A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.     

Fast fingerprint identification for large databases

Fingerprint matching has emerged as an effective tool for human recognition due to the uniqueness, universality and invariability of fingerprints. Many different approaches have been proposed in the literature to determine faithfully if two fingerprint images belong to the same person. Among them, minutiae-based matchers highlight as the most relevant techniques because of their discriminative capabilities, providing precise results. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms.In this paper, we propose a distributed framework for fingerprint matching to tackle large databases in a reasonable time. It provides a general scheme for any kind of matcher, so that its precision is preserved and its time of response can be reduced.To test the proposed system, we conduct an extensive study that involves both synthetic and captured fingerprint databases, which have different characteristics, analyzing the performance of three well-known minutiae-based matchers within the designed framework. With the available hardware resources, our distributed model is able to address up to 400 000 fingerprints in approximately half a second. Additional details are provided at http://sci2s.ugr.es/ParallelMatching.     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

Approximations to auctions of digital goods with share-averse bidders

We consider the case of a digital product for share-averse bidders, where the product can be sold to multiple buyers who experience some disutility from other firms or consumers owning the same product. We model the problem of selling a digital product to share-averse bidders as an auction and apply a Bayesian optimal mechanism design. We also design constant-approximation algorithms in the prior-free setting including both average- and worst-case analyses.     

认知无线网络动态频谱感知决策安全协议框架研究

对认知网络频谱感知安全研究现状进行了分析, 提出了一个频谱感知决策安全协议框架。对认知无线网络模型中相关组件、符号进行了定义, 并对其结构和安全性进行了详细描述。通过与相关方案比较, 结果表明该协议框架能够用来抵御频谱感知所面临的潜在威胁, 使之进行正确的频谱决策。     

一种可扩展的动态数据持有性证明方案

提出了一种能保护数据隐私的动态数据持有性证明方案, 并对方案的安全性进行了证明, 性能分析表明该方案具有低存储、计算和传输负载的特点。通过扩展实现了对公开可验证和多副本检查的支持, 扩展方案的特点在于公开验证中不需要可信第三方的参与, 而多副本检查可以方便地实现对所有副本的批量检查。     

电磁领域中复超越方程的遗传算法求解方法

将遗传算法与参数跟踪策略有效结合,跟踪过程中进行搜索域压缩与位移操作,形成了一种功能强大的新算法,可成功应用于电磁领域中各种各样复超越方程的高精度求解问题.在算法实现过程中,使用参数跟踪策略有效地缩小了搜索区域,保证了解的单一性,提高了运算速度;使用动态搜索域提高了解的精度;应用三阶差商公式预估新的搜索中心,使运算速度得以进一步提高.应用本文发展的算法详细求解了终端短路法测量材料的复介电常数时得到的复超越方程和部分填充矩形波导的特征方程,计算结果表明该算法能够轻松地解决复超越方程中的多值问题,解集完备性好,算法鲁棒性强.     

线传感器网络中基于距离变换的骨架提取*

在无线传感器网络中,利用骨架信息可以提高路由性能,也可以用于网络定位、导航以及分割等.以往的骨架算法往往假定边界节点被全部识别,但在绝大多数网络中,尤其是低密度网络,边界节点往往很难被全部正确识别.提出了一种基于距离变换的低复杂度、分布式骨架提取算法,该算法不要求所有边界节点被识别.实验结果表明,该算法对于边界不准确时能够得到较好骨架,同时对于边界点缺失具有鲁棒性.     

DCAM平台下的分布式方面构件模型及协同框架的研究*

由于方面化中间件平台DCAM的方面内核实现机制没有提供对分布式环境下方面构件通信的支持,扩展了DCAM中方面内核的方面调用机制,提出了分布式方面构件模型以及分布式环境下方面构件的协同框架。扩展后的DCAM平台不仅保持了原平台对POJO的支持,同时通过对分布式协同框架的引入,使平台在分布式环境下具有更好的可扩展性和容错性,满足了按需计算的需求。最后通过应用实例对DCAM的分布式模型进行了可行性验证并给出了性能分析。     

基于EEMD和自相关函数特性的自适应降噪方法

在机械设备早期故障中,反映故障特征的冲击信号非常微弱,容易被噪声淹没,使得故障诊断有一定难度。集合经验模式分解方法将含噪信号分解为多个固有模式分量,其中包括噪声分量和有用信号分量。根据两者自相关函数特性的不同,提出了利用能量集中比找到噪声分量分界点的自适应降噪方法,并利用改进的软阈值方法拾取噪声分量中的高频有用信号。对不同频率的含噪信号进行降噪处理,结果表明,该方法对中低频信号的降噪具有很好的效果。故障轴承振动信号的降噪效果表明该方法的实用性。