114,909 research outputs found
Thermal food processing computation software
The objective of this research consisted of developing the two following thermal food processing software: “F-CALC” is software developed to carry out thermal process calculations based on the well-known Ball's formula method, and “OPT-PROx” is software for thermal food processing optimization based on variable retort temperature processing and global optimization technique. Time-temperature data loaded from Excel-file is used by “F-CALC” software to evaluate the heat penetration parameters jh and fh, as well as to compute process lethality for given process time or vice versa. The possibility of computing the process time and lethality for broken heating curves is included. The diversity of thermal food processing optimization problems with different objectives and required constraints are solvable by “OPT-PROx” software. The adaptive random search algorithm coupled with penalty functions approach, and the finite difference method with cubic spline approximation are utilized by “OPT-PROx” for simulation and optimization thermal food processes. The possibility of estimating the thermal diffusivity coefficient based on the mean squared error function minimization is included. The “OPT-PROx” software was successfully tested on the real thermal food processing problems, namely in the case of total process time minimization with a constraint for average and surface retentions the “OPT-PROx” demonstrates significant advantage over the traditional constant temperature processes in terms of process time and final product quality. The developed user friendly dialogue and used numerical procedures make the “F-CALC” and “OPT-PROx” software extremely useful for food scientists (research and education) and engineers (real thermal food process evaluation and optimization)
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
This paper develops mfEGRA, a multifidelity active learning method using
data-driven adaptively refined surrogates for failure boundary location in
reliability analysis. This work addresses the issue of prohibitive cost of
reliability analysis using Monte Carlo sampling for expensive-to-evaluate
high-fidelity models by using cheaper-to-evaluate approximations of the
high-fidelity model. The method builds on the Efficient Global Reliability
Analysis (EGRA) method, which is a surrogate-based method that uses adaptive
sampling for refining Gaussian process surrogates for failure boundary location
using a single-fidelity model. Our method introduces a two-stage adaptive
sampling criterion that uses a multifidelity Gaussian process surrogate to
leverage multiple information sources with different fidelities. The method
combines expected feasibility criterion from EGRA with one-step lookahead
information gain to refine the surrogate around the failure boundary. The
computational savings from mfEGRA depends on the discrepancy between the
different models, and the relative cost of evaluating the different models as
compared to the high-fidelity model. We show that accurate estimation of
reliability using mfEGRA leads to computational savings of 46% for an
analytic multimodal test problem and 24% for a three-dimensional acoustic horn
problem, when compared to single-fidelity EGRA. We also show the effect of
using a priori drawn Monte Carlo samples in the implementation for the acoustic
horn problem, where mfEGRA leads to computational savings of 45% for the
three-dimensional case and 48% for a rarer event four-dimensional case as
compared to single-fidelity EGRA
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Nonlinear system identification and control using state transition algorithm
By transforming identification and control for nonlinear system into
optimization problems, a novel optimization method named state transition
algorithm (STA) is introduced to solve the problems. In the proposed STA, a
solution to a optimization problem is considered as a state, and the updating
of a solution equates to a state transition, which makes it easy to understand
and convenient to implement. First, the STA is applied to identify the optimal
parameters of the estimated system with previously known structure. With the
accurate estimated model, an off-line PID controller is then designed optimally
by using the STA as well. Experimental results have demonstrated the validity
of the methodology, and comparisons to STA with other optimization algorithms
have testified that STA is a promising alternative method for system
identification and control due to its stronger search ability, faster
convergence rate and more stable performance.Comment: 20 pages, 18 figure
Adaptive Channel Recommendation For Opportunistic Spectrum Access
We propose a dynamic spectrum access scheme where secondary users recommend
"good" channels to each other and access accordingly. We formulate the problem
as an average reward based Markov decision process. We show the existence of
the optimal stationary spectrum access policy, and explore its structure
properties in two asymptotic cases. Since the action space of the Markov
decision process is continuous, it is difficult to find the optimal policy by
simply discretizing the action space and use the policy iteration, value
iteration, or Q-learning methods. Instead, we propose a new algorithm based on
the Model Reference Adaptive Search method, and prove its convergence to the
optimal policy. Numerical results show that the proposed algorithms achieve up
to 18% and 100% performance improvement than the static channel recommendation
scheme in homogeneous and heterogeneous channel environments, respectively, and
is more robust to channel dynamics
Variable Selection in General Multinomial Logit Models
The use of the multinomial logit model is typically restricted to applications with few predictors, because in
high-dimensional settings maximum likelihood estimates tend to deteriorate. In this paper we are proposing a sparsity-inducing penalty that accounts for the special structure of multinomial models. In contrast to existing methods, it penalizes the parameters that are linked to one variable
in a grouped way and thus yields variable selection instead of parameter selection. We develop a proximal gradient method that is able to efficiently compute stable estimates.
In addition, the penalization is extended to the important case of predictors that vary across response categories. We apply our estimator to the modeling of party choice of voters in Germany including voter-specific variables like age and gender but also party-specific features like stance on nuclear energy and immigration
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