The efficiency of systematic sampling in stereology — reconsidered

In the present paper, we summarize and further develop recent research in the estimation of the variance of stereological estimators based on systematic sampling. In particular, it is emphasized that the relevant estimation procedure depends on the sampling density. The validity of the variance estimation is examined in a collection of data sets, obtained by systematic sampling. Practical recommendations are also provided in a separate section.     

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.     

A novel hybrid resampling for semiconductor wafer defect bin classification

Generally, defective dies on semiconductor wafer maps tend to form spatial clusters in distinguishable patterns which contain crucial information on specific problems of equipment or process, thus it is highly important to identify and classify diverse defect patterns accurately. However, in practice, there exists a serious class imbalance problem, that is, the number of the defective dies on semiconductor wafer maps is usually much smaller than that of the non-defective dies. In various machine learning applications, a typical classification algorithm is, however, developed under the assumption that the number of instances for each class is nearly balanced. If the conventional classification algorithm is applied to a class imbalanced dataset, it may lead to incorrect classification results and degrade the reliability of the classification algorithm. In this research, we consider the semiconductor wafer defect bin data combined with wafer warpage information and propose a new hybrid resampling algorithm to improve performance of classifiers. From the experimental analysis, we show that the proposed algorithm provides better classification performance compared to other data preprocessing methods regardless of classification models.     

一种新的进化裂变粒子滤波算法

针对经典自举粒子滤波中的重要性函数选取和重采样所导致的样本枯竭问题,提出了一种基于进化裂变的改进粒子滤波算法．该算法首先采用无迹卡尔曼滤波算法产生重要性函数,然后对重要性采样粒子进行裂变通过进化策略更新粒子集以增加粒子多样性,从而克服经典自举滤波重采样过程中的粒子退化问题．仿真实验表明,该算法能有效地提高跟踪精度, 跟踪性能优于经典粒子滤波算法．     

一种新的基于序列蒙特卡罗方法的符号检测器

在衰落信道下检测传输符号是序列蒙特卡罗方法在通信领域中的一个重要应用.由于在序列蒙特卡罗方法中重要函数的选择直接会影响系统性能,针对这一问题,提出了一种新的混合重要函数,较大幅度提高了衰落信道下传输符号的检测水平,仿真结果也证实了这种新检测器的有效性.     

基于改进粒子滤波的空间红外小目标跟踪方法

为有效跟踪空间红外小目标,常采用基于粒子滤波的检测跟踪算法。粒子滤波中,防止粒子退化的两个关键因素是选择合理的建议分布和重采样算法。在对重采样算法研究的基础上,提出了以有效粒子数作为阈值进行模型切换,分别利用残差重采样和系统重采样的优势,将两种采样模型有机结合,从而得到一种自适应的残差系统重采样（RSR）算法。实验结果表明,所提出的改进重采样算法在时间和跟踪精度上均优于经典重采样算法,有效地提高了空间红外小目标跟踪的效率和稳定性。     

基于不同重采样算法的RFID指纹定位

为满足移动机器人精确定位的需求,提出一种基于不同重采样算法的粒子滤波指纹定位法。定位阶段首先利用机器人运动学建立运动模型作为粒子预测分布, 并将当前的观测信息和环境指纹融入, 以改善滤波效果, 减少所需粒子数;然后给出精致重采样(ER)算法,以提高粒子的细化能力,减少粒子匮乏效应并提高定位精度;最后分析不同重采样算法对定位精度的影响,且从不同的实验角度进一步验证定位算法的精确性以及可靠性。实验结果表明, 该算法在定位精度和鲁棒性方面都有显著提高。     

基于小生境遗传算法的粒子滤波算法

重采样是解决粒子滤波退化问题的主要方法,重采样的基本思想是采取复制保留权值较高的粒子,删除权值较低的粒子,而这导致了粒子多样性的减弱,特别是在样本受限条件下,甚至导致滤波发散。针对上述问题,提出改进的粒子滤波算法,将Mean Shift与粒子滤波融合,在重采样部分引入小生境遗传算法,提高粒子的多样性,避免粒子退化。实验表明,改进后的算法状态估计精度更高,效果更好。     

MIT-BIH数据库心电数据重采样研究

为了将采样率为360 Hz的MIT-BIH数据库心电数据转换到250 Hz采样率,设计了一个25/36倍采样率转换系统,同时对原数据库中的注释信息重新进行了标注,建立了采样率为250 Hz的MIT-BIH重采样数据库。实验表明,重采样结果准确,注释信息标示正确。     

基于贝叶斯估计的漏磁缺陷轮廓重构方法研究

漏磁缺陷轮廓重构是指由检测到的漏磁信号重构缺陷轮廓及参数,是实现漏磁反演的关键。目前常用的反演方法包括神经网络法和优化法,但神经网络法的计算精度受噪声影响严重,优化法计算量大。针对这些问题,提出基于递推贝叶斯估计的漏磁缺陷重构算法。建立缺陷轮廓与漏磁信号的状态空间模型,将反演问题描述为基于状态和观测方程的典型的离散时间跟踪问题,对漏磁信号进行了反演,并在不同信噪比下对神经网络法和所提方法进行了反演效果的比较。结果表明：基于递推贝叶斯估计方法的漏磁信号反演算法精度高,同时对噪声具有鲁棒性,是一种有效可行的漏磁反演新方法。