Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the constr...Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.展开更多
A vertical carbon nanotube field-effect transistor(CNTFET) based on silicon(Si) substrate has been proposed and simulated using a semi-classical theory. A single-walled carbon nanotube(SWNT) and an n-type Si nanowire ...A vertical carbon nanotube field-effect transistor(CNTFET) based on silicon(Si) substrate has been proposed and simulated using a semi-classical theory. A single-walled carbon nanotube(SWNT) and an n-type Si nanowire in series construct the channel of the transistor. The CNTFET presents ambipolar characteristics at positive drain voltage(Vd) and n-type characteristics at negative Vd. The current is significantly influenced by the doping level of n-Si and the SWNT band gap. The n-branch current of the ambipolar characteristics increases with increasing doping level of the n-Si while the p-branch current decreases. The SWNT band gap has the same influence on the p-branch current at a positive Vd and n-type characteristics at negative Vd. The lower the SWNT band gap, the higher the current. However, it has no impact on the n-branch current in the ambipolar characteristics. Thick oxide is found to significantly degrade the current and the subthreshold slope of the CNTFETs.展开更多
A total of 43 prolonged coma patients with diffuse axonal injury received the somatosensory evoked potential examination one month after injury in the First Affiliated Hospital, School of Medicine, Zhejiang University...A total of 43 prolonged coma patients with diffuse axonal injury received the somatosensory evoked potential examination one month after injury in the First Affiliated Hospital, School of Medicine, Zhejiang University in China. Somatosensory evoked potentials were graded as normal, abnormal or absent (grades Ⅰ-Ⅲ) according to N20 amplitude and central conduction time. The outcome in patients with grade Ⅲ somatosensory evoked potential was in each case unfavorable. The prognostic accuracy of grade Ⅲ somatosensory evoked potential for unfavorable and non-awakening outcome was 100% and 80%, respectively. The prognostic accuracy of grade Ⅰ somatosensory evoked potential for favorable and wakening outcome was 86% and 100%, respectively. These results suggest that somatosensory evoked potential grade is closely correlated with coma severity and degree of recovery. Somatosensory evoked potential is a valuable diagnostic tool to assess prognosis in prolonged coma patients with diffuse axonal injury.展开更多
This paper compares the torque characteristics of single stator permanent magnet synchronous motor(PMSM)and double-stator PMSM under different split-ratios,air-gap lengths and shaft diameters by finite element method....This paper compares the torque characteristics of single stator permanent magnet synchronous motor(PMSM)and double-stator PMSM under different split-ratios,air-gap lengths and shaft diameters by finite element method.Firstly,the effects of split-ratio towards the torque characteristics of the two motor structures under different air-gap lengths are researched,the results show that the optimal split-ratios of the two motor structures do not change under different air-gap lengths,and the optimal split-ratio of the double-stator motor is greater than that of single-stator,and the torque of the double-stator motor is greater than that of single-stator motor with arbitrary split-ratio under the same air-gap length;Finally,the effects of the shaft diameter to the torque of the two motor structures are investigated,obtaining that with the increasing of shaft diameter,the electromagnetic torque of the single-stator motor is almost unchanged,however,the torque of the double-stator is gradually reduced,when the shaft diameter reached a certain extent,the electromagnetic torque of the double-stator motor is smaller than that of single-stator motor with the split ratio within a certain range,and the torque/quality ratio of the double-stator motor is smaller than that of single-stator motor with their optimal split ratio separately.展开更多
Owing to their precedent characteristics,micro gas turbines(MGTs)have been favored as popular power machinery in plenty of energy systems such as distributed energy systems,range extenders,solar power generations,fuel...Owing to their precedent characteristics,micro gas turbines(MGTs)have been favored as popular power machinery in plenty of energy systems such as distributed energy systems,range extenders,solar power generations,fuel cell systems and individual power supplies.Their specific features essentially include but are not limited to strong fuel adaptability,low emissions,flexible structure,and easy maintenance.Over the past 20 years,various types of MGTs have been developed.Classical and forward-looking technologies have been employed in the design and production of MGTs and their components.Among them,fully radial flow structures,gas lubricated bearings and efficient recuperators are typical approaches to enhance the overall performance and compactness,however,the exploitation of ceramic based materials and intelligent algorithms in component design can also assist in improving the performance.The applications of MGTs have been expanded to many fields,and the research on related components has also made new progress.Due to the time frame,there is no systematic summary of the latest relevant research,so it is essential to have a comprehensive understanding of the applications of MGTs and their pertinent components.This paper aims to present a comprehensive review on MGTs,covering the development status,applications,factors of performance and representative explorations of their components.Some investigations regarding the characteristics of commercial MGTs are also conducted.Applications in distributed energy,range extenders,solar generations,and fuel cell systems are distinctly introduced.Recent research work on compressors,turbines,combustors,recuperators,and rotor systems are reviewed and analyzed.The technologies and methods associated with materials,manufacturing,and cycles beneficial to the future development of MGTs are also explained and discussed in some detail.展开更多
Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world...Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only.Off-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available.To overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd counting.Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains.Specifically,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning process.Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.展开更多
Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, p...Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.展开更多
Whether habit stimulation is effective in DOC patient arousal has not been reported. In this paper, we analyzed the responses of DOC patients to habit stimulation. Nineteen DOC patients with alcohol consumption or smo...Whether habit stimulation is effective in DOC patient arousal has not been reported. In this paper, we analyzed the responses of DOC patients to habit stimulation. Nineteen DOC patients with alcohol consumption or smoking habits were recruited and 64-channel EEG signals were acquired both at the resting state and at three stimulation states. Wavelet transformation and nonlinear dynamics were used to extract the features of EEG signals and four brain lobes were selected to investigate the degree of EEG response to habit stimulation. Results showed that the highest degree of EEG response was from the callname stimulation, followed by habit and music stimulations. Significant differences in EEG wavelet energy and response coefficient were found both between habit and music stimulation, and between habit and call-name stimulation. These findings prove that habit stimulation induces relatively more intense EEG responses in DOC patients than music stimulation, suggesting that it may be a relevant additional method for eliciting patient arousal.展开更多
基金supported by the research project of the Jiangsu water conservancy science and technology project (Contract Number:2021067).
文摘Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.
基金support by National High Technology Research and Development Program of China (No. 2011AA050504)the analysis supports from Instrumental Analysis Center of SJTU
文摘A vertical carbon nanotube field-effect transistor(CNTFET) based on silicon(Si) substrate has been proposed and simulated using a semi-classical theory. A single-walled carbon nanotube(SWNT) and an n-type Si nanowire in series construct the channel of the transistor. The CNTFET presents ambipolar characteristics at positive drain voltage(Vd) and n-type characteristics at negative Vd. The current is significantly influenced by the doping level of n-Si and the SWNT band gap. The n-branch current of the ambipolar characteristics increases with increasing doping level of the n-Si while the p-branch current decreases. The SWNT band gap has the same influence on the p-branch current at a positive Vd and n-type characteristics at negative Vd. The lower the SWNT band gap, the higher the current. However, it has no impact on the n-branch current in the ambipolar characteristics. Thick oxide is found to significantly degrade the current and the subthreshold slope of the CNTFETs.
基金funded by Zhejiang Medicines &Health Sciences Research Fund (Class A) in 2009, No.2009A086
文摘A total of 43 prolonged coma patients with diffuse axonal injury received the somatosensory evoked potential examination one month after injury in the First Affiliated Hospital, School of Medicine, Zhejiang University in China. Somatosensory evoked potentials were graded as normal, abnormal or absent (grades Ⅰ-Ⅲ) according to N20 amplitude and central conduction time. The outcome in patients with grade Ⅲ somatosensory evoked potential was in each case unfavorable. The prognostic accuracy of grade Ⅲ somatosensory evoked potential for unfavorable and non-awakening outcome was 100% and 80%, respectively. The prognostic accuracy of grade Ⅰ somatosensory evoked potential for favorable and wakening outcome was 86% and 100%, respectively. These results suggest that somatosensory evoked potential grade is closely correlated with coma severity and degree of recovery. Somatosensory evoked potential is a valuable diagnostic tool to assess prognosis in prolonged coma patients with diffuse axonal injury.
基金supported in part by the National Natural Science Foundation of China under Grant 51977011。
文摘This paper compares the torque characteristics of single stator permanent magnet synchronous motor(PMSM)and double-stator PMSM under different split-ratios,air-gap lengths and shaft diameters by finite element method.Firstly,the effects of split-ratio towards the torque characteristics of the two motor structures under different air-gap lengths are researched,the results show that the optimal split-ratios of the two motor structures do not change under different air-gap lengths,and the optimal split-ratio of the double-stator motor is greater than that of single-stator,and the torque of the double-stator motor is greater than that of single-stator motor with arbitrary split-ratio under the same air-gap length;Finally,the effects of the shaft diameter to the torque of the two motor structures are investigated,obtaining that with the increasing of shaft diameter,the electromagnetic torque of the single-stator motor is almost unchanged,however,the torque of the double-stator is gradually reduced,when the shaft diameter reached a certain extent,the electromagnetic torque of the double-stator motor is smaller than that of single-stator motor with the split ratio within a certain range,and the torque/quality ratio of the double-stator motor is smaller than that of single-stator motor with their optimal split ratio separately.
基金the financial support provided by the National Science and Technology Major Project(Grant No.2017- Ⅲ-0003-0027).
文摘Owing to their precedent characteristics,micro gas turbines(MGTs)have been favored as popular power machinery in plenty of energy systems such as distributed energy systems,range extenders,solar power generations,fuel cell systems and individual power supplies.Their specific features essentially include but are not limited to strong fuel adaptability,low emissions,flexible structure,and easy maintenance.Over the past 20 years,various types of MGTs have been developed.Classical and forward-looking technologies have been employed in the design and production of MGTs and their components.Among them,fully radial flow structures,gas lubricated bearings and efficient recuperators are typical approaches to enhance the overall performance and compactness,however,the exploitation of ceramic based materials and intelligent algorithms in component design can also assist in improving the performance.The applications of MGTs have been expanded to many fields,and the research on related components has also made new progress.Due to the time frame,there is no systematic summary of the latest relevant research,so it is essential to have a comprehensive understanding of the applications of MGTs and their pertinent components.This paper aims to present a comprehensive review on MGTs,covering the development status,applications,factors of performance and representative explorations of their components.Some investigations regarding the characteristics of commercial MGTs are also conducted.Applications in distributed energy,range extenders,solar generations,and fuel cell systems are distinctly introduced.Recent research work on compressors,turbines,combustors,recuperators,and rotor systems are reviewed and analyzed.The technologies and methods associated with materials,manufacturing,and cycles beneficial to the future development of MGTs are also explained and discussed in some detail.
基金Project supported by the National Natural Science Foundation of China(Nos.62176059,62101136,and U1811463)the Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01)+3 种基金Zhangjiang Lab,the Shanghai Municipal of Science and Technology Project(No.20JC1419500)the Shanghai Sailing Program(No.21YF1402800)the Natural Science Foundation of Shanghai(No.21ZR1403600)the Shanghai Center for Brain Science and Brain-inspired Technology。
文摘Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only.Off-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available.To overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd counting.Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains.Specifically,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning process.Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.
基金Project supported by the National Natural Science Foundation of China(Nos.62306075 and 62101136)the China Postdoctoral Science Foundation(No.2022TQ0069)+2 种基金the Natural Science Foundation of Shanghai,China(No.21ZR1403600)the Shanghai Municipal of Science and Technology Project,China(No.20JC1419500)the Shanghai Center for Brain Science and Brain-Inspired Technology,China。
文摘Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.
基金supported by the National Natural Science Foundation of China(81671038)
文摘Whether habit stimulation is effective in DOC patient arousal has not been reported. In this paper, we analyzed the responses of DOC patients to habit stimulation. Nineteen DOC patients with alcohol consumption or smoking habits were recruited and 64-channel EEG signals were acquired both at the resting state and at three stimulation states. Wavelet transformation and nonlinear dynamics were used to extract the features of EEG signals and four brain lobes were selected to investigate the degree of EEG response to habit stimulation. Results showed that the highest degree of EEG response was from the callname stimulation, followed by habit and music stimulations. Significant differences in EEG wavelet energy and response coefficient were found both between habit and music stimulation, and between habit and call-name stimulation. These findings prove that habit stimulation induces relatively more intense EEG responses in DOC patients than music stimulation, suggesting that it may be a relevant additional method for eliciting patient arousal.
