Maneuver target interception based on multiple model adaptive estimator

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the “assumed steady-state mean vector”, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.

: We apply multiple model adaptive estimation (MMAE), a proven method of system identification widely used in engineering applications, to the problem of determining Bayesian probability distributions of the final cost and completion time of on-going research and development (R&D) programs, conditioned on actual cost of w9rk performed (ACWP) data. Modeling cumulative expenditures with Rayleigh distributions, we produce graphs of the results that give useful assessments of cost and schedule risks. The procedure is implemented in a convenient computer program. We give three examples of its application to actual data, and results of a Monte Carlo analysis verify the method. (KAR) P. 1

Cooperative interception with fast multiple model adaptive estimation

Molecular simulation studies of reversed-phase liquid chromatography

Polya tree Monte Carlo method

Monte Carlo (MC) method is widely used in resolving mathematical problems that are too complicated to solve analytically. The method involves with sampling process of the random numbers and probability to estimate the result. As MC method depending on a large number of good quality random numbers to produce a high accuracy result, developing a good random number generator (RNG) is vital. Most of random number generators are developed in software based, with the improvement of Field Programmable Gate Arrays (FPGA) density and speed in recent days, implementing random number generator (RNG) directly into hardware is feasible. Random Walk on the Boundary (WOB) is one of MC methods that applied to calculate the unit cube capacitance. The unique requirement of this method is the random numbers produced by RNG must fall within the Gaussian distribution and the maximum decimal values in the range of [0, 1]. Thus, in this paper we presented a novel hardware RNG for Random WOB method to calculate unit cube capacitance in FPGA. The RNG is implemented in floating-point base, using the combination of Cellular Automata Shift Register (CASR) and Linear Feedback Shift Register (LFSR) then channeled through Box-Muller transformation. There is linear approximation in computing logarithmic function applied in Box-Muller transformation. Based on statistical tests, the random numbers generated is nearly 97.5% resembling the standard normal distribution.

The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better.Highlights• In-depth analysis of various artificial intelligence optimization techniques.• Prediction efficiency of artificial intelligence and machine learning algorithms.• 98% dye removal and 100% regeneration of graphite intercalation compound.• Advanced statistical analysis of targeted responses and data fitting techniques.• Analysis of uncertainties and variability using Monte Carlo simulation.

We consider the problem of allocating indivisible goods fairly among n agents who have additive and submodular valuations for the goods. Our fairness guarantees are in terms of the maximin share, which is defined to be the maximum value that an agent can ensure for herself, if she were to partition the goods into n bundles, and then receive a minimum valued bundle. Since maximin fair allocations (i.e., allocations in which each agent gets at least her maximin share) do not always exist, prior work has focused on approximation results that aim to find allocations in which the value of the bundle allocated to each agent is (multiplicatively) as close to her maximin share as possible. In particular, Procaccia and Wang (2014) along with Amanatidis et al. (2015) have shown that under additive valuations, a 2/3-approximate maximin fair allocation always exists and can be found in polynomial time. We complement these results by developing a simple and efficient algorithm that achieves the same approximation guarantee. Furthermore, we initiate the study of approximate maximin fair division under submodular valuations. Specifically, we show that when the valuations of the agents are nonnegative, monotone, and submodular, then a 0.21-approximate maximin fair allocation is guaranteed to exist. In fact, we show that such an allocation can be efficiently found by using a simple round-robin algorithm. A technical contribution of the article is to analyze the performance of this combinatorial algorithm by employing the concept of multilinear extensions.

In this article, a comparison is made between the robustness and efficiency of the CLEAR algorithm and the SIMPLE algorithm on nonorthogonal curvilinear coordinates for compressible flows. Thirteen different high-order convection schemes are employed in the calculations. Subsonic flow, transsonic flow, and supersonic flow in a channel with a circular arc bump and compressible flow in a Laval nozzle are used as test cases. The CLEAR algorithm shows huge potential to compute the transsonic flow in the Laval nozzle and high-speed compressible flows. Results with the ADBQUICKEST scheme, the HLPA scheme, and the MUSCL scheme are stable for both the compressible SIMPLE and CLEAR algorithms for all the mentioned cases.

The theory of adjoint operators is widely used in solving applied multidimensional problems with Monte Carlo method. Efficient algorithms are constructed using the duality principle for many problems described in linear integral equations of the second kind. On the other hand, important applications of adjoint equations for designing experiments were suggested by G.Marchuk and his colleagues in their respective works. Some results obtained in these fields are also generalized to the case of nonlinear operators. Linearization methods were mostly used for that purpose. Results for Lyapunov-Schmidt nonlinear polynomial equations were obtained in the theory of Monte Carlo methods. However, many interesting questions in this subject area are remained open. New results about dual processes used for solving polynomial equations with Monte Carlo method are presented. In particular, an adjoint Markov process for the branching process and the corresponding unbiased estimate of the functional of the solution to the equation are constructed in a general form. The possibility of constructing an adjoint operator to a nonlinear one is discussed.

Training a neural network is a difficult optimization problem because of the nonconvex objective function. Therefore, as an alternative to local search algorithms, many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm at obtaining a low value of the objective function. This study examines, through Monte-Carlo simulations, the relative efficiency of a local search algorithm to 8 stochastic global algorithms: 2 simulated annealing algorithms, 1 simple random stochastic algorithm, 1 genetic algorithm and 4 evolutionary strategy algorithms. The results show that even ignoring the computational requirements of the global algorithms, there is little evidence to support the use of the global algorithms examined for training neural networks.

Extreme wave and wind response predictions

This paper examines the potential financial advantages and disadvantages of a project called Feudo Turistico, which proposes the establishment of a slow tourism destination between Rome and Fiuggi. The objective of the project is to reduce the concentration of tourist demand in the city centers of Rome and Fiuggi and instead distribute it across the Lazio region by providing a local cultural and active tourism experience with new services that are currently unavailable in the region. To assess the project’s feasibility, a cost-benefit analysis is conducted using Monte Carlo Simulation with 10,000 trials. This method allows for obtaining the net present value, internal rate of return, and discounted payback period for each simulated combination of input variables and then exploring the location and dispersion of these indicators. A simple random sampling technique is employed to simulate different sets of input parameters using a factor range between pessimistic and optimistic scenarios, where minimum and maximum values are determined based on existing literature, historical data in statistical reports of research institutions, and calculations derived from this data. Furthermore, two different settings are considered regarding the initial distribution of input parameters, and their outcomes are compared. The results and distribution of net present value, internal rate of return, and discounted payback period indicate that the Feudo Turistico project possesses a financially self-sufficient business model, generating cash flows that can repay the initial investment within a few years with a high probability. Keywords: slow tourism, cost-benefit analysis, discounted cash flow, Monte Carlo Simulation

Electronic, magnetic properties and magnetocaloric effect in La0.67Sr0.33MnO3 compound: Ab initio calculations and Monte Carlo simulation

Purpose: The electron linac ElectronFlash installed at Institut Curie (Orsay, France) is entirely dedicated to FLASH irradiation for radiobiological and pre-clinical studies. The system was designed to deliver an ultra-high-dose rate per pulse (UHDR) (above 106 Gy/s) and a very high average dose rate at different energies and pulse durations. A campaign of tests and measurements was performed to obtain a full reliable characterizations of the electron beam and of the delivered dose, which are necessary to the radiobiological experiments. Methods: A Faraday cup was used to measure the electron charges in a single RF pulse. The percentage depth dose (PDD) and the transverse dose profiles, at the energies of 5 MeV and 7 MeV, were evaluated employing Gafchromic films EBT-XD for two Poly-methylmethacrylate (PMMA) applicators with irradiation sizes of 30 mm and 120 mm, normally used for in vivo and in vitro experiments, respectively. The results were compared with Monte Carlo (MC) simulations. Results: The measurements were performed during a period of a few months in which the experimental set up was adapted and tuned in order to characterize the electron beam parameters and the values of delivered doses before the radiobiological experiments. The measurements showed that the dose parameters, obtained at the energy of 5 MeV and 7 MeV with different applicators, fulfill the FLASH regime, with a maximum value of an average dose rate of 4750 Gy/s, a maximum dose per pulse of 19 Gy and an instantaneous dose rate up to 4.75 ×106 Gy/s. By means of the PMMA applicators, a very good flatness of the dose profiles was obtained at the cost of a reduced total current. The flatness of the large field is reliable and reproducible in radiobiological experiments. The measured PDD and dose profiles are in good agreement with Monte Carlo simulations with more than 95% of the gamma-index under the thresholds of 3 mm/3%. Conclusions: The results show that the system can provide UHDR pulses totally satisfying the FLASH requirements with very good performances in terms of beam profile flatness for any size of the fields. The monitoring of electron beams and the measurement of the dose parameters played an important role in the in vivo and in vitro irradiation experiments performed at the Institut Curie laboratory.