The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legisla...The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted ...Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.展开更多
The correlation between the longitudinal crack occurrence and integrated heat transfer of the mold with data mining methods was investigated.Firstly,three kinds of support vector machine models based on principal comp...The correlation between the longitudinal crack occurrence and integrated heat transfer of the mold with data mining methods was investigated.Firstly,three kinds of support vector machine models based on principal component analysis with different input features were established to explore the effect of integrated heat transfer on the accuracy of the prediction model for the longitudinal crack.The results show that the accuracy was improved while features including mean and standard deviation of integrated heat transfer were added.Then,the difference in integrated heat transfer between defect and normal samples under the same process parameters was quantitatively compared.Compared with normal samples,the temperature difference of cooling water for defect samples decreased by 0.65%,and the temperature difference fluctuation increased by 31.1%.Finally,the literature data were used to provide support for the quantitative correlation according to defect formation mechanism.A new criterion for the prediction of longitudinal crack and a discovering method for correlation between product quality and process parameters in the manufacturing industry have been provided.展开更多
Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security asses...Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.展开更多
In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive ...In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive link between learning transfer and knowledge innovation is recognized by most scholars,while the learner’s attitude toward big data decision-making,as a cognitive perception,affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm.Thus,learning transfer can be regarded as a result of the learner’s attitude,and it becomes the intermediary state between their attitude and knowledge innovation.This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude,knowledge transfer,and knowledge innovation.展开更多
The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-ro...The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.展开更多
This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditiona...This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, c...In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.展开更多
Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the ...Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.展开更多
To increase the performance of bulk data transfer mission with ultra-long TCP （ transmission control protocol） connection in high-energy physics experiments, a series of experiments were conducted to explore the way...To increase the performance of bulk data transfer mission with ultra-long TCP （ transmission control protocol） connection in high-energy physics experiments, a series of experiments were conducted to explore the way to enhance the transmission efficiency. This paper introduces the overall structure of RC@ SEU （ regional center @ Southeast University） in AMS （alpha magnetic spectrometer）-02 ground data transfer system as well as the experiments conducted in CERNET （China Education and Research Network）/CERNET2 and global academic Internet. The effects of the number of parallel streams and TCP buffer size are tested. The test confirms that in the current circumstance of CERNET, to find the fight number of parallel TCP connections is the main method to improve the throughput. TCP buffer size tuning has little effect now, but may have good effects when the available bandwidth becomes higher.展开更多
With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while ...With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.展开更多
Background: Omission of patient information in perioperative communication is closely linked to adverse events. Use of checklists to standardize the handoff in the post anesthesia care unit (PACU) has been shown to ef...Background: Omission of patient information in perioperative communication is closely linked to adverse events. Use of checklists to standardize the handoff in the post anesthesia care unit (PACU) has been shown to effectively reduce medical errors. Objective: Our study investigates the use of a checklist to improve quantity of data transfer during handoffs in the PACU. Design: A cross-sectional observational study. Setting: PACU at Memorial Sloan Kettering Cancer Center (MSKCC);June 13, 2016 through July 15, 2016. Patients, other participants: We observed the handoff reports between the nurses, PACU midlevel providers, anesthesia staff, and surgical staff. Intervention: A physical checklist was provided to all anesthesia staff and recommended to adhere to the list at all observed PACU handoffs. Main outcome measure: Quantity of reported handoff items during 60 pre- and 60 post-implementation of a checklist. Results: Composite value from both surgical and anesthesia reports showed an increase in the mean report of 8.7 items from pre-implementation period to 10.9 post-implementation. Given that surgical staff reported the mean of 5.9 items pre-implementation and 5.5 items post-implementation without intervention, improvements in anesthesia staff report with intervention improved the overall handoff data transfer. Conclusions: Using a physical 12-item checklist for PACU handoff increased overall data transfer.展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web bas...Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the eXtensible Markup Language （XML） and Hierarchical Data Format （HDF） to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model （JDOM） objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation （RMI） data transfer infrastructure. Client＇s defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.展开更多
基金This paper is a phased result of the“Research on the Issue of China’s Data Export System”(24SFB3035)a research project of the Ministry of Justice of China on the construction of the rule of law and the study of legal theories at the ministerial level in 2024.
文摘The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
基金supported in part by the MOST Major Research and Development Project(Grant No.2021YFB2900204)the National Natural Science Foundation of China(NSFC)(Grant No.62201123,No.62132004,No.61971102)+3 种基金China Postdoctoral Science Foundation(Grant No.2022TQ0056)in part by the financial support of the Sichuan Science and Technology Program(Grant No.2022YFH0022)Sichuan Major R&D Project(Grant No.22QYCX0168)the Municipal Government of Quzhou(Grant No.2022D031)。
文摘Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.
基金the support from National Natural Science Foundation of China(52274318).
文摘The correlation between the longitudinal crack occurrence and integrated heat transfer of the mold with data mining methods was investigated.Firstly,three kinds of support vector machine models based on principal component analysis with different input features were established to explore the effect of integrated heat transfer on the accuracy of the prediction model for the longitudinal crack.The results show that the accuracy was improved while features including mean and standard deviation of integrated heat transfer were added.Then,the difference in integrated heat transfer between defect and normal samples under the same process parameters was quantitatively compared.Compared with normal samples,the temperature difference of cooling water for defect samples decreased by 0.65%,and the temperature difference fluctuation increased by 31.1%.Finally,the literature data were used to provide support for the quantitative correlation according to defect formation mechanism.A new criterion for the prediction of longitudinal crack and a discovering method for correlation between product quality and process parameters in the manufacturing industry have been provided.
基金support from the National Natural Science Foundation of China(Grant No.:71901169)the Shaanxi Province Innovative Talents Promotion Plan-Youth Science and Technology Nova Project(Grant No.:2022KJXX-50).
文摘Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.
基金Natural Science Foundation of Inner Mongolia(Project No.2023LHMS07016)the Fundamental Research Fund for the directly affiliated university of Inner Mongolia(Project No.2022 JBQN056)。
文摘In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive link between learning transfer and knowledge innovation is recognized by most scholars,while the learner’s attitude toward big data decision-making,as a cognitive perception,affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm.Thus,learning transfer can be regarded as a result of the learner’s attitude,and it becomes the intermediary state between their attitude and knowledge innovation.This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude,knowledge transfer,and knowledge innovation.
基金a preliminary result of the Chinese Government Scholarship High-level Graduate Program sponsored by China Scholarship Council(Program No.CSC202206310052)。
文摘The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.
基金funded by Sponsorship of Science and Technology Project of State Grid Xinjiang Electric Power Co.,Ltd.,grant number SGXJ0000TKJS2400168.
文摘This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
文摘In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.
文摘Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.
基金The National Basic Research Program of China (973Program) (No.2003CB314803).
文摘To increase the performance of bulk data transfer mission with ultra-long TCP （ transmission control protocol） connection in high-energy physics experiments, a series of experiments were conducted to explore the way to enhance the transmission efficiency. This paper introduces the overall structure of RC@ SEU （ regional center @ Southeast University） in AMS （alpha magnetic spectrometer）-02 ground data transfer system as well as the experiments conducted in CERNET （China Education and Research Network）/CERNET2 and global academic Internet. The effects of the number of parallel streams and TCP buffer size are tested. The test confirms that in the current circumstance of CERNET, to find the fight number of parallel TCP connections is the main method to improve the throughput. TCP buffer size tuning has little effect now, but may have good effects when the available bandwidth becomes higher.
基金the National Natural Science Foundation of China(51965008)Science and Technology projects of Guizhou[2018]2168Excellent Young Researcher Project of Guizhou[2017]5630.
文摘With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.
文摘Background: Omission of patient information in perioperative communication is closely linked to adverse events. Use of checklists to standardize the handoff in the post anesthesia care unit (PACU) has been shown to effectively reduce medical errors. Objective: Our study investigates the use of a checklist to improve quantity of data transfer during handoffs in the PACU. Design: A cross-sectional observational study. Setting: PACU at Memorial Sloan Kettering Cancer Center (MSKCC);June 13, 2016 through July 15, 2016. Patients, other participants: We observed the handoff reports between the nurses, PACU midlevel providers, anesthesia staff, and surgical staff. Intervention: A physical checklist was provided to all anesthesia staff and recommended to adhere to the list at all observed PACU handoffs. Main outcome measure: Quantity of reported handoff items during 60 pre- and 60 post-implementation of a checklist. Results: Composite value from both surgical and anesthesia reports showed an increase in the mean report of 8.7 items from pre-implementation period to 10.9 post-implementation. Given that surgical staff reported the mean of 5.9 items pre-implementation and 5.5 items post-implementation without intervention, improvements in anesthesia staff report with intervention improved the overall handoff data transfer. Conclusions: Using a physical 12-item checklist for PACU handoff increased overall data transfer.
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
文摘Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the eXtensible Markup Language （XML） and Hierarchical Data Format （HDF） to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model （JDOM） objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation （RMI） data transfer infrastructure. Client＇s defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.
