Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), usi...Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.展开更多
For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fas...For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.展开更多
Chrysopogon serrulatus(false beard-grass)is a dominant component of vegetation in the foothills of the Himalayas.To study whole plant morphology,individuals of C.serrulatus were collected from three plots at each of s...Chrysopogon serrulatus(false beard-grass)is a dominant component of vegetation in the foothills of the Himalayas.To study whole plant morphology,individuals of C.serrulatus were collected from three plots at each of six locations spanning from 400 to 1,400 m.The population colonizing the highest elevation modifications in different plant organs.Roots showed increased metaxylem number and area.In the stem,especially outside of the vascular tissue,there was intensive sclerification indicative of increased xeromorphy as a survival strategy.At the highest elevation,leaves were wider;aerenchyma formation and increased sclerification were noted in the leaf sheath;and a greater proportion of storage parenchyma was observed in the leaf blade,all indicators of succulence.In contrast,leaves at lower elevations had xeric morphological features such as increased epidermal thickness,sclerification and more developed metaxylem area.In conclusion,shifting of morphological features in below-and above-ground plant parts of C.serrulatus were linked to shifts in environmental factors along this elevation gradient,thus enabling the successful distribution of this species along this elevation gradient.展开更多
In through-the-wall detection scenarios with low signal-to-noise ratio(SNR)and strong clutter,existing target detection methods generally suffer from inaccuracies,poor real-time performance,and the limitation of detec...In through-the-wall detection scenarios with low signal-to-noise ratio(SNR)and strong clutter,existing target detection methods generally suffer from inaccuracies,poor real-time performance,and the limitation of detecting only moving or stationary targets.To address these challenges,this paper proposes a throughthe-wall radar(TWR)target detection method based on cross-correlation adaptive robust principal component analysis(CCARPCA)capable of simultaneously detecting multiple moving and stationary targets.First,pulse compression is applied to original echo signals using the inverse fast Fourier transform,resulting in high-resolution one-dimensional range profiles.Second,the principal component analysis algorithm suppresses strong clutter interferences,thereby improving the SNR.Next,the back projection algorithm is employed for multi-channel coherent imaging,enabling the extraction of 2-dimensional information and enhancing the sparsity of cross-correlation data.Lastly,considering the drawbacks of the robust principal component analysis(RPCA),such as long detection time and poor robustness,this paper introduces the cross-correlation coefficient and proposes the CCARPCA algorithm,which completely separates the target from the background noise.The experimental results based on a series of simulated and measured data demonstrate the effectiveness of the proposed method in detecting both moving and stationary targets behind walls.Compared to generalized likelihood ratio test,constant false alarm rate,and RPCA,our method achieves a substantial improvement of over 16.4%in detection accuracy based on measured data while maintaining real-time detection capability.Additionally,its detection performance is less sensitive to changes in initial parameters,indicating its superior robustness.展开更多
基金Supported by the National High-tech Program of China (No. 2001 AA413110).
文摘Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.
基金Supported by the National Natural Science Foundation of China(51174091,61364013,61164013)Earlier Research Project of the State Key Development Program for Basic Research of China(2014CB360502)
文摘For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.
文摘Chrysopogon serrulatus(false beard-grass)is a dominant component of vegetation in the foothills of the Himalayas.To study whole plant morphology,individuals of C.serrulatus were collected from three plots at each of six locations spanning from 400 to 1,400 m.The population colonizing the highest elevation modifications in different plant organs.Roots showed increased metaxylem number and area.In the stem,especially outside of the vascular tissue,there was intensive sclerification indicative of increased xeromorphy as a survival strategy.At the highest elevation,leaves were wider;aerenchyma formation and increased sclerification were noted in the leaf sheath;and a greater proportion of storage parenchyma was observed in the leaf blade,all indicators of succulence.In contrast,leaves at lower elevations had xeric morphological features such as increased epidermal thickness,sclerification and more developed metaxylem area.In conclusion,shifting of morphological features in below-and above-ground plant parts of C.serrulatus were linked to shifts in environmental factors along this elevation gradient,thus enabling the successful distribution of this species along this elevation gradient.
基金supported by the National Key R&D Program of China(2021YFC3090402-03)the National Natural Science Foundation(62171475).
文摘In through-the-wall detection scenarios with low signal-to-noise ratio(SNR)and strong clutter,existing target detection methods generally suffer from inaccuracies,poor real-time performance,and the limitation of detecting only moving or stationary targets.To address these challenges,this paper proposes a throughthe-wall radar(TWR)target detection method based on cross-correlation adaptive robust principal component analysis(CCARPCA)capable of simultaneously detecting multiple moving and stationary targets.First,pulse compression is applied to original echo signals using the inverse fast Fourier transform,resulting in high-resolution one-dimensional range profiles.Second,the principal component analysis algorithm suppresses strong clutter interferences,thereby improving the SNR.Next,the back projection algorithm is employed for multi-channel coherent imaging,enabling the extraction of 2-dimensional information and enhancing the sparsity of cross-correlation data.Lastly,considering the drawbacks of the robust principal component analysis(RPCA),such as long detection time and poor robustness,this paper introduces the cross-correlation coefficient and proposes the CCARPCA algorithm,which completely separates the target from the background noise.The experimental results based on a series of simulated and measured data demonstrate the effectiveness of the proposed method in detecting both moving and stationary targets behind walls.Compared to generalized likelihood ratio test,constant false alarm rate,and RPCA,our method achieves a substantial improvement of over 16.4%in detection accuracy based on measured data while maintaining real-time detection capability.Additionally,its detection performance is less sensitive to changes in initial parameters,indicating its superior robustness.
