面向能耗优化的自适应可重构指令Cache

可重构Cache架构可根据程序的存储资源需求自动调整Cache结构,对系统能耗优化具有重要意义。设计了一种容量和组关联度可重构的指令Cache架构以及与之对应的高效自适应可重构算法。通过选取MiBench和MediaBench中的8个测试例程进行测试验证,提出的自适应可重构Cache与16kB四路组关联配置固定的指令Cache相比,在性能平均仅下降0.34%的情况下,系统总能耗平均降低10.51%。     

Tencel纤维家纺面料的工艺优化

探讨Tencel纤维家纺面料生产工艺。分析论述了Tencel A100纤维的性能特点及在纺纱、织造各工序生产中的工艺技术措施。通过严格控制清梳工艺减少纤维损伤;选择涂炭黑涂料胶辊解决并条返花问题;采用较大粗纱捻系数;优选细纱牵伸工艺;优化浆料配方、采用中压上浆;织机合理调节经位置线、应用大张力、早开口、迟引纬等措施;严格控制各工序温湿度,成功开发和生产出满足要求的家纺面料产品。     

高校培养创新型人才的教学论视角分析

高校教学是培养创新型人才的基本途径,从教学论的角度来思考如何培养创新型人才具有基础性的意义。从内容上讲,需要对培养创新型人才的教学目的、教学内容、教学过程、教学模式、教学方法以及教学环境等要素进行理论分析;从方法上讲,则需要把理论分析与目前高校教学的实际情况结合起来,做到实践性和理论性相统一。     

土木工程计算机辅助教学(CECAI)系统研究

文章针对土木工程专业课程的特点,研究计算机辅助教学(CAI)系统在土木工程中的应用,利用Visual Basic语言,初步搭建起了土木工程计算机辅助教学(CECAI)系统框架,并对概念题、计算题、帮助系统等各部分的开发技术和思路进行了阐述,系统地论述了计算机辅助教学的应用模式。     

精梳棉牛奶纤维混纺小提花织物的开发

探讨精梳棉牛奶纤维混纺小提花织物的生产技术措施。分析了精梳棉牛奶纤维混纺织物的特点,指出:该种织物织前工序应采用"小张力,低速度,重被覆,保伸长,减毛羽"的工艺原则,减少纱线毛羽及伸长;织造工序选用"小张力,适当大开口"的工艺原则,优选经位置线。通过一系列技术措施的实施,织机效率达到87.5%,产品下机一等品率达85%,达到了预期的目标。     

基于B／S结构的远程教学系统的设计与实现

本文对基于Ｂ／Ｓ（Ｂｒｏｗｓｅｒ／Ｓｅｒｖｅｒ）结构的多媒体课件的研制进行了讨论。并结合笔者的实践,阐述了开发过程中关键技术的应用。     

双环测力传感器弹性元件的计算机辅助分析

文中介绍了有限元分析双环测力传感器的弹性元件的应力变分布,并通过机器试验得到该传感器输出性能影响较大的弹性元件的结构参数,得到了对该传感器设计有价值的结论。     

厦门国际会展中心项目控制中的信息管理方案

大型工程在进度、投资、质量等方面对项目管理提出更高要求是项目控制（ProjectControlling）出台的深刻背景。面向决策和计算机辅助信息管理是项目控制的重要特点。标准化的信息管理即统一的编码体系、统一的函件与报表、计算机辅助系统是项目控制的基础。     

Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of breast cell malignancies formation of masses and Breast microcalcifications on the mammogram. When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network (ResNet50) for Breast Cancer Detection and Diagnosis, to obtain the Discriminative Localization, Convolutional Neural Network with Class Activation Map (CAM) has also been used to perform breast microcalcifications detection to find a specific class in the Histopathological image. The test results indicate that this method performed almost 225.15% better at determining the exact location of disease (Discriminative Localization) through breast microcalcifications images. ResNet50 seems to have the highest level of accuracy for images of Benign Tumour (BT)/Malignant Tumour (MT) cases at 97.11%. ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.