There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the...There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.展开更多
Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM...Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.展开更多
An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution func...An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution function. The contrast gain is nonlinearly adjusted to avoid noise overenhancement and ringing artifacts while improving the detail contrast with less computational burden. The effectiveness of our method is demonstrated with radiological images and compared with other algorithms.展开更多
Many MNCs have established China centres to coordinate and control their operations in the country. The paper describes the standardization and localization in HRM practices of MNCs in China. It tries to analyse the d...Many MNCs have established China centres to coordinate and control their operations in the country. The paper describes the standardization and localization in HRM practices of MNCs in China. It tries to analyse the degree of standardization or localization of HRM policies for MNCs in China, in the terms of recruitment and selection, training and development, performance appraisal, compensation and promotion.展开更多
In China, 10 ethnic minorities with a combined population of over 20 million people are followers of Islam. In Ningxia Hui Autonomous Region, the population is nearly 6 million, a-mong which the Islamic population is ...In China, 10 ethnic minorities with a combined population of over 20 million people are followers of Islam. In Ningxia Hui Autonomous Region, the population is nearly 6 million, a-mong which the Islamic population is about 2 million. In China as a whole, more than 20 million people enjoy eating food prepared according to Islamic guidelines, known as hal'al food.展开更多
Santomean pig farmer Simao Vicente was hopeful when he came to ask Zou Rui for help. His pig was suffering from hernia, and Zou, a 42-year-old Chinese agricultural expert working in Sao Tomé and Príncipe, wa...Santomean pig farmer Simao Vicente was hopeful when he came to ask Zou Rui for help. His pig was suffering from hernia, and Zou, a 42-year-old Chinese agricultural expert working in Sao Tomé and Príncipe, was the only person on the island who could provide emergency surgery.展开更多
密度峰值聚类(clustering by fast search and find of density peaks,DPC)算法是一种基于密度的聚类算法,它可以发现任意形状和维度的类簇,是具有里程碑意义的聚类算法。然而,DPC算法的样本局部密度定义不适用于同时发现数据集的稠密...密度峰值聚类(clustering by fast search and find of density peaks,DPC)算法是一种基于密度的聚类算法,它可以发现任意形状和维度的类簇,是具有里程碑意义的聚类算法。然而,DPC算法的样本局部密度定义不适用于同时发现数据集的稠密簇和稀疏簇;此外,DPC算法的一步分配策略使得一旦有一个样本分配错误,将导致更多样本的错误分配,产生“多米诺骨牌效应”。针对这些问题,提出一种新的样本局部密度定义,采用局部标准差指数定义样本局部密度,克服DPC的密度定义缺陷;采用两步分配策略代替DPC的一步分配策略,克服DPC的“多米诺骨牌效应”,得到ESDTS-DPC算法。与DPC及其改进算法KNN-DPC、FKNN-DPC、DPC-CE和经典密度聚类算法DBSCAN的实验比较显示,提出的ESDTS-DPC算法具有更好的聚类准确性。展开更多
基金Project(61374140)supported by the National Natural Science Foundation of China
文摘There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.
基金National Natural Science Foundation of China ( No. 61074079) Shanghai Leading Academic Discipline Project,China ( No.B504)
文摘Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.
基金the National Natural Science Foundation of China(No:3 963 0 1 1 0 ) the National Key Technologies R&D Programme under Con-tract96-92 0 -1 2 -0 1
文摘An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution function. The contrast gain is nonlinearly adjusted to avoid noise overenhancement and ringing artifacts while improving the detail contrast with less computational burden. The effectiveness of our method is demonstrated with radiological images and compared with other algorithms.
文摘Many MNCs have established China centres to coordinate and control their operations in the country. The paper describes the standardization and localization in HRM practices of MNCs in China. It tries to analyse the degree of standardization or localization of HRM policies for MNCs in China, in the terms of recruitment and selection, training and development, performance appraisal, compensation and promotion.
文摘In China, 10 ethnic minorities with a combined population of over 20 million people are followers of Islam. In Ningxia Hui Autonomous Region, the population is nearly 6 million, a-mong which the Islamic population is about 2 million. In China as a whole, more than 20 million people enjoy eating food prepared according to Islamic guidelines, known as hal'al food.
文摘Santomean pig farmer Simao Vicente was hopeful when he came to ask Zou Rui for help. His pig was suffering from hernia, and Zou, a 42-year-old Chinese agricultural expert working in Sao Tomé and Príncipe, was the only person on the island who could provide emergency surgery.
文摘密度峰值聚类(clustering by fast search and find of density peaks,DPC)算法是一种基于密度的聚类算法,它可以发现任意形状和维度的类簇,是具有里程碑意义的聚类算法。然而,DPC算法的样本局部密度定义不适用于同时发现数据集的稠密簇和稀疏簇;此外,DPC算法的一步分配策略使得一旦有一个样本分配错误,将导致更多样本的错误分配,产生“多米诺骨牌效应”。针对这些问题,提出一种新的样本局部密度定义,采用局部标准差指数定义样本局部密度,克服DPC的密度定义缺陷;采用两步分配策略代替DPC的一步分配策略,克服DPC的“多米诺骨牌效应”,得到ESDTS-DPC算法。与DPC及其改进算法KNN-DPC、FKNN-DPC、DPC-CE和经典密度聚类算法DBSCAN的实验比较显示,提出的ESDTS-DPC算法具有更好的聚类准确性。
