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991.
992.
设计了一套用于分层注汽实验的智能化测控系统.主要介绍了该系统的结构组成、功能以及技术指标;系统以计算机技术、现代传感器技术、自动控制技术为基础,能够对实验过程中16路模拟信号(包括压力、温度、液位、流量等)、32路开关信号进实时采集监测,能够自动控制各设备的运行状态,实现实验流程的自动切换,能够满足分层注汽、井下工具密封性检测以及热采工具的高温高压性能检测等实验要求;在系统中建立实时数据库和历史数据库,对采集的数据、键人的命令等进行管理和相关分析,提供多方面的数据参考依据;另外,为保证系统运行稳定、可靠,采取了多项提高可靠性的措施,包括选用高可靠性工控机和专门设备,设计备用手动操作器和现场显示单元. 相似文献
993.
针对一般的具有时变且界未知的非线性不确定性的单输入多输出非线性系统,提出一种自适应滑模跟踪控制器的框架.在该框架内,系统的时变且界未知的非线性不确定性可以通过函数逼近技术(FAT)表示成为一组正交基函数序列的组合,并通过滑模控制技术和直接Lyapunov方法获得基函数系数的更新律以及对不确定性逼近误差的在线自适应补偿,从而得到自适应的滑模控制律.所提出的基于函数逼近技术的自适应滑模跟踪控制策略在直流电机跟踪控制系统实验装置上进行了实际控制实验,并进行了性能的对比与分析. 相似文献
994.
针对一类含有离散和分布时延神经网络,在神经激活函数较弱的约束条件下,通过定义一个更具一般性的Lyapunov泛函,使用凸组合技术,得到了新的基于线性矩阵不等式表示的指数稳定性判据.与现有结果相比,这些判据具有较小的保守性.仿真算例表明,得到的结果是有效的且保守性小. 相似文献
995.
In this article, we propose a novel complex radial basis function network approach for dynamic behavioral modeling of nonlinear power amplifier with memory in 3 G systems. The proposed approach utilizes the complex QR‐decomposition based recursive least squares (QRD‐RLS) algorithm, which is implemented using the complex Givens rotations, to update the weighting matrix of the complex radial basis function (RBF) network. Comparisons with standard least squares algorithms, in batch and recursive process, the QRD‐RLS algorithm has the characteristics of good numerical robustness and regular structure, and can significantly improve the complex RBF network modeling accuracy. In this approach, only the signal's complex envelope is used for the model training and validation. The model has been validated using ADS simulated and real measured data. Finally, parallel implementation of the resulting method is briefly discussed. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009. 相似文献
996.
A method for detection of faulty elements in antenna arrays from far‐field radiation pattern is presented. The proposed technique finds variation of current from correct values in the faulty elements. A step wise approach is proposed to determine magnitude and phase of current excitation and location of faulty element using neural networks. The results with radial basis function neural network and probabilistic neural network are compared. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009. 相似文献
997.
Standardisation initiatives (ISO and IEC) try to answer the problem of managing heterogeneous information, scattered within organizations, by formalising the knowledge related to products technical data. While the product is the centred object from which, along its lifecycle, all enterprise systems, either inside a single enterprise or between cooperating networked enterprises, have a specific view, we may consider it as active as far as it participates to the decisions making by providing knowledge about itself. This paper proposes a novel approach, postulating that the product, represented by its technical data, may be considered as interoperable per se with the many applications involved in manufacturing enterprises as far as it embeds knowledge about itself, as it stores all its technical data, provided that these are embedded on a common model. The matter of this approach is to formalise of all technical data and concepts contributing to the definition of a Product Ontology, embedded into the product itself and making it interoperable with applications, minimising loss of semantics. 相似文献
998.
Sophisticated on-chip interconnects using packet and circuit switching techniques were recently proposed as a solution to non-scalable shared-bus schemes currently used in Systems-on-Chip (SoCs) implementation. Different interconnect architectures have been studied and adapted for SoCs to achieve high throughput, low latency and energy consumption, and efficient silicon area. Recently, a new on-chip interconnect architecture by adapting the WK-recursive network topology structure has been introduced for SoCs. This paper analyses and compares the energy consumption and the area requirements of Wk-recursive network with five common on-chip interconnects, 2D Mesh, Ring, Spidergon, Fat-Tree and Butterfly Fat-Tree. We investigated the effects of load and traffic models and the obtained results show that the traffic models and load that ends processing elements has a direct effect on the energy consumption and area requirements. In these results, WK-recursive interconnect generally has a higher energy consumption and silicon area requirements in heavy traffic load. 相似文献
999.
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
1000.
Katalin Friedl Gábor Ivanyos Miklos Santha Yves F. Verhoeven 《Theory of Computing Systems》2009,45(3):629-646
We present several results on the complexity of various forms of Sperner’s Lemma in the black-box model of computing. We give
a deterministic algorithm for Sperner problems over pseudo-manifolds of arbitrary dimension. The query complexity of our algorithm
is linear in the separation number of the skeleton graph of the manifold and the size of its boundary. As a corollary we get
an
deterministic query algorithm for the black-box version of the problem 2D-SPERNER, a well studied member of Papadimitriou’s complexity class PPAD. This upper bound matches the
deterministic lower bound of Crescenzi and Silvestri. The tightness of this bound was not known before. In another result
we prove for the same problem an
lower bound for its probabilistic, and an
lower bound for its quantum query complexity, showing that all these measures are polynomially related.
Research supported by the European Commission IST Integrated Project Qubit Application (QAP) 015848, the OTKA grants T42559
and T46234, and by the ANR Blanc AlgoQP grant of the French Research Ministry. 相似文献