Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.     

Dynamic Survivability in WDM Mesh Networks Under Dynamic Traffic

Network survivability is a crucial requirement in WDM mesh networks. In this paper, we systematically consider the problem of dynamic survivability with dynamic single link failure in WDM networks under dynamic traffic demands. Specifically, we investigate various protection schemes, such as dedicated path protection (DPP), shared path protection (SPP), dedicated link protection (DLP), shared link protection (SLP), and two restoration schemes, path restoration (PR) and link restoration (LR). Moreover, two new shared protection methods are proposed, i.e., SRLG-based shared link protection (SRLG-SLP) and SRLG-based shared path protection (SRLG-SPP). The SRLG (shared risk link group) constraint defines the availability of protection resources to a working path, which requires that any two working paths sharing the same risk of failure (or in the same SRLG) cannot share the same protection resources. Furthermore, in our study, we consider a more practical dynamic single-link failure model, in which the link-failure-interarrival time and link-failure-holding time are considered as two independent parameters. Based on this link-failure model, extensive simulations are done to analyze and compare the dynamic survivable performance of various protection and restoration schemes. Resource utilization, protection efficiency, restoration efficiency, and service disruption ratio are employed as survivable performance metrics versus traffic load, link-failure frequency, and link-failure reparation time to evaluate the survivable performance. Many meaningful results are given. In addition, we show that the developed SRLG-SLP and SRLG-SPP protection schemes perform very well in terms of protection efficiency and service disruption ratio, while sacrificing some performance in terms of resource utilization.     

Anthony D. Wagner: Award for Distinguished Scientific Early Career Contributions to Psychology.

Presents the citation for Anthony D. Wagner, who received the Award for Distinguished Scientific Early Career Contributions to Psychology (cognition and human learning) "for outstanding and innovative research on the neural basis of memory and executive control." A brief profile and a selected bibliography accompany the citation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

BP神经网络在致密砂岩储层识别中的应用

川西须家河组地层岩性复杂,属于超致密低孔渗储层,所以储层识别是该地层天然气勘探中所面临的关键问题和难点之一。针对常规储层识别准确率不高的状况,提出利用BP神经网络进行储层的气水干层识别,利用模糊聚类和产层测试结果标定建模样本,采取随机抽样形成建模集与测试集,建立BP神经网络模型对23口井的储层进行气水干层预测,正确率达77.9%以上,明显提高了该地区的测井解释精度,并提供了一种准确率较高的储层预测方法。     

A Review of Wavelets for Digital Wireless Communication

Wavelets have been favorably applied in almost all aspects of digital wireless communication systems including data compression, source and channel coding, signal denoising, channel modeling and design of transceivers. The main property of wavelets in these applications is in their flexibility and ability to characterize signals accurately. In this paper recent trends and developments in the use of wavelets in wireless communications are reviewed. Major applications of wavelets in wireless channel modeling, interference mitigation, denoising, OFDM modulation, multiple access, Ultra Wideband communications, cognitive radio and wireless networks are surveyed. The confluence of information and communication technologies and the possibility of ubiquitous connectivity have posed a challenge to developing technologies and architectures capable of handling large volumes of data under severe resource constraints such as power and bandwidth. Wavelets are uniquely qualified to address this challenge. The flexibility and adaptation provided by wavelets have made wavelet technology a strong candidate for future wireless communication. Madan Kumar Lakshmanan was born in Chennai, India, in 1979. He received the B.E. (with distinction) in electrical engineering from the University of Madras, Chennai, India, in 2000. He joined the Indian Software firm, Polaris Software Labs Ltd., in 2000 where he wrote software for Telecommunication applications. At Polaris, he was awarded the “On The Spot Of Excellence Award” for his efforts. In 2003, he moved to the Indian Institute of Technology-Madras, to develop and establish a wireless communications network for rural connectivity. In 2004, he was awarded the Royal Dutch/Shell Chevning scholarship to pursue a Master degree in Telecommunications at the Delft University of Technology (TUDelft). At TUDelft he is affiliated to the International Research Center for Telecommunications-Transmission and Radar (IRCTR) where he is undertaking research in the field of wavelets applications in Wireless Communications. Homayoun Nikookar received his Ph.D. in Electrical Engineering from Delft University of Technology (TUDelft), The Netherlands, in 1995. From 1995 to 1998 he was a postdoc researcher at the International Research Center for Telecommunications-Transmission and Radar, TUDelft, where since 1999 he has been an Assistant Professor. Dr. Nikookar has done research on different areas of wireless communications, including wireless channel modeling, UWB, MIMO, multicarrier transmission, Wavelet-based OFDM and CDMA. He is a senior member of the IEEE.     

震源延迟叠加技术及应用效果

延迟叠加爆炸是针对高分辨率和深层地震勘探而提出来的一种激发方式。这种激发方式能在保证地震分辨率的前提下有效提高地震波能量 ,在地震波的入射角范围内 ,有效克服非球形炸药激发引起的地震波传播的方向性。应用弹性波动力学对多级延迟爆炸进行理论研究 ,结合实际试验资料 ,研究了延迟爆炸情况下激发参数对地震波能量、主频、频宽等的影响。从几何地震学出发 ,分析了不同时间激发的地震波在地下空间中的干涉情况 ,对其能量传播的方向性进行了研究。理论分析和试验效果表明 ,延迟叠加震源与相同药量的普通震源相比 ,激发地震波的主频高 ,频带宽 ,增强了下传能量 ,降低了次生干扰 ,在高分辨率和深层地震勘探中具有明显的优势。     

土钉支护在深基坑开挖中的应用

深圳某银行办公楼的深基坑支护采用了土钉支护，不但保证了施工质量，而且缩短了工期，减少了投资，以此工程为例介绍了土钉支护的土力学参数计算、结构设计、施工方案、技术措施和取得的显著效果。     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Linearly many faults in Cayley graphs generated by transposition trees

We prove that when linearly many vertices are deleted in a Cayley graph generated by a transposition tree, the resulting graph has a large connected component containing almost all remaining vertices.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.