In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the...In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.展开更多
Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connectio...Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connection between convolutional neural networks(CNNs)and Gabor filters.Therefore,some Gabor-based CNN methods have been proposed for HSI classification.However,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process.Moreover,these methods require patch cubes as network inputs.Such patch cubes may contain interference pixels,which will negatively affect the classification results.To address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI classification.More precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training process.Furthermore,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels problem.Experimental results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.展开更多
This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywo...This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywords after indexing national R＆D information in Korea （human resources, project and outcome） and applying expertise calculation algorithm. In consideration of the characteristics of national R＆D information, weight values have been selected. Then, expertise points were calculated by applying weighted values. In addition, joint research and collaborative relations were implemented in a knowledge map format through network analysis using national R＆D information.展开更多
Entrepreneurship corporate social responsibility is concerned with obligations that should be undertaken by an enterprise or ＂enterprise citizen＂ to the society including interrelationship between enterprise and som...Entrepreneurship corporate social responsibility is concerned with obligations that should be undertaken by an enterprise or ＂enterprise citizen＂ to the society including interrelationship between enterprise and some related interest dependents. And it is a sense of value, discipline and respect to the people, community and environment-related policies of the enterprise. Obviously, the core of the notion refers to a commitment of the enterprise in order to improve living standard of related interest counterparts. Nowadays, entrepreneurship corporate social responsibility has been not only an ethical call, but also an institutional constraint. The consensus is that in operation process an enterprise should take into account of its economic, social and ethical effects on consumers, staffs, shareholders, communities, local governments and environment and make a better prospect to them. Based on this point of view, by field work and questionnaire method, this paper discusses specifically the interaction between TNCs＇ R＆D activities and local development of the Pudong New Area, a China＇s largest special economic zone in Shanghai to explore dynamics of the TNCs＇ R＆D activities, growth of the local economy and their roles in promoting flexible innovation networks for sustainable futures. This involves： a. notion of the entrepreneurship corporate social responsibility; b. current state and trend of TNCs＇ R＆D activities; c. mode and linkage tightness between TNCs＇ R＆D activities and the local economy; d. main problems of the TNCs＇ R＆D activities in Pudong; e. the context of flexible innovation networks; and f. manner and ways in creation of flexible innovation networks.展开更多
Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the...Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.展开更多
文摘In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.
基金Project supported by the Fundamental Research Funds in Heilongjiang Provincial Universities(Grant No.145109218)the Natural Science Foundation of Heilongjiang Province of China(Grant No.LH2020F050)
文摘Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connection between convolutional neural networks(CNNs)and Gabor filters.Therefore,some Gabor-based CNN methods have been proposed for HSI classification.However,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process.Moreover,these methods require patch cubes as network inputs.Such patch cubes may contain interference pixels,which will negatively affect the classification results.To address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI classification.More precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training process.Furthermore,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels problem.Experimental results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.
基金Project(N-12-NM-LU01-C01) supported by Construction of NTIS (National Science & Technology Information Service) Program Funded by the National Science & Technology Commission (NSTC), Korea
文摘This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywords after indexing national R＆D information in Korea （human resources, project and outcome） and applying expertise calculation algorithm. In consideration of the characteristics of national R＆D information, weight values have been selected. Then, expertise points were calculated by applying weighted values. In addition, joint research and collaborative relations were implemented in a knowledge map format through network analysis using national R＆D information.
文摘Entrepreneurship corporate social responsibility is concerned with obligations that should be undertaken by an enterprise or ＂enterprise citizen＂ to the society including interrelationship between enterprise and some related interest dependents. And it is a sense of value, discipline and respect to the people, community and environment-related policies of the enterprise. Obviously, the core of the notion refers to a commitment of the enterprise in order to improve living standard of related interest counterparts. Nowadays, entrepreneurship corporate social responsibility has been not only an ethical call, but also an institutional constraint. The consensus is that in operation process an enterprise should take into account of its economic, social and ethical effects on consumers, staffs, shareholders, communities, local governments and environment and make a better prospect to them. Based on this point of view, by field work and questionnaire method, this paper discusses specifically the interaction between TNCs＇ R＆D activities and local development of the Pudong New Area, a China＇s largest special economic zone in Shanghai to explore dynamics of the TNCs＇ R＆D activities, growth of the local economy and their roles in promoting flexible innovation networks for sustainable futures. This involves： a. notion of the entrepreneurship corporate social responsibility; b. current state and trend of TNCs＇ R＆D activities; c. mode and linkage tightness between TNCs＇ R＆D activities and the local economy; d. main problems of the TNCs＇ R＆D activities in Pudong; e. the context of flexible innovation networks; and f. manner and ways in creation of flexible innovation networks.
基金Supported by the Shaanxi Province Key Research and Development Project (No. 2021GY-280)Shaanxi Province Natural Science Basic Research Program (No. 2021JM-459)the National Natural Science Foundation of China (No. 61772417)
文摘Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.
