Ship length distribution modeling on China offshore and ability evaluation of radar ship classification

Radial size estimation using radar high-resolution range profiles(HRRPs) and heading angle estimation are the main means for ship classification.The classification ability is closely related to the range resolution of the radar,precision of radial size estimation,and prior distribution of ship lengths in different offshore areas.We collected the AIS information on about 30 000 ships and their lengths in the four offshore areas of China in the ship information net of China.By fitting the data of ship lengths in each offshore area,it is found that the Weibull distributions provide good-of-fitness to the ship lengths and the parameters in individual area are rather different.Based on the prior distributions of ship lengths,we derived the quantitative relationship between the correct classification probability of big-moderate-small ships and the estimate error of ship radial size.The results indicate that the condition for the big-moderate-small correct classification probability in the offshore areas of China to be up to 90% is that the estimate errors of the ship radial size estimates falls into the interval(-12.67 m,9.41 m) when the heading angle of the ship is between ±75 degrees.     

基于预训练语言模型的关键词感知问题生成

问题生成任务是指根据给定的文本段落和答案来自动生成对应的问题。针对现有问题生成方法存在的误差累积现象以及问题生成任务固有的“一对多”情况，提出一种带有关键词感知功能的问题生成方法。在预训练语言模型的基础上，实现关键词分类模型与问题生成模型的网络结构设计。输入文本段落中蕴含关键词，为使所生成的问题中包含同样的关键词以保证问题与段落的语义一致性，利用关键词分类模型提取出文本段落中的关键词，将关键词与非关键词的区分特征融入问题生成模型的输入中，该特征作为问题生成过程的全局信息，用以消除问题生成模型仅依赖局部最优解的弊端，减少误差累积与“一对多”情况的发生。在SQuAD数据集上的实验结果表明，该方法能够提升问题生成的质量，其BLEU-4指标值可达24，优于带有复制机制、带有语义监督的问题生成模型，目前已经借助百度百科数据平台实现了大规模工业应用。     

Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions

In this paper, we deal with the problem of classification of interval type-2 fuzzy sets through evaluating their distinguishability. To this end, we exploit a general matching algorithm to compute their similarity measure. The algorithm is based on the aggregation of two core similarity measures applied independently on the upper and lower membership functions of the given pair of interval type-2 fuzzy sets that are to be compared. Based on the proposed matching procedure, we develop an experimental methodology for evaluating the distinguishability of collections of interval type-2 fuzzy sets. Experimental results on evaluating the proposed methodology are carried out in the context of classification by considering interval type-2 fuzzy sets as patterns of suitable classification problem instances. We show that considering only the upper and lower membership functions of interval type-2 fuzzy sets is sufficient to (i) accurately discriminate between them and (ii) judge and quantify their distinguishability.     

Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

BackgroundThe application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods.PurposeWe propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers.ConclusionStatistical analysis reveals that our proposed method outperforms other popular classifiers, i.e., support vector machine, self-organizing map, back propagation neural network, and C4.5 decision tree, by conducting experiments on 11 gene expression cancer datasets.     

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

This study investigates how to alleviate the class imbalance problems for constructing unbiased classifiers when instances in one class are more than that in another. Since keeping the data distribution unchanged and expanding class boundaries after synthetic samples have been added influence the classification performance greatly, we take into account the above two factors, and propose a Random Walk Over-Sampling approach (RWO-Sampling) to balancing different class samples by creating synthetic samples through randomly walking from the real data. When some conditions are satisfied, it can be proved that, both the expected average and the standard deviation of the generated samples equal to that of the original minority class data. RWO-Sampling also expands the minority class boundary after synthetic samples have been generated. In this work, we perform a broad experimental evaluation, and experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms.     

基于产品平台的石墨产品分类研究

石墨作为重要的基础原材料和战略矿物资源,应用广泛,产品种类繁多。本文引用离散型工业中"产品平台"的概念与思想,对石墨产品进行纵向分类。将石墨产品中的"材料级石墨产品"替代离散型工业中"产品平台"的概念,并以提纯、细化、改性为技术主导方向对材料级石墨产品进行了系统分析与分类,为石墨产业链的延伸与优化提供依据。     

低渗透储层分类标准及其综合评价——以敖包塔-葡萄花油田FⅡ油层组为例

敖包塔-葡萄花油田钻遇扶杨油层FⅡ油层组有效孔隙度主要分布在10%~15%之间,平均为12.6%;空气渗透率一般分布在0.1~10×10-3μm2之间,属低孔、特低渗透性储层。通过对选用7项宏观参数与7项微观参数确定储层综合评价参数并建立分类标准,确定出每个评价参数的分级标准,将敖包塔-葡萄花油田FⅡ油层组的储层分为四类,其中Ⅰ类最好,为低渗透储层;Ⅱ类次之,为特低渗透储层;Ⅲ类再次之,为超低渗透储层;Ⅳ类储层最差,为极差储层。依据专家经验及大量反复实验的结果上确定了权重系数,预测了FⅡ油层组的储层有利区带,结果表明敖包塔-葡萄花油田FⅡ油层组以Ⅰ类储层和Ⅱ类储层区为主,其中最有利储层区(Ⅰ类储层区)主要呈条带状分布于研究区北部的葡332~葡54~葡115-51、葡31、葡462、敖10等井区。     

A multi-model approach for soft sensor development based on feature extraction using weighted kernel fisher criterion简

Multi-model approach can significantly improve the prediction performance of soft sensors in the proc- ess with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phe- nomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).     

资源约束下的海外工程项目进度风险因素分级研究

目前国内对外承包企业“走出去”步伐日益加快,项目规模、类型、分布有了很大的变化,进度风险日益加剧,受资源约束影响日益显著,承包企业管理难度加大。通过对实施项目的资源种类、重要性、特殊性进行分析,说明资源对海外工程项目实施的重要性。通过文献调研、专家调查等方法,从承包商角度对资源约束下海外项目进度风险因素进行识别、分类,采用层次分析法对各影响因素权重进行分析、排序,建立资源约束下海外项目进度风险影响因素的分级体系,为承包商进行海外工程项目进度风险管理防范提供指导。