Characterisation of mortar morphology in thin sections by digital image processing

A digital imaging approach was applied to investigate mortar morphology in thin sections; in particular, the binder/aggregate ratio and the grading curve of five mortar bars were attained by digital image processing (DIP), using the Image Pro Plus 4.1 software package. The imaging procedure employed image segmentation, to extract mortar aggregate, and image filtering, to fix grain boundaries. The results show that digital image processing may be considered as an alternative method to mechanical sieving for the characterisation of mortar morphology, as it appears to be quicker and more accurate than the traditional method. However, digital image processing exhibits limits, which are discussed in the text.     

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.     

The influence of grinding technique on the liberation of clinker minerals and cement properties

This paper describes a study of the relationship between the physical, chemical and mineralogical parameters of cement products obtained by different grinding mechanisms namely high pressure grinding rolls (HPGR) and ball milling, and their effects upon the properties of cements prepared from the ground clinker. Samples were prepared as narrow size fractions and also as distribution samples. Characterization parameters were ascertained by using XRF, laser sizing, Blaine and BET surface area and image analysis methods. HPGR grinding resulted in higher degrees of liberation of clinker phases arising from the intergranular breakage along the grain boundaries compared to ball mill grinding. As for service properties, water demand of HPGR products was higher than ball mill products resulting from high micro fissured structure. Despite high liberation of particularly alite mineral in HPGR grinding, the compressive strength of ball mill products was slightly higher than HPGR products for narrow size samples. Finally, particle size distribution effect on strength was more obvious for distribution samples; generally ball milling gave higher strength values.     

Ensemble of multiple instance classifiers for image re-ranking

Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate “multi-instance bags (MI-bags)”, which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art.     

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.