20,410 research outputs found
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Reduction of Real Power Loss and Safeguarding of Voltage Constancy by Artificial Immune System Algorithm
In this paper, Artificial Immune System (AIS) algorithm is used for solving reactive power problem. Artificial Immune System Algorithm, also termed as the machine learning approach to Artificial Intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Artificial immune system algorithmic approach to power system optimization these ideas are embedded into proposed algorithm for solving reactive dispatch problem. In order to evaluate the proposed algorithm, it has been tested in standard IEEE 30,118 bus systems and compared to other specified algorithms. Simulation results show better performance of the proposed AIS algorithm in reducing the real power loss and preservation of voltage stability
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de EconomÃa y Competitividad DPI2015-70842-
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Distribution systems optimization with computational intelligence algorithms
A dual particle swarm optimization - immune algorithm solution is presented in this paper to deal with the problem of optimum radial reconfiguration and reactive power compensation in distribution systems. The optimization problem uses as minimization function power losses in the distribution system – lines and transformers – and addresses constraints referring lower and upper voltage limits, nodal reactive power limits, topology supply constraints and the maximum number of capacitor banks. The analysis conducted for a pilot and a complex test system has proven the feasibility of the proposed method
An improved optimization technique for estimation of solar photovoltaic parameters
The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV
Freeform Fabrication of Ionomeric Polymer-Metal Composite Actuators
Ionomeric polymer-metal composite (IPMC) actuators are a type of soft electromechanically active material which offers
large displacement, rapid motion with only ~1V stimulus. IPMC’s are entering commercial applications in toys (Ashley
2003) and biomedical devices (Soltanpour 2001; Shahinpoor 2002; Shahinpoor, Shahinpoor et al. 2003; Soltanpour and
Shahinpoor 2003; Soltanpour and Shahinpoor 2004), but unfortunately they can only actuate by bending, limiting their
utility. Freeform fabrication offers a possible means of producing IPMC with novel geometry and/or tightly integrated with
mechanisms which can yield linear or more complex motion. We have developed materials and processes which allow us to
freeform fabricate complete IPMC actuators and their fabrication substrate which will allow integration within other
freeform fabricated devices. We have produced simple IPMC’s using our multiple material freeform fabrication system, and
have demonstrated operation in air for more than 40 minutes and 256 bidirectional actuation cycles. The output stress scaled
to input power is two orders of magnitude inferior to that of the best reported performance for devices produced in the
traditional manner, but only slightly inferior to devices produced in a more similar manner. Possible explanations and paths
to improvement are presented. Freeform fabrication of complete electroactive polymer actuators in unusual geometries,
with tailored actuation behavior, and integrated with other freeform fabricated active components, will enable advances in
biomedical device engineering, biologically inspired robotics, and other fields. This work constitutes the first
demonstration of complete, functional, IPMC actuators produced entirely by freeform fabrication.Mechanical Engineerin
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