Industry is the main industry of the real economy,the main body of scientific and technological innovation,and essential support for improving the quality and efficiency of the regional economy.In recent years,Guizhou...Industry is the main industry of the real economy,the main body of scientific and technological innovation,and essential support for improving the quality and efficiency of the regional economy.In recent years,Guizhou’s industry has achieved efficiency,motivation,and change by relying on three strategic actions:big data,significant poverty alleviation,and big ecology,which has promoted the development of Guizhou’s s industrial economy.Guided by the ideas and concepts of high-quality development,this paper intends to construct the evaluation index system and evaluation model for the high-quality development of the industrial economy.This paper evaluates the high-quality development of the industrial economy in Guizhou Province from both vertical and horizontal aspects.Vertically,Guizhou in recent years in terms of big data,big ecology,and poverty alleviation strategy under the development trend is good;horizontally,among the four provinces and cities in southwestern China,Guizhou Province has the lowest average score in the past five years,and it is urgent to improve the talent policy and promote the upgrading of industrial structure.Finally,combined with relevant policies,countermeasures and suggestions are put forward for the shortboard of industrial development in Guizhou to promote the high-quality development of Guizhou’s industry.展开更多
In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and ...In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.展开更多
With the continuous development of deep learning,Deep Convolutional Neural Network(DCNN)has attracted wide attention in the industry due to its high accuracy in image classification.Compared with other DCNN hard-ware ...With the continuous development of deep learning,Deep Convolutional Neural Network(DCNN)has attracted wide attention in the industry due to its high accuracy in image classification.Compared with other DCNN hard-ware deployment platforms,Field Programmable Gate Array(FPGA)has the advantages of being programmable,low power consumption,parallelism,and low cost.However,the enormous amount of calculation of DCNN and the limited logic capacity of FPGA restrict the energy efficiency of the DCNN accelerator.The traditional sequential sliding window method can improve the throughput of the DCNN accelerator by data multiplexing,but this method’s data multiplexing rate is low because it repeatedly reads the data between rows.This paper proposes a fast data readout strategy via the circular sliding window data reading method,it can improve the multiplexing rate of data between rows by optimizing the memory access order of input data.In addition,the multiplication bit width of the DCNN accelerator is much smaller than that of the Digital Signal Processing(DSP)on the FPGA,which means that there will be a waste of resources if a multiplication uses a single DSP.A multiplier sharing strategy is proposed,the multiplier of the accelerator is customized so that a single DSP block can complete multiple groups of 4,6,and 8-bit signed multiplication in parallel.Finally,based on two strategies of appeal,an FPGA optimized accelerator is proposed.The accelerator is customized by Verilog language and deployed on Xilinx VCU118.When the accelerator recognizes the CIRFAR-10 dataset,its energy efficiency is 39.98 GOPS/W,which provides 1.73×speedup energy efficiency over previous DCNN FPGA accelerators.When the accelerator recognizes the IMAGENET dataset,its energy efficiency is 41.12 GOPS/W,which shows 1.28×−3.14×energy efficiency compared with others.展开更多
Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn...Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.展开更多
Wireless Mesh Network (WMN) is a new-type wireless network. Its core idea is that any of its wireless equipment can act as both an Access Point (AP) and a router. Each node in the network can send and receive signals ...Wireless Mesh Network (WMN) is a new-type wireless network. Its core idea is that any of its wireless equipment can act as both an Access Point (AP) and a router. Each node in the network can send and receive signals as well as directly communicate with one or several peer nodes. One important issue to be considered in wireless Mesh networks is how to secure reliable data transmission in multi-hop links. To solve the problem, the 3GPP system architecture proposes two functionalities: ARQ and HARQ. This paper presents two HARQ schemes, namely hop-by-hop and edge-to-edge, and three ARQ schemes: hop-by-hop, edge-to-edge, and last-hop. Moreover, it proposes three solutions for WMNs from the perspective of protocol stock design: layered cooperative mechanism, relay ARQ mechanism and multi-hop mechanism.展开更多
A parallel algorithm of circulation numerical model based on message passing interface（MPI） is developed using serialization and an irregular rectangle decomposition scheme. Neighboring point exchange strategy（NPES...A parallel algorithm of circulation numerical model based on message passing interface（MPI） is developed using serialization and an irregular rectangle decomposition scheme. Neighboring point exchange strategy（NPES） is adopted to further enhance the computational efficiency. Two experiments are conducted on HP C7000 Blade System, the numerical results show that the parallel version with NPES（PVN） produces higher efficiency than the original parallel version（PV）. The PVN achieves parallel efficiency in excess of 0.9 in the second experiment when the number of processors increases to 100, while the efficiency of PV decreases to 0.39 rapidly. The PVN of ocean circulation model is used in a fine-resolution regional simulation, which produces better results. The capability of universal implementation of this algorithm makes it applicable in many other ocean models potentially.展开更多
Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approach...Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.展开更多
The in-memory computing(IMC)paradigm emerges as an effective solution to break the bottlenecks of conventional von Neumann architecture.In the current work,an approximate multiplier in spin-orbit torque magnetoresisti...The in-memory computing(IMC)paradigm emerges as an effective solution to break the bottlenecks of conventional von Neumann architecture.In the current work,an approximate multiplier in spin-orbit torque magnetoresistive random access memory(SOTMRAM)based true IMC(STIMC)architecture was presented,where computations were performed natively within the cell array instead of in peripheral circuits.Firstly,basic Boolean logic operations were realized by utilizing the feature of unipolar SOT device.Two majority gate-based imprecise compressors and an ultra-efficient approximate multiplier were then built to reduce the energy and latency.An optimized data mapping strategy facilitating bit-serial operations with an extensive degree of parallelism was also adopted.Finally,the performance enhancements by performing our approximate multiplier in image smoothing were demonstrated.Detailed simulation results show that the proposed 838 approximate multiplier could reduce the energy and latency at least by 74.2%and 44.4%compared with the existing designs.Moreover,the scheme could achieve improved peak signal-to-noise ratio(PSNR)and structural similarity index metric(SSIM),ensuring high-quality image processing outcomes.展开更多
To scientifically and objectively monitor the fermentation quality of black tea,a computer vision system(CVS)and electronic nose(e-nose)were employed to analyze the black tea image and odor eigenvalues of Yinghong No....To scientifically and objectively monitor the fermentation quality of black tea,a computer vision system(CVS)and electronic nose(e-nose)were employed to analyze the black tea image and odor eigenvalues of Yinghong No.9 black tea.First,the variation trends of tea polyphenols,volatile substances,image eigenvalues and odor eigenvalues with the extension of fermentation time were analyzed,and the fermentation process was categorized into three stages for classification.Second,principal component analysis(PCA)was employed on the image and odor eigenvalues obtained by CVS and e-nose.Partial least squares discriminant analysis(PLS-DA)was performed on 117 volatile components,and 51 differential volatiles were screened out based on variable importance in projection(VIP≥1)and one-way analysis of variance(P<0.05),including geraniol,linalool,nerolidol,and α-ionone.Then,image features and odor features are fused by using a data fusion strategy.Finally,the image,smell and fusion information were combined with random forest(RF),K-nearest neighbor(KNN)and support vector machine(SVM)to establish the classification models of different fermentation stages and to compare them.The results show that the feature-level fusion strategy integrating the SVM was the most efficient approach,with classification accuracy rates of 100%for the training sets and 95.6%for the testing sets.The performance of Support Vector Regression(SVR)prediction models for tea polyphenol content based on feature-level fusion data outperformed data-level models(Rc,RMSEC,Rp and RMSEP of 0.96,0.48 mg/g,0.94,0.6 mg/g).展开更多
基金supported by the Guizhou Philosophy and Social Science Planning Project(Grant No.19GZYB04)
文摘Industry is the main industry of the real economy,the main body of scientific and technological innovation,and essential support for improving the quality and efficiency of the regional economy.In recent years,Guizhou’s industry has achieved efficiency,motivation,and change by relying on three strategic actions:big data,significant poverty alleviation,and big ecology,which has promoted the development of Guizhou’s s industrial economy.Guided by the ideas and concepts of high-quality development,this paper intends to construct the evaluation index system and evaluation model for the high-quality development of the industrial economy.This paper evaluates the high-quality development of the industrial economy in Guizhou Province from both vertical and horizontal aspects.Vertically,Guizhou in recent years in terms of big data,big ecology,and poverty alleviation strategy under the development trend is good;horizontally,among the four provinces and cities in southwestern China,Guizhou Province has the lowest average score in the past five years,and it is urgent to improve the talent policy and promote the upgrading of industrial structure.Finally,combined with relevant policies,countermeasures and suggestions are put forward for the shortboard of industrial development in Guizhou to promote the high-quality development of Guizhou’s industry.
基金supported by:the 2023 Basic Public Welfare Research Project of the Wenzhou Science and Technology Bureau“Research on Multi-Source Data Classification and Grading Standards and Intelligent Algorithms for Higher Education Institutions”(Project No.G2023094)Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions(Grant/Award Number:2024QN061)2023 Basic Public Welfare Research Project of Wenzhou(No.:S2023014).
文摘In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.
基金supported in part by the Major Program of the Ministry of Science and Technology of China under Grant 2019YFB2205102in part by the National Natural Science Foundation of China under Grant 61974164,62074166,61804181,62004219,62004220,62104256.
文摘With the continuous development of deep learning,Deep Convolutional Neural Network(DCNN)has attracted wide attention in the industry due to its high accuracy in image classification.Compared with other DCNN hard-ware deployment platforms,Field Programmable Gate Array(FPGA)has the advantages of being programmable,low power consumption,parallelism,and low cost.However,the enormous amount of calculation of DCNN and the limited logic capacity of FPGA restrict the energy efficiency of the DCNN accelerator.The traditional sequential sliding window method can improve the throughput of the DCNN accelerator by data multiplexing,but this method’s data multiplexing rate is low because it repeatedly reads the data between rows.This paper proposes a fast data readout strategy via the circular sliding window data reading method,it can improve the multiplexing rate of data between rows by optimizing the memory access order of input data.In addition,the multiplication bit width of the DCNN accelerator is much smaller than that of the Digital Signal Processing(DSP)on the FPGA,which means that there will be a waste of resources if a multiplication uses a single DSP.A multiplier sharing strategy is proposed,the multiplier of the accelerator is customized so that a single DSP block can complete multiple groups of 4,6,and 8-bit signed multiplication in parallel.Finally,based on two strategies of appeal,an FPGA optimized accelerator is proposed.The accelerator is customized by Verilog language and deployed on Xilinx VCU118.When the accelerator recognizes the CIRFAR-10 dataset,its energy efficiency is 39.98 GOPS/W,which provides 1.73×speedup energy efficiency over previous DCNN FPGA accelerators.When the accelerator recognizes the IMAGENET dataset,its energy efficiency is 41.12 GOPS/W,which shows 1.28×−3.14×energy efficiency compared with others.
基金supported by the National Natural Science Foundation of China(Grant Nos.52231014 and 52271361)the Natural Science Foundation of Guangdong Province of China(Grant No.2023A1515010684).
文摘Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.
文摘Wireless Mesh Network (WMN) is a new-type wireless network. Its core idea is that any of its wireless equipment can act as both an Access Point (AP) and a router. Each node in the network can send and receive signals as well as directly communicate with one or several peer nodes. One important issue to be considered in wireless Mesh networks is how to secure reliable data transmission in multi-hop links. To solve the problem, the 3GPP system architecture proposes two functionalities: ARQ and HARQ. This paper presents two HARQ schemes, namely hop-by-hop and edge-to-edge, and three ARQ schemes: hop-by-hop, edge-to-edge, and last-hop. Moreover, it proposes three solutions for WMNs from the perspective of protocol stock design: layered cooperative mechanism, relay ARQ mechanism and multi-hop mechanism.
基金The National High Technology Research and Development Program(863 Program)of China under contract No.2013AA09A505
文摘A parallel algorithm of circulation numerical model based on message passing interface（MPI） is developed using serialization and an irregular rectangle decomposition scheme. Neighboring point exchange strategy（NPES） is adopted to further enhance the computational efficiency. Two experiments are conducted on HP C7000 Blade System, the numerical results show that the parallel version with NPES（PVN） produces higher efficiency than the original parallel version（PV）. The PVN achieves parallel efficiency in excess of 0.9 in the second experiment when the number of processors increases to 100, while the efficiency of PV decreases to 0.39 rapidly. The PVN of ocean circulation model is used in a fine-resolution regional simulation, which produces better results. The capability of universal implementation of this algorithm makes it applicable in many other ocean models potentially.
基金funded by the National Key Research and Development Program of China(2022YFC3502000)the National Natural Science Foundation of China(82141203,82274172,82430119)+4 种基金Shanghai Municipal Science and Technology Major Project(ZD2021CY001)Key project at central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(2060302)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTDD-202004)the support of Wild Goose Array Project,Zhengzhou Center of PLAJLSF,the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23CGA45,Saisai Tian)Tianjin Health Research Project(TJWJ2024QN100).
文摘Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.
基金supported in part by National Natural Science Foundation of China(Grant Nos.62374055,12327806)supported in part by Natural Science Foundation of Wuhan(Grant No.2024040701010049).
文摘The in-memory computing(IMC)paradigm emerges as an effective solution to break the bottlenecks of conventional von Neumann architecture.In the current work,an approximate multiplier in spin-orbit torque magnetoresistive random access memory(SOTMRAM)based true IMC(STIMC)architecture was presented,where computations were performed natively within the cell array instead of in peripheral circuits.Firstly,basic Boolean logic operations were realized by utilizing the feature of unipolar SOT device.Two majority gate-based imprecise compressors and an ultra-efficient approximate multiplier were then built to reduce the energy and latency.An optimized data mapping strategy facilitating bit-serial operations with an extensive degree of parallelism was also adopted.Finally,the performance enhancements by performing our approximate multiplier in image smoothing were demonstrated.Detailed simulation results show that the proposed 838 approximate multiplier could reduce the energy and latency at least by 74.2%and 44.4%compared with the existing designs.Moreover,the scheme could achieve improved peak signal-to-noise ratio(PSNR)and structural similarity index metric(SSIM),ensuring high-quality image processing outcomes.
基金The authors gratefully acknowledge financial support from the Open Project of Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation and Utilization(2020KF02)Guangzhou Science and Technology Program Project(202002020079)+1 种基金Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams(2022KJ120)Qingyuan Science and Technology Program Project(2022KJJH065).
文摘To scientifically and objectively monitor the fermentation quality of black tea,a computer vision system(CVS)and electronic nose(e-nose)were employed to analyze the black tea image and odor eigenvalues of Yinghong No.9 black tea.First,the variation trends of tea polyphenols,volatile substances,image eigenvalues and odor eigenvalues with the extension of fermentation time were analyzed,and the fermentation process was categorized into three stages for classification.Second,principal component analysis(PCA)was employed on the image and odor eigenvalues obtained by CVS and e-nose.Partial least squares discriminant analysis(PLS-DA)was performed on 117 volatile components,and 51 differential volatiles were screened out based on variable importance in projection(VIP≥1)and one-way analysis of variance(P<0.05),including geraniol,linalool,nerolidol,and α-ionone.Then,image features and odor features are fused by using a data fusion strategy.Finally,the image,smell and fusion information were combined with random forest(RF),K-nearest neighbor(KNN)and support vector machine(SVM)to establish the classification models of different fermentation stages and to compare them.The results show that the feature-level fusion strategy integrating the SVM was the most efficient approach,with classification accuracy rates of 100%for the training sets and 95.6%for the testing sets.The performance of Support Vector Regression(SVR)prediction models for tea polyphenol content based on feature-level fusion data outperformed data-level models(Rc,RMSEC,Rp and RMSEP of 0.96,0.48 mg/g,0.94,0.6 mg/g).
