基于高频电力线模型的整流器传导干扰分析与验证

电力传输线是引起电力电子装置或系统发生电压反射以及无法满足电磁兼容限值的主要原因,为了实现整流系统电磁干扰估算,构建能够精确反映电磁传输特性的电缆模型是关键环节之一。高频时参数的频率变化及集肤效应引起的分布特性差异对导体电流分布密度产生影响,此时计及集总参数的S.Kim模型无法精确表征传输线固有特性。为实现模型宽频带寄生参数准确辨识,故采用基于"N-Branch"理论的传输线等效电路拟合策略,从而实现预测模型精确逼近和稳定收敛,为功率整流器系统的EMC设计提供有效的理论依据。最后,通过数值计算和实验结果对上述建模机理可行性与准确性进行验证,在低频段传导干扰实测值与预测结果几乎一致,而在高频段仅仅产生约5 d B的估算误差,预测趋势基本符合。

Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance

With the rise of the electric vehicle industry,as the power source of electric vehicles,lithium battery has become a research hotspot.The state of charge(SOC)estimation and modelling of lithium battery are studied in this paper.The ampere-hour(Ah)integration method based on external characteristics is analyzed,and the open-circuit voltage(OCV)method is studied.The two methods are combined to estimate SOC.Considering the accuracy and complexity of the model,the second-order RC equivalent circuit model of lithium battery is selected.Pulse discharge and exponential fitting of lithium battery are used to obtain corresponding parameters.The simulation is carried out by using fixed resistance capacitance and variable resistance capacitor respectively.The accuracy of variable resistance and capacitance model is 2.9%,which verifies the validity of the proposed model.

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.

Theoretical generalization of Markov chain random field from potential function perspective

The inner relationship between Markov random field(MRF) and Markov chain random field(MCRF) is discussed. MCRF is a special MRF for dealing with high-order interactions of sparse data. It consists of a single spatial Markov chain(SMC) that can move in the whole space. Generally, the theoretical backbone of MCRF is conditional independence assumption, which is a way around the problem of knowing joint probabilities of multi-points. This so-called Naive Bayes assumption should not be taken lightly and should be checked whenever possible because it is mathematically difficult to prove. Rather than trap in this independence proving, an appropriate potential function in MRF theory is chosen instead. The MCRF formulas are well deduced and the joint probability of MRF is presented by localization approach, so that the complicated parameter estimation algorithm and iteration process can be avoided. The MCRF model is then applied to the lithofacies identification of a region and compared with triplex Markov chain(TMC) simulation. Analyses show that the MCRF model will not cause underestimation problem and can better reflect the geological sedimentation process.

A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.

Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation

A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.

Pole-placement self-tuning control of nonlinear Hammerstein system and its application to pH process control

By taking advantage of the separation characteristics of nonlinear gain and dynamic sector inside a Hammerstein model, a novel pole placement self tuning control scheme for nonlinear Hammerstein system was put forward based on the linear system pole placement self tuning control algorithm. And the nonlinear Hammerstein system pole placement self tuning control(NL-PP-STC) algorithm was presented in detail. The identi fication ability of its parameter estimation algorithm of NL-PP-STC was analyzed, which was always identi fiable in closed loop. Two particular problems including the selection of poles and the on-line estimation of model parameters, which may be met in applications of NL-PP-STC to real process control, were discussed. The control simulation of a strong nonlinear p H neutralization process was carried out and good control performance was achieved.

航空装备研制费的参数化估算方法研究

根据现代航空装备研制特征及研制费内容构成,对航空装备研制费影响因素及其度量方式进行了分析,针对国内同类航空装备研制项目样本数据少而研制费用影响因素多的特点,研究提出了采用偏最小二乘回归方法开展样本数据的主成分分析和综合变量提取、建立参数化费用估算模型的参数化估算方法。案例证明,提出的参数化估算方法适用于小样本多元数据条件下的费用模型构建,构建的模型预测准确性满足费用估算要求。

典型离子化合物标准熵的估算模型

提出了用于估算典型离子化合物标准熵的双参数模型。此模型可以用来估算由碱金属、碱土金属与氧族元素和卤族元素组成的离子化合物的标准熵。模型通过回归求解给出了估算时各元素的估算参数值。由此模型估算离子化合物的标准熵时,平均误差为2.473J.mol-1.K-1,标准差为3.274J.mol-1.K-1。模型估算误差符合正态分布,并且在与离子束缚模型等估算模型比较中显示了较高的精确度,优于现有的其他估算方法。

Application of fuzzy theory on earthquake damage rate estimation of buildings

Variations between earthquakes result in many factors that influence post-earthquake building damage(e.g.,ground motion parameters,building structure,site information,and quality of construction).Consequently,it is necessary to develop an appropriate building damage-rate estimation model.The building damage survey data were recorded and constructed into files by the Architecture and Building Research Institute(ABRI),Taiwan for the 1999 Chi-Chi earthquake in the Nantou region as a basis for developing a building damage rate estimation model by applying fuzzy theory to express the fragility curves of buildings as a membership function.Empirical verification was performed using post-earthquake building damage data in the Taichung city that suffered relatively severe damage.Results indicate that fuzzy theory can be applied to predict building damage rates and that the estimated results are similar to actual disaster figures.Prediction of disaster damage using building damage rates can provide a reference for immediate disaster response during earthquakes and for regular disaster prevention and rescue planning.

Simulating Particle Swarm Optimization Algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

近30年中国农业源氮磷排放的格局特征与水环境影响

揭示农业源氮磷排放的空间格局、变化特征及其与水环境状况的关系,对于农业面源污染治理投入的合理配置以及区域农业生产结构优化均至关重要。基于这种认识,建立参数化估算模型测算了1990—2017年中国省域尺度的农业源氮磷排放;从总排放量、单位面积排放量和排放重心迁移三个方面研究农业源氮磷排放的变化特征,并通过相关分析与回归分析揭示农业源氮磷排放与水环境状况的关系。研究发现:1990—2017年中国农业源氮磷排放整体上呈随机分布,表明氮磷排放在省际之间并无显著的相互依赖性,主要受省内因素影响。研究期内年均氮磷总排放量最高的省份依次是河南、山东、四川、湖北、河北、安徽和江苏省,格局特征相对稳定但排放重心经历了东西向的往复式转移并具有北偏西方向的总体转移趋势,其中,氮、磷排放的重心分别向北偏西33.52°和15.45°方向转移了33.94 km及52.31 km。在格局研究基础上,结合空间维的格局特征和时间维的变化特征,将31个省份的氮磷排放划分为高排放增长型、高排放平稳型、高排放倒“U”型、中排放增长型、中排放倒“U”型、低排放增长型、低排放倒“U”型和低排放近似“U”型八类,并利用2017年的自动监测站水质数据进一步验证了氮磷排放与水环境状况的关系。研究结果可作为农业面源污染治理中重点区域瞄准的依据。

A model for parameter estimation of multistage centrifugal compressor and compressor performance analysis using genetic algorithm

A model for performance prediction of multistage centrifugal compressor is proposed. The model allows the users to predict the compressor performance, e.g. pressure ratio, efficiency and losses using the compressor geometric information and speed by a stage stacking calculation based on the characteristics of each stage. To develop the compressor elemental stage charac- teristics, the compressor losses, such as incidence losses and friction losses, are mathematically modeled. For a composite sys- tems, for instance a gas turbine power plant, the performance of the multistage centrifugal compressor can be evaluated. Since some important parameters of the compressor model, e.g., the slip factor or, shock loss coefficient （and reference diameter DI, are hard to be determined by empirical laws, a genetic algorithm （GA） is used to solve the parameter estimation problem of the proposed model, and in turn the compressor performance analysis and parameters study are performed. The surge line for the multistage centrifugal compressor can also be determined from the simulation results. Furthermore, the model presented here provides a valuable tool for evaluating the multistage centrifugal compressor performance as a function of various operation parameters.