The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor a...The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor and offer high fault-tolerant capability for short-circuit faults.Besides,they provide motor friendly waveforms and four-quadrant operation ability.Therefore,they are suitable for high-power applications of fans,pumps,compressors and wind power generation.The purpose of this paper is to comprehensively review recent developments of key technologies on modulation and control of high-power(HP)PWM-CSC fed electric machines systems,including reduction of low-order current harmonics,suppression of inductor–capacitor(LC)resonance,mitigation of common-mode voltage(CMV)and control of modular PWM-CSC fed systems.In particular,recent work on the overlapping effects during commutation,LC resonance suppression under fault-tolerant operation and collaboration of modular PMW-CSCs are described.Both theoretical analysis and some results in simulations and experiments are presented.Finally,a brief discussion regarding the future trend of the HP CSC fed electric machines systems is presented.展开更多
At present,there are few security models which control the communication between virtual machines(VMs).Moreover,these models are not applicable to multi-level security(MLS).In order to implement mandatory access contr...At present,there are few security models which control the communication between virtual machines(VMs).Moreover,these models are not applicable to multi-level security(MLS).In order to implement mandatory access control(MAC)and MLS in virtual machine system,this paper designs Virt-BLP model,which is based on BLP model.For the distinction between virtual machine system and non-virtualized system,we build elements and security axioms of Virt-BLP model by modifying those of BLP.Moreover,comparing with BLP,the number of state transition rules of Virt-BLP is reduced accordingly and some rules can only be enforced by trusted subject.As a result,Virt-BLP model supports MAC and partial discretionary access control(DAC),well satisfying the requirement of MLS in virtual machine system.As space is limited,the implementation of our MAC framework will be shown in a continuation.展开更多
The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using ...The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using conventional methods.In this paper,a new multilevel design method oriented network environment is proposed,which maps the design problem of large-scale machine system into a hypergraph with degree of linking strength(DLS)between vertices.By decomposition of hypergraph,this method can divide the complex design problem into some small and simple subproblems that can be solved concurrently in a network.展开更多
A hybrid machine （HM） as a typical mechatronic device, is a useful tool to generate smooth motion, and combines the motions of a large constant speed motor with a small servo motor by means of a mechnical linkage me...A hybrid machine （HM） as a typical mechatronic device, is a useful tool to generate smooth motion, and combines the motions of a large constant speed motor with a small servo motor by means of a mechnical linkage mechanism, in order to provide a powerful programmable drive system. To achieve design objectives, a control system is required. To design a better control system and analyze the performance of an HM, a dynamic model is necessary. This paper first develops a dynamic model of an HM with a five-bar mechanism using a Lagrangian formulation. Then, several important properties which are very useful in system analysis, and control system design, are presented. Based on the developed dynamic model, two control approaches, computed torque, and combined computed torque and slide mode control, are adopted to control the HM system. Simulation results demonstrate the control performance and limitations of each control approach.展开更多
This paper researches the main technology of open CNC engraving machine, the DXF identification technology. Agraphic information extraction method is proposed. By this method, the graphic information in DXF file can b...This paper researches the main technology of open CNC engraving machine, the DXF identification technology. Agraphic information extraction method is proposed. By this method, the graphic information in DXF file can be identified and transformed into bottom motion controller’s code. So the engraving machine can achieve trajectory tracking. Then the open CNC engraving machine system is developed with C#. At last, the method is validated on a three axes motion experiment platform. The result shows that this method can efficiently identify the graphic information including line, circle, arc etc. in DXF file and the CNC engraving machine can be controlled well.展开更多
Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as...Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.展开更多
In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers...In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers are required and where they shall be.Eachmachine being represented by a low-order linear model,there is some reservation of the results.In this paper,extensive transient simulationsare performed and each machine is represented by a high-order nonlinear model.Coherent groups are found.A weighted speed deviationindex (SDI) is defined to find out the most unstable machines in the system.PSSs are designed after the decisions of PSS number and sites.Transient simulations are carried out again for the closed-loop system.A system stability index (SSI) is used to evaluate the stability of theclosed-loop system.It is found that three PSSs are sufficient to ensure the stability of the nine-machine system.展开更多
Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future,which have a great impact on energy resources and efficiencies.Due to the ...Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future,which have a great impact on energy resources and efficiencies.Due to the emerging artificial intelligence and machine learning,traditional modeling techniques in these energy systems have met challenges in still leveraging physics model and first principle-based approaches.Moreover,with the rapid development of hardware and computing techniques,new modeling approaches for energy systems have become more and more important for system design,integration,analysis,control,and management.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impact...As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.展开更多
We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semicon...We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.展开更多
The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure cas...The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure casting gating system of a large thin-walled cabin casting.A high-quality dataset was established through orthogonal experiments combined with design criteria for the gating system.Spearman’s correlation analysis was used to select high-quality features.The gating system dimensions were predicted using a gated recurrent unit(GRU)recurrent neural network and an elastic network model.Using EasyCast and ProCAST casting software,a comparative analysis of the flow field,temperature field,and solidification field can be conducted to demonstrate the achievement of steady filling and top-down sequential solidification.Compared to the empirical formula method,this method eliminates trial-and-error iterations,reduces porosity,reduces casting defect volume from 11.23 cubic centimeters to 2.23 cubic centimeters,eliminates internal casting defects through the incorporation of an internally cooled iron,fulfilling the goal of intelligent gating system design.展开更多
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model versi...Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, R_(BC)) exhibited marked regional disparities, with a significant increase in China(5.4% yr^(-1)), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO_(2)/BC). Projections under carbon neutrality reveal that R_(BC) will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The R_(BC) increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.展开更多
The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the ...The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems.There is a lack of explainability,challenges with imbalanced attack classes,and limited consideration of practical edge–cloud deployment strategies in prior works.In the proposed study,we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations(SHAP)-based Explainable AI(XAI)to attack detection and classification in IIoT-CPS settings.It includes not only unsupervised clustering(K-Means and DBSCAN)to extract latent traffic patterns but also supervised classification based on taxonomy to classify 33 different kinds of attacks into seven high-level categories:Flood Attacks,Botnet/Mirai,Reconnaissance,Spoofing/Man-In-The-Middle(MITM),Injection Attacks,Backdoors/Exploits,and Benign.The three machine learning algorithms,Random Forest,XGBoost,and Multi-Layer Perceptron(MLP),were trained on a realworld dataset of more than 1 million network traffic records,with overall accuracy of 99.4%(RF),99.5%(XGBoost),and 99.1%(MLP).Rare types of attacks,such as injection attacks and backdoors,were examined even in the case of extreme imbalance between the classes.SHAP-based XAI was performed on every model to help gain transparency and trust in the model and identify important features that drive the classification decisions,such as inter-arrival time,TCP flags,and protocol type.A workable edge-computing implementation strategy is proposed,whereby lightweight computing is performed at the edge devices and heavy,computation-intensive analytics is performed at the cloud.This framework is highly accurate,interpretable,and has real-time application,hence a robust and scalable solution to securing IIoT-CPS infrastructure against dynamic cyber-attacks.展开更多
Fecal incontinence(FI),which can arise from various pathogenic mechanisms,has attracted considerable attention worldwide.Despite its importance,the reproduction of the defecatory system to study the mechanisms of FI r...Fecal incontinence(FI),which can arise from various pathogenic mechanisms,has attracted considerable attention worldwide.Despite its importance,the reproduction of the defecatory system to study the mechanisms of FI remains limited,largely because of social stigma and being considered inappropriate.Inspired by the rectum’s functionalities,we developed a soft robotic system that includes a power supply,pressure sensors,data acquisition systems,a flushing mechanism,stages,and a rectal module.Specifically,the innovative soft rectal module includes actuators inspired by sphincter muscles,both soft and rigid covers,and a soft rectum mold.The rectal mold,which was fabricated from materials that mimic human rectal tissue,was produced using a mold replication fabrication method.Both the soft and rigid components of the mold were created using three-dimensional(3D)printing technology.In addition,the sphincter muscle-inspired actuators featured double-layer pouch structures that were modeled and optimized based on multilayer perceptron methods to obtain a high contraction ratio(100%),generate high pressure(9.8 kPa),and have a short recovery time(3 s).Upon assembly,this defecation robot could smoothly expel liquid feces,perform controlled solid fecal cutting,and defecate extremely solid long feces,thus closely replicating the functions of the human rectum and anal canal.This defecation robot has the potential to facilitate human understanding of the complex defecation system and contribute to the development of improved quality-of-life devices related to defecation.展开更多
Objective This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus(SLE),identify differential metabolites,and analyze their potential as diagnostic markers for SLE and l...Objective This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus(SLE),identify differential metabolites,and analyze their potential as diagnostic markers for SLE and lupus nephritis(LN).Methods Totally,91 participants were enrolled between February 2023 and January 2024 including 58 SLE patients[30 with nonrenal-SLE and 28 with Lupus nephritis(LN)]and 33 healthy controls(HC).Ultracentrifugation was used to isolate serum exosomes,which were analyzed for their metabolic profiles using liquid chromatography–tandem mass spectrometry(LC–MS/MS).Endogenous metabolites were identified via public metabolite databases.Random Forest,Lasso regression and Support Vector Machine Recursive Feature Elimination(SVM-RFE)algorithms were employed to screen key metabolites,and a prediction model was constructed for SLE diagnosis and LN discrimination.ROC curves were constructed to determine the potential of these differential exosome-derived metabolites for the diagnosis of SLE.Furthermore,Spearman’s correlation was employed to evaluate the potential links between exosome-derived metabolites and the clinical parameters which reflect disease progression.Results A total of 586 endogenous serum exosome-derived metabolites showed differential expression,with 225 exosome-derived metabolites significantly upregulated,88 downregulated and 273 exhibiting no notable changes in the HC and SLE groups.Machine learning algorithms revealed three differential metabolites:Pro-Asn-Gln-Met-Ser,C24:1 sphingolipid,and protoporphyrin IX,which exhibited AUC values of 0.998,0.992 and 0.969 respectively,for distinguishing between the SLE and HC groups,with a combined AUC of 1.0.In distinguishing between the LN and SLE groups,the AUC values for these metabolites were 0.920,0.893 and 0.865,respectively,with a combined AUC of 0.931,demonstrating excellent diagnostic performance.Spearman correlation analysis revealed that Pro-Asn-Gln-Met-Ser and protoporphyrin IX were positively correlated with the SLE Disease Activity Index(SLEDAI)scores,urinary protein/creatinine ratio(ACR)and urinary protein levels,while C24:1 sphingolipid exhibited a negative correlation.Conclusions This study provides the first comprehensive characterization of the exosome-derived metabolites in SLE and established a promising prediction model for SLE and LN discrimination.The correlation between exosome-derived metabolites and key clinical parameters strongly indicated their potential role in SLE pathological progression.展开更多
基金supported in part by the Jiangsu Natural Science Foundation of China under Grant BK20180013in part by the Shenzhen Science and Technology Innovation Committee(STIC)under Grant JCYJ20180306174439784.
文摘The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor and offer high fault-tolerant capability for short-circuit faults.Besides,they provide motor friendly waveforms and four-quadrant operation ability.Therefore,they are suitable for high-power applications of fans,pumps,compressors and wind power generation.The purpose of this paper is to comprehensively review recent developments of key technologies on modulation and control of high-power(HP)PWM-CSC fed electric machines systems,including reduction of low-order current harmonics,suppression of inductor–capacitor(LC)resonance,mitigation of common-mode voltage(CMV)and control of modular PWM-CSC fed systems.In particular,recent work on the overlapping effects during commutation,LC resonance suppression under fault-tolerant operation and collaboration of modular PMW-CSCs are described.Both theoretical analysis and some results in simulations and experiments are presented.Finally,a brief discussion regarding the future trend of the HP CSC fed electric machines systems is presented.
基金Acknowledgements This work was supported by National Key Basic Research and Development Plan(973 Plan)of China(No.2007CB310900)National Natural Science Foundation of China(No.90612018,90715030 and 60970008).
文摘At present,there are few security models which control the communication between virtual machines(VMs).Moreover,these models are not applicable to multi-level security(MLS).In order to implement mandatory access control(MAC)and MLS in virtual machine system,this paper designs Virt-BLP model,which is based on BLP model.For the distinction between virtual machine system and non-virtualized system,we build elements and security axioms of Virt-BLP model by modifying those of BLP.Moreover,comparing with BLP,the number of state transition rules of Virt-BLP is reduced accordingly and some rules can only be enforced by trusted subject.As a result,Virt-BLP model supports MAC and partial discretionary access control(DAC),well satisfying the requirement of MLS in virtual machine system.As space is limited,the implementation of our MAC framework will be shown in a continuation.
文摘The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using conventional methods.In this paper,a new multilevel design method oriented network environment is proposed,which maps the design problem of large-scale machine system into a hypergraph with degree of linking strength(DLS)between vertices.By decomposition of hypergraph,this method can divide the complex design problem into some small and simple subproblems that can be solved concurrently in a network.
基金The work was supported in part by the EPSRC research council(No. GR/M29108/01).
文摘A hybrid machine （HM） as a typical mechatronic device, is a useful tool to generate smooth motion, and combines the motions of a large constant speed motor with a small servo motor by means of a mechnical linkage mechanism, in order to provide a powerful programmable drive system. To achieve design objectives, a control system is required. To design a better control system and analyze the performance of an HM, a dynamic model is necessary. This paper first develops a dynamic model of an HM with a five-bar mechanism using a Lagrangian formulation. Then, several important properties which are very useful in system analysis, and control system design, are presented. Based on the developed dynamic model, two control approaches, computed torque, and combined computed torque and slide mode control, are adopted to control the HM system. Simulation results demonstrate the control performance and limitations of each control approach.
文摘This paper researches the main technology of open CNC engraving machine, the DXF identification technology. Agraphic information extraction method is proposed. By this method, the graphic information in DXF file can be identified and transformed into bottom motion controller’s code. So the engraving machine can achieve trajectory tracking. Then the open CNC engraving machine system is developed with C#. At last, the method is validated on a three axes motion experiment platform. The result shows that this method can efficiently identify the graphic information including line, circle, arc etc. in DXF file and the CNC engraving machine can be controlled well.
文摘Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.
文摘In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers are required and where they shall be.Eachmachine being represented by a low-order linear model,there is some reservation of the results.In this paper,extensive transient simulationsare performed and each machine is represented by a high-order nonlinear model.Coherent groups are found.A weighted speed deviationindex (SDI) is defined to find out the most unstable machines in the system.PSSs are designed after the decisions of PSS number and sites.Transient simulations are carried out again for the closed-loop system.A system stability index (SSI) is used to evaluate the stability of theclosed-loop system.It is found that three PSSs are sufficient to ensure the stability of the nine-machine system.
基金supported by the Ministry of Industry and Information Technology,China,the Science Foundation of the Ministry of Education of China(No.21YJC630072)the Key Talent Project of the Yan Zhao Golden Platform for Talent Attraction in Hebei Province,China(No.HJYB202528).
文摘Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future,which have a great impact on energy resources and efficiencies.Due to the emerging artificial intelligence and machine learning,traditional modeling techniques in these energy systems have met challenges in still leveraging physics model and first principle-based approaches.Moreover,with the rapid development of hardware and computing techniques,new modeling approaches for energy systems have become more and more important for system design,integration,analysis,control,and management.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
文摘As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.
基金the School of Engineering and Built Environment at Anglia Ruskin University,UK,for the supportthe support of IRC-CSS and the Electrical Engineering Department,KFUPM,Saudi Arabia。
文摘We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
基金supported by the National Natural Science Foundation of China(Nos.52074246,52275390,52375394)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)the Key R&D Program of Shanxi Province(No.202102050201011).
文摘The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure casting gating system of a large thin-walled cabin casting.A high-quality dataset was established through orthogonal experiments combined with design criteria for the gating system.Spearman’s correlation analysis was used to select high-quality features.The gating system dimensions were predicted using a gated recurrent unit(GRU)recurrent neural network and an elastic network model.Using EasyCast and ProCAST casting software,a comparative analysis of the flow field,temperature field,and solidification field can be conducted to demonstrate the achievement of steady filling and top-down sequential solidification.Compared to the empirical formula method,this method eliminates trial-and-error iterations,reduces porosity,reduces casting defect volume from 11.23 cubic centimeters to 2.23 cubic centimeters,eliminates internal casting defects through the incorporation of an internally cooled iron,fulfilling the goal of intelligent gating system design.
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金supported by the National Natural Science Foundation of China (42505149,41925023,U2342223,42105069,and 91744208)the China Postdoctoral Science Foundation (2025M770303)+1 种基金the Fundamental Research Funds for the Central Universities (14380230)the Jiangsu Funding Program for Excellent Postdoctoral Talent,and Jiangsu Collaborative Innovation Center of Climate Change。
文摘Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, R_(BC)) exhibited marked regional disparities, with a significant increase in China(5.4% yr^(-1)), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO_(2)/BC). Projections under carbon neutrality reveal that R_(BC) will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The R_(BC) increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.
基金funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP23489127)。
文摘The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems.There is a lack of explainability,challenges with imbalanced attack classes,and limited consideration of practical edge–cloud deployment strategies in prior works.In the proposed study,we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations(SHAP)-based Explainable AI(XAI)to attack detection and classification in IIoT-CPS settings.It includes not only unsupervised clustering(K-Means and DBSCAN)to extract latent traffic patterns but also supervised classification based on taxonomy to classify 33 different kinds of attacks into seven high-level categories:Flood Attacks,Botnet/Mirai,Reconnaissance,Spoofing/Man-In-The-Middle(MITM),Injection Attacks,Backdoors/Exploits,and Benign.The three machine learning algorithms,Random Forest,XGBoost,and Multi-Layer Perceptron(MLP),were trained on a realworld dataset of more than 1 million network traffic records,with overall accuracy of 99.4%(RF),99.5%(XGBoost),and 99.1%(MLP).Rare types of attacks,such as injection attacks and backdoors,were examined even in the case of extreme imbalance between the classes.SHAP-based XAI was performed on every model to help gain transparency and trust in the model and identify important features that drive the classification decisions,such as inter-arrival time,TCP flags,and protocol type.A workable edge-computing implementation strategy is proposed,whereby lightweight computing is performed at the edge devices and heavy,computation-intensive analytics is performed at the cloud.This framework is highly accurate,interpretable,and has real-time application,hence a robust and scalable solution to securing IIoT-CPS infrastructure against dynamic cyber-attacks.
基金supported by Grant-in-Aid for Scientific Research on Innovative Areas from the Japan Society for the Promotion of Science(Nos.18H05473 and 23K13290).
文摘Fecal incontinence(FI),which can arise from various pathogenic mechanisms,has attracted considerable attention worldwide.Despite its importance,the reproduction of the defecatory system to study the mechanisms of FI remains limited,largely because of social stigma and being considered inappropriate.Inspired by the rectum’s functionalities,we developed a soft robotic system that includes a power supply,pressure sensors,data acquisition systems,a flushing mechanism,stages,and a rectal module.Specifically,the innovative soft rectal module includes actuators inspired by sphincter muscles,both soft and rigid covers,and a soft rectum mold.The rectal mold,which was fabricated from materials that mimic human rectal tissue,was produced using a mold replication fabrication method.Both the soft and rigid components of the mold were created using three-dimensional(3D)printing technology.In addition,the sphincter muscle-inspired actuators featured double-layer pouch structures that were modeled and optimized based on multilayer perceptron methods to obtain a high contraction ratio(100%),generate high pressure(9.8 kPa),and have a short recovery time(3 s).Upon assembly,this defecation robot could smoothly expel liquid feces,perform controlled solid fecal cutting,and defecate extremely solid long feces,thus closely replicating the functions of the human rectum and anal canal.This defecation robot has the potential to facilitate human understanding of the complex defecation system and contribute to the development of improved quality-of-life devices related to defecation.
基金funded by National Natural Science Foundation of China to Ping Yang with Grant number No.82202600by Nanjing Drum Tower Hospital to Ping Yang with Grant number No.2024-LCYJ-MS-11then to Shou-bin Zhan with Grant number No.2023-JCYJ-QP-25.
文摘Objective This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus(SLE),identify differential metabolites,and analyze their potential as diagnostic markers for SLE and lupus nephritis(LN).Methods Totally,91 participants were enrolled between February 2023 and January 2024 including 58 SLE patients[30 with nonrenal-SLE and 28 with Lupus nephritis(LN)]and 33 healthy controls(HC).Ultracentrifugation was used to isolate serum exosomes,which were analyzed for their metabolic profiles using liquid chromatography–tandem mass spectrometry(LC–MS/MS).Endogenous metabolites were identified via public metabolite databases.Random Forest,Lasso regression and Support Vector Machine Recursive Feature Elimination(SVM-RFE)algorithms were employed to screen key metabolites,and a prediction model was constructed for SLE diagnosis and LN discrimination.ROC curves were constructed to determine the potential of these differential exosome-derived metabolites for the diagnosis of SLE.Furthermore,Spearman’s correlation was employed to evaluate the potential links between exosome-derived metabolites and the clinical parameters which reflect disease progression.Results A total of 586 endogenous serum exosome-derived metabolites showed differential expression,with 225 exosome-derived metabolites significantly upregulated,88 downregulated and 273 exhibiting no notable changes in the HC and SLE groups.Machine learning algorithms revealed three differential metabolites:Pro-Asn-Gln-Met-Ser,C24:1 sphingolipid,and protoporphyrin IX,which exhibited AUC values of 0.998,0.992 and 0.969 respectively,for distinguishing between the SLE and HC groups,with a combined AUC of 1.0.In distinguishing between the LN and SLE groups,the AUC values for these metabolites were 0.920,0.893 and 0.865,respectively,with a combined AUC of 0.931,demonstrating excellent diagnostic performance.Spearman correlation analysis revealed that Pro-Asn-Gln-Met-Ser and protoporphyrin IX were positively correlated with the SLE Disease Activity Index(SLEDAI)scores,urinary protein/creatinine ratio(ACR)and urinary protein levels,while C24:1 sphingolipid exhibited a negative correlation.Conclusions This study provides the first comprehensive characterization of the exosome-derived metabolites in SLE and established a promising prediction model for SLE and LN discrimination.The correlation between exosome-derived metabolites and key clinical parameters strongly indicated their potential role in SLE pathological progression.
