广东省能源需求预测模型构建及实证分析

为了对广东省的能源需求进行准确的预测,首先分析了影响广东省能源需求的各种因素,构建了预测指标体系.在此基础上,针对能源系统非线性等复杂系统特征,结合粒子群算法和BP神经网络的优点,构建了改进的PSO-BP神经网络的预测模型,并通过主成分分析法对指标体系进行数据降维,以降低神经网络的规模和复杂程度.以广东省1985-2013年的能源需求数据进行模拟与仿真,并对2014-2018年的能源需求量进行预测,理论分析和实证研究表明,该方法能够很好的反映广东省能源需求的特征,预测结果较为准确合理.     

一类非线性时滞网络控制系统的无源性分析

本文研究了一类非线性时滞网络控制系统的无源性问题.利用Lyapunov稳定性理论,结合线性矩阵不等式(LMI)技术,通过构造Lyapunov-Krasovskii泛函,在考虑两种不同时滞的情况下,获得了系统满足无源性的充分条件,最后通过仿真算例验证了结论的正确性和方法的有效性.     

Simulations of remote mutants of dihydrofolate reductase reveal the nature of a network of residues coupled to hydride transfer

Recent experimental and theoretical studies have proposed that enzymes involve networks of coupled residues throughout the protein that participate in motions accompanying chemical barrier crossing. Here, we have examined portions of a proposed network in dihydrofolate reductase (DHFR) using quantum mechanics/molecular mechanics simulations. The simulations use a hybrid quantum mechanics‐molecular mechanics approach with a recently developed semiempirical AM1‐SRP Hamiltonian that provides accurate results for this reaction. The simulations reproduce experimentally determined catalytic rates for the wild type and distant mutants of E. coli DHFR, underscoring the accuracy of the simulation protocol. Additionally, the simulations provide detailed insight into how residues remote from the active site affect the catalyzed chemistry, through changes in the thermally averaged properties along the reaction coordinate. The mutations do not greatly affect the structure of the transition state near the bond activation, but we observe differences somewhat removed from the point of C? H cleavage that affect the rate. The mutations have global effects on the thermally averaged structure that propagate throughout the enzyme and the current simulations highlight several interactions that appear to be particularly important. © 2014 Wiley Periodicals, Inc.     

VANET运动模型解析解与数值解的比照分析

依据车载自组织网络(VANET)的高移动性特征分别建立VANET运动解析模型与运动仿真模型.通过两种运动模型得到VANET度分布的解析解与数值解.对它们进行比照分析,发现两种度分布曲线均呈现出小度值的节点个数众多、大度值节点个数较少的特点,符合幂律函数分布,由此证明VANET是一个无标度网络.另外,从理论分析与仿真实验两个角度证明,VANET对随机性攻击具有较高的鲁棒性,但对针对性攻击则表现出网络的脆弱性.     

基于信道占用率的Ad Hoc网络自适应公平性算法

Ad Hoc网络中,节点通过竞争信道完成通信,如果竞争窗口值选取策略不合理,则容易造成公平性问题.在分析现存典型公平性算法的实现机制基础上,提出了信道占用率的概念,设计了一种基于信道占用率的Ad Hoc网络自适应公平性算法.根据节点对通信历史及当前信道占用率与理想信道占用率之间的关系,将通信情况分为4种类别,再结合当前网络负载情况,动态设置竞争窗口值.仿真结果表明,该算法在改善吞吐量的同时,显著提高了节点信道接入公平性,在各种负载条件下表现良好,优于BEB、MILD、MIMD和NAVB等算法.     

基于惯性测量器件的无线步态分析平台

步态定量测量方法应用于许多领域,如临床医学、双足机器人控制等.采用惯性测量单元结合无线传感器网络建立了一个步态分析平台,将两个无线惯性测量单元传感器节点分别绑定在左右双侧脚踝,以同时采集双脚运动过程中的加速度和角速度信号,并将其通过无线方式发送到远程终端.通过模式识别、时间序列分析、阈值检测和零速修正等多种数据融合方法计算步态参数,并通过融合双足传感器数据得到双支撑相、双脚步行周期等重要的双足步态参数,其中双支撑相参数对人体日常动作的识别有重要意义.实验结果显示该研究具有较高的计算精度.     

Preparation and properties of ZnMoO4 anode materials with polymer network gel method

Polymer network gel method combines the advantages of solid-phase method and liquid phase method, triggering acrylamide (AM) radical polymerization in aqueous solution and N, N′- methylene bis acrylamide (MBAM) active double bond cross-linking reaction, forming polymer chains to form a three-dimensional network. The polymer network space formed by the gel is bound and evenly distributed to the ions in the solution, thereby reducing the contact and aggregation of molecules and achieving the purpose of uniform particle size and small particle size. The principle diagram of network gel is shown in Figure. Using cubic zinc acetate and ammonium molybdate tetrahydrate as raw materials, cubic ZnMoO₄ negative electrode materials were prepared with polymer network gel method. The polymer network gel method has various effects on the structure, morphology and electrochemical properties of materials. Besides, the calcination temperature and calcination time were also the key factors to the electrochemical properties of the materials. In this paper, the effects of the ratio of monomer and crosslinker, calcination temperature and calcination time on ZnMoO₄ materials were studied by single variable method, the preparation process was optimized, and its characterization and electrochemical tests were carried out. After 100 cycles, the optimized ZnMoO₄ electrode has a discharge capacity of 374.0 mAh· g^?1, 332.5, 263.5 and 177.1 mAh · g^?1 at current densities of 0.1, 0.5, 1.0 and 2.0 A g^?1, respectively. The electrochemical results show that the optimized ZnMoO₄ has high capacity, large rate capability and excellent cycle stability.     

Process prediction of Ni–SiC coatings based on RBF-BP model

The preparation of Ni–SiC coatings using magnetic field-assisted jet electrodeposition under various plating settings is described in this study. A RBF-BP composite neural network with 4 × 4 × 4 × 7 × 10 × 1 was used to predict the corrosion resistance of Ni–SiC coatings prepared by employing different plating parameters. The results show that the fitting degree between the expected value and the actual value of the RBF-BP composite neural network is 0.97497. Moreover, the hybrid neural network can accurately predict the corrosion resistance of Ni–SiC coatings prepared under different process parameters. The corrosion weight loss of the coating is the lowest at the current density of 4 A/dm², a jet rate of 3 m/s, a SiC particle concentration of 8 g/L, and at a magnetic field intensity of 0.8 T, demonstrating its corrosion resistance under these conditions. According to the coating characterization analysis, the coating's grain size was significantly refined, and the surface was smoother with a high amount of uniformly sized SiC nanoparticles.     

多组件学习器实现有机分子沸点的精准预测

沸点(BP)是有机分子液体的基本物理化学量, 也是化学工业生产中的重要参数. 有机分子的沸点由分子结构决定, 呈现复杂的结构-沸点关系, 函数法(Function Method)、基团贡献法(Group Contribution Method)等传统方法无法应对复杂多样有机分子结构的预测, 应用范围狭窄, 预测精度低. 本研究中, 我们利用基于人工神经网络(ANN)和支持向量机(SVM)的多组件学习器实现有机分子沸点的精准预测. 我们构建了基于可解释性描述符的ANN、基于相关性描述符的ANN及基于复合分子指纹的SVM三个异质模型, 并通过包含4550个各种类别的有机分子沸点的数据集进行训练得到了三个异质性学习器, 最后集成三个学习器对有机分子沸点进行预测. 相比于传统方法和此前的定量结构性质关系(QSPR)模型, 多组件模型结合了三种模型的优点, 展现出很好的预测精度和泛化能力以及低的过拟合, 实现了对多种类型有机分子的沸点的有效预测.