Optimizing the location of weather monitoring stations using estimation uncertainty

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright (c) 2011 Royal Meteorological Societyinfo:eu-repo/semantics/publishedVersio

Derivation of near-optimal pump schedules for water distribution by simulated annealing

The scheduling of pumps for clean water distribution is a partially discrete non-linear problem with many variables. The scheduling method described in this paper typically produces costs within 1% of a linear program-based solution, and can incorporate realistic non-linear costs that may be hard to incorporate in linear programming formulations. These costs include pump switching and maximum demand charges. A simplified model is derived from a standard hydraulic simulator. An initial schedule is produced by a descent method. Two-stage simulated annealing then produces solutions in a few minutes. Iterative recalibration ensures that the solution agrees closely with the results from a full hydraulic simulation

Analysis of Break-Points in Financial Time Series

A time series is a set of random values collected at equal time intervals; this randomness makes these types of series not easy to predict because the structure of the series may change at any time. As discussed in previous research, the structure of time series may change at any time due to the change in mean and/or variance of the series. Consequently, based on this structure, it is wise not to assume that these series are stationary. This paper, discusses, a method of analyzing time series by considering the entire series non-stationary, assuming there is random change in unconditional mean and variance of the series. Specifically, this paper emphasizes financial time series. The main goal in this process is to break the series into small locally stationary time series on which stationary assumption applies. The most interesting part of this procedure is locating the break-points, where the unconditional mean and/or variance of the series change. After having found what the break-points are, we divide the series into smaller series according to the break points; the number of break-points determines how many small stationary time series we have. The analysis by this method considers each interval on which the series is stationary as an independent time series with its specific parameters. Hence, the overall time series that is naturally nonstationary is broken into small stationary time series that are easier to analyze. Afterwards, by using Bayesian Information Criterion (BIC) we are comparing the local stationary model to the model considering the entire series stationary. In a simulation study with known sample size, unconditional means and variances, for each small stationary series, the result shows that we can locate the exact true break-points when the sample size is greater than 500. After our simulation study, this method is also applied to the real data, S&P 500 series of returns, which is a financial time series. The results obtained by using Maximum Likelihood Estimation (MLE) show that BIC is smaller for the locally stationary model

The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison

Thermoluminescence of zircon: a kinetic model

The mineral zircon, ZrSiO4, belongs to a class of promising materials for geochronometry by means of thermoluminescence (TL) dating. The development of a reliable and reproducible method for TL dating with zircon requires detailed knowledge of the processes taking place during exposure to ionizing radiation, long-term storage, annealing at moderate temperatures and heating at a constant rate (TL measurements). To understand these processes one needs a kinetic model of TL. This paper is devoted to the construction of such amodel. The goal is to study the qualitative behaviour of the system and to determine the parameters and processes controlling TL phenomena of zircon. The model considers the following processes: (i) Filling of electron and hole traps at the excitation stage as a function of the dose rate and the dose for both (low dose rate) natural and (high dose rate) laboratory irradiation. (ii) Time dependence of TL fading in samples irradiated under laboratory conditions. (iii) Short time annealing at a given temperature. (iv) Heating of the irradiated sample to simulate TL experiments both after laboratory and natural irradiation. The input parameters of the model, such as the types and concentrations of the TL centres and the energy distributions of the hole and electron traps, were obtained by analysing the experimental data on fading of the TL-emission spectra of samples from different geological locations. Electron paramagnetic resonance (EPR) data were used to establish the nature of the TL centres. Glow curves and 3D TL emission spectra are simulated and compared with the experimental data on time-dependent TL fading. The saturation and annealing behaviour of filled trap concentrations has been considered in the framework of the proposed kinetic model and comparedwith the EPR data associated with the rare-earth ions Tb3+ and Dy3+, which play a crucial role as hole traps and recombination centres. Inaddition, the behaviour of some of the SiOmn− centres has been compared with simulation results.