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
Sarcoma is a kind of mesenchymal malignant tumor and often has poor chemotherapy response.There are many reasons for the insensitivity of sarcomas to chemotherapy,among which genetic changes are important.As a common ...Sarcoma is a kind of mesenchymal malignant tumor and often has poor chemotherapy response.There are many reasons for the insensitivity of sarcomas to chemotherapy,among which genetic changes are important.As a common type of gene alteration,gene fusion plays an important role in the pathogenesis and progression of tumors.In this study,we report a novel osteosarcoma-associated fusion gene,EWSR1-PSMC5,found in patients insensitive to chemotherapy.This gene is a novel fusion mode and has been found to play an important role in autophagy activation.The fusion gene may lead to the activation of autophagy through various signaling pathways,thus leading to the development of osteosarcoma resistance.We report this new fusion mode for the first time,and it should be noted that there is a less common report of EWSR1-related fusion gene in osteosarcoma.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
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.
基金supported by the National Natural Science Foundation of China(No.82072979).
文摘Sarcoma is a kind of mesenchymal malignant tumor and often has poor chemotherapy response.There are many reasons for the insensitivity of sarcomas to chemotherapy,among which genetic changes are important.As a common type of gene alteration,gene fusion plays an important role in the pathogenesis and progression of tumors.In this study,we report a novel osteosarcoma-associated fusion gene,EWSR1-PSMC5,found in patients insensitive to chemotherapy.This gene is a novel fusion mode and has been found to play an important role in autophagy activation.The fusion gene may lead to the activation of autophagy through various signaling pathways,thus leading to the development of osteosarcoma resistance.We report this new fusion mode for the first time,and it should be noted that there is a less common report of EWSR1-related fusion gene in osteosarcoma.
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.
