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.     

Application of data mining method in power load bad data intelligent identification and correction

There is a number of bad data in the load database produced, thus the data must be cleaned before it is used to forecasting electric load or performing power system analysis. The WKFCM measures distance by kernel functions instead of the complicated Euclidean distance and this kernel based distance is used as dissimilarity function of target clustering formula which can reduce the calculation complexity. After the clustering, a super circle covering neural network based identification model for load data is proposed, and the bad data is modified. It is proved that the proposed data processing model has good effect.     

Application and Development of Chemometrics in Ink Analysis

Ink analysis is an important part of material evidence analysis in forensic science. It not only needs to acquire data by precise chemical analysis instruments, but also needs to parse data by the chemometrics methods such as principal component analysis (PCA), cluster analysis (CA), artificial neural networks (ANN) and pattern recognition and so on. The main applications of chemometrics in ink synthesis, including experimental design and optimization, signal processing, multivariate calibration, pattern recognition, database technology and expert system, have been reviewed. Furthermore, future trends of ink analysis are prospected.     

Temporal stability of multivariate genetic divergence in cacao ( Theobroma cacao L.) in Southern Bahia conditions

Stability of the genetic divergence among five non-commercial cacao cultivars at advanced ages was investigated over a five-year period (1986–1990). Cluster analysis was performed on five yield components measured on harvests from each crop year and on the data pooled over five years. The temporal stability was assessed by using clustering procedure, which involved the calculation Mahalanobis distance (D²) and of Tocher's algorithm applied to the distance matrix. The comparison of D² and of clusters based upon pooled analysis, and taken as the standard, with D² values and clusters obtained from each year showed a stable clustering pattern in the most favourable year. For the rest of the years, inconsistency in number and composition of clusters formed was observed. An optimum environment was shown to be important for better expressing divergence by D². Consequently, the evaluation of the genetic divergence among the studied cultivars may be conducted based on a single favourable year. This will result in considerable savings in man power, financial resources, and time which would be wasted should the evaluation be extended for several years.     

表达序列标签(EST)分析方法及在植物抗病研究中的应用

在国际最大公共数据库GenBank的dbEST数据库中收录了大量各种生物的ESTs,这些大量的EST序列对发现、克隆和定位新基因,构建遗传图谱,开发分子标记,研究基因差异表达、功能及基因间互作是非常有价值的资源。为了充分挖掘EST数据资源所蕴含的生物学信息,掌握系统的分析方法对其研究显得尤为重要。参照目前广泛运用的多种EST数据分析软件,系统地简述了EST的序列分析方法,主要包括EST的序列分析前处理、序列的聚类拼接以及注释、功能分类,指出应根据EST数据的特征来选择合适的序列分析软件,并阐释EST技术在植物抗病基因的发现、克隆和定位研究中的应用。     

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.     

Method of Grey Clustering Decision-making to State Assessment of Power Transformer

The assessment of health state of power transformer is a key step to realize Condition-based Maintenance of transformer. Research on health state assessment of transformer has very important actual significance. Transformer can be recognized as a typical grey system. The assessment of its healthy state using grey clustering decision-making method is put forward. The grey classes of transformer are given, the whitenization weight function of grey classes is established, the information involved in assessing the state of transformer is analyzed, and finally steps of state assessment of transformer using grey clustering decision-making are presented. The analysis of actual example demonstrates that the method has provided a new method to assess the health state of transformer.     

人工神经网络与近红外光谱分析

介绍了人工神经网络的原理、结构、算法和研究进展,以及该方法在近红外光谱分析中的重要地位和应用。     

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。     

ANN Disaster Identifying of Wujiawan Landslide in Wanzhou Urban

The paper aims at ANN disaster-possibility identifying of Wujiawan Landslide. ANN construction and parameter setup are analyzed for landslide disaster identifying by ANN, based on a typical landslide-Wujiawan landslide in Wanzhou urban, by confirming evaluation factor and establishing sample data. The ANN model is trained by the similar landslide sample in Wanzhou urban, then the disaster is identified in several different conditions of Wujiawan landslide. Finally, the same conclusion are found by analyzing combined ANN Disaster-Identifying and limit-equilibrium-method calculation. The results show that AAN is accurate and satisfied to be used landslide disaster-possibility identifying.     

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.     

A Kind of Fuzzy System Modelling and Control Based on Neural Networks

Combining artificial neural networks(ANN) with fuzzy system theory,a kind of modelling & control method of fuzzy system based on ANN is presented.The simulation researches have verified that the proposed approach can be applied effectively to a number of control systems which are defficult to build strict mathematical model.     

Combination of GPS monitoring and ANN prediction for slope deformation

Based on the evolution pattern of slope deformation and failure, stability status of a slope is evaluated through analyzing the data of GPS monitoring. A neural network model of slope displacement time is developed based on the GPS monitoring data. It can be used to forecast slope deformation trends. A neural network displacement prediction model of slope deformation is proposed with Matlab ANN toolbox. Upon a case study, the ANN prediction results based on GPS monitoring data are analyzed.     

“锄禾问天”智慧气象为农服务系统的设计与开发

为满足精准为农气象服务需要,基于Android系统,使用开源软件,采用Java语言,按照大数据采集、大数据分析、智慧感知用户需求、智慧精准靶向推送的设计理念,开发具备自我感知、判断分析以及自适应能力的“锄禾问天”智慧气象为农服务系统,前端系统“锄禾问天”APP可智慧感知用户需求,众筹用户气象指标等信息,后端支持系统进行大数据分析,利用人工智能技术,结合气象实况及预报数据,生成“一对一”的智慧气象服务产品,再通过云服务端精准靶向推送用户。系统已在山东省全面推广应用。     

利用神经网络提取棉花叶片数字图像氮素含量的初步研究

选取6种输入向量组合，利用线性网络、BP网络以及径向基网络等3种神经网络模型进行比较研究，筛选最适宜网络模型和最佳输入组合，建立叶片数字图像彩色信息和叶片氮含量的关系模型，探索利用神经网络技术获取叶片数字图像信息的方法。结果表明，径向基网络在利用数字图像(B，H，G-R，G/R)指标作为网络输入向量时，能够实现获取棉花叶片数字图像氮含量的目标。径向基网络训练的180组样本的训练精度均达到极显著水平（r = 0.9022**），30组测试样本的预测值与实测值也达到极显著相关（r = 0.8674**），径向基网络和(B，H，G-R，G/R)向量是一种适合本研究的数学模型。对利用神经网络提取棉花叶片数字图像氮含量技术的初步探索，拓展了神经网络和数字图像技术在农业生产中的应用。     

Fault Diagnosis in Hydraulic Turbine Governor Based on BP Artificial Neural Network

The governing system of hydraulic turbine generator plays an important role in power system. It is significant to find out the faults of governing system and remove them quickly. This paper sets up a new fault diagnosis model of the hydraulic turbine generator governing system with the advanced ANN (artificial neural net). This 17-in-13-out model consists of three layers. It is proved that this model can find the fault accurately.     

Line-tracking Technology and It'' s Application in the Contest Robots

There are important significations in the industrial production and academy research to manage the intelligent robot walks along the scheduled track. This paper combines the development of the contest robot, and discusses the reliable method to realize this function. By the discussion on the chosen of the key sensors, sensing principle, realization of resisting the interference from surrounding light and self-adaptation adjustment algorithm, the authors analyz the linetracking technology on contest robots precisely. Practice indicates that the method of the hardware and software stated is concise and reliable, and there are some reference significations in the application research of the intelligent robot.     

Genetic characterization of anemone-type chrysanthemum (Chrysanthemum morifolium) using floral morphology and SRAP markers

An appreciation of the genetic diversity retained in the gene pool of anemone-type chrysanthemums is needed to drive further genetic improvement of this commercially important ornamental species. Here, a panel of 67 accessions was characterized with respect to both floral morphology, assessed using a set of 12 inflorescence-associated traits, and to genotype, using sequence-related amplified polymorphism (SRAP) fingerprinting. Abundant variation and a high level of broad-sense heritability (>88%) were revealed for all traits except number of tubular floret lobes. Of the possible 66 pairwise inter-trait combinations, 37 proved to be significantly correlated. A principal component analysis of the trait data showed that the first four principal components explained ~78% of the variance; meanwhile a hierarchical clustering analysis revealed a number of discrete groups reflecting the various cultivated types, but was not predictive of provenance. The SRAP genotyping employed 22 primer combinations and generated 238 informative fragments. A moderately high level of the Jaccard's similarity coefficient (mean 0.67) was calculated for the examined panel. A hierarchical clustering analysis based on the genotypic data revealed four groups, which were clearly reflective of cultivated type; moreover, a comparison of the within-cluster means for each of the two key anemone-related traits—tubular floret length and tubular floret length/style length—showed that the genotype-based clusterings were associated with the anemone levels, albeit imperfectively. A principal coordinate analysis showed that the first two coordinates accounted for about 14% of the total variation. In summary, the present study adds new understanding of the genetic diversity present in the anemone-type chrysanthemum gene pool, and the findings will lay the foundation for implementing a genotype/phenotype association strategy to reveal the genetic and molecular basis of floral form in this valuable ornamental species.     

土壤容重对棉铃虫卵发生的影响

棉铃虫卵量的多少直接决定着危害棉花的幼虫的数量,而在新疆以二代幼虫危害最为严重。不同棉区的气候和土壤类型有差异,而土壤是棉铃虫幼虫入土和化蛹的重要媒介,因此弄清土壤对虫卵产生的作用有着理论和现实的意义。此文通过对南疆（麦盖提县）和北疆（沙湾县）两个植保站的多年虫情资料和全疆30个农业气象试验站的实测数据进行回归分析、通径分析和聚类分析,得出影响南疆麦盖提县虫卵的因子较多,其中土壤容重对其虫卵的发生直接作用较小（-0.025）,指示作用较小,而影响北疆沙湾县虫卵的因子较少,土壤容重对其虫卵的发生直接作用较大（-0.294）,指示作用相对较大。降水对虫卵的影响在南、北疆也不相同。土壤容重对虫卵的影响是负作用,对虫卵的发生起到一定的指示作用。因此,可能运用不同地区的土壤容重,并结合相关的气候和生物因子,可以对棉铃虫卵的发生程度起到一定的指示作用。     

Genetic diversity,population structure and key phenotypic traits driving variation within soyabean (Glycine max) collection in Ghana

Soybean [Glycine max (L.) Merrill] is an important oilseed crop worldwide and it has recently become the crop of interest in Ghana. In this study, 142 soybean accessions were genotyped with 34 SSR markers and concurrently evaluated for five quantitative and two qualitative phenotypic traits. Twenty‐nine of the SSR markers were polymorphic with mean allele number of 5.3, polymorphic information content (PIC) of 0.51 and gene diversity of 0.55. Molecular analysis based on unweighted paired group arithmetic mean (UPGMA) clustering and principal coordinate analysis (PCoA) was similar in explaining the extent of diversity within the accessions. Structure analysis placed most of the accessions into two subpopulations with 18 (12.7%) as admixtures. Principal component analysis (PCA) based on phenotypic traits revealed two clusters. Both UPGMA clustering‐based SSR data and PCA from phenotypic data showed similar results. The assembled germplasm is genetically diverse with high variation in flowering and maturity period, and key yield components which could be exploited in developing superior varieties well adapted to Ghana and West Africa.