The development of network and information technology has brought changes to the production environment of scientific and technological information,leading to the integration of multi-type scien-tific and technologica...The development of network and information technology has brought changes to the production environment of scientific and technological information,leading to the integration of multi-type scien-tific and technological information,which has become one of the primary research focuses in the cur-rent field of scientific and technological information analysis.This article proposes a basic mode to realize the fusion of multi-type scientific and technological information,expounds the corresponding basic construction method,and applies it to the scientific and technological topics identification in the field of artificial intelligence(AI).The research results show that the multi-type scientific and technological information fusion mode proposed in this article has certain feasibility in specific appli-cation scenarios,which lays a foundation for the subsequent research work.展开更多
Due to the data acquired by most optical earth observation satellite such as IKONOS, QuickBird-2 and GF-1 consist of a panchromatic image with high spatial resolution and multiple multispectral images with low spatial...Due to the data acquired by most optical earth observation satellite such as IKONOS, QuickBird-2 and GF-1 consist of a panchromatic image with high spatial resolution and multiple multispectral images with low spatial resolution. Many image fusion techniques have been developed to produce high resolution multispectral image. Considering panchromatic image and multispectral images contain the same spatial information with different accuracy, using the least square theory could estimate optimal spatial information. Compared with previous spatial details injection mode, this mode is more accurate and robust. In this paper, an image fusion method using Bidimensional Empirical Mode Decomposition (BEMD) and the least square theory is proposed to merge multispectral images and panchromatic image. After multi-spectral images were transformed from RGB space into IHS space, next I component and Panchromatic are decomposed by BEMD, then using the least squares theory to evaluate optimal spatial information and inject spatial information, finally completing fusion through inverse BEMD and inverse intensity-hue-saturation transform. Two data sets are used to evaluate the proposed fusion method, GF-1 images and QuickBird-2 images. The fusion images were evaluated visually and statistically. The evaluation results show the method proposed in this paper achieves the best performance compared with the conventional method.展开更多
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e...To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.展开更多
Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind s...Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.展开更多
A theory-based model for predicting the pedestal formation in both ion and electron temperatures, and hydrogenic and impurity density is developed and implemented in the 1.5D BALDUR codes for self-consistently simulat...A theory-based model for predicting the pedestal formation in both ion and electron temperatures, and hydrogenic and impurity density is developed and implemented in the 1.5D BALDUR codes for self-consistently simulating H-mode plasma in tokamak. In the simulation, the transports around pedestal, including the electron and ion thermal, hydrogenie and impurity particle transports are calculated using an （Mixed B/gB） model, which is modified to anomalous semi-empirical mixed Bohm/gyro-Bohm include the effects of ωE×B flow shear and magnetic shear. Because of the reduction of transport, the pedestal can be formed. For a preliminary test, this core-edge model is used to simulate the temporal evolution of plasma current, temperature, and density profiles for DIII-D discharges. It is found that the simulations successfully reproduce the experimental results. A statistical analysis, including RMSE and offset, is used to quantify the agreement between the prediction and the corresponding experimental results. The simulation results show an agreement with average RMSE of 11.87%, 14.53%, 7.59% and 12.21% for electron temperature, ion temperature, electron density, and deuterium density profiles, respectively. In addition, it is found that the suppression function developed is effective only in the edge region.展开更多
Empirical mode decomposition (EMD) is a data-driven and multi-scale transform theory, and it is a nonlinear and non-stationary signal processing theory. But each EMD decomposition theory has its advantages and disadva...Empirical mode decomposition (EMD) is a data-driven and multi-scale transform theory, and it is a nonlinear and non-stationary signal processing theory. But each EMD decomposition theory has its advantages and disadvantages. Synthetic aperture radar (SAR) imaging is an important remote sensing technique to obtain the change information, and SAR image data belongs to non-stationary signal. So EMD is very suitable for SAR image processing. There are two kinds of typical EMD theories, which are the ensemble empirical mode decomposition (EEMD) and bidimensional empirical mode decomposition (BEMD). Based on the deep study of the two methods, this paper proposed a new SAR image change detection algorithm, which is called the FCD-EMD algorithm, i.e. fusion change detection based on EMD. So FCD-EMD algorithm can obtain more accurate information, which not only includes the directional information obtained by EEMD, but also can contain the spatial information got by BEMD. The main contribution of the FCD-EMD algorithm is to fuse the detail information in different directions, so that the results obtained are more accurate than the individual method. On the other hand, it can reduce the influence of speckle noise in SAR images by feature selections. The actual SAR image data verify the algorithm proposed in this paper and good experimental results are obtained, which show that the new method is feasible.展开更多
基金Supported by the National Natural Science Foundation of China(No.72074201).
文摘The development of network and information technology has brought changes to the production environment of scientific and technological information,leading to the integration of multi-type scien-tific and technological information,which has become one of the primary research focuses in the cur-rent field of scientific and technological information analysis.This article proposes a basic mode to realize the fusion of multi-type scientific and technological information,expounds the corresponding basic construction method,and applies it to the scientific and technological topics identification in the field of artificial intelligence(AI).The research results show that the multi-type scientific and technological information fusion mode proposed in this article has certain feasibility in specific appli-cation scenarios,which lays a foundation for the subsequent research work.
文摘Due to the data acquired by most optical earth observation satellite such as IKONOS, QuickBird-2 and GF-1 consist of a panchromatic image with high spatial resolution and multiple multispectral images with low spatial resolution. Many image fusion techniques have been developed to produce high resolution multispectral image. Considering panchromatic image and multispectral images contain the same spatial information with different accuracy, using the least square theory could estimate optimal spatial information. Compared with previous spatial details injection mode, this mode is more accurate and robust. In this paper, an image fusion method using Bidimensional Empirical Mode Decomposition (BEMD) and the least square theory is proposed to merge multispectral images and panchromatic image. After multi-spectral images were transformed from RGB space into IHS space, next I component and Panchromatic are decomposed by BEMD, then using the least squares theory to evaluate optimal spatial information and inject spatial information, finally completing fusion through inverse BEMD and inverse intensity-hue-saturation transform. Two data sets are used to evaluate the proposed fusion method, GF-1 images and QuickBird-2 images. The fusion images were evaluated visually and statistically. The evaluation results show the method proposed in this paper achieves the best performance compared with the conventional method.
文摘To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.
文摘Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.
基金supported by the Commission on Higher Education and the Thailand Research Fund(No.RSA5580041)the Government Annual Research Budget through Thammasat University
文摘A theory-based model for predicting the pedestal formation in both ion and electron temperatures, and hydrogenic and impurity density is developed and implemented in the 1.5D BALDUR codes for self-consistently simulating H-mode plasma in tokamak. In the simulation, the transports around pedestal, including the electron and ion thermal, hydrogenie and impurity particle transports are calculated using an （Mixed B/gB） model, which is modified to anomalous semi-empirical mixed Bohm/gyro-Bohm include the effects of ωE×B flow shear and magnetic shear. Because of the reduction of transport, the pedestal can be formed. For a preliminary test, this core-edge model is used to simulate the temporal evolution of plasma current, temperature, and density profiles for DIII-D discharges. It is found that the simulations successfully reproduce the experimental results. A statistical analysis, including RMSE and offset, is used to quantify the agreement between the prediction and the corresponding experimental results. The simulation results show an agreement with average RMSE of 11.87%, 14.53%, 7.59% and 12.21% for electron temperature, ion temperature, electron density, and deuterium density profiles, respectively. In addition, it is found that the suppression function developed is effective only in the edge region.
文摘Empirical mode decomposition (EMD) is a data-driven and multi-scale transform theory, and it is a nonlinear and non-stationary signal processing theory. But each EMD decomposition theory has its advantages and disadvantages. Synthetic aperture radar (SAR) imaging is an important remote sensing technique to obtain the change information, and SAR image data belongs to non-stationary signal. So EMD is very suitable for SAR image processing. There are two kinds of typical EMD theories, which are the ensemble empirical mode decomposition (EEMD) and bidimensional empirical mode decomposition (BEMD). Based on the deep study of the two methods, this paper proposed a new SAR image change detection algorithm, which is called the FCD-EMD algorithm, i.e. fusion change detection based on EMD. So FCD-EMD algorithm can obtain more accurate information, which not only includes the directional information obtained by EEMD, but also can contain the spatial information got by BEMD. The main contribution of the FCD-EMD algorithm is to fuse the detail information in different directions, so that the results obtained are more accurate than the individual method. On the other hand, it can reduce the influence of speckle noise in SAR images by feature selections. The actual SAR image data verify the algorithm proposed in this paper and good experimental results are obtained, which show that the new method is feasible.
