The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical ...The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical models and human expertise.In the era of data-driven manufacturing,the explosion of data amount revolutionized how data is collected and analyzed.This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis.It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection,due to the complexity and uncertainty during indirect measurement.On the other hand,physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process.Machine learning,especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data,while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions.And these trends can demonstrated be by analyzing some typical applications of manufacturing process.展开更多
This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key de...This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong Unive...In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.展开更多
Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) ...Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the ...Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.展开更多
Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambig...Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.展开更多
AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions ...AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.展开更多
Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impair...Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks.In this paper,a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed.Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately.The proposed novel paradigm consists of(1)representing rock slopes as graph-structured data based on complex systems theory,(2)identifying key nodes in the graph-structured data using graph deep learning,and(3)mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope.Verification experiments and real-case applications are conducted by the proposed method.The verification results demonstrate excellent model performance,strong generalization capability,and effective classification results.Moreover,the real case application is conducted on the northern slope of the Yanqianshan Iron Mine.The results show that the proposed method can accurately identify key blocks in complex rock slopes,which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes,thereby ensuring the stability of rock engineering and the safety of life and property.展开更多
With the constant changes of the times,China's science and technology have entered a period of rapid development.At the same time,the economic structure is also changing with the changes of the times,and the origi...With the constant changes of the times,China's science and technology have entered a period of rapid development.At the same time,the economic structure is also changing with the changes of the times,and the original Haikou logistics industry in the process is also facing new impacts and challenges.And related enterprises want to stand out in the fierce market competition,we must optimize and upgrade the current industry development situation,promote the integrated development of Haikou logistics and manufacturing industry,to constantly promote the innovative application of digital technology in the logistics industry and manufacturing industry,the formation of a multi-force economic development model.This paper mainly starts with the development status of Haikou logistics,analyzes the importance of the integration of Haikou logistics and manufacturing industry under the background of big data drive,and makes an in-depth discussion on the path of the integration of Haikou logistics and manufacturing industry under the drive of big data,hoping to contribute new strength to the development of social economy.展开更多
Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.T...Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.These studies,however,have the following limitations:1)effective supervision is neglected for missing data across different fault types and 2)imbalance in missing rates among fault types results in inadequate learning during model training.To overcome the above limitations,this paper proposes a dynamic relative advantagedriven multi-fault synergistic diagnosis method to accomplish accurate fault diagnosis of motors under imbalanced missing data rates.Firstly,a cross-fault-type generalized synergistic diagnostic strategy is established based on variational information bottleneck theory,which is able to ensure sufficient supervision in handling missing data.Then,a dynamic relative advantage assessment technique is designed to reduce diagnostic accuracy decay caused by imbalanced missing data rates.The proposed method is validated using multi-sensor data from motor fault simulation experiments,and experimental results demonstrate its effectiveness and superiority in improving diagnostic accuracy and generalization under imbalanced missing data rates.展开更多
During the process of urbanization,community environments encounter challenges such as data disconnection and the underutilization of small and micro spaces.The establishment of“complete communities”necessitates the...During the process of urbanization,community environments encounter challenges such as data disconnection and the underutilization of small and micro spaces.The establishment of“complete communities”necessitates the implementation of refined governance strategies.This research develops a path for the precise establishment of community micro-gardens driven by mobile measurement.It involves the collection of environmental data via mobile devices equipped with various types of sensors,the generation of visualization maps that are adjusted for spatio-temporal synchronization,and the identification of environmental paint points,including areas of excessive temperature exposure and zones with elevated noise levels.Based on the aforementioned considerations,various plant allocation strategies have been proposed for distinct areas.For instance,the implementation of a composite shade and cooling vegetation system is recommended for regions experiencing high temperatures,while a triple protection structure is suggested for areas affected by odor contamination.The efficacy of these strategies is demonstrated through a case study of the micro-garden transformation in the Dongjie Community of Wulituo Street,Shijingshan,Beijing.The study presents operational technical pathways and plant response solutions aimed at facilitating data-driven governance of community micro-environments.展开更多
The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quan...The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.展开更多
In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Fir...In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Firstly,this paper introduces the big data-driven forecast model of scenic spot passenger flow.Based on the traditional autoregressive integral moving average model and artificial neural network model,it builds a big data analysis and forecast model.Through the analysis of data source,model building,scenic spot passenger flow accuracy,and modeling time comparison,it affirms the advantages of big data analysis in forecasting scenic spot passenger flow.Finally,it puts forward four commercial operation optimization strategies:adjusting the ticket pricing of scenic spots,upgrading the catering and accommodation services in scenic spots,planning and designing play projects,and formulating accurate scenic spot marketing strategies,in order to provide references for the optimization and upgrading of smart tourist attractions in the future.展开更多
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full ...Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.展开更多
Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function...Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.展开更多
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests...With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.展开更多
Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challen...Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall effectiveness.This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven decision-making.The evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing dataset.The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 score.Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique.The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets.These factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance evaluation.Additionally,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51805260)National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.51925505)National Natural Science Foundation of China(Grant No.51775278).
文摘The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical models and human expertise.In the era of data-driven manufacturing,the explosion of data amount revolutionized how data is collected and analyzed.This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis.It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection,due to the complexity and uncertainty during indirect measurement.On the other hand,physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process.Machine learning,especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data,while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions.And these trends can demonstrated be by analyzing some typical applications of manufacturing process.
基金supported by Poongsan-KAIST Future Research Center Projectthe fund support provided by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.2023R1A2C2005661)。
文摘This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
文摘In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.
基金supported by the National Natural Science Foundation of China(62373224,62333013,U23A20327)the Natural Science Foundation of Shandong Province(ZR2024JQ021)
文摘Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
基金supported by the National Natural Science Foundation of China(62325304,U22B2046,62073079,62376029)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)the China Postdoctoral Science Foundation(2023M730255,2024T171123)
文摘Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.
基金supported by the National Key Research and Development Program of China(No.2022YFF0610003)the BUPT Excellent Ph.D.Students Foundation(No.CX2022218)the Fund of Central University Basic Research Projects(No.2023ZCTH11).
文摘Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.
基金funded by the Taiwan Comprehensive University System and the National Science and Technology Council of Taiwan under grant number NSTC 111-2410-H-019-006-MY3Additionally,this work was financially/partially supported by the Advanced Institute of Manufacturing with High-tech Innovations(AIM-HI)from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan+1 种基金the National Natural Science Foundation of China,No.62402444the Zhejiang Provincial Natural Science Foundation of China,No.LQ24F020012.
文摘AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.
基金supported by the National Natural Science Foundation of China(Grant Nos.42277161,42230709).
文摘Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks.In this paper,a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed.Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately.The proposed novel paradigm consists of(1)representing rock slopes as graph-structured data based on complex systems theory,(2)identifying key nodes in the graph-structured data using graph deep learning,and(3)mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope.Verification experiments and real-case applications are conducted by the proposed method.The verification results demonstrate excellent model performance,strong generalization capability,and effective classification results.Moreover,the real case application is conducted on the northern slope of the Yanqianshan Iron Mine.The results show that the proposed method can accurately identify key blocks in complex rock slopes,which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes,thereby ensuring the stability of rock engineering and the safety of life and property.
基金Research on the Digital Transformation of Financial Management Major and the Training Model of Outstanding Talents(2023122203988)Research on the Integration of Haikou Logistics and Manufacturing Driven by Big Data and Its Consumption Promotion Effect(HKKY2024-ZD-24)。
文摘With the constant changes of the times,China's science and technology have entered a period of rapid development.At the same time,the economic structure is also changing with the changes of the times,and the original Haikou logistics industry in the process is also facing new impacts and challenges.And related enterprises want to stand out in the fierce market competition,we must optimize and upgrade the current industry development situation,promote the integrated development of Haikou logistics and manufacturing industry,to constantly promote the innovative application of digital technology in the logistics industry and manufacturing industry,the formation of a multi-force economic development model.This paper mainly starts with the development status of Haikou logistics,analyzes the importance of the integration of Haikou logistics and manufacturing industry under the background of big data drive,and makes an in-depth discussion on the path of the integration of Haikou logistics and manufacturing industry under the drive of big data,hoping to contribute new strength to the development of social economy.
文摘Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.These studies,however,have the following limitations:1)effective supervision is neglected for missing data across different fault types and 2)imbalance in missing rates among fault types results in inadequate learning during model training.To overcome the above limitations,this paper proposes a dynamic relative advantagedriven multi-fault synergistic diagnosis method to accomplish accurate fault diagnosis of motors under imbalanced missing data rates.Firstly,a cross-fault-type generalized synergistic diagnostic strategy is established based on variational information bottleneck theory,which is able to ensure sufficient supervision in handling missing data.Then,a dynamic relative advantage assessment technique is designed to reduce diagnostic accuracy decay caused by imbalanced missing data rates.The proposed method is validated using multi-sensor data from motor fault simulation experiments,and experimental results demonstrate its effectiveness and superiority in improving diagnostic accuracy and generalization under imbalanced missing data rates.
文摘During the process of urbanization,community environments encounter challenges such as data disconnection and the underutilization of small and micro spaces.The establishment of“complete communities”necessitates the implementation of refined governance strategies.This research develops a path for the precise establishment of community micro-gardens driven by mobile measurement.It involves the collection of environmental data via mobile devices equipped with various types of sensors,the generation of visualization maps that are adjusted for spatio-temporal synchronization,and the identification of environmental paint points,including areas of excessive temperature exposure and zones with elevated noise levels.Based on the aforementioned considerations,various plant allocation strategies have been proposed for distinct areas.For instance,the implementation of a composite shade and cooling vegetation system is recommended for regions experiencing high temperatures,while a triple protection structure is suggested for areas affected by odor contamination.The efficacy of these strategies is demonstrated through a case study of the micro-garden transformation in the Dongjie Community of Wulituo Street,Shijingshan,Beijing.The study presents operational technical pathways and plant response solutions aimed at facilitating data-driven governance of community micro-environments.
基金financially supported by the Innovative Research Group Project of the National Natural Science Foundation of China (22021004)Sinopec Major Science and Technology Projects (321123-1)
文摘The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.
文摘In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Firstly,this paper introduces the big data-driven forecast model of scenic spot passenger flow.Based on the traditional autoregressive integral moving average model and artificial neural network model,it builds a big data analysis and forecast model.Through the analysis of data source,model building,scenic spot passenger flow accuracy,and modeling time comparison,it affirms the advantages of big data analysis in forecasting scenic spot passenger flow.Finally,it puts forward four commercial operation optimization strategies:adjusting the ticket pricing of scenic spots,upgrading the catering and accommodation services in scenic spots,planning and designing play projects,and formulating accurate scenic spot marketing strategies,in order to provide references for the optimization and upgrading of smart tourist attractions in the future.
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
基金supported by the National Natural Science Foundation of China(Nos.92152301,12072282)。
文摘Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.
文摘Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.
基金supported by the National Natural Science Foundation of China (62272078)。
文摘With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.
基金support from the Cyber Technology Institute(CTI)at the School of Computer Science and Informatics,De Montfort University,United Kingdom,along with financial assistance from Universiti Tun Hussein Onn Malaysia and the UTHM Publisher’s office through publication fund E15216.
文摘Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall effectiveness.This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven decision-making.The evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing dataset.The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 score.Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique.The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets.These factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance evaluation.Additionally,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.
