Validation Gating for Non-Linear Non-Gaussian Target Tracking

This paper develops a general theory of validation gating for non-linear non-Gaussian mod- els. Validation gates are used in target tracking to cull very unlikely measurement-to-track associa- tions, before remaining association ambiguities are handled by a more comprehensive (and expensive) data association scheme. The essential property of a gate is to accept a high percentage of correct associ- ations, thus maximising track accuracy, but provide a su±ciently tight bound to minimise the number of ambiguous associations. For linear Gaussian systems, the ellipsoidal vali- dation gate is standard, and possesses the statistical property whereby a given threshold will accept a cer- tain percentage of true associations. This property does not hold for non-linear non-Gaussian models. As a system departs from linear-Gaussian, the ellip- soid gate tends to reject a higher than expected pro- portion of correct associations and permit an excess of false ones. In this paper, the concept of the ellip- soidal gate is extended to permit correct statistics for the non-linear non-Gaussian case. The new gate is demonstrated by a bearing-only tracking example.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

A control and surveillance ITS location model to improve safety

Traffic safety in rural highways can be considered as a constant source of concern in many countries. Nowadays, transportation professionals widely use Intelligent Transportation Systems (ITS) to address safety issues. However, compared to metropolitan applications, the rural highway (non-urban) ITS applications are still not well defined. This paper provides a comprehensive review on the existing ITS safety solutions for rural highways. This research is mainly focused on the infrastructure-based control and surveillance ITS technology, such as Crash Prevention and Safety, Road Weather Management and other applications, that is directly related to the reduction of frequency and severity of accidents. The main outcome of this research is the development of a ‘ITS control and surveillance device locating model’ to achieve the maximum safety benefit for rural highways. Using cost and benefits databases of ITS, an integer linear programming method is utilized as an optimization technique to choose the most suitable set of ITS devices. Finally, computational analysis is performed on an existing highway in Iran, to validate the effectiveness of the proposed locating model.

A Stochastic View of Optimal Regret through Minimax Duality

We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. We show that the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. We use this expression to obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary. Peter L. Bartlett, Alexander Rakhlin

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

Building the foundations : skill acquisition in children

Introduction: Why we need to base childrens’ sport and physical education on the principles of dynamical systems theory and ecological psychology As the childhood years are crucial for developing many physical skills as well as establishing the groundwork leading to lifelong participation in sport and physical activities, (Orlick & Botterill, 1977, p. 11) it is essential to examine current practice to make sure it is meeting the needs of children. In recent papers (e.g. Renshaw, Davids, Chow & Shuttleworth, in press; Renshaw, Davids, Chow & Hammond, in review; Chow et al., 2009) we have highlighted that a guiding theoretical framework is needed to provide a principled approach to teaching and coaching and that the approach must be evidence- based and focused on mechanism and not just on operational issues such as practice, competition and programme management (Lyle, 2002). There is a need to demonstrate how nonlinear pedagogy underpins teaching and coaching practice for children given that some of the current approaches underpinning children’s sport and P.E. may not be leading to optimal results. For example, little time is spent undertaking physical activities (Tinning, 2006) and much of this practice is not representative of the competition demands of the performance environment (Kirk & McPhail, 2002; Renshaw et al., 2008). Proponents of a non- linear pedagogy advocate the design of practice by applying key concepts such as the mutuality of the performer and environment, the tight coupling of perception and action, and the emergence of movement solutions due to self organisation under constraints (see Renshaw, et al., in press). As skills are shaped by the unique interacting individual, task and environmental constraints in these learning environments, small changes to individual structural (e.g. factors such as height or limb length) or functional constraints (e.g. factors such as motivation, perceptual skills, strength that can be acquired), task rules, equipment, or environmental constraints can lead to dramatic changes in movement patterns adopted by learners to solve performance problems. The aim of this chapter is to provide real life examples for teachers and coaches who wish to adopt the ideas of non- linear pedagogy in their practice. Specifically, I will provide examples related to specific issues related to individual constraints in children and in particular the unique challenges facing coaches when individual constraints are changing due to growth and development. Part two focuses on understanding how cultural environmental constraints impact on children’s sport. This is an area that has received very little attention but plays a very important part in the long- term development of sporting expertise. Finally, I will look at how coaches can manipulate task constraints to create effective learning environments for young children.

The constraints-based approach to motor learning : Implications for a non-linear pedagogy in sport and physical education

Recently, a constraints- led approach has been promoted as a framework for understanding how children and adults acquire movement skills for sport and exercise (see Davids, Button & Bennett, 2008; Araújo et al., 2004). The aim of a constraints- led approach is to identify the nature of interacting constraints that influence skill acquisition in learners. In this chapter the main theoretical ideas behind a constraints- led approach are outlined to assist practical applications by sports practitioners and physical educators in a non- linear pedagogy (see Chow et al., 2006, 2007). To achieve this goal, this chapter examines implications for some of the typical challenges facing sport pedagogists and physical educators in the design of learning programmes.

Optimal control of distributed generators and capacitors by hybrid DPSO

In this paper, a comprehensive planning methodology is proposed that can minimize the line loss, maximize the reliability and improve the voltage profile in a distribution network. The injected active and reactive power of Distributed Generators (DG) and the installed capacitor sizes at different buses and for different load levels are optimally controlled. The tap setting of HV/MV transformer along with the line and transformer upgrading is also included in the objective function. A hybrid optimization method, called Hybrid Discrete Particle Swarm Optimization (HDPSO), is introduced to solve this nonlinear and discrete optimization problem. The proposed HDPSO approach is a developed version of DPSO in which the diversity of the optimizing variables is increased using the genetic algorithm operators to avoid trapping in local minima. The objective function is composed of the investment cost of DGs, capacitors, distribution lines and HV/MV transformer, the line loss, and the reliability. All of these elements are converted into genuine dollars. Given this, a single-objective optimization method is sufficient. The bus voltage and the line current as constraints are satisfied during the optimization procedure. The IEEE 18-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate the unavoidable need for optimal control on the DG active and reactive power and capacitors in distribution networks.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.