With the increasing complexity of production processes,there has been a growing focus on online algorithms within the domain of multivariate statistical process control(SPC).Nonetheless,conventional methods,based on t...With the increasing complexity of production processes,there has been a growing focus on online algorithms within the domain of multivariate statistical process control(SPC).Nonetheless,conventional methods,based on the assumption of complete data obtained at uniform time intervals,exhibit suboptimal performance in the presence of missing data.In our pursuit of maximizing available information,we propose an adaptive exponentially weighted moving average(EWMA)control chart employing a weighted imputation approach that leverages the relationships between complete and incomplete data.Specifically,we introduce two recovery methods:an improved K-Nearest Neighbors imputing value and the conventional univariate EWMA statistic.We then formulate an adaptive weighting function to amalgamate these methods,assigning a diminished weight to the EWMA statistic when the sample information suggests an increased likelihood of the process being out of control,and vice versa.The robustness and sensitivity of the proposed scheme are shown through simulation results and an illustrative example.展开更多
针对传统的工件图像识别算法运行速度慢、匹配精度差等问题,提出一种改进的ORB(Oriented FAST and Rotated BRIEF)算法解决工件图像的实时与准确识别问题。该算法的流程是首先利用ORB算法提取工件图像的角点特征,随后为其添加SURF(Speed...针对传统的工件图像识别算法运行速度慢、匹配精度差等问题,提出一种改进的ORB(Oriented FAST and Rotated BRIEF)算法解决工件图像的实时与准确识别问题。该算法的流程是首先利用ORB算法提取工件图像的角点特征,随后为其添加SURF(Speed-Up Robust Features)描述符进行方向分配,得到具有旋转尺度不变性的图像角点,结合快速近似最近邻搜索算法进行特征点的匹配,实现工件图像的识别。实验结果表明:在图像存在旋转尺度变化的情况下,使用改进的ORB算法相比传统的ORB、SIFT(Scale Invariant Feature Transform)和SURF算法以及SIFT+SURF、SURF+FREAK组合算法在工件图像角点提取与目标匹配方面速度更快,识别精度更高,提高了工业机器人在搬运工件过程中对工件图像的识别效率和准确性。展开更多
文摘With the increasing complexity of production processes,there has been a growing focus on online algorithms within the domain of multivariate statistical process control(SPC).Nonetheless,conventional methods,based on the assumption of complete data obtained at uniform time intervals,exhibit suboptimal performance in the presence of missing data.In our pursuit of maximizing available information,we propose an adaptive exponentially weighted moving average(EWMA)control chart employing a weighted imputation approach that leverages the relationships between complete and incomplete data.Specifically,we introduce two recovery methods:an improved K-Nearest Neighbors imputing value and the conventional univariate EWMA statistic.We then formulate an adaptive weighting function to amalgamate these methods,assigning a diminished weight to the EWMA statistic when the sample information suggests an increased likelihood of the process being out of control,and vice versa.The robustness and sensitivity of the proposed scheme are shown through simulation results and an illustrative example.
文摘针对传统的工件图像识别算法运行速度慢、匹配精度差等问题,提出一种改进的ORB(Oriented FAST and Rotated BRIEF)算法解决工件图像的实时与准确识别问题。该算法的流程是首先利用ORB算法提取工件图像的角点特征,随后为其添加SURF(Speed-Up Robust Features)描述符进行方向分配,得到具有旋转尺度不变性的图像角点,结合快速近似最近邻搜索算法进行特征点的匹配,实现工件图像的识别。实验结果表明:在图像存在旋转尺度变化的情况下,使用改进的ORB算法相比传统的ORB、SIFT(Scale Invariant Feature Transform)和SURF算法以及SIFT+SURF、SURF+FREAK组合算法在工件图像角点提取与目标匹配方面速度更快,识别精度更高,提高了工业机器人在搬运工件过程中对工件图像的识别效率和准确性。
