Snake robots, which are a type of bionic robots, have high adaptability to various environments. Because of the unique structure and many degrees of freedom (DoFs) of snake robots, these robots can move on rough terra...
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ISBN:
(纸本)9784907764760
Snake robots, which are a type of bionic robots, have high adaptability to various environments. Because of the unique structure and many degrees of freedom (DoFs) of snake robots, these robots can move on rough terrain or in narrow spaces. However, developing a controller for the locomotion of snake robots with many DoFs is challenging. A control model developed for a snake robot moving in one environment might be unsuitable in other environments. Thus, a mixed compositional pattern-producing network (CPPN)-NeuroEvolution of Augmenting Topologies (NEAT) method is proposed in this paper. This method is based on a neuroevolution algorithm and serpentine locomotion, and it can be used to control the locomotion of a snake-like modular robot in multiple environments with different obstacles. In the proposed method, a group of neural networks is constructed for each environment, and these networks are trained using a neuroevolution algorithm for several generations. Some pretrained neural networks for each environment are selected and then integrated to obtain mixed neural networks. These mixed neural networks are then evolved using a multiobjective genetic algorithm (MOGA) to improve the locomotion of a snake-like modular robot in multiple environments. The results of this study indicated that the mixed CPPN-NEAT method outperformed the combined CPPN-NEAT method and use of MOGA alone (without pretraining, combined networks, and mixed networks) in locomotion control for a snake-like modular robot in three environments with different obstacles.
The use of geneticalgorithms as generative and performance design techniques often involves, in practice, constraint handling, which can be a complex task. Moreover, environmental simulations are computationally expe...
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The use of geneticalgorithms as generative and performance design techniques often involves, in practice, constraint handling, which can be a complex task. Moreover, environmental simulations are computationally expensive and managing constraints can avoid wasting time on infeasible solutions. Despite these two incentives, and the benefits of an immense literature, both applied and theorical, on constrained optimization, there are only few guidelines and tools directly applicable by architects to address this issue. This paper proposes to fill this gap by identifying, classifying, and implementing different constraint management techniques available to architects. Seven methods have been tested for a highly constrained envelope design problem, consisting in the optimization of a sun-shading system. Three of them are easily replicable to different types of projects while the four others need to find a problem-specific heuristic. It appears that the second category is more efficient but implies the use of generative techniques that are more difficult to implement than parametric models.
Clustering ensemble refers to the problem of obtaining a final clustering of some data set from a set of input clustering solutions. In this article, the clustering ensemble problem has been modeled as a multiobjectiv...
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Clustering ensemble refers to the problem of obtaining a final clustering of some data set from a set of input clustering solutions. In this article, the clustering ensemble problem has been modeled as a multiobjective optimization problem and a multiobjective evolutionary algorithm has been used for this purpose. The proposed multiobjective evolutionary clustering ensemble algorithm (MOECEA) evolves a clustering solution from the input clusterings by optimizing two criteria simultaneously. The first objective is to maximize the similarity of the resultant clustering with all the input clusterings, where the similarity between two clustering solutions is computed using adjusted Rand index. The second criteria is to minimize the standard deviation among the similarity scores in order to prevent the evolved clustering solution to be very similar with one of the input clusterings and very dissimilar with the others. The performance of the proposed algorithm has been compared with that of other well-known existing cluster ensemble algorithms for a number of artificial and real-life data sets.
Inconel 617 superalloy is a candidate material for various high temperature components employed in ultra-supercritical power plants. Superalloys are hard to machine materials that easier to process using nontraditiona...
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Inconel 617 superalloy is a candidate material for various high temperature components employed in ultra-supercritical power plants. Superalloys are hard to machine materials that easier to process using nontraditional methods such as wire electrical discharge machining. This paper discusses multi perfor-mance optimization in machining of Inconel 617 using grey relational analysis and geneticalgorithm. The performance characteristics of wire cut EDM are surface roughness, material removal rate, wire wear rate, form and orientation tolerances. Parameters under consideration are pulse on time, pulse off time, current, voltage, flushing pressure, wire tension, table feed and wire speed. The performance measures are consolidated as three performance indices using the grey relational grade. The mathematical models to estimate the performance objectives are developed using regression analysis. multiobjective optimiza-tion is applied through geneticalgorithm and a pareto front containing the optimized responses is com-puted. The optimized parameters are validated experimentally and the results are reported. Copyright (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Con-ference on Functional Material, Manufacturing and Performances
Ionic liquids (ILs) have been experimenta ly proved to be effective for ammonia-containing gas separation and recovery. A systematic strategy including thermodynamic models, process simulation, multiobjectivegenetic ...
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Ionic liquids (ILs) have been experimenta ly proved to be effective for ammonia-containing gas separation and recovery. A systematic strategy including thermodynamic models, process simulation, multiobjective genetic algorithm, and assessment for novel IL-based separation of ammonia-containing tail gas and ammonia recovery process was proposed. The conventional IL ([C(4)Mim][NTf2]) and the functional IL ([C(4)im][NTf2]) were selected, and their NH3 removal performance was investigated. Physical properties of models of IL systems were established with temperature-dependent equations, and gas-liquid phase equilibria of the NH3-IL system were molded with the nonrandom two liquid model equation. Total purification cost (TPC), total process CO2 emission (TPCOE), and thermodynamic efficiency (eta(eff)) were selected as the objective functions to be optimized. Process simulation results indicated that under same operational parameters, using functional ILs results in lower NH3 concentration in purified gas and higher removal efficiency than that of conventional ILs. After optimization, a series of solutions satisfying the constraints was provided by the Pareto front. The lowest objective functions can achieve 0.0211 $/N m(3) (TPC), 265.67 kg CO2/h (TPCOE), and 48.05% (eta(eff)). Moreover, using functional ILs could greatly decrease purification cost and energy consumption and avoid wastewater discharge, which is an inevitable environmental problem in the water scrubbing process.
This paper investigates the impact of using wide bandgap (WBG) technology-based bidirectional interleaved HV DC/DC converters on the performance of battery electric vehicles (BEVs). An existing electric vehicle is upg...
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ISBN:
(纸本)9781665449021
This paper investigates the impact of using wide bandgap (WBG) technology-based bidirectional interleaved HV DC/DC converters on the performance of battery electric vehicles (BEVs). An existing electric vehicle is upgraded using off-the-shelf components. There are a variety of batteries, high voltage (HV) DC/DCs, inverters, electric motors, transmissions, etc., available off-the-shelf;hence, numerous possible combinations can be formed, which make the optimal component selection process more complicated through analytical methods. In this paper, a multiobjective genetic algorithm (MOGA) is adopted to minimize the electric energy consumption by improving drivetrain efficiency based on the optimal variant selection of the components. It is found from the virtual simulation framework in MATLAB/Simulink (R) that overall, there is a 9.2% reduction in the energy consumption over a given driving cycle, i.e., Worldwide Harmonized Light Vehicles Test Procedure-3a (WLTP3a). To this end, the drivetrain performance in terms of acceleration time from 0-90 km/h is also improved by 10.2%, while the efficiency is improved by 1.5% compared to the conventional e-drivetrain.
Valid multipath error model is the prerequisite for high-performance GNSS integrity monitoring. It is indispensable to civil aviation and other Safety-of-Life (SoL) users. The model must perfectly bound multipath erro...
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Valid multipath error model is the prerequisite for high-performance GNSS integrity monitoring. It is indispensable to civil aviation and other Safety-of-Life (SoL) users. The model must perfectly bound multipath error while preventing the constructed model from being too conservative. Nevertheless, no sound methodologies to meet both the requirements have been introduced in previous literatures, and subsequently, practices always require iterative manual trade-offs. To improve the efficiency of multipath modeling, we propose a new automatic multipath error modeling methodology. It quantifies the above requirements in the objective function of multiobjective genetic algorithm (GA) so that multipath modeling can be managed automatically. Moreover, through introducing a new model that is based on two inflation factors, conservatism of modeling results can be significantly reduced. Experiments based on a 4-month dataset of BDS-3 Medium Earth Orbit (MEO) satellites show that constructed multipath models effectively bound actual error in each elevation bin. In addition, the new model form with two inflation factors brings average CDF difference reduction of 67.4% at B1I and 50.6% at B3I, which means significantly mitigation in terms of conservatism.
Emerging devices such as double gate carbon nanotube field effect transistors (DG CNTFETs) have opened up manifold possibilities for reconfigurable logic design. The thickness of gate oxide and the employment of inhom...
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Emerging devices such as double gate carbon nanotube field effect transistors (DG CNTFETs) have opened up manifold possibilities for reconfigurable logic design. The thickness of gate oxide and the employment of inhomogeneous dielectrics over and under the carbon nanotubes (CNTs) impact the operation of DG CNTFETs. In this work, the dielectric constant and the thickness of the gate oxide are optimised to suppress the ambipolar conduction in CNTFETs. A multi-objective geneticalgorithm-based approach is proposed to optimise these parameters. The contributions in this study are two-fold: firstly, a DG CNTFET is fabricated with the optimised parameters. By exploiting the ability to select the conduction behaviour using a second gate in the fabricated DG CNTFETs, the device is electrostatically programmed to behave as an n or p-type CNTFET. Secondly, a one instruction set computer is simulated with the optimised model of DG CNTFET. A universal static logic cell which implements 16 logic functions, a D-Latch and a D-FF were built with DG CNTFETs. The logic circuits with polarity-tunable DG CNTFETs outperform other logic structures. There is a 40% betterment in performance primarily due to the reduced number of logic levels and by 25% due to the reduced delay.
In this study, design optimisation of an axial-flux reluctance magnetic coupling is presented. The optimal design procedure is based on a two-dimensional (2D) semi-analytical model defined at the mean radius combined ...
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In this study, design optimisation of an axial-flux reluctance magnetic coupling is presented. The optimal design procedure is based on a two-dimensional (2D) semi-analytical model defined at the mean radius combined with a multi-objective geneticalgorithm (NSGA-II). In order to take into account the end-effects in the radial direction, a correction factor is defined to improve the torque and the axial-force determination. The obtained results are compared with those of 3D non-linear finite element simulations and experimental results. It is shown that the proposed semi-analytical model is very accurate and requires very little computing time.
In this study, a novel approach based on multi-objective geneticalgorithm (MO-GA) is used for simultaneous tuning the unscented Kalman Filter (UKF) parameters and camera and inertial measurement unit camera calibrati...
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In this study, a novel approach based on multi-objective geneticalgorithm (MO-GA) is used for simultaneous tuning the unscented Kalman Filter (UKF) parameters and camera and inertial measurement unit camera calibration in a vision inertial navigation system (VINS). This system consists of visual odometry and inertial navigation system (INS) which integrates with a UKF. In order to obtain simultaneous tuning and calibration of the parameters and variables, the MO-GA minimises the root mean square error of the position and velocity of the vehicle on a selected trajectory of the benchmark data set. Then, the tuned parameters and calibrated variables are placed in the VINS and an adjusted VINS (AVINS) is obtained. For investigating the AVINS, the mentioned system is compared with INS only, VINS based on calibration data of the benchmark data set, and GPS/INS as Real Data on the identical trajectory. Furthermore, in order to evaluate the results of the proposed approach, the AVINS is examined in the second trajectory. The results indicate the proper performance of the presented approach in the simultaneous tuning the filter parameters and calibrating the variables of sensors that are used in the uncalibrated VINS.
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