Intelligent Extracting Method for Feature Mode of Electric Days Load

Generally there have a number of bad data in the electric load data and it affects the precision of load forecasting,so it is necessary for extracting the feature mode of days load data,then cleaning the load data before it is used to forecasting electric load or performing power system analysis.Inspired by soft clustering thought,a intelligent feature mode of days load data extracting method is proposed based on the mutual offset of fuzzy c-means clustering arithmetic and Kohonen self organization feature map neural network.With the merits of not only high extracting precision and convergent speed but also dynamic calculation capability,the method proposed can supply load forecasting or system analysis procedure with due data.Test results using actual data of Chengqu power supply bureau in Chongqing demonstrate the effectivity and feasibility of the method.     

A multi model composition framework based on bayesian model comparision and its application in soft sensor modeling

In order to improve the prediction performance of single model based soft sensor, the features of the current model combination frameworksby analynizing, a new multi model combination framework based on the bayesian model comparison is proposed. In this framework, fuzzy c means clustering to the historial data is used to analyze the production states, then the prediction performance of sub models at different states are compared based on bayesian model comparison. The comparing results are the basis of the model combination stratery at different states. With adapting cross validation predictive distribution, the samples got from the trained models are used to successfully reduce computation load of model comparion.The framework has obtained good results in the practical application.     

构建作物种质资源核心库的一种有效抽样方法

本文提出了基于基因型值构建作物种质资源核心库的抽样方法。 采用包括基因型与环境互作的遗传模型及混合线性模型统计分析原理, 无偏预测基因型值。 用基因型值计算基因型间的马氏距离, 并采用不加权类平均法进行聚类。 根据树型图, 确定合理的分类水平, 将群体分成若干不同的类群。 计算各基因型的平均离差度, 在各类     

Partition Algorithm of Power Network Based on the Optimum Clustering Principle

The paper construct the specimen of multi-dimensional space based on the definition of clustering, calculate the generator control sensitive to load and the Eulerian distance, partition the power system by the shortest distance method of clustering analysis. After the range of area num-ber is determined, the optimal area number and its validity is decided by Shannon function of entropy and Sugeno-Yasukaw rule. A case of IEEE 39-bus system is presented to verify the method.     

一种新的符合度算法及其应用

在总结分析了几种常用综合评价方法的基础上, 提出了一种反映观察值与理论值之间相似性的新算法--符合度。该算法就评价信息个体(观察值)与标准值(期望值)的马氏距离, 再由马氏距离转化为评价对象与标准的接近程度, 即符合度(r)。首先进行指标数(p)、相似度(r)与马氏距离(d)的模拟试验, 再通过曲面拟合的方法找出它们之间的关系模型。通过大量抽样试验, 验证符合度的次数分布与原先设定的符合度的良好对应关系, 说明模型的可行性与可靠性。以小麦RVA性状为指标, 利用该算法分析扬麦系统若干品种之间的接近程度, 并评价多变数复杂效应回归分析模拟试验的结果。符合度算法不需要数据标准化处理, 直接利用原始数据, 减少了计算工作量, 降低了因数据标准化处理方法不同而引起的评价结果差异, 同时由于不需要赋权, 排除了主观性的影响, 保证了信息的完整性以及评价结果的可靠性。     

自适应加权最小二乘支持向量机的空调负荷预测

为了提高建筑空调负荷的预测精度,在分析空调负荷主要影响因素的基础上提出了一种基于自适应加权最小二乘支持向量机(AWLS-SVM)的建筑空调负荷预测方法。该方法根据预测误差的统计特性,采用基于改进正态分布加权规则,自适应地赋予每个建模样本不同的权值,以克服异常样本点对模型性能的影响。建模过程中采用粒子群优化(PSO)算法对模型参数进行优化,以进一步提高模型预测精度。基于DeST模拟数据将AWLS-SVM方法应用于南方地区某办公建筑的逐时空调负荷预测中,并与径向基神经网络(RBFNN)模型、LS-SVM模型及WLS-SVM模型作比较,其平均预测绝对误差分别降低了51.84 %、13.95 %和3.24 %,并进一步基于实际空调负荷数据将该方法应用于另一办公建筑的逐日空调负荷预测中。预测结果表明：AWLS-SVM预测的累积负荷误差为4.56 MW,亦优于其他3类模型,证明了AWLS-SVM具有较高的预测精度和较好的泛化能力,是建筑空调负荷预测的一种有效方法。     

Cellular automata spatial temporal data model and forecasting approach

The standard cellular automata(CA) model is expanded to meet requests of space time dynamic simulation and forecast under the platform of geographic information system(GIS). Taking power load forecasting of the electric power industry as the specific application, the relations between dynamic model of the land use and power load space are established. The data and attribute data interactive discrete in spatial temporal data management have been solved. The CA theory is practically used to simulate the process of urban land use dynamic development, to forecast future land use types of each small area, to establish spatial load forecasting model. It breaks through the localization of all kinds of forecasting methods of traditional space time separation power prediction. The effectiveness of the prediction method is verified by example.     

A real time monitoring method of energy consumption based on data mining

A real time monitoring method of energy consumption based on data mining techniques is proposed to compensate the deficiency of common energy consumption methods in real time and intelligence. The new method can identify energy consumption patterns by clustering analysis of historical energy consumption data, get the decision tree of energy consumption pattern by classifying the energy consumption data, match the real time energy consumption data with the energy consumption patterns, make outlier analysis with historical data of the same pattern, and then determine whether the current energy consumption is abnormal. The experiment with energy consumption data from the comprehensive building proves that the new method is effective in detecting the abnormal data of energy consumption real timely.     

Two-mode clustering of genotype by trait and genotype by environment data

In this paper, we demonstrate the use of two-mode clustering for genotype by trait and genotype by environment data. In contrast to two separate (one mode) clusterings on genotypes or traits/environments, two-mode clustering simultaneously produces homogeneous groups of genotypes and traits/environments. For two-mode clustering, we first scan all two-mode cluster solutions with all possible numbers of clusters using k-means. After deciding on the final numbers of clusters, we continue with a two-mode clustering algorithm based on a genetic algorithm. This ensures optimal solutions even for large data sets. We discuss the application of two-mode clustering to multiple trait data stemming from genomic research on tomatoes as well as an application to multi-environment data on barley.     

Shear resistant performance of RC columns subjected to vertical near-fault velocity pulse-like earthquake actions

The aim is to evaluate the seismic properties of ancient timber structure after strengthening and analyze the failure process and corresponding failure state. Based on the hysteretic behavior and energy dissipation principle of the dovetail column-frame strengthened with CFRP and Arches Brackets under the low reversed cyclic loading, the “potential of destruction-resisting” of the two energy-consuming components is obtained. The dissipated energy of each energy-consuming component under the various earthquake conditions is calculated combining with the shaking table test of ancient timber structure. The model of seismic damage evaluation of the two energy-consuming components is established on the basis of the “potential of destruction-resisting” and the dissipated energy. By means of the energy distribution coefficient, the relationship of the failure state between energy-consuming components and overall strengthened structure is discovered, and the model of seismic damage evaluation of the overall structure under the various earthquake conditions is presented. With the derived model of seismic damage evaluation, the failure coefficient of the energy-consuming components and the overall strengthened structure is quantitatively calculated. According to the failure state, the corresponding damage grade of overall strengthened structure is obtained. The results can provide a reliable theoretical basis for predicting the destruction before earthquake and re-reinforcement to the strengthened ancient timber structures after earthquake.     

基于布谷鸟搜索神经网络的微波加热温度预测模型

微波加热是一种与被加热物直接相互作用的选择性加热方式,具有清洁、节能、减排等特点。针对工业物料作为微波加热负载时,其温度非线性变化的特点,以微波工业加热过程中的多维、海量参数为研究对象,基于泛函接神经网络模型提取样本数据的深度特征,提出了一种基于布谷鸟搜索算法,优化BP神经网络的网络参数,建立了以"数据驱动"为手段微波加热工业物料温度模型。仿真实验结果证明了所提出模型的准确性、实时性。     

基于标记控制分水岭算法的树冠高程模型分割

树冠高程模型是树冠表面模型和数字高层模型的差值,是森林资源调查的重要数据源。笔者提出利用标记控制分水岭算法进行树冠高程模型分割的方法,首先用高斯滤波平滑树冠高程模型,对树冠边缘做初步检测,然后利用形态学重构方法进行树冠形状确认,在此基础上进行二值图像的距离变换和h-minima变换,并标记树冠顶部,最后对实验数据进行分水岭分割,实现单株木树冠边缘勾勒。误差分析表明,该方法能有效提高分割正确率,当结构元素和h-minima变换中的参数分别等于3、1.5时,树冠分割正确率为78.5%。     

基于粒子群算法和支持向量机的黄花菜叶部病害识别

使用数字图像处理技术,以黄花菜叶部病害图像为识别对象,基于Lab空间和K-means聚类算法分割病害区域,提取目标区域的颜色特征、方向梯度直方图(histogram of oriented gradient,HOG)特征和形状特征,分别建立单一特征模型和特征融合模型,采用粒子群(particle swarm optimization,PSO)算法通过交叉验证优化支持向量机(support vector machine,SVM)模型的惩罚因子和核参数,建立基于PSO-SVM的多特征融合分类模型识别黄花菜病害。基于SVM的多特征融合分类模型识别率高于单一特征分类模型,识别率可达为81.67%;基于PSO-SVM多特征融合分类模型识别率高达92.39%。基于PSO-SVM的多特征分类模型识别率高,可以及时、便捷、高效地识别黄花菜病害。     

Application of a dendrogram seriation algorithm to extract pattern from plant breeding data

A dendrogram is often used to display the results from hierarchical clustering; however, the order of objects in a standard dendrogram is arbitrary and so similarity cannot be readily interpreted. An optimized dendrogram, a dendrogram produced by re-ordering the objects using a seriation method, has a customized ordering that reflects the similarity among objects with most similar objects located closest together. Hierarchical clustering has been applied to the analysis of data from plant breeding programs to identify the patterns in breeding populations and to study genotype by environment interactions. In this paper we demonstrate the advantage of an optimized dendrogram for interpretation of plant breeding data and, given this advantage, argue that an optimized dendrogram should be used as the default whenever hierarchical clustering is used.     

A Fuzzy Regression Method for Long-Term and Medium-Term Load Forecasting

In view of the observation data fuzziness and load pattern fuzziness,a new fuzzy regression prediction method was presented for long-term and medium-term load forecasting. With the established fuzzy regression model, the future load value can be forecasted based on the fuzzy historical observation data. The validity of the proposed method was verified with the numerical example of a practical system.     

Logistic model-based genetic analysis for kernel filling in a maize RIL population

Kernel filling is an important factor that directly affects kernel yield in maize. Based on a Logistic model, the process of kernel filling in maize can be effectively fitted, and the characteristic parameters with biological significance can be estimated. To clarify the genetic mechanism of characteristic parameters of kernel filling in maize, a recombinant inbred line (RIL) population including 208 lines derived from the maize inbred lines DH1M and T877 were evaluated in Nantong in 2015 and in Yangzhou in 2016, respectively. The kernel dry weights of recombinant inbred lines were measured 10, 15, 20, 25, 30, 35, 40, 43, 46, 49, 52, 55, 58 and 61 days after pollination (DAP). A total of 12 characteristic parameters related to kernel filling were estimated in different environments using the Logistic model. These parameters showed abundant phenotypic variation across two environments in the recombinant inbred line population. Some more ideal genotypes were selected through clustering based on BLUP values of characteristic parameters. Genetic analysis indicated that the 12 characteristic parameters conformed to the “major gene plus polygenes” model. The results of two environments were reproduced well. Most of the characteristic parameters related to kernel filling were controlled by two major genes, and a few characteristic parameters were controlled by three or four major genes. In addition, the genetic models of some characteristic parameters differed in the two environments due to interactions between the genes and environments. This study not only laid a foundation for further clarifying the genetic mechanism of maize kernel filling and mapping the related genes but also suggests a new paradigm for dynamic developing traits.     

A BOUNDARY ELEMENT METHOD WITH LOWER-ORDER SINGULARITY

In this paper, a lower-order singular BEM based on original BEM is presented by introduccing a new variable of angle. In the new method, kernel of force is the same as kernel of displacement which is only singularity of InR ( R is the distance between a source and a field point ) . Hence, it results in a new BEM formulation, whose singularity is lower than that of the original BEM formulation. What's more, in calculating stress, it eliminates the boundary-layer effect in the main. A software which is based on lower-order singular formulation is used to analysis the bar of 300T coiningmachine, its result is coincident with that obtained by the original BEM and photo-elastic experiment.     

一种基于似然极大的动态聚类方法及其应用

将传统的动态聚类分析和判别分析相结合,引出一种基于似然极大的动态聚类方法,该方法以EM算法实现的极大似然估计进行类参数估计,以相应的贝叶斯后验概率判别个体的归类。模拟研究表明,该方法通常既可无偏估计类参数,又可判别最佳分类个数。与重心法动态聚类和最小组内平方和法动态聚类相比,稳健性较高。同时通过提高判别标准,可以降低误判率。用Fisher的Iris试验数据验证了方法的可行性,并将之成功应用于一个水稻F₂群体的个体的主基因基因型鉴别。     

Genetic diversity within Australian wheat breeding programs based on molecular and pedigree data

124 wheat cultivars and breeding lines were screened with 19 microsatellite (SSR) loci generating 160 scorable bands which were used to construct a genetic distance (GD) matrix. A distance matrix based on coefficient of parentage (COP) scores was also generated for the cultivars for which good pedigree records were available. The SSR and COP data for 101 of the wheat cultivars were compared with genetic distance scores obtained using1898 scorable restriction fragment length polymorphism (RFLP) bands previously generated. Phylograms were generated based on the SSR, RFLP,combined SSR and RFLP and COP data. The standardised Mantel's Z test showed that the distance matrices generated from all of the data sets were significantly correlated. Bootstrap analysis showed that, although the SSR and RFLP data were correlated, a large number of SSR loci are required for determining robust genetic relationships between large numbers of cultivars. In addition, accurate pedigree records are needed to determine genetic relatedness using COP. The molecular data were also used to determine the level of genetic variability within breeding programs and to assess the impact of the introduction of semidwarf and other germplasm. The results showed that the level of genetic diversity in Australian wheat cultivars has increased over time and that in particular, the introduction of semidwarf germplasm resulted in an increase in the overall diversity. This revised version was published online in August 2006 with corrections to the Cover Date.     

Short-Term Electric Load Forecasting Based Trends Combination Model

In short term load forecasting based on ANN,weather is one of the important factors which impacts on load greatly. In order to capture the effect of weather on load, this paper presents a novel thought based on ANN and trends combination short term load forecasting. Decompose the underlying relationships between load and weather variables into three main trends of weekly, daily and hourly. Three separated ANNs capture each trend. Another ANN to arrive at the final forecast combines the forecasts yielded by individual ANNs. The performances of the proposed model and the traditional model are compared on the basis of one week ahead hourly forecasts. Results indicate that the proposed ANN based model can achieve greater forecasting accuracy than the traditional ANN based model.