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Articoli di riviste sul tema "Optimisation probabiliste"

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Autore: Grafiati

Pubblicato: 15 giugno 2024

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1

Martinet, P., L. Lanfranco, D. Tandé, L. Picard, P. Danneels, S. Jamard, B. Gaborit, C. Danthu, C. Loheac e S. Rézig. "Pyélonéphrite aiguë du greffon : vers une optimisation de l'antibiothérapie probabiliste". Médecine et Maladies Infectieuses Formation 1, n. 2 (giugno 2022): S9. http://dx.doi.org/10.1016/j.mmifmc.2022.03.044.

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2

Pitner, P., H. Procaccia, T. Riffard, B. Granger e B. Flesch. "Optimisation du contrôle et de la maintenance des faisceaux tubulaires des générateurs de vapeur grâce à l'analyse probabiliste". Revue Générale Nucléaire, n. 3 (maggio 1993): 187–94. http://dx.doi.org/10.1051/rgn/19933187.

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3

Carrié, Cédric, Noémie Sauvage e Matthieu Biais. "Optimisation du traitement par β-Lactamines chez le patient de réanimation en hyperclairance rénale". Médecine Intensive Réanimation 30, n. 2 (18 maggio 2021): 157–64. http://dx.doi.org/10.37051/mir-00059.

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Abstract (sommario):

L'optimisation du traitement par β-Lactamines représente toujours un défi complexe chez le patient de soins critiques, compte tenu de la large variabilité des concentrations d'antibiotiques en relation avec d’importantes interactions pharmacocinétiques et pharmacodynamiques (PK/PD). D’une part, il est communément admis que les patients présentant une insuffisance rénale justifient une diminution des posologies afin de limiter le risque de toxicité. D’autre part, certains patients peuvent également présenter une hyperclairance rénale (HCR), désormais reconnue comme un des principaux facteurs de risque de sous-dosage et d’échec thérapeutique des agents anti-infectieux à élimination urinaire. L’hyperclairance est une entité fréquente en réanimation chirurgicale, probablement sous-diagnostiquée en l’absence de mesure de clairance urinaire de la créatinine (CLCR). Pour certaines β-lactamines prescrites en probabiliste, plusieurs études de pharmacocinétique suggèrent une augmentation des posologies recommandées afin d’atteindre les objectifs PK/PD chez les patients atteints d'HCR. En cas d’impossibilité de monitorer les concentrations plasmatiques dans des délais brefs, l'optimisation des posologies de β-lactamines selon le monitorage quotidien de la CLCR est une stratégie sûre et efficace pour améliorer les taux de succès thérapeutique. L’HCR étant un phénomène fluctuant, cette stratégie impose un monitorage quotidien de la CLCR afin d’adapter les posologies et limiter le risque de surdosage.

4

Pasalodos-Tato, María, Timo Pukkala e Alberto Rojo Alboreca. "Optimal management of Pinus pinaster in Galicia (Spain) under risk of fire". International Journal of Wildland Fire 19, n. 7 (2010): 937. http://dx.doi.org/10.1071/wf08150.

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Pinus pinaster is the most important conifer in Galicia in terms of volume and production, occurs mainly in plantations. Forest fires are the main threat to forest plantations, affecting optimal stand management. The aim of this study was to develop management prescriptions for P. pinaster based on growth and yield models and optimisations. The objective function was soil expectation value, calculated taking the expected fire losses into account. Fire risk was assumed to consist of two components, probability of occurrence and damage. As the main cause of forest fires in Galicia is arson, the manager cannot significantly influence fire occurrence, which was assumed to be exogenous. Salvage was treated as an endogenous factor depending on the management schedule followed in the stand. Optimisations were done for different initial stands, timber assortments, discount rates and probabilities of fire occurrence. Based on the optimisation results, regression models were developed for the optimal rotation length as well as the timing and intensity of thinnings. The results show that when fire risk is partly endogenous, optimal rotation lengths become shorter with increasing probability of fire occurrence, and optimal thinning becomes heavier and earlier. However, without a price reduction for burned timber, the optimal rotation length increases with increasing probability of fire.

5

Tayal, Shilpy. "Analysis of Information Geometry for Optimization and Inference Applications". Mathematical Statistician and Engineering Applications 70, n. 1 (31 gennaio 2021): 621–27. http://dx.doi.org/10.17762/msea.v70i1.2516.

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A mathematical framework called "information geometry" investigates the geometrical attributes and features of probability distributions and statistical models. In a variety of domains, including as machine learning, optimisation, and inference, it offers a potent toolkit for analysing and optimising complicated systems. Information geometry and its uses in optimisation and inference are examined in this work.First, we give a general overview of information geometry's foundational ideas and principles, including topics like the Fisher information metre, divergence measures, and exponential families. We go over how to quantify the geometric links between probability distributions and derive practical geometric structures using these ideas.The use of information geometry in optimisation issues is what we investigate next. We show how the Fisher information metric can direct effective search strategies and convergence analysis in optimisation algorithms by utilising its geometric characteristics. We go over the benefits of applying information geometry to a variety of optimisation tasks, including parameter estimation, model choice, and neural network training. We also look into how information geometry affects statistical inference. We emphasise how the development of effective and reliable inference algorithms is made possible by the geometric structures of exponential families. We go over the use of divergence measures to quantify the differences between distributions, making tasks like model comparison and hypothesis testing easier.We also review current developments in information geometry, especially its application to probabilistic programming and deep learning. We go over how information geometry can improve deep neural networks' capacity for generalisation, interpretation, and uncertainty estimation.In this study, information geometry and its uses in optimisation and inference are thoroughly studied. Information geometry provides useful insights and methods for resolving challenging issues in a variety of fields by taking advantage of the geometric aspects of probability distributions.

6

Lypchuk, Vasyl, e Vasyl Dmytriv. "Management of technological process optimisation". Engineering Management in Production and Services 12, n. 3 (1 settembre 2020): 103–15. http://dx.doi.org/10.2478/emj-2020-0022.

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Abstract The research aims to characterise the optimisation of a technological process depending on the main time parameters for production. The optimisation does not require to correct technical parameters of a system, but rather the organisational and managerial factors of the technological process. The workload is taken as an evaluation criterion, which factors in the probability distribution of time characteristics of computer process operations. Time characteristics that represent the performance of an operation influence the workloads of an operator and equipment, determining the productivity of the technological process. Analytical models were developed for the operational control of a production line efficiency considering the probability–statistical parameters pertaining to the performance of operations and technological equipment peculiarities. The article presents research results, which characterise the dependence of a production line efficiency on the type of equipment, and the duration of preparatory and final operations considering their probability. Under an optimal workload of the operator, the duration of the complete program changes linearly, regardless of the time required for the performance of operations by a computer without the involvement of the operator, and depending on the type of equipment. A managerial decision can be optimal under the condition that the factor of technological process efficiency (K_TP) tends to max. The developed method of analytical determination can be used to calculate the workload of both an operator and technological equipment. The calculations of the duration of a production line operation resulted in the methodology for the consideration of probability characteristics pertaining to the time distribution of the period required to perform operations, which influences the unequal efficiency of the production line. The probabilistic character of time distribution related to intervals of performed operations serves as a parameter in the management of technological process optimisation, which can be achieved using simulators of technological processes optimised in terms of their efficiency.

7

Shariatmadar, Keivan, e Mark Versteyhe. "Numerical Linear Programming under Non-Probabilistic Uncertainty Models — Interval and Fuzzy Sets". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, n. 03 (21 maggio 2020): 469–95. http://dx.doi.org/10.1142/s0218488520500191.

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This paper considers a linear optimisation problem under uncertainty with at least one element modelled as a non-probabilistic uncertainty. The uncertainty is expressed in the coefficient matrices of constraints and/or coefficients of goal function. Previous work converts such problems to classical (linear) optimisation problems and eliminates uncertainty by converting the linear programming under uncertainty problem to a decision problem using imprecise probability and imprecise decision theory. Our aim here is to generalise this approach numerically and present three methods to calculate the solution. We investigate what numerical results can be obtained for interval and fuzzy types of uncertainty models and compare them to classical probabilistic cases — for two different optimality criteria: maximinity and maximality. We also provide an efficient method to calculate the maximal solutions in the fuzzy set model. A numerical example is considered for illustration of the results.

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Van Nguyen, N., J. W. Lee, Y. D. Lee e H. U. Park. "A multidisciplinary robust optimisation framework for UAV conceptual design". Aeronautical Journal 118, n. 1200 (febbraio 2014): 123–42. http://dx.doi.org/10.1017/s0001924000009027.

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Abstract This paper describes a multidisciplinary robust optimisation framework for UAV conceptual design. An in-house configuration designer system is implemented to generate the full sets of configuration data for a well-developed advanced UAV analysis tool. A fully integrated configuration designer along with the UAV analysis tool ensures that full sets of configuration data are provided simultaneously while the UAV configuration changes during optimisation. The computational strategy for probabilistic analysis is proposed by implementing a central difference method and fitting distribution for a reduced number of Monte Carlo Simulation sampling points. The minimisation of a new robust design objective function helps to enhance the reliability while other UAV performance criteria are satisfied. In addition, the fully integrated process and a probabilistic analysis strategy method demonstrate a reduction in the probability of failure under noise factors without any noticeable increase in design turnaround time. The proposed robust optimisation framework for UAV conceptual design case study yields a more trustworthy prediction of the optimal configuration and is preferable to the traditional deterministic design approach. The high fidelity analysis ANSYS Fluent 13 is performed to demonstrate the accuracy of proposed framework on baseline, deterministic and RDO configuration.

9

Holický, Milan. "Fuzzy probabilistic optimisation of building performance". Automation in Construction 8, n. 4 (aprile 1999): 437–43. http://dx.doi.org/10.1016/s0926-5805(98)00090-9.

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10

al-Rifaie, Mohammad Majid, e Tim Blackwell. "Cognitive Bare Bones Particle Swarm Optimisation with Jumps". International Journal of Swarm Intelligence Research 7, n. 1 (gennaio 2016): 1–31. http://dx.doi.org/10.4018/ijsir.2016010101.

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The ‘bare bones' (BB) formulation of particle swarm optimisation (PSO) was originally advanced as a model of PSO dynamics. The idea was to model the forces between particles with sampling from a probability distribution in the hope of understanding swarm behaviour with a conceptually simpler particle update rule. ‘Bare bones with jumps' (BBJ) proposes three significant extensions to the BB algorithm: (i) two social neighbourhoods, (ii) a tuneable parameter that can advantageously bring the swarm to the ‘edge of collapse' and (iii) a component-by-component probabilistic jump to anywhere in the search space. The purpose of this paper is to investigate the role of jumping within a specific BBJ algorithm, cognitive BBJ (cBBJ). After confirming the effectiveness of cBBJ, this paper finds that: jumping in one component only is optimal over the 30 dimensional benchmarks of this study; that a small per particle jump probability of 1/30 works well for these benchmarks; jumps are chiefly beneficial during the early stages of optimisation and finally this work supplies evidence that jumping provides escape from regions surrounding sub-optimal minima.

11

Shariatmadar, Keivan, Adriano Arrigo, François Vallée, Hans Hallez, Lieven Vandevelde e David Moens. "Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty". Energies 14, n. 4 (15 febbraio 2021): 1016. http://dx.doi.org/10.3390/en14041016.

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The current energy transition and the underlying growth in variable and uncertain renewable-based energy generation challenge the proper operation of power systems. Classical probabilistic uncertainty models, e.g., stochastic programming or robust optimisation, have been used widely to solve problems such as the day-ahead energy and reserve dispatch problem to enhance the day-ahead decisions with a probabilistic insight of renewable energy generation in real-time. By doing so, the scheduling of the power system becomes, production and consumption of electric power, more reliable (i.e., more robust because of potential deviations) while minimising the social costs given potential balancing actions. Nevertheless, these classical models are not valid when the uncertainty is imprecise, meaning that the system operator may not rely on a unique distribution function to describe the uncertainty. Given the Distributionally Robust Optimisation method, our approach can be implemented for any non-probabilistic, e.g., interval models rather than only sets of distribution functions (ambiguity set of probability distributions). In this paper, the aim is to apply two advanced non-probabilistic uncertainty models: Interval and ϵ-contamination, where the imprecision and in-determinism in the uncertainty (uncertain parameters) are considered. We propose two kinds of theoretical solutions under two decision criteria—Maximinity and Maximality. For an illustration of our solutions, we apply our proposed approach to a case study inspired by the 24-node IEEE reliability test system.

12

Franke, Björn, Michael O'Boyle, John Thomson e Grigori Fursin. "Probabilistic source-level optimisation of embedded programs". ACM SIGPLAN Notices 40, n. 7 (12 luglio 2005): 78–86. http://dx.doi.org/10.1145/1070891.1065922.

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Lógó, J., e E. Pintér. "Numerical Methods in Probabilistic Topology Optimisation: A Review". Computational Technology Reviews 5 (4 settembre 2012): 79–108. http://dx.doi.org/10.4203/ctr.5.3.

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Goudarzi, Pejman. "Probabilistic ownership cost optimisation for video content distribution". International Journal of Communication Networks and Distributed Systems 15, n. 4 (2015): 350. http://dx.doi.org/10.1504/ijcnds.2015.072402.

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Trairatphisan, Panuwat, Andrzej Mizera, Jun Pang, Alexandru Adrian Tantar e Thomas Sauter. "optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks". PLoS ONE 9, n. 7 (1 luglio 2014): e98001. http://dx.doi.org/10.1371/journal.pone.0098001.

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Landes, Juergen, e Jon Williamson. "Objective Bayesian Nets for Integrating Consistent Datasets". Journal of Artificial Intelligence Research 74 (27 maggio 2022): 393–458. http://dx.doi.org/10.1613/jair.1.13363.

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This paper addresses a data integration problem: given several mutually consistent datasets each of which measures a subset of the variables of interest, how can one construct a probabilistic model that fits the data and gives reasonable answers to questions which are under-determined by the data? Here we show how to obtain a Bayesian network model which represents the unique probability function that agrees with the probability distributions measured by the datasets and otherwise has maximum entropy. We provide a general algorithm, OBN-cDS, which offers substantial efficiency savings over the standard brute-force approach to determining the maximum entropy probability function. Furthermore, we develop modifications to the general algorithm which enable further efficiency savings but which are only applicable in particular situations. We show that there are circumstances in which one can obtain the model (i) directly from the data; (ii) by solving algebraic problems; and (iii) by solving relatively simple independent optimisation problems.

17

Wolfgang, A., N. N. Ahmad, S. Chen e L. Hanzo. "Genetic algorithm assisted error probability optimisation for beamforming". Electronics Letters 40, n. 5 (2004): 320. http://dx.doi.org/10.1049/el:20040229.

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Debusscher, Charles Marie J., Tuhfe Göçmen e Søren Juhl Andersen. "Probabilistic surrogates for flow control using combined control strategies". Journal of Physics: Conference Series 2265, n. 3 (1 maggio 2022): 032110. http://dx.doi.org/10.1088/1742-6596/2265/3/032110.

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Abstract Wind farm flow control (WFFC) is a promising technology for improving wind farm operation and design. The presented study focuses on the combination of the two most prominent WFFC strategies, yaw-based wake-steering and axial induction control via constant blade pitch, for maximising the wind farm power production with and without a load constraint. The optimisation is performed via data-driven polynomial-based probabilistic surrogate models, calibrated through a range of LES and aeroelastic simulations for a 2-turbine setup. The results indicate the yaw-based wake-steering to be the driving mechanism to increase the wind farm power production, particularly when loads are not considered. However, axial induction is seen beneficial for load alleviation, especially in close spacings. Overall, the analyses highlight the potential of combined WFFC strategies for power optimisation in a safety-critical system and provides a probabilistic approach for data-driven multi-objective farm flow control.

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Aksezer, Caglar S., e James C. Benneyan. "Multiresponse optimisation of powder metals via probabilistic loss functions". European J. of Industrial Engineering 7, n. 3 (2013): 295. http://dx.doi.org/10.1504/ejie.2013.054130.

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Kim, Sunyong, e Dan M. Frangopol. "Efficient multi-objective optimisation of probabilistic service life management". Structure and Infrastructure Engineering 13, n. 1 (18 luglio 2016): 147–59. http://dx.doi.org/10.1080/15732479.2016.1198405.

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21

Ng, C. T., M. Veidt e H. F. Lam. "Guided wave damage characterisation in beams utilising probabilistic optimisation". Engineering Structures 31, n. 12 (dicembre 2009): 2842–50. http://dx.doi.org/10.1016/j.engstruct.2009.07.009.

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22

Banerjee, Goutam, e Kamalendu Sengupta. "Pore size optimisation of humidity sensor—a probabilistic approach". Sensors and Actuators B: Chemical 86, n. 1 (agosto 2002): 34–41. http://dx.doi.org/10.1016/s0925-4005(02)00142-9.

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23

Green, Michael, e Edward Keith Weatherhead. "A critical comparison of using a probabilistic weather generator versus a change factor approach; irrigation reservoir planning under climate change". Journal of Water and Climate Change 5, n. 1 (22 ottobre 2013): 13–24. http://dx.doi.org/10.2166/wcc.2013.073.

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Abstract (sommario):

In the UK, there is a growing interest in constructing on-farm irrigation reservoirs, however deciding the optimum reservoir capacity is not simple. There are two distinct approaches to generating the future daily weather datasets needed to calculate future irrigation need. The change factor approach perturbs the observed record using monthly change factors derived from downscaled climate models. This assumes that whilst the climate changes, the day-to-day climate variability itself is stationary. Problems may arise where the instrumental record is insufficient or particularly suspect. Alternatively, probabilistic weather generators can be used to identify options which are considered more robust to climate change uncertainty because they consider non-stationary climate variability. This paper explores the difference between using the change factor approach and a probabilistic weather generator for informing farm reservoir design at three sites in the UK. Decision outcomes obtained using the current normal practice of 80% probability of non-exceedance rule and simple economic optimisations are also compared. Decision outcomes obtained using the change factor approach and probabilistic weather generators are significantly different; whether these differences translate to real-world differences is discussed. This study also found that using the 80% probability of non-exceedance rule could potentially result in maladaptation.

24

Tun, Y. W., D. M. Pedroso e Alexander Scheuermann. "Probabilistic Reliability Assessment of Slope Stability Problems with Genetic Algorithms". Applied Mechanics and Materials 846 (luglio 2016): 385–90. http://dx.doi.org/10.4028/www.scientific.net/amm.846.385.

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Uncertainty estimation and consideration in engineering is an important practice to design reliable structures especially in geotechnical engineering since the level of control with regards to the material parameters is much lower. Research has been conducted in order to assess the reliability of geotechnical works using probabilistic methods where challenges in computing the probability density function and predicting the critical failure region must be first overcome. One method to solve this problem is the Monte Carlo simulation; however it requires a high computational effort. Alternatively, reliability indices such as the Hasofer-Lind (HL) index can approximate the probability of failure Pf with fewer computations. Nonetheless, yet an optimisation problem needs to be solved. In this work, a genetic algorithm is developed to compute the HL index using the limit equilibrium method to search for the critical failure surface. Study cases and the analysis of the Vajont landslide are presented in order to illustrate the method.

25

Marková, Jana. "Probabilistic Verification of Structures with Respect to Durability". Applied Mechanics and Materials 732 (febbraio 2015): 313–18. http://dx.doi.org/10.4028/www.scientific.net/amm.732.313.

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General principles on probabilistic approach to structural verification with respect to durability are provided in the international standard ISO 13823 which is currently being implemented into the system of Czech standards. The operational use of the new procedures in practice would require specification of probabilistic durability criteria, physical models of material deteriorations, and theoretical models of basic variables. It appears that the probabilistic methods of optimisation may provide valuable background information facilitating determination of appropriate criteria for durability.

26

PE e F. P. Kelly. "Probability, Statistics and Optimisation: A Tribute to Peter Whittle." Journal of the American Statistical Association 90, n. 432 (dicembre 1995): 1493. http://dx.doi.org/10.2307/2291553.

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27

Albeverio, Sergio, e Frederik S. Herzberg. "Optimisation of Measures on a Hyperfinite Adapted Probability Space". Acta Applicandae Mathematicae 100, n. 1 (30 ottobre 2007): 1–14. http://dx.doi.org/10.1007/s10440-007-9174-2.

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28

Xu, Zixiang, Ahmet Unveren e Adnan Acan. "Probability collectives hybridised with differential evolution for global optimisation". International Journal of Bio-Inspired Computation 8, n. 3 (2016): 133. http://dx.doi.org/10.1504/ijbic.2016.076652.

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Auza, Anna, Ehsan Asadi, Behrang Chenari e Manuel Gameiro da Silva. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles". Energies 16, n. 13 (27 giugno 2023): 4983. http://dx.doi.org/10.3390/en16134983.

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This paper systematically reviews the techniques and dynamics to study uncertainty modelling in the electric grids considering electric vehicles with vehicle-to-grid integration. Uncertainty types and the most frequent uncertainty modelling approaches for electric vehicles are outlined. The modelling approaches discussed in this paper are Monte Carlo, probabilistic scenarios, stochastic, point estimate method and robust optimisation. Then, Scopus is used to search for articles, and according to these categories, data from articles are extracted. The findings suggest that the probabilistic techniques are the most widely applied, with Monte Carlo and scenario analysis leading. In particular, 19% of the cases benefit from Monte Carlo, 15% from scenario analysis, and 10% each from robust optimisation and the stochastic approach, respectively. Early articles consider robust optimisation relatively more frequent, possibly due to the lack of historical data, while more recent articles adopt the Monte Carlo simulation approach. The uncertainty handling techniques depend on the uncertainty type and human resource availability in aggregate but are unrelated to the generation type. Finally, future directions are given.

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Hagh, Mehrdad Tarafdar, Payman Amiyan, Sadjad Galvani e Naser Valizadeh. "Probabilistic load flow using the particle swarm optimisation clustering method". IET Generation, Transmission & Distribution 12, n. 3 (13 febbraio 2018): 780–89. http://dx.doi.org/10.1049/iet-gtd.2017.0678.

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Oelfke, U., e M. Bangert. "SP-0208: The probabilistic planning approach: Including uncertainties into optimisation". Radiotherapy and Oncology 106 (marzo 2013): S82. http://dx.doi.org/10.1016/s0167-8140(15)32514-7.

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Tse, C. T., K. W. Wang, C. Y. Chung e K. M. Tsang. "Parameter optimisation of robust power system stabilisers by probabilistic approach". IEE Proceedings - Generation, Transmission and Distribution 147, n. 2 (2000): 69. http://dx.doi.org/10.1049/ip-gtd:20000270.

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33

Kwak, Seong Woo, e Jung-Min Yang. "Probabilistic optimisation of checkpoint intervals for real-time multi-tasks". International Journal of Systems Science 44, n. 4 (aprile 2013): 595–603. http://dx.doi.org/10.1080/00207721.2011.617890.

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Koukoulekidis, Nikolaos, Hyukjoon Kwon, Hyejung H. Jee, David Jennings e M. S. Kim. "Faster Born probability estimation via gate merging and frame optimisation". Quantum 6 (13 ottobre 2022): 838. http://dx.doi.org/10.22331/q-2022-10-13-838.

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Abstract (sommario):

Outcome probability estimation via classical methods is an important task for validating quantum computing devices. Outcome probabilities of any quantum circuit can be estimated using Monte Carlo sampling, where the amount of negativity present in the circuit frame representation quantifies the overhead on the number of samples required to achieve a certain precision. In this paper, we propose two classical sub-routines: circuit gate merging and frame optimisation, which optimise the circuit representation to reduce the sampling overhead. We show that the runtimes of both sub-routines scale polynomially in circuit size and gate depth. Our methods are applicable to general circuits, regardless of generating gate sets, qudit dimensions and the chosen frame representations for the circuit components. We numerically demonstrate that our methods provide improved scaling in the negativity overhead for all tested cases of random circuits with Clifford+T and Haar-random gates, and that the performance of our methods compares favourably with prior quasi-probability simulators as the number of non-Clifford gates increases.

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Cho, G. A., M. A. Ebert, L. Holloway, Z. Kuncic, C. Baldock e D. I. Thwaites. "Radiation treatment dose optimisation using Poisson tumour control probability parameters". Journal of Physics: Conference Series 489 (24 marzo 2014): 012047. http://dx.doi.org/10.1088/1742-6596/489/1/012047.

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Dahlqvist, C. H., F. Cantalloube e O. Absil. "Auto-RSM: An automated parameter-selection algorithm for the RSM map exoplanet detection algorithm". Astronomy & Astrophysics 656 (dicembre 2021): A54. http://dx.doi.org/10.1051/0004-6361/202141446.

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Abstract (sommario):

Context. Most of the high-contrast imaging (HCI) data-processing techniques used over the last 15 years have relied on the angular differential imaging (ADI) observing strategy, along with subtraction of a reference point spread function (PSF) to generate exoplanet detection maps. Recently, a new algorithm called regime switching model (RSM) map has been proposed to take advantage of these numerous PSF-subtraction techniques; RSM uses several of these techniques to generate a single probability map. Selection of the optimal parameters for these PSF-subtraction techniques as well as for the RSM map is not straightforward, is time consuming, and can be biased by assumptions made as to the underlying data set. Aims. We propose a novel optimisation procedure that can be applied to each of the PSF-subtraction techniques alone, or to the entire RSM framework. Methods. The optimisation procedure consists of three main steps: (i) definition of the optimal set of parameters for the PSF-subtraction techniques using the contrast as performance metric, (ii) optimisation of the RSM algorithm, and (iii) selection of the optimal set of PSF-subtraction techniques and ADI sequences used to generate the final RSM probability map. Results. The optimisation procedure is applied to the data sets of the exoplanet imaging data challenge, which provides tools to compare the performance of HCI data-processing techniques. The data sets consist of ADI sequences obtained with three state-of-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. The results of our analysis demonstrate the interest of the proposed optimisation procedure, with better performance metrics compared to the earlier version of RSM, as well as to other HCI data-processing techniques.

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Słowik, Agnieszka, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden e Lawrence C. Paulson. "Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 10 (3 aprile 2020): 13919–20. http://dx.doi.org/10.1609/aaai.v34i10.7232.

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Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.

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Lachout, Petr, Eckhard Liebscher e Silvia Vogel. "Strong convergence of estimators as ε n -minimisers of optimisation problemsof optimisation problems". Annals of the Institute of Statistical Mathematics 57, n. 2 (giugno 2005): 291–313. http://dx.doi.org/10.1007/bf02507027.

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Uzulina, Ginta. "OPTIMISATION OF GOVERNMENTAL DECISIONS". Journal of Business Economics and Management 4, n. 3 (30 settembre 2003): 198–202. http://dx.doi.org/10.3846/16111699.2003.9636054.

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The report discusses the role of impact assessment and evaluation of governmental decisions. The main objective of report is to develop a statistical model for optimisation of the set of policy instruments. The Draft of Rural Development Plan is used as example in order to develop and test the statistical model for optimising the allocation and use of funds. The statistical model is developed like as a small software version for policy‐makers to evaluate critical decisions. The statistical decision making theory is used in seeking solutions to optimise the choice of governmental decisions. The functions of losses or gains are defined on the basis of statistical data in two levels ‐ macroeconomic level and microeconomic level. In the statistical model the principles of the subjective probability are also applied in the case of unreliable and unavailable information.

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Morio, Jérôme, Isabelle Junqua, Solange Bertuol e Jean-Philippe Parmantier. "Optimisation of Segregation Distances between Electric Cable Bundles Embedded in a Structure". Applied Sciences 12, n. 4 (18 febbraio 2022): 2132. http://dx.doi.org/10.3390/app12042132.

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This paper presents the optimisation of the segregation distance between two electric cable bundles installed in an aircraft structure under electromagnetic compatibility constraints. We first describe the problem formulation where a probabilistic constraint has to be verified during the optimisation process. To overcome the nonlinearity of the constraint function and guarantee the algorithm convergence, we propose a joint approach between Monte Carlo sampling and a Kriging surrogate to estimate the optimum distance with a low computational cost. This methodology was tested on a realistic use-case of distance segregation between cable bundles.

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Li, Xiaodong, Xiang Song e Djamila Ouelhadj. "A Cost Optimisation Model for Maintenance Planning in Offshore Wind Farms with Wind Speed Dependent Failure Rates". Mathematics 11, n. 13 (22 giugno 2023): 2809. http://dx.doi.org/10.3390/math11132809.

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This paper presents an optimisation model for cost optimisation of maintenance at an offshore wind farm (OWF). The model is created for OWF project developers to optimise strategic resources to meet their maintenance demand. The model takes into account various maintenance categories on a full range of wind turbine components; the failure rate associated with each component is dependent on wind speed in order to consider weather uncertainty. Weibull distribution is used to predict the probability of wind speed occurring during a given period based on available historical data. The performance of the proposed optimisation model has been validated using reference cases and a UK OWF in operation. Various optimal solutions are investigated for the problems with increased and decreased mean turbine failure rates as a sensitivity test of the model.

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Gautier, Athénaïs, David Ginsbourger e Guillaume Pirot. "Goal-oriented adaptive sampling under random field modelling of response probability distributions". ESAIM: Proceedings and Surveys 71 (agosto 2021): 89–100. http://dx.doi.org/10.1051/proc/202171108.

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In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space. We consider cases where the spatial variation of these response distributions does not only concern their mean and/or variance but also other features including for instance shape or uni-modality versus multi-modality. Our contributions build upon a non-parametric Bayesian approach to modelling the thereby induced fields of probability distributions, and in particular to a spatial extension of the logistic Gaussian model. The considered models deliver probabilistic predictions of response distributions at candidate points, allowing for instance to perform (approximate) posterior simulations of probability density functions, to jointly predict multiple moments and other functionals of target distributions, as well as to quantify the impact of collecting new samples on the state of knowledge of the distribution field of interest. In particular, we introduce adaptive sampling strategies leveraging the potential of the considered random distribution field models to guide system evaluations in a goal-oriented way, with a view towards parsimoniously addressing calibration and related problems from non-linear (stochastic) inversion and global optimisation.

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Jaimungal, Sebastian. "Reinforcement learning and stochastic optimisation". Finance and Stochastics 26, n. 1 (23 dicembre 2021): 103–29. http://dx.doi.org/10.1007/s00780-021-00467-2.

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Humeida, Yousif, Paul D. Wilcox, Michael D. Todd e Bruce W. Drinkwater. "A probabilistic approach for the optimisation of ultrasonic array inspection techniques". NDT & E International 68 (dicembre 2014): 43–52. http://dx.doi.org/10.1016/j.ndteint.2014.07.007.

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Spinelli, Andrea, e Timoleon Kipouros. "PDOPT: A Python library for Probabilistic Design space exploration and OPTimisation". Journal of Open Source Software 9, n. 95 (5 marzo 2024): 6110. http://dx.doi.org/10.21105/joss.06110.

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Murray, Andrew, Michael Cashmore, Ashwin Arulselvan e Jeremy Frank. "Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract)". Proceedings of the International Symposium on Combinatorial Search 15, n. 1 (17 luglio 2022): 305–7. http://dx.doi.org/10.1609/socs.v15i1.21794.

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Abstract (sommario):

Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the problem of determining Chance Constrained PSTN SC as a Joint Chance Constrained optimisation problem via column generation, lifting the usual assumptions of independence and Boole's inequality typically leveraged in PSTN literature. Our approach offers on average a 10 times reduction in cost versus previous methods.

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Spinelli, Andrea, Luchien Anderson, Hossein Balaghi Enalou, Bahareh Zaghari, Timoleon Kipouros e Panagiotis Laskaridis. "Application of Probabilistic principles to Set-Based Design for the optimisation of a hybrid-electric propulsion system". IOP Conference Series: Materials Science and Engineering 1226, n. 1 (1 febbraio 2022): 012064. http://dx.doi.org/10.1088/1757-899x/1226/1/012064.

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Abstract Current research in hybrid-electric aircraft propulsion has outlined the increased complexity in design when compared with traditional propulsion. However, current design methodologies rely on aircraft-level analysis and do not include the consideration of the impact of new technologies and their uncertainty. This can be a key factor for the development of future hybrid-electric propulsion systems. In this paper, we present a methodology for exploring the design space using the principles of Set-Based Design, which incorporates probabilistic assessment of requirements and multidisciplinary optimisation with uncertainty. The framework can explore every design parameter combination using a provided performance model of the system under design and evaluate the probability of satisfying a minimum required figure of merit. This process allows to quickly discard configurations incapable of meeting the goals of the optimiser. A multidisciplinary optimiser then is used to obtain the best points in each surviving configuration, together with their uncertainty. This information is used to discard undesirable configurations and build a set of Pareto optimal solutions. We demonstrate an early implementation of the framework for the design of a parallel hybrid-electric propulsion system for a regional aircraft of 50 seats. We achieve a considerable reduction to the required function evaluations and optimisation run time by avoiding the ineffective areas of the design space but at the same time maintaining the optimality potential of the selected sets of design solutions.

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Saad, Wasan Kadhim, Ibraheem Shayea, Bashar J. Hamza, Hafizal Mohamad, Yousef Ibrahim Daradkeh e Waheb A. Jabbar. "Handover Parameters Optimisation Techniques in 5G Networks". Sensors 21, n. 15 (31 luglio 2021): 5202. http://dx.doi.org/10.3390/s21155202.

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The massive growth of mobile users will spread to significant numbers of small cells for the Fifth Generation (5G) mobile network, which will overlap the fourth generation (4G) network. A tremendous increase in handover (HO) scenarios and HO rates will occur. Ensuring stable and reliable connection through the mobility of user equipment (UE) will become a major problem in future mobile networks. This problem will be magnified with the use of suboptimal handover control parameter (HCP) settings, which can be configured manually or automatically. Therefore, the aim of this study is to investigate the impact of different HCP settings on the performance of 5G network. Several system scenarios are proposed and investigated based on different HCP settings and mobile speed scenarios. The different mobile speeds are expected to demonstrate the influence of many proposed system scenarios on 5G network execution. We conducted simulations utilizing MATLAB software and its related tools. Evaluation comparisons were performed in terms of handover probability (HOP), ping-pong handover probability (PPHP) and outage probability (OP). The 5G network framework has been employed to evaluate the proposed system scenarios used. The simulation results reveal that there is a trade-off in the results obtained from various systems. The use of lower HCP settings provides noticeable enhancements compared to higher HCP settings in terms of OP. Simultaneously, the use of lower HCP settings provides noticeable drawbacks compared to higher HCP settings in terms of high PPHP for all scenarios of mobile speed. The simulation results show that medium HCP settings may be the acceptable solution if one of these systems is applied. This study emphasises the application of automatic self-optimisation (ASO) functions as the best solution that considers user experience.

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Pronzato, Luc, e Éric Thierry. "Sequential experimental design and response optimisation". Statistical Methods & Applications 11, n. 3 (ottobre 2002): 277–92. http://dx.doi.org/10.1007/bf02509828.

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Kidney, Donnacha Oisín, e Nicolas Wu. "Algebras for weighted search". Proceedings of the ACM on Programming Languages 5, ICFP (22 agosto 2021): 1–30. http://dx.doi.org/10.1145/3473577.

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Weighted search is an essential component of many fundamental and useful algorithms. Despite this, it is relatively under explored as a computational effect, receiving not nearly as much attention as either depth- or breadth-first search. This paper explores the algebraic underpinning of weighted search, and demonstrates how to implement it as a monad transformer. The development first explores breadth-first search, which can be expressed as a polynomial over semirings. These polynomials are generalised to the free semimodule monad to capture a wide range of applications, including probability monads, polynomial monads, and monads for weighted search. Finally, a monad transformer based on the free semimodule monad is introduced. Applying optimisations to this type yields an implementation of pairing heaps, which is then used to implement Dijkstra's algorithm and efficient probabilistic sampling. The construction is formalised in Cubical Agda and implemented in Haskell.