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101.
在部队通信服务问题的研究中,数据链仿真是数据链武器装备论证的重要研究内容和方法,对军队数据链建设具有重要意义.以美军现役战术数据链Link-16为背景,比较分析了数据链系统的四类信息传输方式,为提高抗干扰性能,介绍了主要的抗干扰技术.利用Simulink软件对数据链系统建立了链路仿真平台,重点构建了直序扩频模块和跳频模块,对在加性高斯白噪声和莱斯衰落影响下的信号传输进行了仿真并进行了抗干扰性能分析.通过仿真与分析可以看出,RS编码、直序扩频、跳频等技术保证了Link-16数据链的高抗干扰性能,使其满足军用通信要求. 相似文献
102.
Photoshop的抠图技巧分析 总被引:1,自引:0,他引:1
本文介绍了抠图的概念,分析了选区在Photoshop图像处理过程中的关键作用,提出了选区创建的直接法与间接法,具体分析了各种操作方法的优缺点及使用的广泛性。 相似文献
103.
Sebastiano Battiato Giovanni Maria Farinella Giovanni Giuffrida Catarina Sismeiro Giuseppe Tribulato 《Multimedia Tools and Applications》2009,42(1):5-30
Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience.
Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect
to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing
process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in
particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate
that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes
a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features
to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS)
show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed
approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
Giuseppe TribulatoEmail: |
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
104.
Credit scoring using support vector machines with direct search for parameters selection 总被引:1,自引:1,他引:0
Ligang Zhou Kin Keung Lai Lean Yu 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(2):149-155
Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the
model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring
model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment
(DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility
of the method. The results show that the direct search method can find the effective model with high classification accuracy
and good robustness and keep less dependency on the initial search space or point setting. 相似文献
105.
106.
随着区块链技术的不断发展,区块的传输延迟成为区块链系统可扩展性的性能瓶颈。远程直接内存访问(RDMA)技术能够支持高带宽和低时延的数据传输,为低延迟区块传输提供了新的思路。因此,结合RDMA原语的特性,设计了用于区块信息共享的区块目录结构,并在此基础上设计并实现了区块传输的基本工作过程。实验结果表明,相较于基于TCP的方案,在1 MB大小的区块上基于RDMA的区块传输机制将节点间的区块传输延迟降低了44%,全网络的区块传输延迟降低了24.4%,在10 000节点规模的区块链上,区块链发生临时分叉的数量降低了22.6%。可见,基于RDMA的区块传输机制充分发挥了高速网络的性能优势,降低了区块传输延迟及临时分叉的数量,提高了现有区块链系统的可扩展性。 相似文献
107.
108.
投加阻垢剂是解决反渗透(Reverse osmosis,RO)膜结垢问题简单有效的方法。综述了RO系统中阻垢剂的合成应用、阻垢特征、机理及存在问题。含磷类阻垢剂因导致水体磷污染已经基本被其它几类阻垢剂替代,绿色环保类阻垢剂在阻垢效果和可降解性方面均比聚合物类好。但任何阻垢剂单独使用都难于满足(RO)系统阻垢的实际需求,阻垢剂的复配可相互弥补单一阻垢剂的缺陷,同时发挥其协同效应,而阻垢剂的特征改性具有更强的针对性和适用性。因此,阻垢剂的复配和改性是其研究的趋势和重点。 相似文献
109.
《Advanced Powder Technology》2023,34(1):103880
The momentum exchange between the phases plays a vital role in modelling of gas–solid flows and it is mathematically described by drag models. However, no consensus exists on which drag model gives the most accurate prediction of the drag force, and, despite the increase in available computing power, the same drag models are used in two-dimensional and three-dimensional simulations. In this study, direct numerical simulations of gas flow through multiple random configurations of static monodisperse particles are performed. The variations of solid volume fraction and particle Reynolds number are in the ranges of 0.05–0.4 and 13.7–136.9, respectively. The drag force exerted on particles is calculated and properly averaged. Based on the simulation results, thirteen drag models are compared and correction factors are introduced using the stochastic gradient descent algorithm. The correction factors provide a simple adjustment for the models to be used in 2D modelling. 相似文献
110.
This paper deals with the computation of Hopf bifurcation points in fluid mechanics. This computation is done by coupling a bifurcation indicator proposed recently (Cadou et al., 2006) [1] and a direct method (Jackson, 1987; Jepson, 1981) [2] and [3] which consists in solving an augmented system whose solutions are Hopf bifurcation points. The bifurcation indicator gives initial critical values (Reynolds number, Strouhal frequency) for the direct method iterations. Some classical numerical examples from fluid mechanics, in two dimensions, are studied to demonstrate the efficiency and the reliability of such an algorithm. 相似文献