基于GRA-GRNN模型的露天金属矿爆破后矿岩界线位移分析

为了避免露天金属矿爆破后导致爆堆边缘矿石品位贫化损失,需要根据最低品位阈值重新计算矿岩边界,而影响矿岩边界发生改变因素众多,需要确定主要影响因素。因此,利用爆堆爆破前地形方向和爆堆地质数据,构建灰色关联-广义回归神经网络模型(GRA-GRNN)分析爆堆矿岩边界变化主要影响因素。首先对爆堆钻孔品位数据使用析取克里金法进行空间插值,并根据矿山工艺最低品位阈值提取爆破前的矿岩边界;其次,将爆破前后的数字DEM模型进行求差,求得爆破后的爆堆数字DEM模型,并构建爆破前后爆堆数字DEM模型空间分布椭圆,从而确定爆堆爆破后的水平形变方向;对影响爆堆爆破后形变的可能因素进行提取,并应用GRA-GRNN模型选取主要影响因素及对其强度进行分析,并将其结果与BP神经网络模型预测结果进行了对比。从实验结果可知,影响爆堆爆破后形变强度排前三的因素为:爆破前地形方向、爆孔排距和列距,强度分别为0.880、0.760和0.755,预测结果优于BP模型。     

Densely connected convolutional network block based autoencoder for panorama map compression

As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

基于RFID标签阵列的睡眠期间呼吸量连续监测系统

睡眠期间连续且准确的呼吸量监测有助于推断用户的睡眠阶段以及提供一些慢性疾病的线索。现有工作主要针对呼吸频率进行感知和监测,缺乏对呼吸量进行连续监测的手段。针对上述问题提出了一种基于商用无线射频识别(RFID)标签的无线感知用户睡眠期间呼吸量的系统——RF-SLEEP。RF-SLEEP通过阅读器连续收集附着在胸部表面的标签阵列返回的相位值及时间戳数据,计算出呼吸引起的胸部不同点的位移量,基于广义回归神经网络(GRNN)构建胸部不同点的位移量与呼吸量之间的关系模型,从而实现对用户睡眠期间呼吸量的评估。RF-SLEEP通过在用户肩膀处附着双参考标签,消除用户睡眠期间翻转身体对胸部位移计算造成的误差。实验结果表明,RFSLEEP对不同用户睡眠期间的呼吸量连续监测的平均精确度为92.49%。     

基于卷积神经网络的表情识别

在传统静态表情识别研究基础上,提出一种简单的人脸裁剪方法,再用浅层卷积神经网络进一步提取特征并进行表情识别。以CK+和JAFFE为实验数据集,进行预处理效果对比实验、数据增强实验、单种表情识别实验和跨数据集六分类实验。结果表明,针对数据量较少的情况,提出的表情识别方法效果明显且鲁棒性更优。     

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.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.