Process monitoring method based on correlation variable classification and vine copula

Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

测井资料在优选压裂层位中的应用

压裂是低孔、低渗油层改造的主要增产措施。根据常规测井资料、岩石物理实验分析数据及试油成果。研究储层四性关系，确定区块的低孔、低渗储层评价标准。进一步对油层进行分类，有针对性地为油层压裂提供施工层位和准确的储层参数。通过商543区块实例，阐述了选择压裂层位的方法。     

接轨站设置位置和布置图型的影响因素及其分类

通过对接轨站设置位置和布置图型主要影响因素的分析。按照能够充分反映接轨站作业特点的交接方式。对接轨站所采用的布置图型进行了归纳和分类。     

低流度油藏分类及开发特征研究

低流度油藏指流度低于30×10-3μm2/mPa.s的油藏。根据流度大小以及油藏在开发生产中表现出的特征,可将低流度油藏分为一般低流度油藏、特低流度油藏和超低流度油藏。对大港低流度油藏的研究表明,由于低流度油藏地质情况复杂,地层认识不清,因而开发特征复杂,表现为采油速度低,采出程度和采收率低;注水效果差,油藏井距不适应;油井见水后含水迅速上升,产量则急剧下降。     

3G用户细分研究

对3G用户的细分方法及3G目标市场的定位进行了初步的研究，提出了3G用户细分的体系框架和3G目标市场定位的考虑要素及初步的定位建议。     

地震属性解释技术的研究及确定性分析

随着油气勘探开发的不断发展，储层预测、属性分析等技术得到了广泛的应用，在生产中取得了较好的效果。但地震属性的概念，分类方法，地震属性之间、地震属性与储层参数之间的相互关系仍较模糊，各种地震属性解释方法虽然种类繁多，但缺乏系统的研究，这使得地震属性在应用中存在较大的不确定性。文章通过系统研究地震属性的概念、储层地震响应的基本特征，提出了地震响应的核心是反映地震波在介质传播过程中的能量变化，所有能引起地震能量发生变化的介质的不均质性均可导致地震属性的变化，由此提出了一种新的地震属性分类方法，并认为大部分地震属性只是基本属性在不同域中的不同表现形式。在分析现有地震解释技术的基础上，提出了对地震属性解释的新认识，强调了地震属性对地层岩性油气藏勘探的重要性，对属性间的关系及地震属性解释技术的确定性进行了研究，提出了岩性油气藏地震解释的工作流程及对地震资料采集和处理的要求。     

分类挖掘技术在高校人事管理中的应用

探讨分类挖掘技术在高校实际工作中的应用方式与应用领域。以高校人事管理为模型，应用数据挖掘中决策树算法，对高校人力资源数据源中的信息进行分析，发现其中有价值的数据模式，寻找其中存在的关系和规则，对高校人才规划提供比较客观的决策支持。     

智能组合式充电机的开发与研制

针对集中供电式地震勘探仪器在地震勘探作业中对电瓶充电的特殊要求，我们研制出一种智能组合式充电机。文中阐述了智能式充电机的设计原理，介绍了其性能指标及使用效果。