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Search Results: 1 - 10 of 273310 matches for " 李宇 "
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p-叶亚纯函数的新子类
New Subclass of p-Valent Meromorphic Functions
 [PDF]

杨静, 书海
Pure Mathematics (PM) , 2012, DOI: 10.12677/PM.2012.23019
Abstract: 本文运用线性算子L p (a,c)和从属关系定义了含绝对值形式的p-叶亚纯函数\"\"的一个新子类\"\"。讨论了该函数类的系数不等式,包含关系,极值定理以及凸半径和星象半径。本文将已有的某些单叶亚纯函数及p-叶亚纯函数的相关结果推广至p-叶亚纯数子类\"\"
In this paper, we introduce a subclass \"\" of the class  \"\" by use of Hadamard operator  L p (a,c)  and subordination principle. The main objective of this paper is to provide coefficient inequality, inclusion properties, extreme points, convexity and starlike radius of this class. In this paper, we extend relevant results of univalent meromorphic functions and p-valent meromorphic functions to the subclass \"\"本文运用线性算子L p (a,c)
基于最小二乘支持向量机的森林火灾预测研究
Prediction of Forest Fires Based on Least Squares Support Vector Machine
 [PDF]

恩来,
Hans Journal of Data Mining (HJDM) , 2016, DOI: 10.12677/HJDM.2016.61003
Abstract:
森林火灾是一个主要的环境问题,造成经济损失和生态破坏而且危及生命。如何预测、防治或减少森林火灾的危害成为诸多学科领域共同关注的科学任务。传统的做法是使用卫星,红外线扫描仪和局部传感器。但是由于卫星定位的延迟和扫描仪高昂的设备成本和维护成本,这些方案不能用来解决所有的情况。然而,研究表明气象因素对森林火灾有重要的影响。因此,有不少的学者建立森林火灾预测系统并将气象数据纳入量化指标体系。随机计算机的迅速发展,不少的学者将机器学习的方法运用到森林火灾等级预测模型中,但是其预测效果并不十分理想。本文提出基于机器学习中支持向量机方法的改进方法-最小二乘支持向量机,由于最小二乘支持向量机对处理样本容量较小的数据具有较高的准确度而且耗时较短。本文选用UCI数据库中的森林火灾数据进行预测处理,选用高斯函数(径向基函数)作为最小二乘支持向量机的核函数,根据一对一的多分类算法设计出最小二乘支持向量机的多分类器,使用粒子群算法选择最优参数。最后与支持向量机、BP神经网络、决策树等方法进行对比。
Forest fire is a major environmental problem, resulting in economic loss and ecological damage, and endangering life. How to predict, prevent or reduce the damage of forest fire has become a scientific task of many disciplines. The traditional approach is to use a satellite, an infrared scanner, and a local sensor. However, due to the delay of the satellite positioning and the high cost of the scanner’s equipment and maintenance costs, these solutions can not be used to solve all the situation. However, the study shows that the meteorological factors have an important influence on forest fire. Therefore, many scholars have established system for forest fire prediction and the meteorological data into the quantitative index system. With the rapid development of random computer, many scholars have applied the method of machine learning to forest fire grade prediction model, but the effect is not very ideal. This paper presents an improved method of support vector machine method based on machine learning, because the least squares support vector machine is with a higher accuracy and shorter time consuming to process small sample size of the data. In this paper, we select the UCI database of forest fire forecast data processing, select Gaussian function (radial basis function) as the kernel function of least squares support vector machine, according to one of multiple classification algorithm design of least squares support vector machine classifier, using particle swarm optimization algorithm to choose the optimal parameters. Finally, it is compared with the support vector machine, BP neural network, decision tree and so on.
云南省CPI序列的分析与预测—基于SARIMA模型
Analysis and Prediction of Yunnan CPI Series—Based on SARIMA Model
 [PDF]

卓然, 孙晓
Statistics and Applications (SA) , 2016, DOI: 10.12677/SA.2016.52015
Abstract: 本文以云南省为例,运用近20年来的月度数据对CPI进行建模预测。分析表明,CPI数据呈现周期为12的季节性;文章通过建立季节性ARIMA模型,预测2016年第二季度云南省CPI将在第一季度的基础上逐渐上升,且能够保持在稳定增长的范围内。
Taking Yunnan Province as an example, monthly data nearly 20 years were used on CPI forecast modeling. Analysis shows that the CPI data present seasonal cycle of 12. Through the establishment of the seasonal ARIMA models in this article, we predict the CPI of Yunnan Province in 2016 in the second quarter will gradually rise on the basis of the first quarter. And it is able to maintain within the scope of stable growth.
硬拷贝迎接GIS的挑战

中国图象图形学报 , 1998,
Abstract: 硬拷贝迎接GIS的挑战在远古时代,人们已开始将信息与地理位置相联系,最早的地理硬拷贝输出来自于3500年前法国南部的洞穴壁画上,记录的是猎手绘制的捕捉猎物的踪迹。今天,地理信息系统(GIS)开始借助于计算机的帮助,并且用途更加广泛。但他们仍具有共同的...
嵌入大学科技园的紧密型产学研结合机制及区域创新驱动模式研究

科技进步与对策 , 2013, DOI: 10.6049/kjjbydc.2011110393
Abstract: 产学研结合的紧密程度不仅依赖中间力量对产学研主体的连接作用,更取决于产学研结合主体之间的互补性。首先分析了松散型产学研结合机制和紧密型产学研结合机制的成因,以及大学作为创新源头的缺位和原有辅助机构嵌入方式导致高新区发展的“空心化”和“外援化”困境,并进一步指出当前大力发展大学科技园的必要性,总结出辅助机构嵌入大学科技园的紧密型产学研平台的多种模式。最后,结合我国大力建设大学科技园及高新区二次创业并举的背景,阐述了大学科技园与高新区协同发展的条件,提出了“区中园、一体化、虚拟结合”这3种大学科技园与高新区有机结合的“双核”区域创新驱动模式。大学科技园产学研合作区域创新产学研结合机制
债权让与的优先顺序与公示制度

法学研究 , 2012,
Abstract: 债权让与的优先顺序,即债权让与对抗债务人以外第三人的效力问题,系债法上分歧最大的问题之一,事关保理、资产证券化等现代债权融资交易发展,且为拓宽中小企业、农户等弱势当事人融资渠道的关键因素之一,具有深层次社会经济意义。运用“制度内情境分析法”,依次考察让与主义、通知主义、登记主义三种主要制度对各方当事人及第三人的影响,可发现登记主义更为公平、更有效率。各国的历史与经验证据支持此结论。中国合同法对此未作规定,存在不足。物权法新创应收账款质押登记制度,越来越多的债权转让亦可能在该系统登记。未来有必要对债权完全让与及担保性让与采统一登记制度。债权让与登记制,表明公示原则为适用于一切财产权转让的基本原则,性质为财产法总则上的原则。
服务网格标准规范体系*

现代图书情报技术 , 2007,
Abstract: ?分析开放网格服务系统的架构及其相关的规范体系结构,并描述了相关的应用案例,最后总结了现在存在的问题和可能的发展。
梁启超与图书馆事业渊源考

图书馆杂志 , 2013,
Abstract: ?梁启超是我国近代著名的政治家、思想家,主张维新救国,变革图强,深受国人敬仰,梁启超的一生与我国图书馆事业有着很深的渊源,他是近代图书馆事业的重要探索者,为形成“中国特色图书馆学思想体系”,创建中国图书馆学做出了突出贡献,他的理论对当代图书馆事业的进步和发展仍起着重要的启示作用。
学习列宁的社会统计理论和方法

社会学研究 , 1986,
Abstract: 列宁在1917年写《统计学和社会学》一文,准备用普·皮留乔夫的笔名,印成公开发行的单行本,但是他只写好了一部分。它是一篇体现列宁应用统计研究民族国家的立场、观点和方法的文章,是马克思主义社会统计学的好教材。
用于云存储数据服务器的I/O请求调度算法
I/O Scheduling Algorithm for Data Servers in Cloud Storage Environments


- , 2018, DOI: 10.3969/j.issn.0258-2724.2018.04.026
Abstract: 在云存储系统的体系架构中,当前对数据服务器守护进程的I/O请求调度采用先来先服务(first in first out)策略,这种调度策略没有考虑不同类型I/O请求的时效性要求,容易造成时效性要求高的I/O请求因被阻塞而无法得到及时处理,从而降低整个系统的服务质量.为解决该问题,本文提出一种用于云存储数据服务器的I/O请求调度算法.该算法首先对来自客户端的I/O请求进行分类,并赋予不同的优先级;然后以合适的时长作为周期、以分时间片的方式对不同优先级的I/O请求进行周期性的调度.分布式文件系统仿真实验结果表明,该算法在重负载情况下对实时请求的响应速度提高了20%,同时也兼顾了低优先级请求的响应性能.
:Distributed file systems (DFSs) are generally employed for storing user data while designing a cloud storage system. The primary aspects of DFSs include efficient storage and management of metadata, data distribution strategies, and reliability of user data. In the case of data servers of a DFS, the FIFO (first in first out) strategy is adopted for scheduling I/O requests which are received by a data server daemon. The FIFO algorithm prioritises all such requests equally; requests that require better quality of services may therefore be blocked for long durations. To address this issue, a new priority based periodic scheduling algorithm (PPSA) has been proposed. Initially, PPSA classifies requests into different priority queues. Then, it periodically schedules requests according to their respective priorities and dedicated time slices. The obtained DFS simulation results show that PPSA can increase the response performance of heavy-load real-time requests by 20%, and can also ascertain the lowest response time performance for other requests
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