共查询到20条相似文献，搜索用时 183 毫秒
1.
2.
在人体运动跟踪建模中,需要对样本集的多样性特征进行贫化处理,以提高全关节驱动模式运动状态跟踪的准确性。传统方法采用量子进化和粒子滤波算法进行人体运动跟踪贫化算法实现,算法在全关节多样化样本特征运动模式下,跟踪效果不好。提出一种采用动态分层二值进化处理的改进的量子进化粒子滤波全关节驱动模式跟踪方法,解决多样本特征的贫化问题。进行人体全关节驱动模式动力学分析及人体运动跟踪模型构建,通过动态分层处理技术,获得二值前景图像,求得人体关节的全方位信息特征,通过动态分层二值进化方法,准确地找到各关节位置,构建亮度模型函数,实现贫化处理。实验表明,改进算法能实现对体操运动员运动幅度大的肘、腕、踝部位均得到了准确的跟踪结果,贫化效果较好,运动状态估计精度较高。 相似文献
3.
4.
文章针对单参数恒虚警(CFAR)检测器在weibull杂波模型下CFAR性能的不足,提出采用最优线性无偏估计(BLUE)算法设计局部检测器,并在全局最优条件对局部检测器的双门限参数以及判决融合规则进行优化.实验仿真表明,该方法能有效改善分布式CFAR检测系统的性能. 相似文献
5.
为提高彩色图像分割精度,解决传统分水岭图像分割算法误分割率高等问题,本文提出了一种基于改进分水岭算法的彩色图像分割方法。建立了基于偏微分方程的去噪模型,既可以抑制噪声又可以有效地保护图像轮廓。结合数学形态学、图像信息熵、区域合并实现图像分割。在彩色图像RGB空间利用信息熵求取形态学梯度,然后对彩色梯度图进行分水岭分割,最后进行区域合并。仿真结果表明:本文所述分割方法准确度和清晰度较好,噪声抑制效果理想而且分割速度较快。 相似文献
6.
7.
8.
本文在基于灰度图像分割的基础上对传统遗传算法进行改进,提出基于染色体、基因位的改进遗传算法.该方法利用图像的直方图,对进行初始种群预处理,减少遗传算法的迭代次数.实验表明,改进的遗传算法应用于灰度图像分割能取得较好的效果. 相似文献
9.
《内蒙古科技与经济》2021,(13)
针对换向器表面检测面临缺陷类型多样、样本少等问题,提出了一种基于贝叶斯生成对抗网络的换向器缺陷检测方法,该方法包括两个阶段:第一阶段利用贝叶斯生成对抗网络对样本进行数据增强,第二阶段利用分类网络与分割网络结合的方式对缺陷进行检测;在缺陷检测模块中,分割网络输出预测缺陷位置热图,将其作为注意力机制融入分类网络,从而提高分类网络精度。 相似文献
10.
11.
This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods. 相似文献
12.
针对提取水体信息时阈值设定模糊问题,提出一种具有稳定阈值的在反射率为0时水体与其他地物分离度最好的自动水体信息提取方法。利用不同时间段的艾比湖、博斯腾湖、鄱阳湖、阿拉湖的Landsat 8卫星影像为数据源,通过分析水体与不同地物的反射率及主分量特征,构建一种新型水体指数——温度植被水体指数（Temperature Vegetation Water Index, TVWI）,利用监督分类得到的水体面积为参考值,分别与传统水体提取方法进行对比验证。结果表明：①利用TVWI分别对2013年与2016年的艾比湖进行水体信息提取,总体精度分别为98.51%、97.33%,博斯腾湖为99.66%,鄱阳湖为98.06%,阿拉湖为99.72%,总体精度较高;②在TVWI指数中,水体与非水体区域区分度较高,在水体边界处的非水体区域出现极高的噪音值,而水体区域值大于0;③通过普适性分析得知,TVWI对湖泊适应性较好,对不同类型的湖泊水体提取精度均较高。因此,利用TVWI进行高精度无模糊阈值设定的水体信息提取是可行的。 相似文献
13.
We propose a novel form of nonlinear stochastic filtering based on an iterative evaluation of a Kalman-like gain matrix computed within a Monte Carlo scheme as suggested by the form of the parent equation of nonlinear filtering (Kushner–Stratonovich equation) and retains the simplicity of implementation of an ensemble Kalman filter (EnKF). The numerical results, presently obtained via EnKF-like simulations with or without a reduced-rank unscented transformation, clearly indicate remarkably superior filter convergence and accuracy vis-à-vis most available filtering schemes and eminent applicability of the methods to higher dimensional dynamic system identification problems of engineering interest. 相似文献
14.
《Information processing & management》2001,37(2):187-198
Information filtering (IF) systems usually filter data items by correlating a vector of terms that represent the user profile with similar vectors of terms that represent data items. Terms that represent data items can be determined by experts or automatic indexing methods. In this study we employ an artificial neural network (ANN) as an alternative method for both IF and term selection and compare its effectiveness to that of “traditional” methods. In an earlier study we developed and examined the performance of an IF system that employed content-based and stereotypic rule-based filtering methods in the domain of e-mail messages. In this study, we train a large-scale ANN-based filter, which uses meaningful terms in the same database as input, and use it to predict the relevance of those messages. Our results reveal that the ANN relevance prediction out-performs the prediction of the IF system. Moreover, we found very low correlation between the terms in the user profile (explicitly selected by the users) and the positive causal-index (CI) terms of the ANN, which indicate the relative importance of terms in messages. This implies that the users underestimate the importance of some terms, failing to include them in their profiles. This may explain the rather low prediction accuracy of the IF system. 相似文献
15.
主要研究了均值聚类图像分割问题。针对传统的聚类图像分割算法对图像地分割精度较低等问题,提出一种基于模糊控制的C-均值聚类快速图像分割新方法。本文采用快速模糊C-均值聚类算法对图像分割。实验结果表明,图像分割边缘清晰,分割效果明显优于传统的聚类图像分割算法。 相似文献
16.
《Journal of The Franklin Institute》2019,356(8):4732-4746
This paper develops an Aitken based modified Kalman filtering stochastic gradient algorithm for dual-rate nonlinear models. The Aitken based method can increase the convergence rate and the modified Kalman filter can improve the estimation accuracy. Thus compared to the traditional auxiliary model based stochastic gradient algorithm, the proposed algorithm in this paper is more effective, and this is proved by the convergence analysis. Furthermore, two simulated examples are given to illustrate the effectiveness of the proposed algorithm. 相似文献
17.
A novel H∞ filter design methodology has been presented for a general class of nonlinear systems. Different from existing nonlinear filtering design, the nonlinearities are approximated using neural networks, and then are modeled based on linear difference inclusions, which makes the structure of the desired filter simpler and parameter turning easier and has the advantages of guaranteed stability, numeral robustness, bounded estimation accuracy. A unified framework is established to solve the addressed H∞ filtering problem by exploiting linear matrix inequality (LMI) approach. A numerical example shows that the filtering error systems will work well against bounded error between a nonlinear dynamical system and a multilayer neural network. 相似文献
18.
一种基于局部K-分布的新的SAR图像舰船检测算法 总被引：1，自引：0，他引：1
提出了一种基于局部K-分布的新的SAR图像舰船检测算法.取目标窗口和背景窗口,通过把泄露到背景窗口中的舰船部分去除,对背景窗口中的剩余部分统计均值和方差,最终得到杂波分布概率模型进行恒虚警检测.相对于K-分布CFAR检测算法和基于局部窗口的K-分布CFAR检测算法,该算法能够适应杂波的局部变化, 对距离很近的舰船不会产生漏检.仿真结果表明了方法的有效性. 相似文献
19.
Privacy-preserving collaborative filtering schemes focus on eliminating the privacy threats inherent in single preference values, and the privacy risks in the multi-criteria preference domain are disregarded. In this work, we introduce randomized perturbation-based privacy-preserving approaches for multi-criteria collaborative filtering systems. Initially, the privacy protection methods efficiently used in traditional single-criterion systems are adapted onto multi-criteria ratings. However, these systems require intelligent protection mechanisms that are flexible and adapting to the structure of each sub-criterion. To achieve such a goal, we introduce a novel privacy-preserving protocol by adapting an entropy-based randomness determination procedure that can recover accuracy losses. The proposed protocol adjusts privacy-controlling parameters concerning the information inherent in each criterion. We experimentally evaluate the proposed schemes on three subsets of Yahoo!Movies multi-criteria preference dataset to demonstrate the effects of the proposed privacy-preserving schemes on both user privacy levels and prediction accuracy for differing sparsity rates. According to the obtained experimental outcomes, the proposed entropy-based privacy-preserving scheme can produce significantly more accurate predictions while maintaining an identical level of privacy provided by the traditional privacy protection scenario. The experimental results also confirm that the novel entropy-based privacy-preserving scheme maintains the confidentiality of personal preferences without severely compromising prediction accuracy. 相似文献
20.
Engin Bozdag 《Ethics and Information Technology》2013,15(3):209-227
Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping. 相似文献