This paper describes a novel electrostatically actuated microgripper with freeform geometries designed by a genetic *** new semiautomated design methodology is capable of designing near-optimal MEMS devices that are r...
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This paper describes a novel electrostatically actuated microgripper with freeform geometries designed by a genetic *** new semiautomated design methodology is capable of designing near-optimal MEMS devices that are robust to fabrication *** use of freeform geometries designed by a genetica* algorithm significantly improves the performance of the *** experiment shows that the designed microgripper has a large displacement(91.5μm)with a low actuation voltage(47.5 V),which agrees well with the *** microgripper has a large actuation displacement and can handle micro-objects with a size from 10 to 100μm.A grasping experiment on human hair with a diameter of 77μm was performed to prove the functionality of the *** result confirmed the superior performance of the new design methodology enabling freeform *** design method can also be extended to the design of many other MEMS devices.
In this study, a strategy based on the particle swarm optimization (PSO)a* algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, nam...
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In this study, a strategy based on the particle swarm optimization (PSO)a* algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, namely, material models, optimality criterions, and a PSO algo rithm. The material models are utilized to predict microstructure information, such as recrystallization volume fraction and average grain size. The optimality crite? non can be determined by the designed target microstructures and random errors. The developed strategy is resolved using the PSOa* algorithm, which is an intelligent optimala* algorithm. Thisa* algorithm does not need a derivable objective function, which renders it suitable for dealing with the complex hot forging process of alloy components. The optimal processing parameters (deformation temperature and strain rate) are obtained by the developed strategy and validated by the hot forging experiments. Uniform and fine target microstructures can be obtained using the optimized processing parameters, which indicates that the developed strategy is effective for controlling the microstructural evolution during the hot forging of the studied superalloy.
Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging *** paper proposes a novel stochastic optimization algori...
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Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging *** paper proposes a novel stochastic optimizationa* algorithm using Latin hypercube sampling and Cholesky decomposition combined with scenario bundling and sensitivity analysis(LC-SB-SA) to address this *** deal with the uncertainty of natural inflows, Latin hypercube sampling is implemented to provide an adequate number of sampling scenarios efficiently, and Cholesky decomposition is introduced to describe the correlated natural inflows among cascaded *** addition, to overcome the difficulties in solving the objectives of all the scenarios, scenario bundling and sensitivity analysisa* algorithms are developed to improve the computational *** results from both two-station and tenstation systems indicate that the proposed method has the merits in accuracy as well as calculation speed for the midterm cascaded hydro generation *** consideration of natural inflow correlation makes the formulated problem more realistic.
This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enh...
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This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetica* algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetica* algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures.
In order to solve the springback problem in sheet metal forming, the trial and error method is a widely used method in the factory, which is time-consuming and costly for its non-direction and non-quantitative. Finite...
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In order to solve the springback problem in sheet metal forming, the trial and error method is a widely used method in the factory, which is time-consuming and costly for its non-direction and non-quantitative. Finite element simulation is an e ective method to predict the springback of complex shape parts, but its precision is sensitive to the simulation model, particularly material model and boundary conditions. In this paper, the simple iterative method is introduced to establish the iterative compensationa* algorithm, and the convergence criterion of iterative parameters is put forward. In addition, the newa* algorithm is applied to the V-free bending and stretch-bending processes, and the convergence of curvature and bending angle is proved theoretically and verified experimentally. At the same time,the iterative compensation experiments for plane bending show that, the new method can predict the next compensaantido tnh ev atlaureg ebta cseurdv oatnu trhe ew sitphri tnhgeb earcrko ro fo fe laecshs ttehsat,n s0 o. 5 th%a ta rteh eo btatraigneet db aefntedri n2 g-3 a nitgelrea tiwoitnhs.t Thhei se rrreosre aorf clhe sps rtohpaons e±s 0 a.1%new iterative compensationa* algorithm to predict springback in sheet metal forming process, where each compensation value depends only on the iteration parameter di erence before and after springback for the same forming process of same material.
Crystal structure prediction has been widely used to accelerate the discovery of new materials in recent *** to this day,it remains a challenge to predict the stable stoichiometries and structures of ternary or more c...
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Crystal structure prediction has been widely used to accelerate the discovery of new materials in recent *** to this day,it remains a challenge to predict the stable stoichiometries and structures of ternary or more complex systems due to the explosive increase of the size of the chemical and configurational *** novel materials with a series of unique characteristics are expected to be found in this virgin territory while newa* algorithms to predict crystal structures in complex systems are urgently called *** by co-evolution in biology,here we propose a co-evolutionarya* algorithm,which we name COPEX,and which is based on the well-known evolutionarya* algorithm *** this proposeda* algorithm,a few USPEX calculations for ternary systems and multiple for energetically-favored pseudobinary or fixed-composition systems are carried out in parallel,and coevolution is achieved by sharing structural information on the fittest individuals among different USPEX sub-processes during the joint *** have applied thea* algorithm to W–Cr–B,Mg–Si–O,and Hf–Ta–C,three very different systems,and many ternary compounds have been *** results clearly demonstrate that the COPEXa* algorithm combines efficiency and reliability even for complex systems.
Quantuma* algorithms are emerging tools in the design of functional materials due to their powerful solution space search *** to balance the high price of quantum computing resources and the growing computing needs has ...
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Quantuma* algorithms are emerging tools in the design of functional materials due to their powerful solution space search *** to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be *** propose a novel optimization strategy based on an active learning scheme that combines the Quantum-inspired Genetica* algorithm(QGA)with machine learning surrogate model *** Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments,thereby improving the optimization ***,a genetica* algorithm embedded with quantum mechanics,combines the advantages of quantum computing and genetica* algorithms,enabling faster and more robust convergence to the *** the design of planar multilayer photonic structures for transparent radiative cooling as a testbed,we show superiority of oura* algorithm over the classical genetica* algorithm(CGA).Additionally,we show the precision advantage of the Random Forest(RF)model as a flexible surrogate model,which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimizationa* algorithms(e.g.,quantum annealing needs Ising model as a surrogate).
In nanoparticle sizing using the ultrafast image-based dynamic light scattering (UIDLS)method,larger impurities and dark noise from the complementary metal-oxide-semiconductor (CMOS)detector affect measurement *** sol...
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In nanoparticle sizing using the ultrafast image-based dynamic light scattering (UIDLS)method,larger impurities and dark noise from the complementary metal-oxide-semiconductor (CMOS)detector affect measurement *** solve this problem,a two-dimensional self-adapting fast Fourier transform (2D-SAFFT)algorithm is proposed for *** light scattering images of nanoparticles are processed using 2D fast Fourier transforms,and a high-frequency threshold and a low-frequency threshold are then set using the self-adaptinga* algorithm to eliminate the effects of the dark noise of the CMOS detector and the *** signals caused by the dark noise of the CMOS detector and the impurities are cut off using the high-frequency threshold and the low-frequency *** signals without the high- and low-frequency components are then processed again using an inverse Fourier transform to obtain new images without the dark noise and impurities *** mean diameters of the measured nanoparticles can be obtained from images obtained using *** standard latex nanoparticles (46,100, 203,508,994nm)and commercial nanoparticles (antimony-doped tin oxide,indium tin oxide,TWEEN- 80,nano-Fe,and nano-Al2O3)were measured using this new *** show that 2D-SAFFT can effectively eliminate the effects of dark noise from the CMOS detector and the impurities.
Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidationa* algorithm named PVDE(prediction-...
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Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidationa* algorithm named PVDE(prediction-based VM deploymenta* algorithm for energy efficiency) is presented. The proposeda* algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selectiona* algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposeda* algorithms. Experimental results show that, in contrast to other energy-savinga* algorithms, thea* algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.
In this paper, a tailored four-step Adams-Bashforth-Moulton (ABM)a* algorithm is applied to a semirecursive formulation to perform a real-time simulation of a semitrailer truck. In the ABMa* algorithm, each integration st...
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In this paper, a tailored four-step Adams-Bashforth-Moulton (ABM)a* algorithm is applied to a semirecursive formulation to perform a real-time simulation of a semitrailer truck. In the ABMa* algorithm, each integration step involves two function evaluations, namely predictor and corrector. This is fundamentally different when compared to the classic fourth-order Runge-Kutta (RK) integrator approach that contains four function evaluations. A semitrailer truck under investigation is modeled in term of a semirecursive method and simulated by using the presented ABMa* algorithm. The results show that the four-step ABM method can reduce CPU time almost 50% for solving the truck dynamics with very similar accuracy, in comparison to the fourth-order RK method. The presented ABMa* algorithm could be used in the semirecursive formulation to carry out accurate real-time simulation of medium-large vehicle systems.
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