一种新的水平集停止项函数选取方法研究

现今的停止项采用的全局阈值法很难使经典水平集算法得到较准确的分割效果。为此,构建了一个新的停止项函数,该函数在梯度较小时为一个常数,超过某个阈值后为单调递减函数,直至到某个梯度时递减为零。这保证了采用该函数的水平集方法能够实现在同质区加速演化,在边缘区停止演化的目的。实验结果表明,采用新的停止项函数能够使水平集方法获得更好的分割结果。     

单车型确定性动态车辆调配问题

给出了单车型确定性动态车辆调配问题的定义,引入网络图描述该问题.建立了问题的线性规划模型,鉴于线性模型的缺点,利用函数逼近技术构造一个特殊的线性函数来近似目标函数中未来时段部分,从而建立起问题的时空分解模型,把问题从时间和空间上分解为多个单时段单节点问题,并根据单时段单节点问题特点设计简单的排序求解方法.最后,给出了模型的完整求解过程,从而使问题得到有效地解决.     

干线运输优化的两阶段局部搜索启发式算法

干线运输的车货匹配及车辆运输路径优化对于降低大规模、多分布网点的物流企业的运输成本有重要意义.建立了干线运输的发车费用模型及总费用模型,提出应用一种基于直达网络搜索和车辆行驶路径搜索的两阶段局部搜索启发式算法进行求解.通过对企业实际数据的计算,表明该算法优化了干线运输的运输路径,减少了运输车辆数,降低了运输总费用,具有较好的应用前景.     

基于上下层MAS的SAR图像海洋分布目标检测

针对SAR图像中的海洋分布目标,从人的认知过程出发提出了一种基于上下层多Agent系统(top bottom multi Agent system, TBMAS)检测算法。该算法针对上下层Agent定义了不同的行为。下层Agent获得初步的视觉知觉,上层Agent加入联想分析等处理获得最终检测结果。算法从分布目标的局部统计特性考虑,不需要进行统计模型和参数的估计,利用局域直方图和Kullback Leibler散度进行差异度分析。实验表明,对于多种分布目标均有较好的检测效果。     

Automatic patent document summarization for collaborative knowledge systems and services

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.     

Testing serial correlation in semiparametric varying coefficient partially linear errors-in-variables model

The authors propose a V _{N, p} test statistic for testing finite-order serial correlation in a semiparametric varying coefficient partially linear errors-in-variables model. The test statistic is shown to have asymptotic normal distribution under the null hypothesis of no serial correlation. Some Monte Carlo experiments are conducted to examine the finite sample performance of the proposed V _{N, p} test statistic. Simulation results confirm that the proposed test performs satisfactorily in estimated size and power. This research is supported by the National Natural Science Foundation of China under Grant Nos. 10871217 and 40574003; the Science and Technology Project of Chongqing Education Committee under Grant No. KJ080609; the Doctor's Start-up Research Fund under Grant No. 08-52204; and the Youth Science Research Fund of Chongqing Technology and Business University under Grant No. 0852008.     

Security transmission and recognition of F-knowledge

The concept of F-knowledge is presented by employing S-rough sets. By engrafting and penetrating between the F-knowledge generated by S-rough sets and the RSA algorithm, the security transmission and recognition of multi-agent F-knowledge are proposed, which includes the security transmission of multi-agent F-knowledge with positive direction secret key and the security transmission of multi-agent F-knowledge with reverse direction secret key. Finally, the recognition criterion and the applications of F-knowledge are presented. The security of F-knowledge is a new application research direction of S-rough sets in information systems.     

基于脸部结构知识的三维人脸建模

将Sibson坐标用于局部特征分析,提出了一个新的局部形变模型.该模型具有较强的局部性,可以精细地刻画人脸个性特征,且能够根据人脸形状的先验信息进行整体控制.然后,在局部形变的基础上采用双重形变,实现了两种互补的形变算法在特征级的融合,进一步提高了建模精度.实验结果表明,本算法具有较小的重建误差和较快的处理速度,能够生成光滑、逼真的三维人脸模型.     

基于样本重采样的电路非平衡数据预处理方法

针对机载设备电子电路故障状态测试数据少、整体测试数据不均衡的问题,提出了一种基于样本重采样的数据预处理方法。首先,采用超限学习机对原始数据集进行训练以挑选出分类准确的样本。然后,对其中的少数类和多数类分别采用合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)进行过采样和局部密度欠采样处理;并将错误分类的多数类样本作为干扰因素进行删除。通过以上两种手段可以均衡数据集,并控制数据规模防止过拟合,提高对故障样本的检测率。实测数据处理结果表明,相比于其他重采样算法,所提算法整体效果优良且稳定,对电子电路故障诊断具有一定的应用价值。     

层次任务网络中的重新规划研究综述

层次任务网络(hierarchical task network, HTN)作为智能规划技术的重要组成部分,已成功应用于无人平台任务规划、应急方案制定等各领域中。由于日益增加的不确定因素对计划执行效果的影响,迫切需要重新规划技术,因此基于HTN的重新规划技术成为近年来的研究热点。首先,通过对当前研究现状的总结梳理,提出了基于HTN的3层重新规划框架。然后,分别从框架中的计划修复、局部重规划和全局重规划3个层次阐述了相应的技术路线与实现细节,针对不同层次指出了目前方法的优势与不足。最后,对该问题未来的研究方向进行了展望,并提出了待解决的关键问题及解决思路。