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1.
指数滤波器是一类新构造出来的输出信噪比和目标时延分辨力随指数变化的滤波器,该滤波器在损失一定输出信噪比的前提下可以有效提高目标时延分辨力,从而提高目标时延估计精度,但仅采用单个指数滤波器仍存在输出信噪比和目标时延分辨力均达不到实际需求的情况。在乘积型高阶模糊函数乘积运算的启发下,在指数滤波器的基础上提出了一种新的乘积型指数滤波器,并分析了该乘积型指数滤波器的输出信噪比及目标时延分辨力等性能。仿真实验表明,所提的乘积型指数滤波器在低信噪比情况下可以更有效提高多目标时延估计精度,且算法简单易于实现,适用于背景复杂的多目标参数估计任务。 相似文献
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Mahama Yahaya Wenbo Fan Chuanyun Fu Xiang Li Yue Su 《International journal of injury control and safety promotion》2020,27(3):266-275
Abstract The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof. 相似文献
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为了提升车道线检测的准确性和实时性,改良车道偏离预警系统的性能,提出了一种新的车道线识别算法。首先应用投影法对采集到的图像设立感兴趣区域,以此来减少图像中存在的干扰信息;其次应用一种改进后的自适应高斯滤波算法对采集所得图像进行平滑处理,减少图像中不必要的细节;最后采用边缘绘制算法进行边缘检测,在此基础上,提出一种线段检测算法——Edline算法提取边缘线,对检测到的直线段进行筛选和聚类。利用引用计数法对车道线进行跟踪和预测。结果表明,新算法的平均处理时间为17.1 ms,准确率为96.19%,将其应用在车道偏离预警系统中可以有效地提高预警效率,提升预警的准确性和响应速度。研究结果丰富了车道线识别理论,可为车道偏离预警系统的应用及基础研究提供参考。 相似文献
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We construct new measures of fund style, performance and activity from linear combinations of off‐the‐shelf stock‐market indices. A fund's benchmark portfolio is a linear combination of two or more reference portfolios that in a least‐squares sense most closely approximates the fund's portfolio. The resulting linear combination scalar is itself a measure of fund style and the distance between a fund and its benchmark is a measure of fund activity. Our approach has a number of advantages over existing characteristic‐matching methods. We illustrate our approach using a data set of US institutional funds. 相似文献
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Online booking is one of the most popular ways of making reservations for hotel guests. Thus, hoteliers are paying increasing attention to hotel website information presentation and design. The purpose of this study, then, is to examine the joint influence of choice set size and information filtering mechanisms on consumers’ decision confidence towards online hotel booking. Choice set size was operationalized through 3-, 9-, and 30-hotel room choice sets. Through experimental design, this study shows that the presence of an information filtering mechanism reduces consumers’ perceptions of choice overload with a large number of choices (30 choices), whereas its impact is attenuated with smaller choice sets (3 and 9 choices). In addition, choice overload mediates the impact of choice set size on decision confidence. Theoretical contribution and managerial implications are also discussed. 相似文献
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During a trip planning, tourists gather information from different sources, select and rank the places to visit according to their personal interests, and try to devise daily tours among them. This paper addresses the complex selection and touring problem and proposes a “filter-first, tour-second” framework for generating personalized tour recommendations for tourists based on information from social media and other online data sources. Collaborative filtering is applied to identify a subset of optional points of interest that maximize the potential satisfaction, while there are some preselected mandatory points that the tourists must visit. Next, the underlying orienteering problem is solved via an Iterated Tabu Search algorithm. The goal is to generate tours that contain all mandatory points and maximize the total score collected from the optional points visited daily, taking into account different day availabilities and opening hours, limitations on the tour lengths, budgets and other restrictions. Computational experiments on benchmark datasets indicate that the proposed touring algorithm is very competitive. Furthermore, the proposed framework has been evaluated on data collected from Foursquare. The results show the practical utility and the temporal efficacy of the recommended tours. 相似文献