Statistical Approach Based on POS for Chinese Time Word Disambiguity

Segmentation Ambiguity is an important factor influencing accuracy of Chinese auto-segmentation system. Time words include expressions both indicating exact time positions and those scattering in a treriod of time. On the foundations of modern Chinese corpus processing principles and certain type time word segmentation ambiguity, this paper proposes problem, a statistical language model and corresponding approach based on maximum likelihood to solve the ambiguous and it reaches a 90% accuracy which shows the effectiveness of the algorithm.     

Appoach To Object Semanteme Based Image Segmentation and Classification

There exist potential problems in current region-based image retrievals. This paper proposes a novel approach to object semanteme based image segmentation and classification. First, a hierarchical region growing image segmentation is established using HRGSeg algorithm, which can effectively get rid of weak object semantemes and play down the side effect of over-segmentation. Based on it, low-level features like color, edge and texture are extracted mapped into high-level object semantics hierarchically by using SVM. A fairly good experiment result is achieved and shows the feasibility of our approach.     

基于GWO优化SVM的小麦籽粒优劣分级研究

小麦籽粒优劣不仅是产量及品质的重要决定因素,也是育种适应性的综合指标。为了提高小麦籽粒优劣分级的准确率,同时克服神经网络中存在的收敛速度慢、容易陷入局部极值等缺陷,提出一种灰狼算法（GWO）优化支持向量机（SVM）的小麦籽粒优劣分级方法,以航麦8805为研究对象,利用图像处理技术对小麦籽粒图像进行预处理并提取小麦籽粒的形态、颜色和纹理等21个特征。然后采用灰狼算法对支持向量机的两个参数（c、σ）进行优化,建立GWO-SVM模型,从而对小麦籽粒进行优劣分级。与其他算法相比,GWO优化SVM的算法对小麦籽粒的分级准确率有明显的提高,对小麦籽粒优劣分级的准确率可达到95.08%。     

Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

This paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheat fields using multi-spectral QuickBird satellite imagery for site-specific weed management. The research was conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and 2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluate the wild oat patches mapping at the field level, both pixel- and object-based image analyses were tested with six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The results showed that weed patches could be accurately detected with both analyses obtaining global accuracies between 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate for both the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis, all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentation based on two scales. The first segmentation scale was classified using the MD and ML algorithms to discriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and 88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentation scale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based on the most accurate results obtained in the field-level study, pixel-based classifications using the MD, ML and SVM algorithms were applied to the wheat fields identified. The results of these broad-level analyses showed that wild oat patches were accurately discriminated in all the wheat fields present in the entire images with accuracies greater than 91% for all the classifiers tested.     

Multi classification Method of GA SVM on Identifying Grade of Expansive Soils

Aiming at the fact that parameters in support vector machine(SVM) model were difficult to be identified, a genetic algorithm SVM(GA SVM) was proposed to avoid the blindness in parameter choosing and improve the estimation ability of SVM, in which the parameters in SVM and kernel function were searched by genetic algorithm. And it was then applied to the classification for the swell and shrink grade of expansive soils. Five indexes including liquid limit, total swell shrink ratio, plasticity index, water contents and free expansive ratio were adopted as discriminated factors. And the four grades of the expansive soils were the outputs correspondingly. Classification function was obtained through training a large set of expansive samples. And it was shown that the classification method of GA SVM was effective and with high accuracy.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Segmentation of Color Image Based on Feature Divergence and Fuzzy Theory

In order to improve general adaptive capability of algorithm,the new color image segmentation algorithm based on feature divergence and fuzzy theory(FDCIS) is proposed.The algorithm introduces feature divergence and fuzzy dissimilarity function into calculation in order to measure the dissimilarity of feature vector,clusters data by means of feature divergence,and accomplishes the merge of image region.The experimental results demonstrate that the color image segmentation result of the proposed approach reduce calculation on large sample of color image,simply and effectively solve over-segmentation of color image,avoid the dependence of the algorithm on initial condition,and hold favorable consistency in terms of human perception.     

渔业科学数据智能RSS阅读器的设计研究

针对传统RSS阅读器在实际应用中接收大量冗余信息的问题,提出一种智能化的RSS阅读器。该阅读器基于渔业科学数据平台,采用向量空间模型,运用中文分词、对象持久化等技术实现智能原理,设计实现了智能化RSS阅读器。实验证明：该阅读器的过滤有效性为86.2%,过滤准确性为82.4%,能够较好地过滤掉与用户不相关的信息。应用结果表明：渔业科学数据智能RSS阅读器的实现可使用户获得更精准的信息。     

Partial discharge pattern recognition based on S transform and two-directional 2DPCA

A new feature extraction method is proposed to recognize different types of partial discharge (PD) signals. Firstly,four typical categories of PD artificial defect models are made and S transform (ST) is employed to obtain a time-frequency representation of the recorded UHF signals. Then,two-directional two-dimensional principal component analysis ((2D) 2PCA) is applied to compress the ST amplitude (STA) matrix to extract features. Finally,support vector machine (SVM) combined with particle swarm optimization (PSO) algorithm is introduced to accomplish the recognition of experimental samples. Classification results demonstrate that the average recognition rate of (10,5) combination is the highest while the one of (5,5) combination is the lowest among four kinds of feature dimension combinations. Moreover,PSO can obviously improve the classification performance of SVM. Specifically,all the average recognition rates of PSO-SVM are higher than 94.43%and the maximum value comes to 97.67%. Therefore,the feature sets extracted by ST and (2D) 2PCA can not only achieve dramatic dimension reduction,but also retain the major information of original data. It is proved that the proposed algorithm can obtain ideal results in PD pattern recognition.     

基于颜色和纹理特征的花生仁外观品质检测研究

为了提高对花生仁外观缺陷的在线分类准确率及效率。通过对采集完好、破损、霉变的花生仁RGB图像进行均值位移法、灰度处理以及阈值分割等预处理,研究提取了花生仁HSV颜色空间下的H、S、V各分量的一阶矩和二阶矩共6个颜色特征值,再基于灰度共生矩阵法提取能量、熵、对比度、逆差分矩共4个纹理特征值,构建颜色和纹理结合的特征向量,最后分别采用BP神经网络和SVM分类器对花生仁进行分类识别。结果表明：在花生仁的整体识别准确率上,BP神经网络为96.67%,SVM分类器为97.22%,后者优于前者,在识别时间上BP和SVM分别为2.5 s和1.1 s,识别效率上也是SVM更好,综合识别准确率和效率两方面考虑,优先选择SVM分类器模型来对花生仁进行分类识别。     

An incomplete binary tree SVM multi class ciassification algorithm based on hypersphere

On the base of current researches on multiclass classification with support vector machine, an incomplete binary tree SVM multi class classification algorithm based on hypersphere is proposed. The algorithm adopts hypersphere SVM algorithm to calculate the distribution of each sample groups. Then, the distance formula is used to calculate the distance among the sample classes. According to the principle that the class which can be separated easiest must be split first, the algorithm designs binary tree to improve the classification accuracy. Compared with many classification methods, the effectiveness of the algorithm is verified by simulation experiments.     

Markov Logic Networks with its application in De duplication

In order to solve the limitation that the traditional De duplications are mostly used for a specific field and only address one aspect of a problem,a scheme based on Markov Logic Networks (MLNs)is proposed, which is a new Statistical Relational Learning (SRL) model. With its advantage of computing the probability distribution of worlds to serve for the inference, the De duplication is formalized. Discriminative learning algorithm is adopted for Markov Logic Networks weights, MC SAT algorithm is adopted for inference. It shows how to capture the essential features of different aspects in De duplication with a small number of predicate rules and also combines these rules together to compose all kinds of model. The experiment results prove that the method based on Markov Logic Networks not only covers the original Fellegi Sunter model, but also achieves a better result than the traditional methods based on Clustering Algorithms and Similarity Measures in De duplication. It reveals that the Markov Logic Networks can play an important part in practical application.     

采空区危险性的支持向量机识别

针对采空区危险性影响因素与其危险性等级之间存在着复杂非线性关系的特点,笔者提出采用支持向量机最优分类理论来识别采空区的危险性等级。研究选取岩体结构、地质构造、岩石抗压强度、弹性模量、采空区形状、矿体倾角、高跨比、空区体积等8个参数作为主要影响因素,根据支持向量机理论,提出了1-V-1的采空区分类算法,并在Matlab中编程,建立了分类预测的SVM模型。以某矿山的实测采空区为例,利用该模型进行了识别,并与BP神经网络预测结果作对比。实例研究表明,采用该方法的分类结果比神经网络更准确,与采空区调查结果一致性好,用支持向量机理论进行采空区危险性评价是可行的。     

Labor Market Segmentation in Urumqi,Xinjiang: Exposing Labor Market Segments and Testing the Relationship between Migration and Segmentation

Labor market segmentation and migration are two phenomena that are dramatically reshaping the spatial, economic, and social relationships of many urban cities in both developed and developing countries. To this point, the bulk of Chinese literature falls within the context of area studies, without much effort to link Chinese migration and emerging labor market outcomes to larger global trends and discourse. This research attempts to link the body of internal Chinese migration and emerging labor markets to labor market segmentation theory, primarily developed by urban economists and sociologists. My findings provide evidence that applying labor market segmentation theory to examine emerging markets in China offers fruitful results that help to identify the new urban stratification that exists in China. I employ a set of quantitative methods using employee‐level field data that I collected in Urumqi in 2008 to identify distinct segments within Urumqi's labor market and argue that migration is a major driver of labor market segmentation. Cluster analysis shows Uyghur minorities and women are found to be overwhelmingly concentrated in the lower sector, composed mostly of “bad” jobs. Discriminant analysis reveals that migrant status and ethnicity are the most important variables that deepen the gap among the labor market segments. The social inequality created as a result of market segmentation can partially explain Uyghur discontent in the region and the July 2009 riots, one of the worst riots in Xinjiang's modern history.     

基于人工蜂群算法优化支持向量机的 采场底板破坏深度预测

为确定合理的底板防水煤岩柱尺寸,减少底板突水安全事故的发生,利用支持向量机(SVM)与人工蜂群算法(ABCA)综合研究底板破坏深度问题。由于SVM训练参数惩罚因子 C 和核函数宽度 g 的选择对预测精度的影响显著,采用ABCA优化该训练参数的选择过程,建立基于SVM的底板破坏深度预测模型。选取采深、煤层倾角、采厚、工作面斜长、底板抗破坏能力和是否有切穿断层或破碎带作为影响底板破坏深度的主要影响指标,利用现场实测的30组数据作为样本对该模型进行训练和预测。结果表明：该预测模型的平均相对误差为12.5%,平均绝对误差为 0.986 m ,均方误差为0.005,平方相关系数为0.980,较其他预测模型具有更强的泛化能力和更高的预测精度。     

Impact of Chinese market segmentation on regional collaborative governance of environmental pollution: A new approach to complex system theory

The market segmentation among local governments under decentralization of China not only directly triggered the “fragmentation” of regional development, but also hindered collaborative actions in environmental governance. This study adopts a new method to calculate regional environmental collaborative governance using a synergy degree model of a complex system, and then it empirically analyzes the impact of Chinese market segmentation. We find that the regional environmental collaborative governance in China shows a growing trend during the period of investigation, but the level is still low. Market segmentation has significantly inhibited the regional collaborative governance of environmental pollution among local governments of China. The effect of market segmentation on the order of personnel input and capital input is significantly negative, while the effect on the order of policy input and organizational input is not significant. In the areas where the air pollution and water pollution are serious, the effect of market segmentation is also significantly negative. The conclusions are helpful to understanding the institutional factors that hinder regional environmental collaborative governance in China more comprehensively, and provide insights for improving the performance of environmental governance.     

A Fast One-Step Scanning Thinning Algorithm

A new quick thinning algorithm was presented in this paper,which assigneddifferent weights to different pixels near the skeleton pixels and rules out the im possible pixels in thethinning procedure. It needs neither iteration on pixel panel nor detection on every pixel such thatthe time consumed is greatly lessened. A filling algorithm is also offered for better results.Experiment on variety of binary patterns showed that it could get both a high speed and a goodskeleton shape compared with other algorithms,This new algorithm reaches perfect result.     

Adaptive Image Segmentation Method Based on Ncut

In order to solve the problem that requires some factors by manual in the traditional Ncut algorithm, limit the generality of the algorithm, an adaptive image segmentation method is proposed by improving the traditional Ncut algorithm. First, instead of the two control parameters on the calculation of weight matrix that influence the segmentation results in the traditional Ncut algorithm by groups of potential theory; then in order to reduce the sensitive to the number of the cluster and the center of the cluster in the K-means algorithm, calculate on the eigenvector of the Ncut algorithm by the minimum spanning tree, to get the final number of cluster and the center, and then uses the K-means clustering algorithm to get the final segmentation result. The experimental results show that the proposed method not only improves the versatility of the algorithm, and the segmentation is good.     

基于智能算法优化SVM的横向通风过程中温度场预测方法探究

将改进的智能预测技术应用于储粮横向通风过程中的粮堆温度预测,为粮食通风智能预测与决策提供了一种新思路。选取河北清苑国家粮食储备库冬季横向通风的实时监测数据,在分析主要影响因素的基础上,应用三种智能优化算法——网格寻优算法、GA遗传算法寻优、PSO粒子群算法,结合回归支持向量机理论,对粮堆的通风过程进行建模。结果表明,优化过的回归预测模型能较好地拟合粮食温度与其他变量之间的非线性关系,尤其是当样本数量较为有限时,该方法具有更高的拟合精度,更适合对储粮通风这一强非线性过程的预测研究,对于人工干预操作具有一定的现实指导意义。