict the effects of management intervention on spread, the detection of spread, evidence for containment failure and metrics for absolute or partial containment. Case studies documenting either near-absolute (Orobanche ramosa L., branched broomrape) or partial (Parthenium hysterophorus (L.) King and Robinson, parthenium) containment are presented. Results: While useful for informing containment strategies, predictive models cannot be employed in containment programme evaluation owing to the highly stochastic nature of realized weed spread. The quality of observations is critical to the timely detection of weed spread. Effectiveness of surveillance and monitoring activities will be improved by utilizing information on habitat suitability and identification of sites from which spread could most compromise containment. Proof of containment failure may be difficult to obtain. The default option of assuming that a new detection represents containment failure could lead to an underestimate of containment success, the magnitude of which will depend on how often this assumption is made. Main conclusions: Evaluation of weed containment programmes will be relatively straightforward if containment is either absolute or near-absolute and may be based on total containment area and direct measures of containment failure, for example, levels of dispersal, establishment and reproduction beyond (but proximal to) the containment line. Where containment is only partial, other measures of containment effectiveness will be required. These may include changes in the rates of detection of new infestations following the institution of interventions designed to reduce dispersal, the degree of compliance with such interventions, and the effectiveness of tactics intended to reduce fecundity or other demographic drivers of spread. © 2012 Blackwell Publishing Ltd.

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

Interaction between production characteristics and postharvest performance and practices for fresh fruit

Significant interactions have been demonstrated between production factors and postharvest quality of fresh fruit. Accordingly, there is an attendant need for adaptive postharvest actions to modulate preharvest effects. The most significant preharvest effects appear to be mediated through mineral nutrition influences on the physical characteristics of fruit. Examples of specific influencers include fertilisers, water availability, rootstock, and crop load effects on fruit quality attributes such as skin colour, susceptibility to diseases and physiological disorders, and fruit nutritional composition. Also, rainfall before and during harvest can markedly affect fruit susceptibility to skin blemishes, physical damage, and diseases. Knowledge of preharvest-postharvest interactions can help determine the basis for variability in postharvest performance and thereby allow refinement of postharvest practices to minimise quality loss after harvest. This knowledge can be utilised in predictive management systems. Such systems can benefit from characterisation of fruit nutritional status, particularly minerals, several months before and/or at harvest to allow informed decisions on postharvest handling and marketing options. Other examples of proactive management practices include adjusting harvesting and packing systems to account for rainfall effects before and/or during harvest. Improved understanding of preharvest-postharvest interactions is contributing to the delivery of consistently higher quality of fruit to consumers. This paper focuses on the state of knowledge for sub-tropical and tropical fruits, in particular avocado and mango.

Job performance ratings: The relative importance of mental ability, conscientiousness, and career adaptability

According to career construction theory, continuous adaptation to the work environment is crucial to achieve work and career success. In this study, we examined the relative importance of career adaptability for job performance ratings using an experimental policy-capturing design. Employees (N = 135) from different vocational backgrounds rated the overall job performance of fictitious employees in 40 scenarios based on information about their career adaptability, mental ability, conscientiousness, and job complexity. We used multilevel modeling to investigate the relative importance of each factor. Consistent with expectations, career adaptability positively predicted job performance ratings, and this effect was relatively smaller than the effects of conscientiousness and mental ability. Job complexity did not moderate the effect of career adaptability on job performance ratings, suggesting that career adaptability predicts job performance ratings in high-, medium-, and low-complexity jobs. Consistent with previous research, the effect of mental ability on job performance ratings was stronger in high- compared to low-complexity jobs. Overall, our findings provide initial evidence for the predictive validity of employees' career adaptability with regard to other people's ratings of job performance.

Interaction between production characteristics and postharvest performance and practices for fresh fruit

Significant interactions have been demonstrated between production factors and postharvest quality of fresh fruit. Accordingly, there is an attendant need for adaptive postharvest actions to modulate preharvest effects. The most significant preharvest effects appear to be mediated through mineral nutrition influences on the physical characteristics of fruit. Examples of specific influencers include fertilisers, water availability, rootstock, and crop load effects on fruit quality attributes such as skin colour, susceptibility to diseases and physiological disorders, and fruit nutritional composition. Also, rainfall before and during harvest can markedly affect fruit susceptibility to skin blemishes, physical damage, and diseases. Knowledge of preharvest-postharvest interactions can help determine the basis for variability in postharvest performance and thereby allow refinement of postharvest practices to minimise quality loss after harvest. This knowledge can be utilised in predictive management systems. Such systems can benefit from characterisation of fruit nutritional status, particularly minerals, several months before and/or at harvest to allow informed decisions on postharvest handling and marketing options. Other examples of proactive management practices include adjusting harvesting and packing systems to account for rainfall effects before and/or during harvest. Improved understanding of preharvest-postharvest interactions is contributing to the delivery of consistently higher quality of fruit to consumers. This paper focuses on the state of knowledge for sub-tropical and tropical fruits, in particular avocado and mango.

Activities of daily living in children with developmental coordination disorder: Performance, learning, and participation

Background Children with developmental coordination disorder (DCD) face evident motor difficulties in daily functioning. Little is known, however, about their difficulties in specific activities of daily living (ADL). Objective The purposes of this study were: (1) to investigate differences between children with DCD and their peers with typical development for ADL performance, learning, and participation, and (2) to explore the predictive values of these aspects. Design. This was a cross-sectional study. Methods In both a clinical sample of children diagnosed with DCD (n=25 [21 male, 4 female], age range=5-8 years) and a group of peers with typical development (25 matched controls), the children’s parents completed the DCDDaily-Q. Differences in scores between the groups were investigated using t tests for performance and participation and Pearson chi-square analysis for learning. Multiple regression analyses were performed to explore the predictive values of performance, learning, and participation. Results Compared with their peers, children with DCD showed poor performance of ADL and less frequent participation in some ADL. Children with DCD demonstrated heterogeneous patterns of performance (poor in 10%-80% of the items) and learning (delayed in 0%-100% of the items). In the DCD group, delays in learning of ADL were a predictor for poor performance of ADL, and poor performance of ADL was a predictor for less frequent participation in ADL compared with the control group. Limitations A limited number of children with DCD were addressed in this study. Conclusions This study highlights the impact of DCD on children’s daily lives and the need for tailored intervention.

Randomised Evaluation of New Technologies within the Population‐Based Cervical Cancer Screening Programme in Finland: Cross‐Sectional Performance and Validity

A randomised and population-based screening design with new technologies has been applied to the organised cervical cancer screening programme in Finland. In this experiment the women invited to routine five-yearly screening are individually randomised to be screened with automation-assisted cytology, human papillomavirus (HPV) test or conventional cytology. By using the randomised design, the ultimate aim is to assess and compare the long-term outcomes of the different screening regimens. The primary aim of the current study was to evaluate, based on the material collected during the implementation phase of the Finnish randomised screening experiment, the cross-sectional performance and validity of automation-assisted cytology (Papnet system) and primary HPV DNA testing (Hybrid Capture II assay for 13 oncogenic HPV types) within service screening, in comparison to conventional cytology. The parameters of interest were test positivity rate, histological detection rate, relative sensitivity, relative specificity and positive predictive value. Also, the effect of variation in performance by screening laboratory on age-adjusted cervical cancer incidence was assessed. Based on the cross-sectional results, almost no differences were observed in the performance of conventional and automation-assisted screening. Instead, primary HPV screening found 58% (95% confidence interval 19-109%) more cervical lesions than conventional screening. However, this was mainly due to overrepresentation of mild- and moderate-grade lesions and, thus, is likely to result in overtreatment since a great deal of these lesions would never progress to invasive cancer. Primary screening with an HPV DNA test alone caused substantial loss in specificity in comparison to cytological screening. With the use of cytology triage test, the specificity of HPV screening improved close to the level of conventional cytology. The specificity of primary HPV screening was also increased by increasing the test positivity cutoff from the level recommended for clinical use, but the increase was more modest than the one gained with the use of cytology triage. The performance of the cervical cancer screening programme varied widely between the screening laboratories, but the variation in overall programme effectiveness between respective populations was more marginal from the very beginning of the organised screening activity. Thus, conclusive interpretations on the quality or success of screening should not be based on performance parameters only. In the evaluation of cervical cancer screening the outcome should be selected as closely as possible to the true measure of programme effectiveness, which is the number of invasive cervical cancers and subsequent deaths prevented in the target population. The evaluation of benefits and adverse effects of each new suggested screening technology should be performed before the technology becomes an accepted routine in the existing screening programme. At best, the evaluation is performed randomised, within the population and screening programme in question, which makes the results directly applicable to routine use.

The usefulness of fair values in improving the predictive ability of earnings: Evidence from international banks

One of the objectives of general-purpose financial reporting is to provide information about the financial position, financial performance and cash flows of an entity that is useful to a wide range of users in making economic decisions. The current focus on potentially increased relevance of fair value accounting weighed against issues of reliability has failed to consider the potential impact on the predictive ability of accounting. Based on a sample of international (non-U.S.) banks from 24 countries during 2009-2012, we test the usefulness of fair values in improving the predictive ability of earnings. First, we find that the increasing use of fair values on balance-sheet financial instruments enhances the ability of current earnings to predict future earnings and cash flows. Second, we provide evidence that the fair value hierarchy classification choices affect the ability of earnings to predict future cash flows and future earnings. More precisely, we find that the non-discretionary fair value component (Level 1 assets) improves the predictability of current earnings whereas the discretionary fair value components (Level 2 and Level 3 assets) weaken the predictive power of earnings. Third, we find a consistent and strong association between factors reflecting country-wide institutional structures and predictive power of fair values based on discretionary measurement inputs (Level 2 and Level 3 assets and liabilities). Our study is timely and relevant. The findings have important implications for standard setters and contribute to the debate on the use of fair value accounting.

A Predictive Perfomance Model for Superscalar Processors

Designing and optimizing high performance microprocessors is an increasingly difficult task due to the size and complexity of the processor design space, high cost of detailed simulation and several constraints that a processor design must satisfy. In this paper, we propose the use of empirical non-linear modeling techniques to assist processor architects in making design decisions and resolving complex trade-offs. We propose a procedure for building accurate non-linear models that consists of the following steps: (i) selection of a small set of representative design points spread across processor design space using latin hypercube sampling, (ii) obtaining performance measures at the selected design points using detailed simulation, (iii) building non-linear models for performance using the function approximation capabilities of radial basis function networks, and (iv) validating the models using an independently and randomly generated set of design points. We evaluate our model building procedure by constructing non-linear performance models for programs from the SPEC CPU2000 benchmark suite with a microarchitectural design space that consists of 9 key parameters. Our results show that the models, built using a relatively small number of simulations, achieve high prediction accuracy (only 2.8% error in CPI estimates on average) across a large processor design space. Our models can potentially replace detailed simulation for common tasks such as the analysis of key microarchitectural trends or searches for optimal processor design points.

Impact-Angle-Constrained Suboptimal Model Predictive Static Programming Guidance of Air-to-Ground Missiles

A nonlinear suboptimal guidance law is presented in this paper for successful interception of ground targets by air-launched missiles and guided munitions. The main feature of this guidance law is that it accurately satisfies terminal impact angle constraints in both azimuth as well as elevation simultaneously. In addition, it is capable of hitting the target with high accuracy as well as minimizing the lateral acceleration demand. The guidance law is synthesized using recently developed model predictive static programming (MPSP). Performance of the proposed MPSP guidance is demonstrated using three-dimensional (3-D) nonlinear engagement dynamics by considering stationary, moving, and maneuvering targets. Effectiveness of the proposed guidance has also been verified by considering first. order autopilot lag as well as assuming inaccurate information about target maneuvers. Multiple munitions engagement results are presented as well. Moreover, comparison studies with respect to an augmented proportional navigation guidance (which does not impose impact angle constraints) as well as an explicit linear optimal guidance (which imposes the same impact angle constraints in 3-D) lead to the conclusion that the proposed MPSP guidance is superior to both. A large number of randomized simulation studies show that it also has a larger capture region.

Generalized model predictive static programming and its application to 3D impact angle constrained guidance of air-to-surface missiles

A new `generalized model predictive static programming (G-MPSP)' technique is presented in this paper in the continuous time framework for rapidly solving a class of finite-horizon nonlinear optimal control problems with hard terminal constraints. A key feature of the technique is backward propagation of a small-dimensional weight matrix dynamics, using which the control history gets updated. This feature, as well as the fact that it leads to a static optimization problem, are the reasons for its high computational efficiency. It has been shown that under Euler integration, it is equivalent to the existing model predictive static programming technique, which operates on a discrete-time approximation of the problem. Performance of the proposed technique is demonstrated by solving a challenging three-dimensional impact angle constrained missile guidance problem. The problem demands that the missile must meet constraints on both azimuth and elevation angles in addition to achieving near zero miss distance, while minimizing the lateral acceleration demand throughout its flight path. Both stationary and maneuvering ground targets are considered in the simulation studies. Effectiveness of the proposed guidance has been verified by considering first order autopilot lag as well as various target maneuvers.

Generalized State Estimation and Model Predictive Guidance for Spiraling and Ballistic Targets

An extended Kalman filter based generalized state estimation approach is presented in this paper for accurately estimating the states of incoming high-speed targets such as ballistic missiles. A key advantage of this nine-state problem formulation is that it is very much generic and can capture spiraling as well as pure ballistic motion of targets without any change of the target model and the tuning parameters. A new nonlinear model predictive zero-effort-miss based guidance algorithm is also presented in this paper, in which both the zero-effort-miss as well as the time-to-go are predicted more accurately by first propagating the nonlinear target model (with estimated states) and zero-effort interceptor model simultaneously. This information is then used for computing the necessary lateral acceleration. Extensive six-degrees-of-freedom simulation experiments, which include noisy seeker measurements, a nonlinear dynamic inversion based autopilot for the interceptor along with appropriate actuator and sensor models and magnitude and rate saturation limits for the fin deflections, show that near-zero miss distance (i.e., hit-to-kill level performance) can be obtained when these two new techniques are applied together. Comparison studies with an augmented proportional navigation based guidance shows that the proposed model predictive guidance leads to a substantial amount of conservation in the control energy as well.

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.