动力煤洗前干法脱粉新技术

介绍了煤炭振动流化床气力分级新技术的工作原理和特点;通过该机组在郑煤集团米村矿的应用实例,说明了该技术可实现煤炭选前0～10mm内任一粒度干法分级,从而降低动力煤洗选粒度下限,优化产品结构,减少煤泥量,提高企业经济效益。     

A demerit-fuzzy rating system,monitoring scheme and classification for manufacturing processes

For monitoring online manufacturing processes, the proportion of weights imposed on each type of product’s defects (nonconformities or demerits) has a profoundly effective impact on control charts’ performance. Apparently, the demerit-chart approach is superior than the widely-used c-chart scheme, because it allows us to place relative precise weights (real numbers) on defects according to their distinctly inferior degrees affecting the product quality so that the abnormal variations of processes can be literally exposed. However, in many applications, the seriousness of defects is evaluated partially or entirely by the inspectors’ perceptive judgement or knowledge, so with the precise-weight assignment, the demerit rating mechanism is considered to be somewhat constrained and subjective which inevitably leads to the targeted manufacturing process with limited and possibly biased information for online surveillance. To cope with the drawback, a demerit-fuzzy rating system and monitoring scheme is proposed in this paper. We first incorporate fuzzy weights (fuzzy numbers) to properly reflect the severity measures of defects which are categorized linguistically. Then, based on properties of fuzzy set theory and proposed approaches for fuzzy-number ranking, we develop the demerit-fuzzy charting scheme which is capable of discriminating process conditions into multi-intermittent statuses between in-control and out-of-control. This approach improves the traditional process control techniques with the binary-classification restraint for the process conditions. Finally, the proposed demerit-fuzzy rating system, monitoring scheme, and classification is elucidated by an application in garment industry to monitor textile-stitching nonconformities conditions.     

Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.     

基于Bayes特征判别的选煤建模与识别

研究了煤块的Harr小波变换和幅度投影分析,在对原始图像进行特征判别分析的基础上,建立了煤块和煤矸石的统计模型,提出了用Bayes判别函数分选煤块和煤矸石的算法,实现了用计算机视觉系统对样本进行了识别检验.并运用神经网络BP算法进行训练识别,检验其正确性,最后通过实验验证了算法有效性.     

一种基于深度卷积网络的鲁棒头部姿态估计方法

针对头部姿态估计方法受特征提取限制导致姿态估计效果不佳的问题,提出使用深度卷积网络自动学习有效特征并进行分类的头部姿态估计方法。首先,利用DCNN非线性映射和自动提取图像结构信息的能力,设计一个深度卷积网络实现对姿态鲁棒特征的提取;然后再将提取的特征用于分类器训练并最终实现头部姿态估计。在Pointing’04和FacePix数据库上的测试结果表明,本文设计的深度卷积网络能有效的进行特征学习,避免了人工设计特征的不足,与现有的基于人工设计特征方法相比,本文方法在两个数据库上达到的预测平均绝对误差分别为4.05〫和2.04〫,充分证实了本文算法的稳定性和可靠性。     

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.     

Segmentation of abdominal organs from CT using a multi-level,hierarchical neural network strategy

Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision. Recent studies on multiple feature–classifier combinations show that hierarchical systems outperform composite feature–single classifier models. In this study, how hierarchical formations can translate to improved accuracy, when large size feature spaces are involved, is explored for the problem of abdominal organ segmentation. As a result, a semi-automatic, slice-by-slice segmentation method is developed using a novel multi-level and hierarchical neural network (MHNN). MHNN is designed to collect complementary information about organs at each level of the hierarchy via different feature–classifier combinations. Moreover, each level of MHNN receives residual data from the previous level. The residual data is constructed to preserve zero false positive error until the last level of the hierarchy, where only most challenging samples remain. The algorithm mimics analysis behaviour of a radiologist by using the slice-by-slice iteration, which is supported with adjacent slice similarity features. This enables adaptive determination of system parameters and turns into the advantage of online training, which is done in parallel to the segmentation process. Proposed design can perform robust and accurate segmentation of abdominal organs as validated by using diverse data sets with various challenges.     

青霉分类方法的研究进展

介绍了青霉的传统分类方法，综述了随机扩增多态性（RAPD）、限制性片断长度多态性（RFLP）、扩增片断长度多态性（AFLP）和rDNA／rRNA序列分析等几种分子标记技术的基本原理、方法及其青霉分类鉴定中的研究进展。     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.