This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor gro...This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three p...This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.展开更多
Due to the signal reflection and diffraction,site-specific unmodeled errors like multipath effect and Non-Line-of-Sight reception are significant error sources in Global Navigation Satellite System since they cannot b...Due to the signal reflection and diffraction,site-specific unmodeled errors like multipath effect and Non-Line-of-Sight reception are significant error sources in Global Navigation Satellite System since they cannot be easily mitigated.However,how to characterize and model the internal mechanisms and external influences of these site-specific unmodeled errors are still to be investigated.Therefore,we propose a method for characterizing and modeling site-specific unmodeled errors under reflection and diffraction using a data-driven approach.Specifically,we first consider all the popular potential features,which generate the site-specific unmodeled errors.We then use the random forest regression to comprehensively analyze the correlations between the site-specific unmodeled errors and the potential features.We finally characterize and model the site-specific unmodeled errors.Two 7-consecutive datasets dominated by signal reflection and diffraction were conducted.The results show that there are significant differences in the correlations with potential features.They are highly related to the application scenarios,observation types,and satellite types.Notably,the innovation vector often shows a strong correlation with the code site-specific unmodeled errors.For the phase site-specific unmodeled errors,they have high correlations with elevation,azimuth,number of visible satellites,and between-frequency differenced phase observations.In the environments of reflection and diffraction,the sum of the correlations of the top six potential features can reach approximately 88.5 and 87.7%,respectively.Meanwhile,these correlations are stable for different observation types and satellite types.With the integration of a transformer model with the random forest method,a high-precision unmodeled error prediction model is established,demonstrating the necessity to include multiple features for accurate and efficient characterization and modeling of site-specific unmodeled errors.展开更多
Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards.Parallel-serial structures,commonly found in modern production systems,offer a unique ba...Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards.Parallel-serial structures,commonly found in modern production systems,offer a unique balance of flexibility and efficiency by combining parallel processes with sequential workflows.However,their inherent complexity poses significant challenges,particularly in optimizing energy efficiency and ensuring consistent product quality.In data-driven manufacturing environments,it is not clear how to leverage production data to enhance the energy efficiency of production systems.Therefore,this paper studied a data-driven approach to improving energy efficiency in parallel-serial production lines with product quality issues.Firstly,the authors developed a data-driven performance analysis method to evaluate the effects of disruption events,such as energy-saving control actions,machine breakdowns,and product quality failures,on system throughput and energy consumption.Secondly,a periodic energy-saving control method was developed to enhance system energy efficiency using a non-linear programming model.To reduce complexity and improve computational efficiency,the model was simplified by leveraging the intrinsic properties of parallel-serial production lines and solved using an adaptive genetic algorithm.Finally,the effectiveness of the proposed data-driven approach was validated through case studies,providing actionable insights into achieving data-driven energy efficiency optimization in complex production systems.展开更多
Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency...Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.展开更多
The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this chal...The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.展开更多
A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis ca...A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.展开更多
With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal referen...With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.展开更多
In a smart grid,state estimation(SE)is a very important component of energy management system.Its main functions include system SE and detection of cyber anomalies.Recently,it has been shown that conventional SE techn...In a smart grid,state estimation(SE)is a very important component of energy management system.Its main functions include system SE and detection of cyber anomalies.Recently,it has been shown that conventional SE techniques are vulnerable to false data injection(FDI)attack,which is a sophisticated new class of attacks on data integrity in smart grid.The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model,which is different from the traditional weighted least square based SE model.This SE model has a number of unique advantages compared with traditional SE models.First,the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors.Second,the proposed SE model can learn the actual power system states.Finally,this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors.The proposed FDI attack detection technique is evaluated on a number of standard bus systems.The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-ofthe-art techniques.Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.展开更多
Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural v...Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural ventilation,it is essential to develop models to understand the relationship between wind flow characteristics and the building's design.Significantly more effort is still needed for developing such reliable,accurate,and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics(CFD)simulation.This paper,therefore,presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings.The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building.Once the surface pressures have been obtained,multizone modelling is used to predict the air change per hour(ACH)for different flats in various configurations.Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error,mean absolute percentage error,and a fusion algorithm respectively.These data-driven models are used to predict the ACH of 25 flats.The results from multizone modelling and data-driven modelling are compared.The results imply a high accuracy of the data-driven prediction in comparison with physics-based models.The fusion algorithm-based neural network performs best,achieving 96%accuracy,which is the highest of all models tested.This study contributes a more efficient and robust method for predicting wind-induced natural ventilation.The findings describe the relationship between building design(e.g.,plan layout),distribution of surface pressure,and the resulting ACH,which serve to improve the practical design of sustainable buildings.展开更多
Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)a...Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure.In response to the inconvenience of the online prediction method and the inaccu-racy of the offline prediction method,an improved offline prediction method based on data feedback is presented in this paper.FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves.The crack length corresponding to the cycle is trained using a support vector regression(SVR)and back propagation neural network(BP NN)algorithms.FCP prediction lives of test specimens are performed according to the online,offline,and improved offline prediction methods.Effects of the number of feedback data,the sequence length(SL)in the input set,and the cycle interval on prediction accuracy are discussed.The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature.The larger the number of feedback data,the higher the prediction accuracy.The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5,respectively.Furthermore,the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.展开更多
Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,exp...Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.展开更多
The COVID-19 pandemic has significantly impacted global health,social,and economic situations since its emergence in December 2019.The primary focus of this study is to propose a distinct vaccination policy and assess...The COVID-19 pandemic has significantly impacted global health,social,and economic situations since its emergence in December 2019.The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach,concentrating on the year 2021.We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated(SEIRV)model,incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis(EDA)approach.While no vaccine guarantees total immunity against the disease,and vaccine immunity wanes over time,it is critical to include and accurately estimate vaccine efficacy,as well as a constant vaccine immunity decay or wane factor,to better simulate the dynamics of vaccine-induced protection over time.Based on the distribution and effectiveness of vaccines,we integrated a data-driven estimation of vaccine efficacy,calculated at 75%for Malaysia,underscoring the model's realism and relevance to the specific context of the country.The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters.The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy.Our findings reveal that this distinct vaccination policy,which emphasizes an accelerated vaccination rate during the initial stages of the program,is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections.The study found that vaccinating 57–66%of the population(as opposed to 76%in the real data)with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections.The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination,offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies,particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy.While the methodology used in this study is specifically applied to national data from Malaysia,its successful application to local regions within Malaysia,such as Selangor and Johor,indicates its adaptability and potential for broader application.This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes,implying its usefulness for similar datasets from various geographical regions.展开更多
Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,de...Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are available.In this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and pressure.We call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data.Using the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established.We came up with 12 reservoir types.Subsequently,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types.Based on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability distribution.The investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.展开更多
Non-learning based motion and path planning of an Unmanned Aerial Vehicle(UAV)is faced with low computation efficiency,mapping memory occupation and local optimization problems.This article investigates the challenge ...Non-learning based motion and path planning of an Unmanned Aerial Vehicle(UAV)is faced with low computation efficiency,mapping memory occupation and local optimization problems.This article investigates the challenge of quadrotor control using offline reinforcement learning.By establishing a data-driven learning paradigm that operates without real-environment interaction,the proposed workflow offers a safer approach than traditional reinforcement learning,making it particularly suited for UAV control in industrial scenarios.The introduced algorithm evaluates dataset uncertainty and employs a pessimistic estimation to foster offline deep reinforcement learning.Experiments highlight the algorithm's superiority over traditional online reinforcement learning methods,especially when learning from offline datasets.Furthermore,the article emphasizes the importance of a more general behavior policy.In evaluations,the trained policy demonstrated versatility by adeptly navigating diverse obstacles,underscoring its real-world applicability.展开更多
The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demand...The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.展开更多
In this paper,a data-driven method for disturbance estimation and rejection is presented.The proposed approach is divided into two stages:an inner stabilization loop,to set the desired reference model,together with an...In this paper,a data-driven method for disturbance estimation and rejection is presented.The proposed approach is divided into two stages:an inner stabilization loop,to set the desired reference model,together with an outer loop for disturbance estimation and compensation.Inspired by the active disturbance rejection control framework,the exogenous and endogenous disturbances are lumped into a lotal disturbance signal.This signal is estimaed using an on-line algorithm based on a data-driven predictor scheme,whose parameters are chosen Io salisfy high robustnessperformance criteria.The above process is presented as a novel enhancement lo design a disturbance observer,w hich constitutes the main contribution of the paper.In addition,the control strategy is completely presented in discrete time,avoiding the use of discretization methods for its digital implementation.As a case study,the voltage control of a DC-DC synchronous buck converter aflected by disturbances in the input voltage and the load is considered.Finally,experimental results that validate the proposed stralegy and some comparisons with the classical disturbance observer-based control are presented.展开更多
Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic ...Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic process modelling(DSPM)approach into dynamic simulation of the railway vehicle collision.This DSPM approach consists of two steps:(i)process description,four kinds of kernels are used to describe the uncertainty inherent in collision processes;(ii)solving,stochastic variational inferences and mini-batch algorithms can then be used to accelerate computations of stochastic processes.By applying DSPM,Gaussian process regression(GPR)and finite element(FE)methods to two collision scenarios(i.e.lead car colliding with a rigid wall,and the lead car colliding with another lead car),we are able to achieve a comprehensive analysis.The comparison between the DSPM approach and the FE method revealed that the DSPM approach is capable of calculating the corresponding confidence interval,simultaneously improving the overall computational efficiency.Comparing the DSPM approach with the GPR method indicates that the DSPM approach has the ability to accurately describe the dynamic response under unknown conditions.Overall,this research demonstrates the feasibility and usability of the proposed DSPM approach for stochastic dynamics simulation of the railway vehicle collision.展开更多
基金National Natural Science Foundation of China(Project No.:12371428)Projects of the Provincial College Students’Innovation and Training Program in 2024(Project No.:S202413023106,S202413023110)。
文摘This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
文摘This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.
基金funded by the National Natural Science Foundation of China(42374014,42004014).
文摘Due to the signal reflection and diffraction,site-specific unmodeled errors like multipath effect and Non-Line-of-Sight reception are significant error sources in Global Navigation Satellite System since they cannot be easily mitigated.However,how to characterize and model the internal mechanisms and external influences of these site-specific unmodeled errors are still to be investigated.Therefore,we propose a method for characterizing and modeling site-specific unmodeled errors under reflection and diffraction using a data-driven approach.Specifically,we first consider all the popular potential features,which generate the site-specific unmodeled errors.We then use the random forest regression to comprehensively analyze the correlations between the site-specific unmodeled errors and the potential features.We finally characterize and model the site-specific unmodeled errors.Two 7-consecutive datasets dominated by signal reflection and diffraction were conducted.The results show that there are significant differences in the correlations with potential features.They are highly related to the application scenarios,observation types,and satellite types.Notably,the innovation vector often shows a strong correlation with the code site-specific unmodeled errors.For the phase site-specific unmodeled errors,they have high correlations with elevation,azimuth,number of visible satellites,and between-frequency differenced phase observations.In the environments of reflection and diffraction,the sum of the correlations of the top six potential features can reach approximately 88.5 and 87.7%,respectively.Meanwhile,these correlations are stable for different observation types and satellite types.With the integration of a transformer model with the random forest method,a high-precision unmodeled error prediction model is established,demonstrating the necessity to include multiple features for accurate and efficient characterization and modeling of site-specific unmodeled errors.
基金supported in part by Major Project of the National Social Science Fund of China,under Grant No.23&ZD050in part by National Natural Science Foundation of China(NSFC),under Grant Nos.72402031 and 71971052+2 种基金in part by Open Project Program of State Key Laboratory of Massive Personalized Customization System and Technology,under Grant No.H&C-MPC-2023-04-03in part by the Fundamental Research Funds for the Central Universities,under Grant No.N25ZJL015the Joint Funds of the Natural Science Foundation of Liaoning,under Grant No.2023-BSBA-139.
文摘Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards.Parallel-serial structures,commonly found in modern production systems,offer a unique balance of flexibility and efficiency by combining parallel processes with sequential workflows.However,their inherent complexity poses significant challenges,particularly in optimizing energy efficiency and ensuring consistent product quality.In data-driven manufacturing environments,it is not clear how to leverage production data to enhance the energy efficiency of production systems.Therefore,this paper studied a data-driven approach to improving energy efficiency in parallel-serial production lines with product quality issues.Firstly,the authors developed a data-driven performance analysis method to evaluate the effects of disruption events,such as energy-saving control actions,machine breakdowns,and product quality failures,on system throughput and energy consumption.Secondly,a periodic energy-saving control method was developed to enhance system energy efficiency using a non-linear programming model.To reduce complexity and improve computational efficiency,the model was simplified by leveraging the intrinsic properties of parallel-serial production lines and solved using an adaptive genetic algorithm.Finally,the effectiveness of the proposed data-driven approach was validated through case studies,providing actionable insights into achieving data-driven energy efficiency optimization in complex production systems.
文摘Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.
文摘The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.
基金This work was supported by the National Natural Science Foundation of China(Grant No.42050104)the Science Foundation of SINOPEC Group(Grant No.P20030).
文摘A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.
文摘With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.
文摘In a smart grid,state estimation(SE)is a very important component of energy management system.Its main functions include system SE and detection of cyber anomalies.Recently,it has been shown that conventional SE techniques are vulnerable to false data injection(FDI)attack,which is a sophisticated new class of attacks on data integrity in smart grid.The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model,which is different from the traditional weighted least square based SE model.This SE model has a number of unique advantages compared with traditional SE models.First,the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors.Second,the proposed SE model can learn the actual power system states.Finally,this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors.The proposed FDI attack detection technique is evaluated on a number of standard bus systems.The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-ofthe-art techniques.Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.
基金supported by the Hong Kong University of Science and Technology Research Grant(project no.IGN17EG04).
文摘Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural ventilation,it is essential to develop models to understand the relationship between wind flow characteristics and the building's design.Significantly more effort is still needed for developing such reliable,accurate,and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics(CFD)simulation.This paper,therefore,presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings.The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building.Once the surface pressures have been obtained,multizone modelling is used to predict the air change per hour(ACH)for different flats in various configurations.Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error,mean absolute percentage error,and a fusion algorithm respectively.These data-driven models are used to predict the ACH of 25 flats.The results from multizone modelling and data-driven modelling are compared.The results imply a high accuracy of the data-driven prediction in comparison with physics-based models.The fusion algorithm-based neural network performs best,achieving 96%accuracy,which is the highest of all models tested.This study contributes a more efficient and robust method for predicting wind-induced natural ventilation.The findings describe the relationship between building design(e.g.,plan layout),distribution of surface pressure,and the resulting ACH,which serve to improve the practical design of sustainable buildings.
文摘Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure.In response to the inconvenience of the online prediction method and the inaccu-racy of the offline prediction method,an improved offline prediction method based on data feedback is presented in this paper.FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves.The crack length corresponding to the cycle is trained using a support vector regression(SVR)and back propagation neural network(BP NN)algorithms.FCP prediction lives of test specimens are performed according to the online,offline,and improved offline prediction methods.Effects of the number of feedback data,the sequence length(SL)in the input set,and the cycle interval on prediction accuracy are discussed.The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature.The larger the number of feedback data,the higher the prediction accuracy.The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5,respectively.Furthermore,the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.
基金support by the National Key Research and Development Program of China(grant no.2018YFA0703600)the National Natural Science Foundation of China(grant no.51825104).
文摘Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.
基金his work was funded by the James Watt PhD Scholarship program supported by Heriot-Watt University.
文摘The COVID-19 pandemic has significantly impacted global health,social,and economic situations since its emergence in December 2019.The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach,concentrating on the year 2021.We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated(SEIRV)model,incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis(EDA)approach.While no vaccine guarantees total immunity against the disease,and vaccine immunity wanes over time,it is critical to include and accurately estimate vaccine efficacy,as well as a constant vaccine immunity decay or wane factor,to better simulate the dynamics of vaccine-induced protection over time.Based on the distribution and effectiveness of vaccines,we integrated a data-driven estimation of vaccine efficacy,calculated at 75%for Malaysia,underscoring the model's realism and relevance to the specific context of the country.The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters.The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy.Our findings reveal that this distinct vaccination policy,which emphasizes an accelerated vaccination rate during the initial stages of the program,is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections.The study found that vaccinating 57–66%of the population(as opposed to 76%in the real data)with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections.The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination,offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies,particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy.While the methodology used in this study is specifically applied to national data from Malaysia,its successful application to local regions within Malaysia,such as Selangor and Johor,indicates its adaptability and potential for broader application.This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes,implying its usefulness for similar datasets from various geographical regions.
基金This research is supported and partially funded by Parahyangan Catholic University in Bandung,Indonesia.
文摘Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are available.In this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and pressure.We call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data.Using the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established.We came up with 12 reservoir types.Subsequently,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types.Based on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability distribution.The investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.
基金supported by the National Natural Science Foundation of China(No.52272382)the Aeronautical Science Foundation of China(No.20200017051001)the Fundamental Research Funds for the Central Universities,China。
文摘Non-learning based motion and path planning of an Unmanned Aerial Vehicle(UAV)is faced with low computation efficiency,mapping memory occupation and local optimization problems.This article investigates the challenge of quadrotor control using offline reinforcement learning.By establishing a data-driven learning paradigm that operates without real-environment interaction,the proposed workflow offers a safer approach than traditional reinforcement learning,making it particularly suited for UAV control in industrial scenarios.The introduced algorithm evaluates dataset uncertainty and employs a pessimistic estimation to foster offline deep reinforcement learning.Experiments highlight the algorithm's superiority over traditional online reinforcement learning methods,especially when learning from offline datasets.Furthermore,the article emphasizes the importance of a more general behavior policy.In evaluations,the trained policy demonstrated versatility by adeptly navigating diverse obstacles,underscoring its real-world applicability.
基金National Natural Sciences Foundation of China,Grant/Award Numbers:62125302,62203087Sci-Tech Talent Innovation Support Program of Dalian,Grant/Award Number:2022RG03+1 种基金Liaoning Revitalization Talents Program,Grant/Award Number:XLYC2002087Young Elite Scientist Sponsorship Program by CAST,Grant/Award Number:YESS20220018。
文摘The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.
文摘In this paper,a data-driven method for disturbance estimation and rejection is presented.The proposed approach is divided into two stages:an inner stabilization loop,to set the desired reference model,together with an outer loop for disturbance estimation and compensation.Inspired by the active disturbance rejection control framework,the exogenous and endogenous disturbances are lumped into a lotal disturbance signal.This signal is estimaed using an on-line algorithm based on a data-driven predictor scheme,whose parameters are chosen Io salisfy high robustnessperformance criteria.The above process is presented as a novel enhancement lo design a disturbance observer,w hich constitutes the main contribution of the paper.In addition,the control strategy is completely presented in discrete time,avoiding the use of discretization methods for its digital implementation.As a case study,the voltage control of a DC-DC synchronous buck converter aflected by disturbances in the input voltage and the load is considered.Finally,experimental results that validate the proposed stralegy and some comparisons with the classical disturbance observer-based control are presented.
基金supported by the National Key Research and Development Project(No.2019YFB1405401)the National Natural Science Foundation of China(No.5217120056)。
文摘Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic process modelling(DSPM)approach into dynamic simulation of the railway vehicle collision.This DSPM approach consists of two steps:(i)process description,four kinds of kernels are used to describe the uncertainty inherent in collision processes;(ii)solving,stochastic variational inferences and mini-batch algorithms can then be used to accelerate computations of stochastic processes.By applying DSPM,Gaussian process regression(GPR)and finite element(FE)methods to two collision scenarios(i.e.lead car colliding with a rigid wall,and the lead car colliding with another lead car),we are able to achieve a comprehensive analysis.The comparison between the DSPM approach and the FE method revealed that the DSPM approach is capable of calculating the corresponding confidence interval,simultaneously improving the overall computational efficiency.Comparing the DSPM approach with the GPR method indicates that the DSPM approach has the ability to accurately describe the dynamic response under unknown conditions.Overall,this research demonstrates the feasibility and usability of the proposed DSPM approach for stochastic dynamics simulation of the railway vehicle collision.
