基于局部重构的无监督特征选择方法

利用局部线性嵌入(LLE)算法中获得局部邻域之间的重构关系与使用最小角回归方法解决L1归一化问题都使用回归方法,针对在通过映射获得低维嵌入空间与通过特征选择获得低维空间上有着一致的思想,提出一种能保持局部重构关系的无监督谱特征选择方法.该方法利用最小二乘法计算样本的邻域重构系数,并用这些系数表示样本之间的关系,通过解决稀疏特征值问题获得能够保持样本间关系的低维嵌入空间,最后通过解决L1归一化问题实现自动特征选择.通过在四个不同数据集上的聚类实验结果证明,该方法能更准确地评价每个特征的重要性,能自动适应不同的数据集,受参数影响更小,可以明显提升聚类效果.     

基于蚁群聚集信息素的半监督文本分类算法

半监督文本分类中已标记数据与未标记数据分布不一致,可能导致分类器性能较低。为此,提出一种利用蚁群聚集信息素浓度的半监督文本分类算法。将聚集信息素与传统的文本相似度计算相融合,利用Top-k策略选取出未标记蚂蚁可能归属的种群,依据判断规则判定未标记蚂蚁的置信度,采用随机选择策略,把置信度高的未标记蚂蚁加入到对其最有吸引力的训练种群中。在标准数据集上与朴素贝叶斯算法和EM算法进行对比实验,结果表明,该算法在精确率、召回率以及F1度量方面都取得了更好的效果。     

基于改进卡方统计的微博特征提取方法

通过对微博文本特征信息的分析与研究,提出一种基于改进卡方统计的微博特征提取方法。扩充微博信息分类特征,在传统的卡方统计量的基础上,引入了频度等因素,改进特征选择方法;在传统的特征项权值计算的基础上,提出了新的改进卡方统计量的方法,改进权重计算效果。对上述方法利用经典KNN和SVM算法进行了测试,实验结果表明该方法提高了微博信息分类的准确率。     

黄河水水质配方实验及其应用

该文通过水质调查和大量实验数据，对循环水改为黄河水质运行的配方进行了筛选，并提出了自然ｐＨ全有机配方的概念，通过监测数据，对黄河水配方运行情况进行了总结，该配方投用后，不但能节约地下水资源，而且经济效益显著。     

化工防腐仪表的选择

介绍工业自动化仪表在氮肥，磷肥生产过程中，对检测控制仪表，热电偶，热电阻，变送器，流量计，液位计的选择。     

长河坝水电站初期导流洞下闸封堵方案优选研究

长河坝水电站初期导流洞堵头挡正常蓄水位的水头高达212.00 m,封堵工程量大、程序复杂、直接影响电站发电工期。结合导流洞封堵施工的特点,通过结构稳定计算分析,拟定永久堵头挡水、闸门挡水、临时堵头与永久堵头结合、2号导流洞堵头位置下移4种下闸封堵方案,并从发电工期、施工进度、工程结构安全、工程投资等方面进行全面的经济技术比较,进而优选出最佳方案。研究成果可为类似高堆石坝工程导流洞的下闸封堵设计提供借鉴。     

超声CO激光的现状和展望(续)

介绍了200kWCW超声电子束连续CO激光器（SSCO－EIL）开环实物模型的能量和光谱特征的实验研究结果，以及增加SSCO－EIL的输出功率，谱线选择性，Q－开关激光作用的数字计算结果，同时讨论了SSCO－EIL的特性对几种激光技术效率的影响，由于激光能量利用率的增加和能量成本的降低，SSCO－EIL拥有的无与伦比的能量和特有的特征，将大大拓宽激光技术的应用领域。     

工业PTA氧化过程4-CBA浓度的模糊神经网络模型

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.