A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

新形势下普通高校大学生行为管理模式研究

高校是培养人才的摇篮,肩负着科教兴国、人才强国两大历史性战略任务。由于社会环境的复杂化,人际关系的交错性,新时代的大学生行为状况给高校学生管理工作带来了新的挑战。针对新时期大学生行为特点,我们通过加强党建工作、强化学风建设、做好班级自主管理和深化社会服务四个方面工作进行分析,提出了全面引导、约束、规范和优化大学生的行为的建议和对策,从而有效解决新形势下普通高校学生管理工作的实际困难。     

冷轧平整机机前甩尾轧制工艺的开发与应用

本文介绍了柳钢冷轧板带厂1550 mm单机架平整机机前甩尾轧制的工艺。改进前轧制每卷钢卷生产效率低,钢卷成材率低,改进后生产效率、成材率明显提高,部分规格的钢卷能实现带尾剪切长度为零。     

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

课程群案例嵌入协同教学模式的探索

在课程群的教学中由于每门课程各自独立开展教学,缺乏知识的融合和衔接,导致学生运用综合知识解决问题的能力较弱。在课程群的教学中采用案例嵌入协同教学模式,将完整的工程案例嵌入到课程群各门课程的教学中,协同规划各门课程的教学任务,每门课程再围绕案例展开研究性教学。通过嵌入的工程案例衔接各门课程的知识点,帮助学生建构完整的知识体系,强化工程应用的概念；同时通过研究性教学,培养学生分析问题和解决问题的能力,两部分相结合,提高了学生运用综合知识解决复杂问题的能力。     

基于USB总线的虚拟频谱分析仪设计

文章详细介绍了基于USB总线的虚拟频谱分析仪的设计过程。该仪器使用嵌入式混合处理器MC56F8323来实现动态信号的实时采集、频谱分析和数字滤波等功能。处理结果由USB2．0接口芯片CY7C68013上传至PC机．以完成数据显示、存储、绘制图形等功能。其中，CY7C68013工作于Slave FIFO模式，以实现与MC56F8323间的数据传输。该系统可同时对两路动态信号进行实时谱分析，每路采样频率高选500kHz。     

Failure mode transition and energy dissipation in naturally occurring composites

The microstructural aspects of compressive inelastic deformation in balsa wood are investigated with emphasis on the failure mode transition and its effects on energy dissipation characteristics. The architectural features as well as the composite character of cell wall ultrastructure are discussed in a framework to understand the complex interrelationship between microstructure and macroscopic behavior in this extremely lightweight cellular biocomposite. Based on this discussion and experimental results, it is concluded that the biomimetic approach may prove to be a viable strategy in designing composite structures with high specific energy absorption capacity.     

提取地震信号高频信息方法的若干问题

岩性油气藏勘探已成为当代油气勘探的重要组成部分,因此提取地震资料高频信息已成为一个重要研究课题。文中对分辨率、分频处理地震信号、波阻抗反演中的一些问题进行了较深入的讨论,并得出以下认识:1要提高垂向和横向分辨率,必须设法提高野外采集地震资料的频率;2地震分频处理是偏移后提高分辨率的常用方法之一,这种方法应该限定在地震波的有效频带内,而超出有效频带,可能会带来很多假像;3低频合成地震记录中高频反射系数存在不确定性。在基于模型波阻抗反演的方法中,反演结果受初始模型的影响非常大,初始模型是依据测井资料建立的,初始模型与实际情况越接近,反演效果就越好;否则,会产生模型化的现象;4在相对波阻抗数据体(相当于地震数据体)作为约束条件下的SP测井参数协克里金插值是正演过程,消除了模型化的问题,使反演结果更接近真实情况,提高了砂层真实的分辨率。