Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node’s perspective i.e., migrating the service of faulty node to it’s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don’t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level.     

Novel neural network modeling method and applications

Neural networks play an important role for designing the parametric model of electromagnetic structures. The current neural network methods are unfit for a circuit model with many input variables because it is costly to extract a large number of the training data and test data to complete the highly nonlinear mapping approximation. This article proposes a new neural network modeling method—the multidimensional neural network model, which can be used to solve the issue of multivariable radiofrequency and microwave passive device modeling. The entire multidimensional neural network modeling problem is simplified into a set of neural network submodels through decomposition method. Then the submodels are combined into an equivalent model, and the final entire model is produced through the neural‐network mapping model developed with the submodels and equivalent model. A microstrip hairpin filter model is developed using the proposed method. The simulation results show the correctness and the effectivity of the proposed method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:769–779, 2015.     

基于遗传算法的空间伸展臂展开过程优化控制

针对空间伸展臂展开过程建立多体系统动力学模型,并以控制力为变量建立优化控制模型,通过最优控制力的选取,使空间伸展臂得以平稳、快速展开.为了避免基于梯度的优化方法在寻找最优控制力过程中大量复杂的灵敏度计算,采用遗传算法进行全局寻优.同时,优化迭代过程中动力学方程采用高阶变分数值积分方法求解,具有更高的稳定性.最后,以剪叉式空间伸展臂为例,利用遗传算法对其展开过程进行优化控制.     

Tree-based data retrieval algorithm for multi-item request with deadline in wireless networks

The data retrieval problem in wireless communication has attracted increasing attentions in many applications such as electronic information sharing and e-business. However, most existing studies do not consider how to retrieve the requested data items in their deadlines. In this paper, we discuss the so-called deadline-based multi-item data retrieval problem, which aims at finding an access pattern for retrieving requested data items in certain deadlines such that the access latency and data miss rate are minimized. We propose two algorithms, named EDFS and UR, to deal with the above problem. The EDFS algorithm gives priority to retrieve the requested data item with the least deadline, while the UR algorithm introduces the urgent rate that is computed by the broadcast time and deadline of requested data item for further reducing the access latency and data miss ratio. Moreover, we design a tree-based data retrieval algorithm that finds the longest downloading sequence of data items in one broadcast cycle and is applied to EDFS and UR for finding data retrieval sequence of requested data items. The efficiency of our proposed schemes has been validated by the extensive experiments.     

核酸适体靶向红荧烯有机纳米探针用于肿瘤成像研究

近二十年来,肿瘤在我国的发生率和死亡率呈现不断增高的趋势。若能早期发现癌变位点并及时对其进行靶向治疗,将有助于提高癌症的治愈率。伴随着纳米技术的发展,用于癌症早期诊断成像和治疗的纳米材料的开发也得到了人们的广泛关注。文章介绍了一种基于核酸适体靶向肿瘤细胞的有机荧光纳米探针,其制备过程快速简便,所制得的纳米颗粒不仅表现出优异的靶向能力,而且具有良好的生物相容性和稳定性,该研究为肿瘤的靶向诊疗提供了有力工具。     

天津市水功能区划要求下海河干流排涝问题研究

按新的海河干流水功能区划要求,海河干流将不再承担上游来水的行洪任务。因而有必要分析探讨新形势下海河干流的排涝问题。在充分考虑海河干流水功能区划要求的基础上,通过推求设计暴雨、进行产汇流计算,建立海河干流汇水区一维水动力模型,模拟了海河干流在经历不同重现期(3年、5年及10年一遇)设计暴雨下的排涝水位。在此基础上,对多种排涝方案进行比选,给出了适当的排涝方案,以缓解水功能区划要求下天津市海河干流的排涝矛盾。     

灌溉结合发电水库调度图编制的改进算法研究

在使用《水利工程水利计算规范》(SL 104—95)编制灌溉结合发电水库综合调度图的过程中,对初步计算结果进行反复检验和修改后仍难以准确符合设计要求。为解决这一问题,提出了改进限制灌溉线、灌溉防破坏线的计算方法,协调了限制灌溉区、保证灌溉区、灌溉发电共同保证区之间的关系,统一了年调节与多年调节水库调度图的计算方法,并用系列年代替了典型年。结果表明:采用改进方法计算和编制的综合调度图可以一次性通过检验,无需进行修改;按改进方法编制的调度图进行长系列径流调节计算,可以准确地符合灌溉和发电的各种设计要求,提高时历法编制综合调度图的理论合理性和实用价值。     

非一致性条件下城市设计洪水的计算方法

在分析城市化后产流变化的基础上,可采用平均洪量模数增量修正设计洪峰频率曲线的均值,进而推算城市化后的设计洪峰流量,以宝象河流域为实例进行了验证。结果表明:该算法具有合理性,以实测洪水资料为基础进行的非一致性条件下的设计洪水计算结果比基于设计暴雨推求设计洪水的误差更小。     

宽尾墩对跌坎底流消力池影响的试验研究

针对低水头、大单宽流量、下游水位变幅大的水电站中的泄洪消能问题,本文结合官地水电站工程进行了水工模型试验研究,从消力池的水流流态、临底流速以及底板时均动水压强对比几方面,分析了宽尾墩对跌坎底流消力池的影响。结果表明,宽尾墩改善了消力池内的水流流态,降低了消力池内的临底流速和时均压强,充分利用了宽尾墩技术优势,提高了泄洪设施的总消能率,有效解决了下游水位变幅较大的水电工程类泄洪消能问题。     

VFP和CAD技术在生成流速流向图中的应用

主要介绍了数据库编程语言VFP和CAD在生成流速流向图中的应用情况。在进行水文测验过程中各断面位置所在的垂线实测到的流速流向需要制作成图件,为了能够规模化、标准化成图,采用VFP编程对数据进行批量处理,并生成CAD图,进而可以对文字、线条进行随意设置以及多条垂线的图形合并比较。并可根据需要转换成PDF图形文件,作为成果报告的附件,满足工作和生产的需要。