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基于传感器阵列与前馈神经网络的气体辨识系统 总被引:9,自引:0,他引:9
将气体传感器阵列与前馈神经网络模式识别技术相结合形成气体辨识技术相结合形成气体辨识系统,通过实验比较了不同的传感器信号预处理方法、前馈神经网络的结构和参数对气体辨识系统性能的影响,研究结果具有一定的工程应用价值。 相似文献
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提出将气体传感器阵列检测与最近邻域法相结合的方法实现气体的模式识别。设计了用该方法进行气体识别的实验系统。该方法具有实验次数少,且识别准确度高的优点。实验以3只金属氧化物半导体气体传感器组成的阵列为例,详细讨论了该方法的实验过程与识别结果。通过对CH4,H,CO 3种气体进行识别实验,结果表明:该方法的正确识别率达到100%,具有很高的实用价值。 相似文献
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隐变量模型是一类有效的降维方法,但是由非线性核映射建立的隐变量模型不能保持数据空间的局部结构。为了克服这个缺点,文中提出一种保持数据局部结构的隐变量模型。该算法充分利用局部保持映射的保局性质,将局部保持映射的目标函数作为低维空间中数据的先验信息,对高斯过程隐变量中的低维数据进行约束,建立局部保持的隐变量。实验结果表明,相比原有的高斯过程隐变量,文中算法较好地保持数据局部结构的效果。 相似文献
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利用薄膜技术制作的半导体金属氧化物气体传感器阵列是由一个基底上的四个传感器单元组成的。基本结构是在4英寸的硅片上制作完成的。首先,沉积金属铂电极,加热棒和温度传感器。其次,沉积半导体金属氧化物SnO2。然后进行传感器阵列电极的焊接,封装。最后进行测量,测量结果显示了传感器阵列对不同气体甲烷(CH4),一氧化碳(CO),氢气(H2),二氧化氮(NO2)和氨气(NH3)的响应。 相似文献
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由电池驱动以微机械技术制造的气体传感器的功耗是十分关键的实际问题.为了研制低功耗气体传感器,在传感器阵列上表面制备完成后,从下表面腐蚀传感器芯片的基底,以造成传感器芯片和封装室之间形成一个空气夹层.从而对有空气夹层的传感器芯片和没有空气夹层的传感器芯片的能耗做比较.实验结果显示,腐蚀过的传感器芯片具有较低的功耗. 相似文献
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Top-K数据查询是无线传感器网络的一个重要应用,如何节省能耗是Top-K数据查询的一个重要课题.针对传统的Top-K数据查询是多跳传输,节点过滤窗口更新代价大等缺点,提出一种基于分簇的无线传感器网络Top-K数据查询算法.通过对节点进行分簇进而减少数据的传输跳数,通过设置过滤器值对数据过滤,减少冗余数据的传输,增加探寻过程,保证数据的完整性和可靠性,实现降低网络节点整体通信能耗的目的.仿真结果表明:与传统算法相比,该算法可有效降低网络的整体能耗,提高能量有效性. 相似文献
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对于大规模的传感器网络而言,通常采用基于簇的分层路由策略。针对传感器网络中数据的小波压缩,提出了一种基于粗糙数据相关的反馈型成簇算法。该算法首先根据节点数据间的粗糙相关度,形成数据相关性较好的簇结构,然后通过比较Sink反馈的部分小波重构数据与其相应真实数据,进一步优化簇结构。理论分析和仿真实验表明,这种成簇机制使簇内数据的小波压缩具有误差小、压缩比大的优点,且能防止小波压缩中数据淹没现象的产生。 相似文献
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码书生成是基于矢量量化压缩体绘制的关键之一。在码书生成中,初始码书对码书生成算法有较大的影响。现有的码书初始化方法需要对原始海量数据进行多次迭代,数据频繁在硬盘、内存和GPU(图形处理器)之间进行数据传输,导致算法效率不高。本文针对码书生成的初始码书提取问题,提出了基于数据流聚类策略的初始码书生成算法。其基本思想是将海量三维数据体当作一个数据流(分块),对每一部分数据形成局部码书,再对所有的局部码书进行分类形成最终的初始码书。利用本方法可以极大的减少数据的读取和传输的次数,同时,充分利用GPU并行计算能力。通过仿真结果分析表明,本文提出的方法在效率上和效果上都有较大的提高。 相似文献
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现代智慧医疗需要操作简洁、反应迅速,能够提供智慧诊断的信息化平台,提出基于物联网无线传感器技术的智慧医疗模型。系统利用附着在患者身上的各类传感器采集到的生理信息数据,采用基于密度的带有噪声的空间聚类(DBSCAN)算法的数据分析方法,用非线性映射把患者的生理信息数据转换到高纬度的特征空间,对变换后的矢量数据进行聚类分析,从而提升聚类结果并有效辅助医务人员进行诊断。 相似文献
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Latent variable models are powerful dimensionality reduction approaches in machine learning and pattern recognition. However, this kind of methods only works well under a necessary and strict assumption that the training samples and testing samples are independent and identically distributed. When the samples come from different domains, the distribution of the testing dataset will not be identical with the training dataset. Therefore, the performance of latent variable models will be degraded for the reason that the parameters of the training model do not suit for the testing dataset. This case limits the generalization and application of the traditional latent variable models. To handle this issue, a transfer learning framework for latent variable model is proposed which can utilize the distance (or divergence) of the two datasets to modify the parameters of the obtained latent variable model. So we do not need to rebuild the model and only adjust the parameters according to the divergence, which will adopt different datasets. Experimental results on several real datasets demonstrate the advantages of the proposed framework. 相似文献
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In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results. 相似文献
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Capture-recapture methods are used to estimate the prevalence of diseases in the field of epidemiology. The information used for estimation purposes are available from multiple lists, whereby giving rise to the problems of list dependence and heterogeneity. In this paper, modelling is focused on the heterogeneity part. We present a new binomial latent class model which takes into account both the observed and unobserved heterogeneity within capture-recapture data. We adopt the conditional likelihood approach and perform estimation via the EM algorithm. We also derive the mathematical expressions for the computation of the standard error of the unknown population size. An application to data on diabetes patients in a town in northern Italy is discussed. 相似文献
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Surrogate-assisted evolutionary optimization has proved to be effective in reducing optimization time, as surrogates, or meta-models can approximate expensive fitness functions in the optimization run. While this is a successful strategy to improve optimization efficiency, challenges arise when constructing surrogate models in higher dimensional function space, where the trade space between multiple conflicting objectives is increasingly complex. This complexity makes it difficult to ensure the accuracy of the surrogates. In this article, a new surrogate management strategy is presented to address this problem. A k-means clustering algorithm is employed to partition model data into local surrogate models. The variable fidelity optimization scheme proposed in the author's previous work is revised to incorporate this clustering algorithm for surrogate model construction. The applicability of the proposed algorithm is illustrated on six standard test problems. The presented algorithm is also examined in a three-objective stiffened panel optimization design problem to show its superiority in surrogate-assisted multi-objective optimization in higher dimensional objective function space. Performance metrics show that the proposed surrogate handling strategy clearly outperforms the single surrogate strategy as the surrogate size increases. 相似文献
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提出一种基于Dijkstra的无线传感器网络分簇路由算法--DEUC.该算法将改进的Dijkstra算法应用到簇间路由机制中,寻找簇头到基站的最短路径,使得离SINK较远的簇头节点沿着最短路径传输信息,从而有效减少传输路径长度与相应的网络延时.该算法还将传感器网络进行区域划分,使得距离SINK较近的簇头拥有少量成员节点,因此,靠近SINK的簇首可以为簇问的数据转发预留能量,达到均衡簇头能量消耗的目的.仿真结果表明,该算法在延长网络生存周期方面相比低功耗自适应分簇路由协议(LEACH)和能量高效的非均匀分簇算法(EEUC)分别提高约35%和25%. 相似文献