In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to laten...In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.展开更多
Short-term surveys provide precious information for economic fluctuations analysis. In short-term surveys like the business tendency survey, most of the questions are qualitative and concern the evolution of different...Short-term surveys provide precious information for economic fluctuations analysis. In short-term surveys like the business tendency survey, most of the questions are qualitative and concern the evolution of different economic factors of the business activity. They provide economic information on the present situation and short-term perspectives. Usually, the respondents have to choose between three possible evolutions： increase （improvement, favourable, level higher than the normal）, stability （normal） or decrease （unfavourable, level lower than the normal）. The balance of opinion is defined as the difference between the proportion of respondents expressing a positive opinion and the proportion expressing a negative opinion. To analyze these types of surveys, the methods are well standardized and use both the multidimensional approach and time series （scoring, dynamic factor analysis, etc.） In this paper, the authors propose a new method of calculating a robust composite indicator based on range median statistics, and on a lexicographical order relation of the individual data. A confidence interval is constructed around these statistics. The indicator＇s advantage is simplicity of calculation in comparison with the Mitchell, Smith and Weale （2004） index （MSW）, while its effectiveness seems to be of the same order. It was used on a Ukrainian dataset for the construction sector. This procedure can be applied to the surveys that contain correlated ordered qualitative answers.展开更多
The advent of convolutional neural networks has led to remarkable progress in dense stereo labeling problem,achieving superior performance over the traditional methods.However,the ill-posed nature of stereo matching m...The advent of convolutional neural networks has led to remarkable progress in dense stereo labeling problem,achieving superior performance over the traditional methods.However,the ill-posed nature of stereo matching makes noise(outliers)in winner-takes-all(WTA)disparity maps inevitable.This paper presents a robust statistical approach to noise detection and refinement of WTA disparity maps.In the context of noise detection,the input noisy WTA disparity map is segmented into regular grid cells(regions)with the aim of leveraging Markov random field(MRF)to infer candidate disparity labels.However,there are two key problems:there can be large severe outliers in the regions;second,the regular partition process may produce regions with mixed disparity distributions.To overcome these problems,we optimize a robust objective function over the segmented disparity map.By obtaining the optimal solution of the objective function through a maximum a posteriori estimation in a probabilistic model,we are able to infer MRF candidate disparity labels.We then apply a soft-segmentation constraint on the estimated MRF candidate disparity labels to describe and detect outliers in the disparity map.Next,an edge-preserving statistical inference that leverages the joint statistics of the disparity map and its guidance reference image is used to select correct candidate disparity for each detected outlier.Finally,a weighted median filter is applied to remove small spikes and irregularities in the resulting disparity map.Rigorous and comprehensive experiments showed that the proposed method is distributionally robust and outlier resistant,and can effectively detect and correct outliers in disparity maps.Middlebury evaluation benchmark validated the competitive performance of the proposed method.展开更多
This paper presents a new robust global motion estimation method based on pre-analysis of the video content. The novel idea in the proposed method, compared to classical robust statistics-based estimation methods, is...This paper presents a new robust global motion estimation method based on pre-analysis of the video content. The novel idea in the proposed method, compared to classical robust statistics-based estimation methods, is to classify the video sequences into 3 classes based on the analysis of scene content before motion estimation. Different motion models and estimation methods are applied to different classes of image sequences. As a result, outliers can be identified and removed from the dominant motion estimate to solve the problem of inaccurate initial descending direction estimates associated with classical global motion estimation methods. The pre-analysis of scene content is based on the Spatial Temporal Gradient Scale (STGS) images derived from the original image sequences. The extra computation time for STGS-image-based pre-analysis of scene content is negligible compared to the overall speed and accuracy improvement achieved with the proposed method. Evaluations based on extensive experiments have shown that the proposed method significantly improves the speed of robust global motion estimation methods (saving about 50% of the execution time of classical methods).展开更多
文摘In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.
文摘Short-term surveys provide precious information for economic fluctuations analysis. In short-term surveys like the business tendency survey, most of the questions are qualitative and concern the evolution of different economic factors of the business activity. They provide economic information on the present situation and short-term perspectives. Usually, the respondents have to choose between three possible evolutions： increase （improvement, favourable, level higher than the normal）, stability （normal） or decrease （unfavourable, level lower than the normal）. The balance of opinion is defined as the difference between the proportion of respondents expressing a positive opinion and the proportion expressing a negative opinion. To analyze these types of surveys, the methods are well standardized and use both the multidimensional approach and time series （scoring, dynamic factor analysis, etc.） In this paper, the authors propose a new method of calculating a robust composite indicator based on range median statistics, and on a lexicographical order relation of the individual data. A confidence interval is constructed around these statistics. The indicator＇s advantage is simplicity of calculation in comparison with the Mitchell, Smith and Weale （2004） index （MSW）, while its effectiveness seems to be of the same order. It was used on a Ukrainian dataset for the construction sector. This procedure can be applied to the surveys that contain correlated ordered qualitative answers.
基金the 2020 Guangdong International Cooperation Project(No.2019A050510007).
文摘The advent of convolutional neural networks has led to remarkable progress in dense stereo labeling problem,achieving superior performance over the traditional methods.However,the ill-posed nature of stereo matching makes noise(outliers)in winner-takes-all(WTA)disparity maps inevitable.This paper presents a robust statistical approach to noise detection and refinement of WTA disparity maps.In the context of noise detection,the input noisy WTA disparity map is segmented into regular grid cells(regions)with the aim of leveraging Markov random field(MRF)to infer candidate disparity labels.However,there are two key problems:there can be large severe outliers in the regions;second,the regular partition process may produce regions with mixed disparity distributions.To overcome these problems,we optimize a robust objective function over the segmented disparity map.By obtaining the optimal solution of the objective function through a maximum a posteriori estimation in a probabilistic model,we are able to infer MRF candidate disparity labels.We then apply a soft-segmentation constraint on the estimated MRF candidate disparity labels to describe and detect outliers in the disparity map.Next,an edge-preserving statistical inference that leverages the joint statistics of the disparity map and its guidance reference image is used to select correct candidate disparity for each detected outlier.Finally,a weighted median filter is applied to remove small spikes and irregularities in the resulting disparity map.Rigorous and comprehensive experiments showed that the proposed method is distributionally robust and outlier resistant,and can effectively detect and correct outliers in disparity maps.Middlebury evaluation benchmark validated the competitive performance of the proposed method.
基金the State High- Tech Developments Plan of China!(No.86 3- 30 6 - 0 3- 0 7)
文摘This paper presents a new robust global motion estimation method based on pre-analysis of the video content. The novel idea in the proposed method, compared to classical robust statistics-based estimation methods, is to classify the video sequences into 3 classes based on the analysis of scene content before motion estimation. Different motion models and estimation methods are applied to different classes of image sequences. As a result, outliers can be identified and removed from the dominant motion estimate to solve the problem of inaccurate initial descending direction estimates associated with classical global motion estimation methods. The pre-analysis of scene content is based on the Spatial Temporal Gradient Scale (STGS) images derived from the original image sequences. The extra computation time for STGS-image-based pre-analysis of scene content is negligible compared to the overall speed and accuracy improvement achieved with the proposed method. Evaluations based on extensive experiments have shown that the proposed method significantly improves the speed of robust global motion estimation methods (saving about 50% of the execution time of classical methods).
