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
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
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
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
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
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
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 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
BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction...BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction,though concerns regarding model interpretability,reliance on retrospective data,and variability in performance persist.AIM To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.METHODS A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019.The most frequently used ML models were deep learning(37.5%),random forests(37.5%),support vector machines(31.25%),and ensemble methods(18.75%).The dataset sizes varied from 134 to 14177 patients,with nine studies incorporating external validation.RESULTS The reported area under the curve values were 0.669–0.980 for overall survival,0.920–0.960 for cancer-specific survival,and 0.710–0.856 for disease-free survival.These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.CONCLUSION Despite challenges concerning retrospective studies and a lack of interpretability,ML models show promise;prospective trials and multidimensional data integration are recommended for improving their clinical applicability.展开更多
Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when impl...Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.展开更多
Geared-rotor systems are critical components in mechanical applications,and their performance can be severely affected by faults,such as profile errors,wear,pitting,spalling,flaking,and cracks.Profile errors in gear t...Geared-rotor systems are critical components in mechanical applications,and their performance can be severely affected by faults,such as profile errors,wear,pitting,spalling,flaking,and cracks.Profile errors in gear teeth are inevitable in manufacturing and subsequently accumulate during operations.This work aims to predict the status of gear profile deviations based on gear dynamics response using the digital model of an experimental rig setup.The digital model comprises detailed CAD models and has been validated against the expected physical behavior using commercial finite element analysis software.The different profile deviations are then modeled using gear charts,and the dynamic response is captured through simulations.The various features are then obtained by signal processing,and various ML models are then evaluated to predict the fault/no-fault condition for the gear.The best performance is achieved by an artificial neural network with a prediction accuracy of 97.5%,which concludes a strong influence on the dynamics of the gear rotor system due to profile deviations.展开更多
The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model...The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model-based boring indexes(derived from rock fragmentation mechanisms)and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters.Key performance indicators-thrust(F),torque(T),and penetration(p)-were used to calculate three model-based boring indexes(a,b,k),which quantify dynamic rock fragmentation behavior.A dataset of 359 candidate samples,reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia,China,was utilized to validate the framework.The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance,achieving high accuracy.Specifically,the mean absolute error(MAE)for rotation speed(n)was 0.10 r/min,corresponding to a mean absolute percentage error(MAPE)of 1.09%.For advance rate(v),the MAE was 3.4 mm/min,with a MAPE of 4.50%.The predicted thrust(F)and torque(T)values exhibited strong agreement with field measurements,with MAEs of 270 kN and 178 kN∙m,respectively.Field applications demonstrated a 30%reduction in parameter adjustment time compared to empirical methods.This work provides a robust solution for real-time TBM control,advancing intelligent tunneling in complex geological environments.展开更多
This paper explores the possibility of using machine learning algorithms to predict type 2 diabetes.We selected two commonly used classification models:random forest and logistic regression,modeled patients’clinical ...This paper explores the possibility of using machine learning algorithms to predict type 2 diabetes.We selected two commonly used classification models:random forest and logistic regression,modeled patients’clinical and lifestyle data,and compared their prediction performance.We found that the random forest model achieved the highest accuracy,demonstrated excellent classification results on the test set,and better distinguished between diabetic and non-diabetic patients by the confusion matrix and other evaluation metrics.The support vector machine and logistic regression perform slightly less well but achieve a high level of accuracy.The experimental results validate the effectiveness of the three machine learning algorithms,especially random forest,in the diabetes prediction task and provide useful practical experience for the intelligent prevention and control of chronic diseases.This study promotes the innovation of the diabetes prediction and management model,which is expected to alleviate the pressure on medical resources,reduce the burden of social health care,and improve the prognosis and quality of life of patients.In the future,we can consider expanding the data scale,exploring other machine learning algorithms,and integrating multimodal data to further realize the potential of artificial intelligence(AI)in the field of diabetes.展开更多
Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-value...Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.展开更多
BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is an effective intervention for managing complications of portal hypertension,particularly acute variceal bleeding(AVB).While effective in reducing portal...BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is an effective intervention for managing complications of portal hypertension,particularly acute variceal bleeding(AVB).While effective in reducing portal pressure and preventing rebleeding,TIPS is associated with a considerable risk of overt hepatic encephalopathy(OHE),a complication that significantly elevates mortality rates.AIM To develop a machine learning(ML)model to predict OHE occurrence post-TIPS in patients with AVB using a 5-year dataset.METHODS This retrospective single-center study included 218 patients with AVB who underwent TIPS.The dataset was divided into training(70%)and testing(30%)sets.Critical features were identified using embedded methods and recursive feature elimination.Three ML algorithms-random forest,extreme gradient boosting,and logistic regression-were validated via 10-fold cross-validation.SHapley Additive exPlanations analysis was employed to interpret the model’s predictions.Survival analysis was conducted using Kaplan-Meier curves and stepwise Cox regression analysis to compare overall survival(OS)between patients with and without OHE.RESULTS The median OS of the study cohort was 47.83±22.95 months.Among the models evaluated,logistic regression demonstrated the highest performance with an area under the curve(AUC)of 0.825.Key predictors identified were Child-Pugh score,age,and portal vein thrombosis.Kaplan-Meier analysis revealed that patients without OHE had a significantly longer OS(P=0.005).The 5-year survival rate was 78.4%,with an OHE incidence of 15.1%.Both actual OHE status and predicted OHE value were significant predictors in each Cox model,with model-predicted OHE achieving an AUC of 88.1 in survival prediction.CONCLUSION The ML model accurately predicts post-TIPS OHE and outperforms traditional models,supporting its use in improving outcomes in patients with AVB.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.
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.
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.
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.
文摘BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction,though concerns regarding model interpretability,reliance on retrospective data,and variability in performance persist.AIM To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.METHODS A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019.The most frequently used ML models were deep learning(37.5%),random forests(37.5%),support vector machines(31.25%),and ensemble methods(18.75%).The dataset sizes varied from 134 to 14177 patients,with nine studies incorporating external validation.RESULTS The reported area under the curve values were 0.669–0.980 for overall survival,0.920–0.960 for cancer-specific survival,and 0.710–0.856 for disease-free survival.These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.CONCLUSION Despite challenges concerning retrospective studies and a lack of interpretability,ML models show promise;prospective trials and multidimensional data integration are recommended for improving their clinical applicability.
基金funded by the McGill University Graduate Excellence Fellowship Award(00157)the Mitacs Accelerate Program(IT13369)the McGill Engineering Doctoral Award(MEDA).
文摘Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.
文摘Geared-rotor systems are critical components in mechanical applications,and their performance can be severely affected by faults,such as profile errors,wear,pitting,spalling,flaking,and cracks.Profile errors in gear teeth are inevitable in manufacturing and subsequently accumulate during operations.This work aims to predict the status of gear profile deviations based on gear dynamics response using the digital model of an experimental rig setup.The digital model comprises detailed CAD models and has been validated against the expected physical behavior using commercial finite element analysis software.The different profile deviations are then modeled using gear charts,and the dynamic response is captured through simulations.The various features are then obtained by signal processing,and various ML models are then evaluated to predict the fault/no-fault condition for the gear.The best performance is achieved by an artificial neural network with a prediction accuracy of 97.5%,which concludes a strong influence on the dynamics of the gear rotor system due to profile deviations.
基金supported by the National Key R&D Program of China(2022YFE0200400).
文摘The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model-based boring indexes(derived from rock fragmentation mechanisms)and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters.Key performance indicators-thrust(F),torque(T),and penetration(p)-were used to calculate three model-based boring indexes(a,b,k),which quantify dynamic rock fragmentation behavior.A dataset of 359 candidate samples,reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia,China,was utilized to validate the framework.The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance,achieving high accuracy.Specifically,the mean absolute error(MAE)for rotation speed(n)was 0.10 r/min,corresponding to a mean absolute percentage error(MAPE)of 1.09%.For advance rate(v),the MAE was 3.4 mm/min,with a MAPE of 4.50%.The predicted thrust(F)and torque(T)values exhibited strong agreement with field measurements,with MAEs of 270 kN and 178 kN∙m,respectively.Field applications demonstrated a 30%reduction in parameter adjustment time compared to empirical methods.This work provides a robust solution for real-time TBM control,advancing intelligent tunneling in complex geological environments.
文摘This paper explores the possibility of using machine learning algorithms to predict type 2 diabetes.We selected two commonly used classification models:random forest and logistic regression,modeled patients’clinical and lifestyle data,and compared their prediction performance.We found that the random forest model achieved the highest accuracy,demonstrated excellent classification results on the test set,and better distinguished between diabetic and non-diabetic patients by the confusion matrix and other evaluation metrics.The support vector machine and logistic regression perform slightly less well but achieve a high level of accuracy.The experimental results validate the effectiveness of the three machine learning algorithms,especially random forest,in the diabetes prediction task and provide useful practical experience for the intelligent prevention and control of chronic diseases.This study promotes the innovation of the diabetes prediction and management model,which is expected to alleviate the pressure on medical resources,reduce the burden of social health care,and improve the prognosis and quality of life of patients.In the future,we can consider expanding the data scale,exploring other machine learning algorithms,and integrating multimodal data to further realize the potential of artificial intelligence(AI)in the field of diabetes.
基金supported by the National Key Project of the Ministry of Science and Technology of China(No.2022YFC3701200)the National Natural Science Foundation of China(No.42090030).
文摘Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.
基金Natural Science Foundation of Guangdong Province,No.2024A1515013069.
文摘BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is an effective intervention for managing complications of portal hypertension,particularly acute variceal bleeding(AVB).While effective in reducing portal pressure and preventing rebleeding,TIPS is associated with a considerable risk of overt hepatic encephalopathy(OHE),a complication that significantly elevates mortality rates.AIM To develop a machine learning(ML)model to predict OHE occurrence post-TIPS in patients with AVB using a 5-year dataset.METHODS This retrospective single-center study included 218 patients with AVB who underwent TIPS.The dataset was divided into training(70%)and testing(30%)sets.Critical features were identified using embedded methods and recursive feature elimination.Three ML algorithms-random forest,extreme gradient boosting,and logistic regression-were validated via 10-fold cross-validation.SHapley Additive exPlanations analysis was employed to interpret the model’s predictions.Survival analysis was conducted using Kaplan-Meier curves and stepwise Cox regression analysis to compare overall survival(OS)between patients with and without OHE.RESULTS The median OS of the study cohort was 47.83±22.95 months.Among the models evaluated,logistic regression demonstrated the highest performance with an area under the curve(AUC)of 0.825.Key predictors identified were Child-Pugh score,age,and portal vein thrombosis.Kaplan-Meier analysis revealed that patients without OHE had a significantly longer OS(P=0.005).The 5-year survival rate was 78.4%,with an OHE incidence of 15.1%.Both actual OHE status and predicted OHE value were significant predictors in each Cox model,with model-predicted OHE achieving an AUC of 88.1 in survival prediction.CONCLUSION The ML model accurately predicts post-TIPS OHE and outperforms traditional models,supporting its use in improving outcomes in patients with AVB.
