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Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of “semi-connection” that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution time and achieves better performances than the state-of-art solutions. [ABSTRACT FROM AUTHOR]

This paper formulates a control problem for systems that are affected by uncertain inputs and are vulnerable to risks as a chance-constrained optimization problem (CCP) with two chance-constraints (CCs). The first CC encompasses requirements of the normal operations of the system, whereas the second CC ensures the avoidance of risks associated with rare events. CCPs are in general difficult to solve, and this paper proposes a scenario-based optimization, testing, and improving algorithm to find approximate solutions to such problems within a stochastic model predictive control setting in a computationally cheap manner. The proposed approach is applied to a river control problem with flood avoidance, and the controller performed well in realistic simulations of the upper part of Murray River in Australia. [ABSTRACT FROM AUTHOR]

This paper shows how existing methods of probabilistic least-squares classification based on Gaussian processes can be used to construct a probabilistic representation of a habitat map using data collected by an autonomous underwater vehicle (AUV). A novel adaptive habitat classifying algorithm is presented, which uses a Monte Carlo simulation technique to select the most informative mission from a set of candidates. Approximations are presented to assign upper and lower bounds to the information expected from the candidate missions. This allows missions to be efficiently pruned from the set of candidates so that the algorithm converges to a preferred candidate with a reduced number of simulations. The performance of this algorithm is evaluated in an experiment carried out using an AUV at Jervis Bay, Australia. [ABSTRACT FROM AUTHOR]

The article highlights the use of optimal designs for two-colour microarray experiments in Australia. It states that microarrays facilitate the study of gene expression and enables the measurement of gene expression. Moreover, the multiple objective metaheuristic method of Pareto simulated annealing has been adapted generate an approximation to the set of Pareto optimal designs. Furthermore, to search systematically for the optimal values of the tuning parameters based on Pareto simulated annealing and to response surface methodology, algorithms are developed.