Advanced Materials Science and Technology is a peer-reviewed open access journal published semi-annual online by Omniscient Pte. Ltd. The journal covers the properties, applications and synthesis of new materials related to energy, environment, physics, chemistry, engineering, biology and medicine, including ceramics, polymers, biological, medical and composite materials and so on.
本文探讨了基于单片机技术设计智能电子产品的方法和策略.首先,介绍了单片机技术的概念、基本原理和特点,以及其在智能电子产品中的应用领域和发展历程.其次,详细讨论了智能电子产品设计的一般流程和步骤,并强调了单片机在设计流程中的重要性和作用.再次,针对具体的设计方法,探讨了如何根据产品需求选择适当的单片机型号和配置,以及硬件设计和软件开发的关键考虑因素.最后,通过案例分析展示了基于单片机技术设计智能电子产品的过程和要点.
水汽羽和入射激光辐射之间的交互影响着远程激光成型.对于发射波长约为 1μm的激光系统,根据一个重要的损耗机理可以追溯到纳米材料粒子成型的消失.通过数学模型(瑞利近似公式)研究颗粒成型与小孔内蒸发速度之间的耦合关系,可知颗粒成型对锁眼几何及由此而来的工艺参数(如进给速度和激光功率)具有很强的依赖性.为了验证颗粒成型和工艺参数之间的关系,在光纤激光成型过程的水汽羽中引导使用了宽波段发光二极管的光线.本文分析了减弱探测光束对波长的依赖性,并利用高速成像技术研究水汽羽的传播.
With the advent of the era of big data,with the emergence of massive data and explosive growth of information,the phenomenon of information overload is becoming increasingly serious.Personalized recommendation system is a powerful tool to solve information overload,and its core is recommendation algorithm,and collaborative filtering recommendation algorithm is the most widely used recommendation algorithm at present.By briefly summarizing the research progress of collaborative filtering recommendation algorithm,this paper summarizes and analyzes some key problems existing in the algorithm and related improvement schemes.Finally,it analyzes the problems to be solved and the future development direction of collaborative filtering recommendation algorithm.
随着科学技术的不断创新与发展,数据的种类变得越来越多样化,数据的维度也呈显著的增长趋势.传统模式下的机器学习算法已无法满足有限周期内完成要解决任务的要求,因此,本文提出了一种基于Hadoop的数量关联规则的数据挖掘算法.首先该算法运用分布式计算系统(MapReduce)计算模式,对系统内数据进行多模块划分,其次利用数量参数间的关联规则挖掘有效数据,并将挖掘后的有效数据进行合并归类,以此获得频繁参量集.最后通过测试实验可知,优化后的算法在运算时间、运算效率等方面均优于传统算法,使其在一定程度上有效解决传统Apriori算法效率低的问题.
