绿色专利设计知识的获取方法研究

为了获得绿色专利文献中的功能、结构与绿色特征的相互关系知识,提出一种新方法,即基于特征关联的知识获取方法.通过提取专利文献的权利要求书中零部件的特征,分析标题或摘要中的目的功能和绿色特征,建立零部件特征-目的功能和目的功能-绿色特征的训练集,并采用数据挖掘算法提取它们之间的关联规则,获得绿色特征、目的功能及零部件特征之间关系的知识,以辅助产品的绿色创新设计,并以实例验证了所提出的方法.实验结果表明了方法的有效性.     

Theranostic USPIO‐Loaded Microbubbles for Mediating and Monitoring Blood‐Brain Barrier Permeation

Efficient and safe drug delivery across the blood‐brain barrier (BBB) remains one of the major challenges of biomedical and (nano‐) pharmaceutical research. Here, it is demonstrated that poly(butyl cyanoacrylate)‐based microbubbles (MB), carrying ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles within their shell, can be used to mediate and monitor BBB permeation. Upon exposure to transcranial ultrasound pulses, USPIO‐MB are destroyed, resulting in acoustic forces inducing vessel permeability. At the same time, USPIO are released from the MB shell, they extravasate across the permeabilized BBB and they accumulate in extravascular brain tissue, thereby providing non‐invasive R ₂*‐based magnetic resonance imaging information on the extent of BBB opening. Quantitative changes in R ₂* relaxometry are in good agreement with 2D and 3D microscopy results on the extravascular deposition of the macromolecular model drug fluorescein isothiocyanate (FITC)‐dextran into the brain. Such theranostic materials and methods are considered to be useful for mediating and monitoring drug delivery across the BBB and for enabling safe and efficient treatment of CNS disorders.     

基于突发公共卫生事件应急医疗设施的 城郊森林公园研究

面对新型冠状肺炎的突然暴发,国家快速启动应急医疗设施的建设。选址科学性和建设时效性是建设过程中面临的两大问题。利用城市绿地的防灾功能,通过平灾结合可在一定程度上缓解以上问题。通过比较发现,城郊森林公园在选址、规模、环境、基础设施、覆盖率等诸方面与应急医疗设施需求具有高度的契合性。将城郊森林公园纳入突发公共卫生事件应急预案,可以通过早期的合理选址、布局,预先勘探、铺设管网,加强污水、垃圾处理系统,合理规划康养功能等手段,大大提升灾时应急医疗设施建设的速度,并在一定程度上降低传染性疾病对人口和环境造成的危害。     

模型与知识驱动的电缆加工工艺快速设计技术

为解决航天电缆组件设计工艺数据协同效率低、工艺知识积累应用不足、工艺准备重复性工作量大且周期长等问题,提出一种模型与知识驱动的航天电缆组件加工工艺快速设计技术。分析基于MBD的电缆结构化设计信息提取、电缆结构化工艺设计知识库构建、基于知识的电缆加工工艺智能生成等关键技术;构建B/S架构的软件系统并实现型号工程应用验证。结果表明：该系统能实现电缆工艺快速设计,大幅提高电缆组件加工工艺设计效率,提升文件规范性和工艺设计过程知识化、智能化水平。     

基于深度学习的小口径弹药装配设备故障诊断专家系统

针对小口径弹药装配设备故障诊断智能化程度不足、诊断效率低以及误诊率高等问题,开展小口径弹药装配设备故障诊断技术和专家系统研究。为提高复杂装备故障知识推理及判别精度,研究基于知识图谱和故障树的故障类别知识库构建方法,提出基于规则和案例的故障知识推理方法,采用深度学习算法进行故障知识推理和更新,构建故障诊断模型并用于小口径弹药装配设备故障诊断专家系统。结果表明：该系统可实现对小口径弹药装配设备故障的智能预测和分析,符合制造装备智能化发展要求,为制造装备智能化、信息化的研制发展和推广应用提供参考。     

基于知识地图的标准知识链接服务研究

为解决当前标准知识及相关信息资源在资源整合、关联表达和快速获取中的问题,提供直观、全面和智能化的标准知识链接服务。本文在对标准知识及其要素的理论分析基础上,以知识地图为工具,以知识链接为方法,从整体上设计了标准知识系统的功能模块和系统架构。基于Django WEB开发框架,建立了标准知识数据库,实现了标准知识地图系统。以纺织产业为应用实例,利用所开发的知识地图系统对相关国内外标准法规进行文本挖掘。实验证明本文所构建的理论和标准知识系统能够提供领域内标准知识之间的关联导航,实现基于产品流程的标准知识聚合,为纺织企业提供基于语义的智能化标准知识链接服务。     

Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast

In order to solve the problem of noise amplification, low contrast and image distortion in the process of medical image enhancement, a new algorithm is proposed which combines NSCT (nonsubsampled contourlet transform) and improved fuzzy contrast. The image is decomposed by NSCT. Firstly, linear enhancement method is used in low frequency coefficients; secondly the improved adaptive threshold function is used to deal with the high frequency coefficients. Finally, the improved fuzzy contrast is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experimental results show that the proposed algorithm can improve the image visual effects, remove the noise and enhance the details of medical images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 7–14, 2015     

Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor

In this article, we analyze the performance of artificial neural network, in classification of medical images using wavelets as feature extractor. This work classifies the mammographic image, MRI images, CT images, and ultrasound images as either normal or abnormal. We have tested the proposed approach using 50 mammogram images (13 normal and 37 abnormal), 24 MRI brain images (9 normal and 15 abnormal), 33 CT images (11 normal and 22 abnormal), and 20 ultrasound images (6 normal and 14 abnormal). Four kind of neural network models such as BPN (Back Propagation Network), Hopfield, RBF (Radial Basis Function), and PNN (Probabilistic neural network) were chosen for study. To improve diagnostic accuracy, the feature extracted using wavelets such as Harr, Daubechies (db2, db4, and db8), Biorthogonal and Coiflet wavelets are given as input to the neural network models. Good classification percentage of 96% was achieved using the RBF when Daubechies (db4) wavelet based feature extraction was used. We observed that the classification rate is almost high under the RBF neural network for all the dataset considered. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 33–40, 2015     

中文电子病历命名实体标注语料库构建

针对中文电子病历命名实体语料标注空白的现状,研究了中文电子病历命名实体标注语料库的构建。参考2010年美国国家集成生物与临床信息学研究中心(1282)给出的电子病历命名实体类型及修饰类型的定义,在专业医生的指导下制定了详尽的中文电子病历标注规范;通过对大量中文电子病历的分析,提出了一套完整的中文电子病历命名实体标注方案,而且采用预标注和正式标注的方法,建立了一定规模的中文电子病历命名实体标注语料库,其标注语料的一致性达到了92%以上。该工作对中文电子病历的命名实体识别及信息抽取研究提供了可靠的数据支持,对医疗知识挖掘也有重要意义。     

大学生创新思维与高校学科知识结构构建

人才培养是高等教育的本质要求和根本使命,建设创新型国家,全面实施创新驱动战略,需要学生具备与培养目标相适应的学科知识和构建技能。综述高校学科知识结构相关概念,讨论高校学科知识体系,研究高校学科知识结构的创新思维与构建思维,阐述四阶段学科知识结构构建过程。