基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

A simulation study of protons heated by left/right-handed Alfvén waves generated by electromagnetic proton-proton instability

Most protons in the solar wind belong to one of two different populations,the less dense beam protons and the denser core protons.The beam protons,with a velocity of(1-2)VA(VA is the local Alfvén speed),always drift relative to the core protons;this kind of distribution is unstable and stimulates several kinds of wave mode.In this study,using a 2D hybrid simulation model,we find that the original right-handed elliptically polarized Alfvén waves become linearly polarized,and eventually become right-handed and circularly polarized.Given that linearly polarized waves are a superposition of left-handed and right-handed waves,cyclotron resonance in the right-handed/left-handed component heats beam/core protons perpendicularly.The resonance between beam protons and right-handed polarized waves is stronger when the beam relative density is lower,resulting in more dramatic perpendicular heating of beam protons,whereas the situation is reversed when the beam relative density is larger.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

Boosted 2D graphene nanosheets by organic-inorganic hybrid cross-linker for an efficient and stable supercapacitor

Using covalent graphene derivatives in energy storage applications is promising. From this view, covalently cross-linked graphene oxide (GO) nanosheets are designed using polyoligomeric silsesquioxanes-propyl-NH₂ (POPN). Then, by incorporating cobalt sulfide nanoparticles into the porous scaffold, a high-value nanocomposite is formed. In a typical three-electrode cell, this nanocomposite declared substantial specific capacity of 454 and 438 Fg^-1 using cyclic voltammetry (CV) and charge-discharge (GCD) assessments. The device is assembled via two identical electrodes containing RGO-SiO₃-NH2-poss-NH2-SiO₃-RGO/cobalt sulfide (RGO-Si-POPN-Si-RGO/CoS₂). Utilizing CV and GCD methods, specific capacitances of 328 and 315 Fg^-1 are realized at a sweep rate and current density of 2 mVs^?1 and 0.5 Ag^-1, respectively. The device presents desirable energy density of 18.5 Whkg^?1 at the power density of 325 Wkg^-1. More impressively, around 97.9% of the specific capacitance is retained after 5000 charge-discharge cycles. The results confirm exceptional capacitive capabilities and super stability of the nanocomposite suitable for practical systems.     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.     

Pattern design and optimization of yarn-dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn-dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn-dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.