An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction ste...An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.展开更多
In the pitching motion,the unsteady transition and relaminarization position plays an important role in the dynamic characteristics of the airfoil.In order to facilitate the computer to automatically and accurately ca...In the pitching motion,the unsteady transition and relaminarization position plays an important role in the dynamic characteristics of the airfoil.In order to facilitate the computer to automatically and accurately calculate the position of the transition and relaminarization,a Variable Slip Window Technology(VSWT)suitable for airfoil dynamic data processing was developed using the S809 airfoil experimental data in this paper and two calculation strategies,i.e.,global strategy and single point strategy,were proposed:global strategy and single point strategy.The core of the VSWT is the selection of the window function h and the parameters setting in the h function.The effect of the VSWT was evaluated using the dimensionless pulse strength value(INB),which can be used to evaluate the signal characteristics,of the root mean square(RMS)value of the fluctuating pressure.It is found that:the h function characteristics have a significant influence on the VSWT.The suitable functions are Hn function constructed in this paper and step function.For the left boundary of the magnified area,the step function can obtain the largest INB value,but the robustness is not good.The H1 function(Gaussian-like function,n=1)can show higher robustness while ensuring a large INB value.The two computing strategies,which are single point strategy and global strategy,have their own advantages and disadvantages.The former strategy,that is the single point strategy,can achieve a higher INB value,but the RMS magnification at the feature position needs to be known in advance.Although the INB value obtained by the latter strategy,that is the global strategy,is slightly smaller than the calculation results of the former strategy,it is not necessary to know the RMS magnification at the feature position in advance.So the global strategy has better robustness.The experimental data of NACA0012 airfoil was used to further validate the developed VSWT in this paper,and the results show that the VSWT developed in this paper can still double the INB value of the transition/relaminarization position.The VSWT developed in this paper has certain practicability,which is convenient for the computer to automatically determine the transition/relaminarization characteristics.展开更多
Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field wit...Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.展开更多
The filter operator used in normal multichannel matching filter is physically realizable.This filter operator only delays seismic data in the filtering process.A noncausal multichannel matching filter based on a least...The filter operator used in normal multichannel matching filter is physically realizable.This filter operator only delays seismic data in the filtering process.A noncausal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is Iater than real data.The diferences between causal and noncausal multichannel matching filters are compared using a synthetic shot gather,which demonstrates the validity of the noncausal matching filter.In addition,a variable length sliding window which changes with ofset and layer velocity is proposed to solve the count of events increasing with increasing ofset in a fixed Iength sliding window.This variable length sliding window is also introduced into the modified and expanded multichannel matching filter.This method is applied to the Pluto1.5 synthetic data set.The benefits of the non.causal filter operator and variable length sliding window are demonstrated bv the good multiple attenuation result.展开更多
Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is...Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is applicable for layers of stable thickness.When a layer exhibits variable thickness in the seismic response,a fixed time window cannot provide comprehensive geologic information for the target interval.Therefore,we propose a novel approach for a waveform clustering workfl ow based on a variable time window to enable broader applications.The dynamic time warping(DTW)distance is fi rst introduced to effectively measure the similarities between seismic waveforms with various lengths.We develop a DTW distance-based clustering algorithm to extract centroids,and we then determine the class of all seismic traces according to the DTW distances from centroids.To greatly reduce the computational complexity in seismic data application,we propose a superpixel-based seismic data thinning approach.We further propose an integrated workfl ow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms.We evaluated the performance by applying the proposed workfl ow to synthetic seismograms and seismic survey data.Compared with the the traditional waveform clustering method,the synthetic seismogram results demonstrate the enhanced capability of the proposed workfl ow to detect boundaries of diff erent lithologies or lithologic associations with variable thickness.Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workfl ow correlates well with the geological characteristics of wells in terms of reservoir thickness.展开更多
Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the g...Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.展开更多
基金Supported by National High-Tech Program of China (No. 2001AA413110).
文摘An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.
基金the Youth Science Foundation(No.20181111502212)for their support。
文摘In the pitching motion,the unsteady transition and relaminarization position plays an important role in the dynamic characteristics of the airfoil.In order to facilitate the computer to automatically and accurately calculate the position of the transition and relaminarization,a Variable Slip Window Technology(VSWT)suitable for airfoil dynamic data processing was developed using the S809 airfoil experimental data in this paper and two calculation strategies,i.e.,global strategy and single point strategy,were proposed:global strategy and single point strategy.The core of the VSWT is the selection of the window function h and the parameters setting in the h function.The effect of the VSWT was evaluated using the dimensionless pulse strength value(INB),which can be used to evaluate the signal characteristics,of the root mean square(RMS)value of the fluctuating pressure.It is found that:the h function characteristics have a significant influence on the VSWT.The suitable functions are Hn function constructed in this paper and step function.For the left boundary of the magnified area,the step function can obtain the largest INB value,but the robustness is not good.The H1 function(Gaussian-like function,n=1)can show higher robustness while ensuring a large INB value.The two computing strategies,which are single point strategy and global strategy,have their own advantages and disadvantages.The former strategy,that is the single point strategy,can achieve a higher INB value,but the RMS magnification at the feature position needs to be known in advance.Although the INB value obtained by the latter strategy,that is the global strategy,is slightly smaller than the calculation results of the former strategy,it is not necessary to know the RMS magnification at the feature position in advance.So the global strategy has better robustness.The experimental data of NACA0012 airfoil was used to further validate the developed VSWT in this paper,and the results show that the VSWT developed in this paper can still double the INB value of the transition/relaminarization position.The VSWT developed in this paper has certain practicability,which is convenient for the computer to automatically determine the transition/relaminarization characteristics.
基金financially supported by the National Natural Science Foundation of China(No.52004029)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-20-06).
文摘Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.
基金supported by the National 863 Program (Grant No. 2006AA09A102-09)the National 973 Program (GrantNo. 2007CB209606)
文摘The filter operator used in normal multichannel matching filter is physically realizable.This filter operator only delays seismic data in the filtering process.A noncausal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is Iater than real data.The diferences between causal and noncausal multichannel matching filters are compared using a synthetic shot gather,which demonstrates the validity of the noncausal matching filter.In addition,a variable length sliding window which changes with ofset and layer velocity is proposed to solve the count of events increasing with increasing ofset in a fixed Iength sliding window.This variable length sliding window is also introduced into the modified and expanded multichannel matching filter.This method is applied to the Pluto1.5 synthetic data set.The benefits of the non.causal filter operator and variable length sliding window are demonstrated bv the good multiple attenuation result.
基金supported by the National Science and Technology Major Project (No. 2017ZX05001-003)。
文摘Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is applicable for layers of stable thickness.When a layer exhibits variable thickness in the seismic response,a fixed time window cannot provide comprehensive geologic information for the target interval.Therefore,we propose a novel approach for a waveform clustering workfl ow based on a variable time window to enable broader applications.The dynamic time warping(DTW)distance is fi rst introduced to effectively measure the similarities between seismic waveforms with various lengths.We develop a DTW distance-based clustering algorithm to extract centroids,and we then determine the class of all seismic traces according to the DTW distances from centroids.To greatly reduce the computational complexity in seismic data application,we propose a superpixel-based seismic data thinning approach.We further propose an integrated workfl ow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms.We evaluated the performance by applying the proposed workfl ow to synthetic seismograms and seismic survey data.Compared with the the traditional waveform clustering method,the synthetic seismogram results demonstrate the enhanced capability of the proposed workfl ow to detect boundaries of diff erent lithologies or lithologic associations with variable thickness.Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workfl ow correlates well with the geological characteristics of wells in terms of reservoir thickness.
基金supported by General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China (2012IK169)National Natural Science Youth Foundation of China (21205053).
文摘Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.
