Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique

This method presents extraction of effective color and shape features for the analysis of dermatology images. We employ three phases of operation in order to perform efficient retrieval of images of skin lesions. Our proposed algorithm used color and shape feature vectors and the features are normalized using Min–Max normalization. Particle swarm optimization (PSO) technique for multi-class classification is used to converge the search space more efficiently. The results using receiver operating characteristic (ROC) curve proved that the proposed architecture is highly contributed to computer-aided diagnosis of skin lesions. Experiments on a set of 1450 images yielded a specificity of 98.22% and a sensitivity of 94%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. We present explicit combinations of feature vectors corresponding to healthy and lesion skin.     

Feature extraction and soft computing methods for aerospace structure defect classification

This study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one; and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method – namely the novel CBIR-based one outperforms all the competitors – and they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application.     

自动化立体仓库系统在飞机钣金模具管理中的应用

通过设计与规划自动化立体仓库管理系统,提出了以满足工段生产需求为目标的物流配送思想,以此思想为导向,优化模具管理流程,理顺内部客户关系,实现了飞机钣金零件模具的高效、准时配送。在飞机钣金模具管理中,与传统的模具仓库管理模式相比,自动化立体仓库系统节省了场地面积,降低了人工成本,提高了零件生产效率。     

基于Hadoop的分布式视频车辆检索方法*

针对传统单机视频检索效率低下的问题,设计了一种基于Hadoop的分布式视频车辆检索方法。该方法首先将视频切割成多个分块,然后利用MapReduce和FFMPEG在分布式环境下提取视频帧,再通过车牌识别算法检索其中出现的车牌号码,最后计算出车辆在视频中出现的时间。实验结果表明,与单机检索方式相比,该方式具有更强的数据处理能力和更高的检索效率。     

改进相关反馈技术在CBVR人体动作识别中的应用研究

针对CBVR人体动作识别问题,提出利用相关反馈技术与其它技术相结合。首先,分析了从视频中提取人类行为并将其表示成特征集的方法;然后研究了各种基于内容的视频检索方法;最后,将这些技术进行组合得到基于相关反馈技术的最优CBVR人体动作识别方法。在三个行为数据库中(包括UCF运动,UCF视频网站和HOHA2)评估了由上述几种技术组合而成的性能,实验结果为开发高效的基于内容的视频检索系统提供了有益的借鉴。     

基于分块直方图和共生矩阵的图像检索方法

提出了一种基于分块直方图和共生矩阵相结合的图像检索方法。传统的基于颜色直方图特征的图像检索方法简单且易于实现，但是失去了颜色的空间分布信息。分块直方图法根据图像目标和背景的不同，将图像分成意义不同的8个子块，充分利用了图像分块的特点，改善了颜色特征缺乏空间信息的缺点。共生矩阵法根据不同图像间共生矩阵的差别来实现图像检索。最后，综合利用上述两个特征对图像进行共同检索。实验表明：综合2种特征检索图像比单一特征检索效果更好。     

化学化工文献检索课程建设探讨

将教师的科研实践经历,例如参加学术会议的经历等,融入《化学化工文献检索》课,从而即引起学生的兴趣,又使学生从多方面了解科研。还将经济观念培养引入文献检索课,说明科学研究常常需要基金的支持,合理地使用经费是科研工作的重要部分之一。此外,通过多种教学手段,例如双语教学,调动学生的学习积极性。     

Hanford罐贮高放废物回取及废物最小化经验

介绍了美国Hanford场址罐贮高放废物的回取,包括废物回取标准,回取技术——改进型冲洗法、盐饼溶解法、真空回取法和移动回取法等,以及在实施罐贮废物的回取中的废物最小化方面的经验等。