An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decompo...An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.展开更多
This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach.Considering that the dynamic characteristics of microorganisms differ across g...This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach.Considering that the dynamic characteristics of microorganisms differ across growth stages,we introduced the concept of multi-stage sensitivity analysis,in which each stage was investigated separately.The fuzzy C-means(FCM)algorithm was employed to cluster process data under nominal conditions,thereby dividing the penicillin fermentation process into distinct growth stages.Based on this division,the Latin hypercube sampling with partial rank correlation coefficient(LHS-EPRCC)method was applied to conduct sensitivity analysis for each stage,identifying an importance parameter set(IPS)that corresponds to the stage-specific growth characteristics.Re-estimation and correction of the IPS were then performed to enhance the predictive accuracy of the model.In a penicillin fermentation process deviating from nominal conditions,the proposed method was applied for model correction.Simulation results demonstrate that the corrected model aligns well with the actual process,thereby verifying the effectiveness of the proposed multistage sensitivity analysis approach in addressing complex fermentation processes and environmental uncertainties.展开更多
为了提高基于拍摄方式的文档图像的二值化效果,降低光学字符识别(optical character recognition,OCR)系统的文字识别错误率,提出了一种全局阈值与局部阈值相结合的二值化算法——VFCM。该算法使用最大方差比方法产生全局阈值,使用FCM(F...为了提高基于拍摄方式的文档图像的二值化效果,降低光学字符识别(optical character recognition,OCR)系统的文字识别错误率,提出了一种全局阈值与局部阈值相结合的二值化算法——VFCM。该算法使用最大方差比方法产生全局阈值,使用FCM(FuzzyC-Means)聚类方法产生局部阈值。这两种方法的结合能够较好地保留字符的笔画细节,并能有效地消除伪影。实验结果表明,该算法可以取得比较好的二值化效果,并能带来OCR系统识别率的有效提高。展开更多
摘要An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.
基金National Natural Science Foundation of China(62471204,62473175,62403215,61833007)Natural Science Foundation of Jiangsu Province(BK20241607,BE2023022-2)Research start-up fund for high-level talent(928201/186)。
摘要This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach.Considering that the dynamic characteristics of microorganisms differ across growth stages,we introduced the concept of multi-stage sensitivity analysis,in which each stage was investigated separately.The fuzzy C-means(FCM)algorithm was employed to cluster process data under nominal conditions,thereby dividing the penicillin fermentation process into distinct growth stages.Based on this division,the Latin hypercube sampling with partial rank correlation coefficient(LHS-EPRCC)method was applied to conduct sensitivity analysis for each stage,identifying an importance parameter set(IPS)that corresponds to the stage-specific growth characteristics.Re-estimation and correction of the IPS were then performed to enhance the predictive accuracy of the model.In a penicillin fermentation process deviating from nominal conditions,the proposed method was applied for model correction.Simulation results demonstrate that the corrected model aligns well with the actual process,thereby verifying the effectiveness of the proposed multistage sensitivity analysis approach in addressing complex fermentation processes and environmental uncertainties.
摘要为了提高基于拍摄方式的文档图像的二值化效果,降低光学字符识别(optical character recognition,OCR)系统的文字识别错误率,提出了一种全局阈值与局部阈值相结合的二值化算法——VFCM。该算法使用最大方差比方法产生全局阈值,使用FCM(FuzzyC-Means)聚类方法产生局部阈值。这两种方法的结合能够较好地保留字符的笔画细节,并能有效地消除伪影。实验结果表明,该算法可以取得比较好的二值化效果,并能带来OCR系统识别率的有效提高。