Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since...Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.展开更多
Heavy oil is a complicated mixture and a potential resource and has attracted much attention since the end of last century. It is important to characterize the composition of heavy oil to enhance its recovery efficien...Heavy oil is a complicated mixture and a potential resource and has attracted much attention since the end of last century. It is important to characterize the composition of heavy oil to enhance its recovery efficiency. A designed unilateral Nuclear Magnetic Resonance （NMR） sensor with a Larmor frequency of 20 MHz and a well-defined constant gradient of 23.25 T/m was employed to acquire three- dimensional （3D） data for three heavy oil samples. The highly-constant gradient is advantageous for diffusion coefficient measurement of heavy oil. A fast data-implementation procedure including specially designed 3D pulse sequence and Inversion Laplace Transform （ILT） algorithm was adopted to process the data and extract 3D T1-D-T2 probability function. It indicates that NMR relaxometry and diffusometry are useful to characterize the components of heavy oil samples. NMR results were compared with independent measurements of fractionation and gas chromatography analysis.展开更多
Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in...Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.展开更多
For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will b...For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.展开更多
According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteris...According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.展开更多
Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniq...Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.展开更多
Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension all...Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.展开更多
Despite the proficiency of lithium(Li)-7 NMR spectroscopy in delineating the physical and chemical states of Li metal electrodes,challenges in specimen preparation and interpretation impede its progress.In this study,...Despite the proficiency of lithium(Li)-7 NMR spectroscopy in delineating the physical and chemical states of Li metal electrodes,challenges in specimen preparation and interpretation impede its progress.In this study,we conducted a comprehensive postmortem analysis utilizing ^(7)Li NMR,employing a stan-dard magic angle spinning probe to examine protective-layer coated Li metal electrodes and LiAg alloy electrodes against bare Li metal electrodes within Li metal batteries(LMBs).Our investigation explores the effects of sample burrs,alignment with the magnetic field,the existence of liquid electrolytes,and precycling on the ^(7)Li NMR signals.Through contrasting NMR spectra before and after cycling,we identi-fied alterations in Li^(0) and Li^(+) signals attributable to the degradation of the Li metal electrode.Our NMR analyses decisively demonstrate the efficacy of the protective layer in mitigating dendrite and solid elec-trolyte interphase formation.Moreover,we noted that Li*ions near the Li metal surface exhibit magnetic susceptibility anisotropy,revealing a novel approach to studying diamagnetic species on Li metal elec-trodes in LMBs.This study provides valuable insights and practical guidelines for characterizing distinct lithium states within LMBs.展开更多
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
The combination of electrochemical measurements with spectroscopic characterizations provides valuable insights into reaction mechanisms.Nuclear magnetic resonance(NMR)spectroscopy,as a powerful technique due to its a...The combination of electrochemical measurements with spectroscopic characterizations provides valuable insights into reaction mechanisms.Nuclear magnetic resonance(NMR)spectroscopy,as a powerful technique due to its atomic specificity and versatility in studying gas,liquid,and solid,allows the study of electrolyte solution,catalyst and catalyst-adsorbate interfaces.When applied in operando,NMR can offer molecular-level insights into various electrochemical processes.Operando NMR has been applied extensively in battery research,but relatively underexplored for electrocatalysis in the past two decades.In this mini review,we first introduce the operando electrochemical NMR setups,categorized by different probe designs.Then we review the applications of operando NMR for monitoring the electrolyte solution and the catalyst-adsorbate interface.Considering the high environmental impact of electrochemical conversion of CO_(2)into value-added products,we zoom in to the use of operando NMR in studying electrochemical CO_(2)reduction.Finally,we provide our perspective on further developing and applying operando NMR methods for understanding the complex reaction network of Cu-catalyzed electrochemical CO_(2)reduction.展开更多
Silkworms and spiders are capable of generating fibers that are both highly durable and elastic in a short span of time,using a silk solution stored within their bodies at room temperature and normal atmospheric press...Silkworms and spiders are capable of generating fibers that are both highly durable and elastic in a short span of time,using a silk solution stored within their bodies at room temperature and normal atmospheric pressure.The dragline silk fiber,which is essentially a spider's lifeline,surpasses the strength of a steel wire of equivalent thickness.Regrettably,humans have yet to replicate this process to produce fibers with similar high strength and elasticity in an eco-friendly manner.Therefore,it is of utmost importance to thoroughly comprehend the extraordinary structure and fibrillation mechanism of silk,and leverage this understanding in the manufacturing of high-strength,high-elasticity fibers.This review will delve into the recent progress in comprehending the structure of silks derived from silkworms and spiders,emphasizing the distinctive attributes of solidstate NMR.展开更多
基金support from the National Natural Science Foundation of China (No.62005164,62222507,62175101,and 62005166)the Shanghai Natural Science Foundation (23ZR1443700)+3 种基金Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission (23SG41)the Young Elite Scientist Sponsorship Program by CAST (No.20220042)Science and Technology Commission of Shanghai Municipality (Grant No.21DZ1100500)the Shanghai Municipal Science and Technology Major Project,and the Shanghai Frontiers Science Center Program (2021-2025 No.20).
文摘Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.
基金financial supports from the National Science Foundation of China (Grant No.41074102 and 41130417)"111 Program,Supported by the Programme of Introducing Talents of Discipline to Universities"(B13010)Program for Changjiang Scholars and Innovative Research Team in University
文摘Heavy oil is a complicated mixture and a potential resource and has attracted much attention since the end of last century. It is important to characterize the composition of heavy oil to enhance its recovery efficiency. A designed unilateral Nuclear Magnetic Resonance （NMR） sensor with a Larmor frequency of 20 MHz and a well-defined constant gradient of 23.25 T/m was employed to acquire three- dimensional （3D） data for three heavy oil samples. The highly-constant gradient is advantageous for diffusion coefficient measurement of heavy oil. A fast data-implementation procedure including specially designed 3D pulse sequence and Inversion Laplace Transform （ILT） algorithm was adopted to process the data and extract 3D T1-D-T2 probability function. It indicates that NMR relaxometry and diffusometry are useful to characterize the components of heavy oil samples. NMR results were compared with independent measurements of fractionation and gas chromatography analysis.
文摘Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.
基金supported in part by the NSFC(Grant No.11471332)The research of Gao-wei Cao was supported in part by the NSFC(Grant No.11701551).
文摘For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.
基金supported by the Undergraduate Education and Teaching Reform Research Project of Yunnan Province(JG2023157)Support Program for Yunnan Talents(CA23138L010A)+2 种基金Yunnan Higher Education Undergraduate Teaching Achievement Project(202246)National First class Undergraduate Course Construction Project of Software Engineering(109620210004)Software Engineering Virtual Teaching and Research Office Construction Project of Kunming University of Science and Technology(109620220031)。
文摘According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.
文摘Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.
基金supported by the National Natural Science Foundation of China(No.61901465,82222032,82172050).
文摘Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.
基金the Basic Research Project(C123000,C210200,C310200,&C421000)of the Korea Basic Science Institute(KBSI)funded by the Korea Ministry of Science and ICT(MSIT)the Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea(NRF)funded by MSIT(NRF-2021M1A2A2038141).O.H.Han thanks to Prof.I.S.Yang at Ewha Womans University for insightful discussion.
文摘Despite the proficiency of lithium(Li)-7 NMR spectroscopy in delineating the physical and chemical states of Li metal electrodes,challenges in specimen preparation and interpretation impede its progress.In this study,we conducted a comprehensive postmortem analysis utilizing ^(7)Li NMR,employing a stan-dard magic angle spinning probe to examine protective-layer coated Li metal electrodes and LiAg alloy electrodes against bare Li metal electrodes within Li metal batteries(LMBs).Our investigation explores the effects of sample burrs,alignment with the magnetic field,the existence of liquid electrolytes,and precycling on the ^(7)Li NMR signals.Through contrasting NMR spectra before and after cycling,we identi-fied alterations in Li^(0) and Li^(+) signals attributable to the degradation of the Li metal electrode.Our NMR analyses decisively demonstrate the efficacy of the protective layer in mitigating dendrite and solid elec-trolyte interphase formation.Moreover,we noted that Li*ions near the Li metal surface exhibit magnetic susceptibility anisotropy,revealing a novel approach to studying diamagnetic species on Li metal elec-trodes in LMBs.This study provides valuable insights and practical guidelines for characterizing distinct lithium states within LMBs.
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
基金support from Radboud University Start-up and NWO Open Competition ENW-M grant (OCENW.M.21.308)support from China Scholarship Council
文摘The combination of electrochemical measurements with spectroscopic characterizations provides valuable insights into reaction mechanisms.Nuclear magnetic resonance(NMR)spectroscopy,as a powerful technique due to its atomic specificity and versatility in studying gas,liquid,and solid,allows the study of electrolyte solution,catalyst and catalyst-adsorbate interfaces.When applied in operando,NMR can offer molecular-level insights into various electrochemical processes.Operando NMR has been applied extensively in battery research,but relatively underexplored for electrocatalysis in the past two decades.In this mini review,we first introduce the operando electrochemical NMR setups,categorized by different probe designs.Then we review the applications of operando NMR for monitoring the electrolyte solution and the catalyst-adsorbate interface.Considering the high environmental impact of electrochemical conversion of CO_(2)into value-added products,we zoom in to the use of operando NMR in studying electrochemical CO_(2)reduction.Finally,we provide our perspective on further developing and applying operando NMR methods for understanding the complex reaction network of Cu-catalyzed electrochemical CO_(2)reduction.
基金support by a JSPS KAKENHI,Grant-in-Aid for Scientific Research(C),Grant Number JP19K05609.
文摘Silkworms and spiders are capable of generating fibers that are both highly durable and elastic in a short span of time,using a silk solution stored within their bodies at room temperature and normal atmospheric pressure.The dragline silk fiber,which is essentially a spider's lifeline,surpasses the strength of a steel wire of equivalent thickness.Regrettably,humans have yet to replicate this process to produce fibers with similar high strength and elasticity in an eco-friendly manner.Therefore,it is of utmost importance to thoroughly comprehend the extraordinary structure and fibrillation mechanism of silk,and leverage this understanding in the manufacturing of high-strength,high-elasticity fibers.This review will delve into the recent progress in comprehending the structure of silks derived from silkworms and spiders,emphasizing the distinctive attributes of solidstate NMR.
