基于SOA的宁波地铁信息系统架构设计

为有效改善宁波地铁企业信息系统中各个信息系统间信息的有效互通、数据共享、统一管理等问题, 结合宁波市轨道交通的规划、建设、运营和开发“四位一体”战略, 设计了面向智慧地铁的信息系统架构, 整个架构的内容包括基础设施、基础平台、一体化的信息系统平台、信息门户及网站、微信、移动APP等公众服务平台, 并设计配置了信息化管理体系, 进而实现异构、分散的应用系统之间的有效集成, 提升信息化系统资源协同工作和业务管理的效率.     

Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment

A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.     

MATLAB拟合工具箱在PN结物理特性综合实验中的应用

介绍了PN结物理特性综合实验原理,利用绘图软件Matlab对实验数据进行曲线拟合,得到了PN结伏安特性函数表达式和PN结正向压降与温度间关系的拟合曲线,计算了常温下玻尔兹曼常数和PN结温度传感器灵敏度及T=0 K时的半导体近似禁带宽度。     

Origin绘图功能在物理实验中的应用

用实例介绍使用origin软件的绘图功能进行绘图和数据处理的步骤和方法,全面地呈现了origin绘图功能在物理实验数据处理中的应用。     

三维面形测量中基于模版匹配的变形条纹修复

在基于条纹投影和相位分析的三维面形测量中,由于被测物体表面标志点或复杂面形的阴影遮挡存在,会造成变形条纹局部区域的条纹数据缺失,影响相位和高度信息的最终重建,需要人为地对缺失图像信息进行修复。提出了一种新的缺失条纹数据修复方法——基于模版匹配的图像修复算法,通过图像中已有条纹信息(特别是与待修复区域周围相位信息相似度较高的已知条纹信息)对缺失的变形条纹信息进行估算,实现数据修复。该方法修复效果好,运算过程无需人为参与,便于计算机自动实现,尤其适合于待修复图像整体结构明显、纹理清晰图像的数据修复,有助于提高被测物体相位计算质量和在此基础上的三维面形重建质量。     

基于FPGA的超高速线阵CCD信号采集系统的设计与实现

线阵CCD已广泛应用于在线检测、图像识别等系统,目前高帧率采集系统多在200~500Hz之间。高速线阵CCD采集系统,如1K甚至10KHz以上的采集要求,设计难度大,电路实现复杂,需要专用处理器,产品成本高,提出了一种采用并行高速FPGA驱动线阵CCD,通过常规分立元件完成模拟信号处理,实现数字信号实时传输的方案。该方案不仅简化了硬件设计上的难度,在同等性能情况下,可实现每秒万帧的高速采样,大幅度降低了成本。方案选用Altera FPGA作为控制核心,实现高速信号采集的同时,在片上实现一定的图像算法,不仅加速了图像处理速度,同时降低了计算机的处理压力。最后,本电路通过USB2.0接口,完成数据的实时传输。设计具有高帧率、高灵敏度、性能稳定,便携使用等特点,同时还有一定的通用性,已应用于一些光学系统中。     

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.     

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.     

An approach to estimating and extrapolating model error based on inverse problem methods:towards accurate numerical weather prediction

Model error is one of the key factors restricting the accuracy of numerical weather prediction(NWP). Considering the continuous evolution of the atmosphere, the observed data(ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers’ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.     

Efficient and flexible memory architecture to alleviate data and context bandwidth bottlenecks of coarse-grained reconfigurable arrays

The computational capability of a coarse-grained reconfigurable array(CGRA)can be significantly restrained due to data and context memory bandwidth bottlenecks.Traditionally,two methods have been used to resolve this problem.One method loads the context into the CGRA at run time.This method occupies very small on-chip memory but induces very large latency,which leads to low computational efficiency.The other method adopts a multi-context structure.This method loads the context into the on-chip context memory at the boot phase.Broadcasting the pointer of a set of contexts changes the hardware configuration on a cycle-by-cycle basis.The size of the context memory induces a large area overhead in multi-context structures,which results in major restrictions on application complexity.This paper proposes a Predictable Context Cache(PCC)architecture to address the above context issues by buffering the context inside a CGRA.In this architecture,context is dynamically transferred into the CGRA.Utilizing a PCC significantly reduces the on-chip context memory and the complexity of the applications running on the CGRA is no longer restricted by the size of the on-chip context memory.Data preloading is the most frequently used approach to hide input data latency and speed up the data transmission process for the data bandwidth issue.Rather than fundamentally reducing the amount of input data,the transferred data and computations are processed in parallel.However,the data preloading method cannot work efficiently because data transmission becomes the critical path as the reconfigurable array scale increases.This paper also presents a Hierarchical Data Memory(HDM)architecture as a solution to the efficiency problem.In this architecture,high internal bandwidth is provided to buffer both reused input data and intermediate data.The HDM architecture relieves the external memory from the data transfer burden so that the performance is significantly improved.As a result of using PCC and HDM,experiments running mainstream video decoding programs achieved performance improvements of 13.57%–19.48%when there was a reasonable memory size.Therefore,1080p@35.7fps for H.264high profile video decoding can be achieved on PCC and HDM architecture when utilizing a 200 MHz working frequency.Further,the size of the on-chip context memory no longer restricted complex applications,which were efficiently executed on the PCC and HDM architecture.