Topology Optimization for Harmonic Excitation Structures with Minimum Length Scale Control Using the Discrete Variable Method

Continuumtopology optimization considering the vibration response is of great value in the engineering structure design.The aimof this studyis toaddress the topological designoptimizationof harmonic excitationstructureswith minimumlength scale control to facilitate structuralmanufacturing.Astructural topology design based on discrete variables is proposed to avoid localized vibration modes,gray regions and fuzzy boundaries in harmonic excitation topology optimization.The topological design model and sensitivity formulation are derived.The requirement of minimum size control is transformed into a geometric constraint using the discrete variables.Consequently,thin bars,small holes,and sharp corners,which are not conducive to the manufacturing process,can be eliminated from the design results.The present optimization design can efficiently achieve a 0–1 topology configuration with a significantly improved resonance frequency in a wide range of excitation frequencies.Additionally,the optimal solution for harmonic excitation topology optimization is not necessarily symmetric when the load and support are symmetric,which is a distinct difference fromthe static optimization design.Hence,one-half of the design domain cannot be selected according to the load and support symmetry.Numerical examples are presented to demonstrate the effectiveness of the discrete variable design for excitation frequency topology optimization,and to improve the design manufacturability.

Empirical mode decomposition using variable filtering with time scale calibrating

A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （EMD）, which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response （FIR） filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original EMD, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.

Shape Measures for the Distribution of a Qualitative Variable

There are several shape measures for quantitative variables, some of which can also be applied to ordinal variables. In quantitative variables, symmetry, peakedness, and kurtosis are essential properties to evaluate the deviation from assumptions, particularly normality. They aid in selecting the most appropriate method for estimating parameters and testing hypotheses. Initially, these properties serve a descriptive role in qualitative variables. Once defined, they can be considered to check for non-compliance with assumptions and to propose modifications for testing procedures. The objective of this article is to present three measures of the shape of the distribution of a qualitative variable. The concepts of qualitative asymmetry and peakedness are defined. The measurement of the first concept involves calculating the average frequency difference between qualitative categories matched by frequency homogeneity or proximity. For the second concept, the peak-to-shoulder difference and the qualitative percentile kurtosis are taken into consideration. This last measurement is a less effective option than the peak-to-shoulder difference to measure peakedness. A simulated example of the application of these three measures is given and the paper closes with some conclusions and suggestions.

Shape Measures for the Distribution of a Qualitative Variable

There are several shape measures for quantitative variables, some of which can also be applied to ordinal variables. In quantitative variables, symmetry, peakedness, and kurtosis are essential properties to evaluate the deviation from assumptions, particularly normality. They aid in selecting the most appropriate method for estimating parameters and testing hypotheses. Initially, these properties serve a descriptive role in qualitative variables. Once defined, they can be considered to check for non-compliance with assumptions and to propose modifications for testing procedures. The objective of this article is to present three measures of the shape of the distribution of a qualitative variable. The concepts of qualitative asymmetry and peakedness are defined. The measurement of the first concept involves calculating the average frequency difference between qualitative categories matched by frequency homogeneity or proximity. For the second concept, the peak-to-shoulder difference and the qualitative percentile kurtosis are taken into consideration. This last measurement is a less effective option than the peak-to-shoulder difference to measure peakedness. A simulated example of the application of these three measures is given and the paper closes with some conclusions and suggestions.

On the Cosmic Evolution of the Quantum Vacuum Using Two Variable G Models and Winterberg’s Thesis

We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two varying G models, where G, is Newton’s constant, and G-1, increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if G increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, ± the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low CMB temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.

THE METHOD OF MULTIPLE SCALES APPLIED TO THE NONLINEAR STABILITY PROBLEM OF A TRUNCATED SHALLOW SPHERICAL SHELL OF VARIABLE THICKNESS WITH THE LARGE GEOMETRICAL PARAMETER

Using the modified method of multiple scales, the nonlinear stability of a truncated shallow spherical shell of variable thickness with a nondeformable rigid body at the center under compound loads is investigated. When the geometrical parameter k is larger, the uniformly valid asymptotic solutions of this problem are obtained and the remainder terms are estimated.

Role of Accessibility and Socio-Economic Variables in Modelling Population Change at Varying Scale

During past decades, effects of accessibility to growth have been considered extensively. In previous studies, however, matter of scale has been largely ignored. In this study, relevance of travel accessibility and essential socio-economic variables for explaining population change analysed with a multi-scalar study setting by the case of Finland. The analytical framework of the study relies on applied geographical information systems (GIS). Relationships between population change and explanatory variables were established with non-linear multiple regression, generalised additive models (GAMs). The data consist of population and socio-economic grid cell databases based on authentic records, transport network models, including complete digital road network database. Models were established at six resolutions between 2 km × 2 km and 24 km × 24 km. The main result is that the performance of the models, explaining and predicting population change, strongly relies on potential accessibility particularly at accurate resolutions. An important finding is also that it is possible to establish explain and predict population change tolerably at accurate resolutions and well at coarse resolutions, as the performance of models increases with scale.

Variable selection for skew-normal mixture of joint location and scale models

Although there are many papers on variable selection methods based on mean model in the nite mixture of regression models,little work has been done on how to select signi cant explanatory variables in the modeling of the variance parameter.In this paper,we propose and study a novel class of models:a skew-normal mixture of joint location and scale models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population.The problem of variable selection for the proposed models is considered.In particular,a modi ed Expectation-Maximization(EM)algorithm for estimating the model parameters is developed.The consistency and the oracle property of the penalized estimators is established.Simulation studies are conducted to investigate the nite sample performance of the proposed methodolo-gies.An example is illustrated by the proposed methodologies.

Discrete Scale Relativity and SX Phoenicis Variable Stars

Discrete Scale Relativity proposes a new symmetry principle called discrete cosmological self-similarity which relates each class of systems and phenomena on a given Scale of nature’s discrete cosmological hierarchy to the equivalent class of analogue systems and phenomena on any other Scale. The new symmetry principle can be understood in terms of discrete scale invariance involving the spatial, temporal and dynamic parameters of all systems and phenomena. This new paradigm predicts a rigorous discrete self-similarity between Stellar Scale variable stars and Atomic Scale excited atoms undergoing energy-level transitions and sub-threshold oscillations. Previously, methods for demonstrating and testing the proposed symmetry principle have been applied to RR Lyrae, δ Scuti and ZZ Ceti variable stars. In the present paper we apply the same analytical methods and diagnostic tests to a new class of variable stars: SX Phoenicis variables. Double-mode pulsators are shown to provide an especially useful means of testing the uniqueness and rigor of the conceptual principles and discrete self-similar scaling of Discrete Scale Relativity. These research efforts will help theoretical physicists to understand the fundamental discrete self-similarity of nature, and to model both stellar and atomic systems with one unified physics.

Stability Enhancement of Small-Scale Power Grid with Renewable Power Sources by Variable Speed Diesel Power Plant

This paper proposes a power control method to improve a stability of a small-scale power grid with renewable energy sources. In an isolated small- scale power grid such as an island, diesel power plant is main power source which has an environmental burden and expensive running cost due to high priced fossil fuel. Thus, expanding installation of the renewable energy sources such as a wind power is strongly desirable. Such fluctuating energy sources, however, harm power quality of the small-scale power grid, and in addition, conventional power plant in the small-scale power grid cannot, in general, stabilize the grid system with such fluctuating power sources. In this study, Variable Speed Doubly-Fed Induction Generator (VS-DFIG) is proposed to be in-stalled at a diesel power plant instead of a conventional Fixed Speed Synchronous Generator (FS-SG), because quick control of a power balance in the small-scale power grid can be achieved by using the inertial energy of VS-DFIG. In addition, utilization of a Battery Energy Storage System (BESS) is also considered to assist cooperatively the VS-DFIG control. As a result of the simulation analysis by using the proposed method, it is verified that frequency fluctuations due to renewable energy source can be effectively reduced by quick power control of the VS-DFIG compared to the conventional FS-SG, and further control ability can be obtained by utilizing BESS. Moreover, the transient stability of a small-scale power grid during a grid fault can also be enhanced.

Comparative Study of Response Surface Designs with Errors-in-Variables Model

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors.The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables.Three performance criteria are proposed.The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors.The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors.The last performance measure is the mean squared scaled prediction variance change with and without errors.In the simulations,1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design.The independent variables are coded variables in these designs.Comparative results show that for the low level errors in variables,central composite face-centered design is optimal;otherwise,Box-Behnken design has a relatively better performance.

Crowdsourced Sampling of a Composite Random Variable: Analysis, Simulation, and Experimental Test

A composite random variable is a product (or sum of products) of statistically distributed quantities. Such a variable can represent the solution to a multi-factor quantitative problem submitted to a large, diverse, independent, anonymous group of non-expert respondents (the “crowd”). The objective of this research is to examine the statistical distribution of solutions from a large crowd to a quantitative problem involving image analysis and object counting. Theoretical analysis by the author, covering a range of conditions and types of factor variables, predicts that composite random variables are distributed log-normally to an excellent approximation. If the factors in a problem are themselves distributed log-normally, then their product is rigorously log-normal. A crowdsourcing experiment devised by the author and implemented with the assistance of a BBC (British Broadcasting Corporation) television show, yielded a sample of approximately 2000 responses consistent with a log-normal distribution. The sample mean was within ~12% of the true count. However, a Monte Carlo simulation (MCS) of the experiment, employing either normal or log-normal random variables as factors to model the processes by which a crowd of 1 million might arrive at their estimates, resulted in a visually perfect log-normal distribution with a mean response within ~5% of the true count. The results of this research suggest that a well-modeled MCS, by simulating a sample of responses from a large, rational, and incentivized crowd, can provide a more accurate solution to a quantitative problem than might be attainable by direct sampling of a smaller crowd or an uninformed crowd, irrespective of size, that guesses randomly.

A Study on Evaluation Model of IT Industry’s Innovation Capability Based Variable Weight Theory

In this paper, IT Industry＇s innovation capability is considered to be the innovation output capability after complex operation of industry input in industry system. In this complex process, R＆D personnel input and R＆D expense input are un-substitutable, and for evaluation of innovation capability, innovation input and innovation output also are un-substitutable. Based on this theory, an evaluation model of sustaining strength index is put forward. Considering both input scale and output contribution of IT industry＇s innovation system, this model reflects the un-substitutability of every evaluation aspects. The measurement result not only shows the industry innovation capability, but also reflects the support degree to economy. At last the data of IT industry in China are provided between 1994 and 2004 for empirical study.

Criticality in Two-Variable Earthquake Model on a Random Graph

A two-variable earthquake model on a quenched random graph is established here.It can be seen as a generalization of the OFC models.We numerically study the critical behavior of the model when the system is nonconservative:the result indicates that the model exhibits self-organized criticality deep within the nonconservative regime.The probability distribution for avalanche size obeys finite size scaling.We compare our model with the model introduced by Stefano Lise and Maya Paczuski [Phys.Rev.Lett.88 (2002) 228301],it is proved that they are not in the same universality class.

CMA-MESO千米尺度变分同化系统中极小化控制变量的重构

重构GRAPES(Global/Regional Assimilation and Prediction System)全球、区域一体化变分同化系统中的极小化控制变量,提升中、小尺度同化分析能力,为中国气象局业务区域数值预报系统CMA-MESO提供千米尺度适用的同化方案。新方案用纬向风速(u)和经向风速(v)替代原有流函数和势函数作为新的风场控制变量,采用温度和地面气压(T,ps)替代原有非平衡无量纲气压作为新的质量场控制变量,同时不再考虑准地转平衡约束,而是采用连续方程弱约束保证分析平衡。背景误差参数统计和数值试验结果表明,采用重构后的极小化控制变量,观测信息传播更加局地,分析结构更加合理,避免了原方案在中、小尺度应用时存在的虚假相关问题。连续方程弱约束的引入,限制了同化分析中辐合、辐散的不合理增长,帮助新方案在分析更加局地的同时保证分析平衡。为期1个月的连续同化循环和预报试验结果表明,新方案可以减小风场和质量场分析误差,CMAMESO系统地面降水和10 m风场的预报评分显著提升。

基于心率变异性的阵发性心房颤动预测方法

基于心率变异性(HRV)的特征分析,提出一种患者阵发性房颤(PAF)发作的预测系统方法。首先,基于一种新的自适应滤波技术逐次平滑滤波并粗粒化HRV后,采用熵量化HRV在多个自适应尺度的复杂性特征;其次,特征经MinMax归一化和序列前向选择特征子集,输入支持向量机识别HRV类型,预测PAF发作。经50例时长5 min HRV序列集的五折交叉验证,得到最优预测结果为:准确率98%,敏感性100%,特异性96%,性能表现优越。另外,实验表明远离和紧随PAF时的HRV复杂性特征值在不同频率段内,分别具有不同的显著变化(P<0.05),反映受试者神经系统调节心脏节律改变,以及调控机体、应激等适应外界环境变化能力的下降。

基于变论域模糊PID的金属热处理过程温度控制方法

为高效且安全地提取金属合金内的可用材质并分析其性能,利用变论域模糊PID技术,实现对热处理过程的温度控制。设定在金属规则状态不变的情况下,论域会随着偏差的缩减出现伸缩,即需要增添新的规则。若偏差率能够支持模糊量化取整,控制器的伸缩因子可能会陷入无法取值的情况。此时,通过二元函数分片双一次插值法计算伸缩因子,在临近分档之间填充新规则,确保伸缩因子取值的连续性。然后,在模糊PID控制过程中加入变论域,依靠控制器实现对金属热处理过程温度的控制。根据实验验证情况可知:该方法有效提高了控制效果,在应用该方法后,缩短了温度控制时间,在100s内就可以是加热温度到达拟定值。

基于AVMD多尺度模糊熵和VPMCD算法的宽频振荡分类

电力系统宽频振荡具有宽频域、非线性和时变性的特点,对振荡分类在准确性、快速性等方面提出了更高的要求。为此,提出一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)的多尺度模糊熵(multi-scale fuzzy entropy,MFE)和变量预测模型(variable predictive model-based class discriminate,VPMCD)相结合的宽频振荡分类新方法。首先,对宽频振荡信号进行AVMD,得到固有模态分量(intrinsic mode functions,IMFS)。然后,引入MFE对IMFS进行时域特征描述,同时实现对IMFS构造特征向量的降维处理。最后,采用VPMCD对MFE降维后的特征向量实现宽频振荡的分类检测。通过仿真和实测数据分析,结果表明,所提方法的宽频振荡分类检测准确率比支持向量机(support vector machines,SVM)、BP神经网络方法的分类准确率更高,分类时间更短。

保持主导振荡模态的双馈风电场动态聚合等值方法

针对风电场详细建模出现的“维数灾”问题,计及双馈风电机组各控制环节与系统振荡模态的关系,以保持等值前后主导振荡模态一致为目标,提出考虑风电机组多时间尺度级联特性的风电场站等值聚合方法。首先考虑风电机组多控制环节级联建立风电场状态空间模型;其次,利用振荡能量级判别系统主导振荡模态,并基于参与因子分析不同模态的关键影响因素;在此基础上,以主导振荡模态的关键影响因素为分群指标,结合自编码器进行数据降维处理,经改进聚合算法聚类和拓扑变换获取等值参数等环节后得到等值模型。最后,在Matlab平台分别搭建单机模型和含24台双馈风电机组的风电场模型,验证了所提等值方法的有效性。

致密油整体缝网压裂技术在杏树岗油田杏69-1井区扶余油层的应用实践

针对大庆致密油储层地质条件逐年变差,以及采用常规压裂改造工艺达不到预期增产效果的问题,创新采用整体缝网压裂工艺,主要从地质选层、缝储匹配、液性组合、有效改造4个方面进行方案优化。依托杏树岗油田杏69-1井区扶余油层“井缝控藏”理念,纵向上通过甜点优选压裂层段,平均单井压裂层数由4层减少至3层。横向上考虑砂体连通关系,以缝控储量最大为目标,优化施工规模,平均单井液体规模降低16%,支撑剂规模降低31.4%,单井成本降低4.7%。同时采用变黏压裂液、控缝高工艺,积极推行全链条挖潜增效,实现该井区扶余油层整体缝网压裂效益开发。现场应用表明,整体缝网压裂技术提高了缝控储量规模,实现了储层改造最大化、缝控储量最大化。实际平均单井日产油5.7t/d,比设计值提高103%;实际建设产能1.62×10^(4)t,超额完成22.7%。