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81.
It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy. 相似文献
82.
Michael Shneier Tommy Chang Tsai Hong Will Shackleford Roger Bostelman James S. Albus 《Autonomous Robots》2008,24(1):69-86
Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability
of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate
the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question
of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment
are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the
vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection
into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis
of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the
geometry and the appearance of the scene.
The models are used to classify color images and assign traversability costs to regions. The classification does not use the
range or position information, but only color images. Traversability determined during the model-building phase is stored
in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color
images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and
how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA
Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is
presented.
相似文献
James S. AlbusEmail: |
83.
In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG. 相似文献
84.
Wei-Chou Chen Shian-Shyong Tseng Tzung-Pei Hong 《Expert systems with applications》2008,34(4):2858-2869
Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach. 相似文献
85.
86.
k-best MIRA和动态k-best MIRA 总被引:1,自引:0,他引:1
MIRA(Margin Infused Relaxed Algorithm)是一种超保守算法,在分类、排序、回归等应用领域都取得不错成绩.文中在传统MIRA算法基础上进行改进,提出k-best MIRA(K-MIRA)与动态k-best MIRA(DK-MIRA)算法.这两种算法能够根据学习进程自动调整优化约束条件,从而提高算法的收敛速度与性能.将K-MIRA与DK-MIRA用于定义类问题回答中的句子排序任务,取得较为满意的实验结果. 相似文献
87.
针对因天气变化而产生的色彩转移、光照变化以及相似地形(如土地和沙地)的反射频谱模糊性等因素造成的地形分类性能下降的问题,提出了一种基于高斯混合模型(GMM)的地形分类算法.首先,提取不同光照条件下地形的特征,这种特征是基于改进的离散余弦变换(DCT)纹理特征和在Y1Q空间提取的颜色特征的融合特征,然后用这些特征数据训练GMM,对于GMM组成模型数目则采用贝叶斯信息准则(BIC)加以确定.另外针对不同地形区域边界上分类性能差以及同一地形在相同条件下非一致性问题,还提出了一种分类策略.该策略的原理是利用当前特征窗口周围邻域的所属类别概率的平均值大小来决定当前特征窗口的类别.应用提出的算法在两个数据库上进行了实验,取得了令人满意的效果. 相似文献
88.
89.
基于排序的关联分类算法 总被引:1,自引:0,他引:1
提出了一种基于排序的关联分类算法.利用基于规则的分类方法中择优方法偏爱高精度规则的思想和考虑尽可能多的规则,改进了CBA(Classification Based on Associations)只根据少数几条覆盖训练集的规则构造分类器的片面性.首先采用关联规则挖掘算法产生后件为类标号的关联规则,然后根据长度、置信度、支持度和提升度等对规则进行排序,并在排序时删除对分类结果没有影响的规则.排序后的规则加上一个默认分类便构成最终的分类器.选用20个UCI公共数据集的实验结果表明,提出的算法比CBA具有更高的平均分类精度. 相似文献
90.
利用LS—SVM模块化决策系统求解EEG源参数 总被引:1,自引:1,他引:0
给定头皮脑电位的分布推算脑内电活动的源是脑电研究的一个重要的方面.研究涉及到信息科学、电磁场计算及生物医学工程等多个学科领域,其研究成果在神经疾病诊断、探索人的感觉和认知过程等方面具有蕈要意义.基于最小二乘支持向最机(LS-SVM)算法建立模块化决策系统,首先对脑电数据进行分类,然后依据分类结果提取数据样本,并建立回归模型,最后求解多种偶极子源参数.从而建立起头皮电压和脑电源参数之间的内在联系,为脑电动态分析提出一种实时的研究思路.计算机仿真计算结果证明了此方法的有效性. 相似文献