A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed. Firstly, the system framework and a rapid modeling method of CNC machine tool...A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed. Firstly, the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software. Then, a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language（VRML） by attaining communication among VRML, JavaApplet, JavaScript and Html so as to realize the virtual operation for CNC machine tool. Moreover, the algorithm of material removed simulation based on VRML Z-map is presented. The advantages of this algorithm include less memory requirement and much higher computation. Lastly, the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.展开更多
Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a p...Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation(HILS)realization of electric machine drives using commercial real time machines.HILS implementation using digital s...This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation(HILS)realization of electric machine drives using commercial real time machines.HILS implementation using digital signal processors(DSPs)and field programmable gate array(FPGA)for electric machine drives has been investigated but those methods have drawbacks such as complexity in development and verification.Among various HILS implementation approaches,more efficient development and verification for electric machine drives can be achieved through use of commercial real time machines.As well as implementation of the HILS,accurate modeling of a control target system plays an important role.Therefore,modeling trend in electric machine drives for HILS implementation is needed to be reviewed.This paper provides a background of HILS and commercially available real time machines and characteristics of each real time machine are introduced.Also,recent trends and progress of permanent magnet synchronous machines(PMSMs)modeling are presented for providing more accurate HILS implementation approaches in this paper.展开更多
The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper ...The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.展开更多
BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are p...BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.展开更多
BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in ...BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.展开更多
Background and Objective The effectiveness of radiofrequency ablation(RFA)in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma(HCC)measuring 5 cm or less remains uncertain.Thi...Background and Objective The effectiveness of radiofrequency ablation(RFA)in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma(HCC)measuring 5 cm or less remains uncertain.This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA.Methods This study was performed using the Surveillance,Epidemiology,and End Results(SEER)database from 2004 to 2017,focusing on patients diagnosed with a solitary HCC lesion≤5 cm in size.We compared the overall survival(OS)and cancer-specific survival(CSS)rates of these patients with those of patients who received hepatectomy,radiotherapy,or chemotherapy or who were part of a blank control group.To enhance the reliability of our findings,we employed stabilized inverse probability treatment weighting(sIPTW)and stratified analyses.Additionally,we conducted a Cox regression analysis to identify prognostic factors.XGBoost models were developed to predict 1-,3-,and 5-year CSS.The XGBoost models were evaluated via receiver operating characteristic(ROC)curves,calibration plots,decision curve analysis(DCA)curves and so on.Results Regardless of whether the data were unadjusted or adjusted for the use of sIPTWs,the 5-year OS(46.7%)and CSS(58.9%)rates were greater in the RFA group than in the radiotherapy(27.1%/35.8%),chemotherapy(32.9%/43.7%),and blank control(18.6%/30.7%)groups,but these rates were lower than those in the hepatectomy group(69.4%/78.9%).Stratified analysis based on age and cirrhosis status revealed that RFA and hepatectomy yielded similar OS and CSS outcomes for patients with cirrhosis aged over 65 years.Age,race,marital status,grade,cirrhosis status,tumor size,and AFP level were selected to construct the XGBoost models based on the training cohort.The areas under the curve(AUCs)for 1,3,and 5 years in the validation cohort were 0.88,0.81,and 0.79,respectively.Calibration plots further demonstrated the consistency between the predicted and actual values in both the training and validation cohorts.Conclusion RFA can improve the survival of patients diagnosed with a solitary HCC lesion≤5 cm.In certain clinical scenarios,RFA achieves survival outcomes comparable to those of hepatectomy.The XGBoost models developed in this study performed admirably in predicting the CSS of patients with solitary HCC tumors smaller than 5 cm following RFA.展开更多
Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer type.This disease arises when cells in the breast undergo unregulate...Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer type.This disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding tissues.It is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in women.Early detection is pivotal in mitigating its mortality rate.The key to curbing its mortality lies in early detection.However,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and patients.In this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)dataset.We applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each method.The analysis showed that the random forest performs better and yields the highest result with 99.46%accuracy.After performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.展开更多
Tunnels are vital in connecting crucial transportation hubs as transportation infrastructure evolves.Variations in tunnel design standards and driving conditions across different levels directly impact driver visual p...Tunnels are vital in connecting crucial transportation hubs as transportation infrastructure evolves.Variations in tunnel design standards and driving conditions across different levels directly impact driver visual perception and traffic safety.This study employs a Gaussian hybrid clustering machine learning model to explore driver gaze patterns in highway tunnels and exits.By utilizing contour coefficients,the optimal number of classification clusters is determined.Analysis of driver visual behavior across tunnel levels,focusing on gaze point distribution,gaze duration,and sweep speed,was conducted.Findings indicate freeway tunnel exits exhibit three distinct fixation point categories aligning with Gaussian distribution,while highway tunnels display four such characteristics.Notably,in both tunnel types,65%of driver gaze is concentrated on the near area ahead of their lane.Differences emerge in highway tunnels due to oncoming traffic,leading to 13.47%more fixation points and 0.9%increased fixation time in the right lane compared to regular highway tunnel conditions.Moreover,scanning speeds predominantly fall within the 0.25-0.3 range,accounting for 75.47%and 31.14%of the total sweep speed.展开更多
Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes...Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.展开更多
The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for ident...The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.展开更多
Conversion of dryland to paddy fields(CDPF)is an effective way to transition from rain-fed to irrigated agricul ture,helping to mitigate the effects of climate change on agriculture and increase yields to meet growing...Conversion of dryland to paddy fields(CDPF)is an effective way to transition from rain-fed to irrigated agricul ture,helping to mitigate the effects of climate change on agriculture and increase yields to meet growing food demand.However,the suitability of CDPF is spatio-temporally dynamic but has often been neglected in previous studies.To fill this knowledge gap,this research developed a novel method for quantifying the suitability of CDPF,based on the MaxEnt model for application in Northeast China.We explored the spatiotemporal characteristics of the suitability of CDPF under the baseline scenario(2010-2020),and future projections(2030-2090)coupled with climate change and socioeconomic development scenarios(SSP126,SSP245,and SSP585),and revealed the driving factors behind it.Based on this,we identified potential priority areas for future CDPF implementation.The results show that the suitability of CDPF projects implemented in the past ten years is relatively high.Com pared with the baseline scenario,the suitability of CDPF under the future scenarios will decline overall,with the lightest decrease in the RCP585 and the most severe decrease in the RCP245.The key drivers affecting the suitability of CDPF are elevation,slope,population count,total nitrogen,soil organic carbon content,and precip itation seasonality.The potential priority areas for the future CDPF range from 6,284.61 km^(2)to 37,006.02 km^(2).These findings demonstrate the challenges of CDPF in adapting to climate change and food security,and provide insights for food-producing regions around the world facing climate crises.展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.展开更多
The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning mode...The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.展开更多
In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-vio...In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.展开更多
This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ...This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.展开更多
Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measur...Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.展开更多
Fine-grained weather forecasting data,i.e.,the grid data with high-resolution,have attracted increasing attention in recent years,especially for some specific applications such as the Winter Olympic Games.Although Eur...Fine-grained weather forecasting data,i.e.,the grid data with high-resolution,have attracted increasing attention in recent years,especially for some specific applications such as the Winter Olympic Games.Although European Centre for Medium-Range Weather Forecasts(ECMWF)provides grid prediction up to 240 hours,the coarse data are unable to meet high requirements of these major events.In this paper,we propose a method,called model residual machine learning(MRML),to generate grid prediction with high-resolution based on high-precision stations forecasting.MRML applies model output machine learning(MOML)for stations forecasting.Subsequently,MRML utilizes these forecasts to improve the quality of the grid data by fitting a machine learning(ML)model to the residuals.We demonstrate that MRML achieves high capability at diverse meteorological elements,specifically,temperature,relative humidity,and wind speed.In addition,MRML could be easily extended to other post-processing methods by invoking different techniques.In our experiments,MRML outperforms the traditional downscaling methods such as piecewise linear interpolation(PLI)on the testing data.展开更多
基金Selected from Proceedings of the 7th International Conference on Frontiers of Design and Manufacturing (ICFDM'2006)This project is supported by National Natural Science Foundation of China (No.50775047)Scientific and Technological Foundation of Guangdong Province,China(No.2004B10201032).
文摘A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed. Firstly, the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software. Then, a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language（VRML） by attaining communication among VRML, JavaApplet, JavaScript and Html so as to realize the virtual operation for CNC machine tool. Moreover, the algorithm of material removed simulation based on VRML Z-map is presented. The advantages of this algorithm include less memory requirement and much higher computation. Lastly, the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.
文摘Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金supported in part by the National Research Foundation of Korea(NRF)grant funded by Korea government(No.2020R1C1C1013260)in part by INHA UNIVERSITY Research Grant.
文摘This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation(HILS)realization of electric machine drives using commercial real time machines.HILS implementation using digital signal processors(DSPs)and field programmable gate array(FPGA)for electric machine drives has been investigated but those methods have drawbacks such as complexity in development and verification.Among various HILS implementation approaches,more efficient development and verification for electric machine drives can be achieved through use of commercial real time machines.As well as implementation of the HILS,accurate modeling of a control target system plays an important role.Therefore,modeling trend in electric machine drives for HILS implementation is needed to be reviewed.This paper provides a background of HILS and commercially available real time machines and characteristics of each real time machine are introduced.Also,recent trends and progress of permanent magnet synchronous machines(PMSMs)modeling are presented for providing more accurate HILS implementation approaches in this paper.
文摘The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.
文摘BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.
基金This study has been reviewed and approved by the Clinical Research Ethics Committee of Wenzhou Central Hospital and the First Hospital Affiliated to Wenzhou Medical University,No.KY2024-R016.
文摘BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.
文摘Background and Objective The effectiveness of radiofrequency ablation(RFA)in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma(HCC)measuring 5 cm or less remains uncertain.This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA.Methods This study was performed using the Surveillance,Epidemiology,and End Results(SEER)database from 2004 to 2017,focusing on patients diagnosed with a solitary HCC lesion≤5 cm in size.We compared the overall survival(OS)and cancer-specific survival(CSS)rates of these patients with those of patients who received hepatectomy,radiotherapy,or chemotherapy or who were part of a blank control group.To enhance the reliability of our findings,we employed stabilized inverse probability treatment weighting(sIPTW)and stratified analyses.Additionally,we conducted a Cox regression analysis to identify prognostic factors.XGBoost models were developed to predict 1-,3-,and 5-year CSS.The XGBoost models were evaluated via receiver operating characteristic(ROC)curves,calibration plots,decision curve analysis(DCA)curves and so on.Results Regardless of whether the data were unadjusted or adjusted for the use of sIPTWs,the 5-year OS(46.7%)and CSS(58.9%)rates were greater in the RFA group than in the radiotherapy(27.1%/35.8%),chemotherapy(32.9%/43.7%),and blank control(18.6%/30.7%)groups,but these rates were lower than those in the hepatectomy group(69.4%/78.9%).Stratified analysis based on age and cirrhosis status revealed that RFA and hepatectomy yielded similar OS and CSS outcomes for patients with cirrhosis aged over 65 years.Age,race,marital status,grade,cirrhosis status,tumor size,and AFP level were selected to construct the XGBoost models based on the training cohort.The areas under the curve(AUCs)for 1,3,and 5 years in the validation cohort were 0.88,0.81,and 0.79,respectively.Calibration plots further demonstrated the consistency between the predicted and actual values in both the training and validation cohorts.Conclusion RFA can improve the survival of patients diagnosed with a solitary HCC lesion≤5 cm.In certain clinical scenarios,RFA achieves survival outcomes comparable to those of hepatectomy.The XGBoost models developed in this study performed admirably in predicting the CSS of patients with solitary HCC tumors smaller than 5 cm following RFA.
基金supported by the Researchers Supporting Project(RSPD2024R846),King Saud University,Riyadh,Saudi Arabia.
文摘Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer type.This disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding tissues.It is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in women.Early detection is pivotal in mitigating its mortality rate.The key to curbing its mortality lies in early detection.However,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and patients.In this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)dataset.We applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each method.The analysis showed that the random forest performs better and yields the highest result with 99.46%accuracy.After performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.
基金supported by the National Natural Science Foundation of China(52302437)the Cangzhou Science and Technology Plan Project(213101011)+1 种基金the Science and Technology Program Projects of Shandong Provincial Department of Transportation(2024B28)the Doctoral Scientific Research Start-up Foundation of Shandong University of Technology(422049).
文摘Tunnels are vital in connecting crucial transportation hubs as transportation infrastructure evolves.Variations in tunnel design standards and driving conditions across different levels directly impact driver visual perception and traffic safety.This study employs a Gaussian hybrid clustering machine learning model to explore driver gaze patterns in highway tunnels and exits.By utilizing contour coefficients,the optimal number of classification clusters is determined.Analysis of driver visual behavior across tunnel levels,focusing on gaze point distribution,gaze duration,and sweep speed,was conducted.Findings indicate freeway tunnel exits exhibit three distinct fixation point categories aligning with Gaussian distribution,while highway tunnels display four such characteristics.Notably,in both tunnel types,65%of driver gaze is concentrated on the near area ahead of their lane.Differences emerge in highway tunnels due to oncoming traffic,leading to 13.47%more fixation points and 0.9%increased fixation time in the right lane compared to regular highway tunnel conditions.Moreover,scanning speeds predominantly fall within the 0.25-0.3 range,accounting for 75.47%and 31.14%of the total sweep speed.
文摘Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.
文摘The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.
基金supported by the Ministry of Education of Human-ities and Social Science project,China(Grant No.21YJA630121)the National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2023YFD1500103)+2 种基金the Tsinghua Rural Studies PhD Scholarship(Grant No.202323)2023 Gradu-ate Innovation Fund Project of China University of Geosciences,Beijing(Grant No.ZD2023YC043)National Social Science Fund of China(Grants No.19ZDA096 and 20&ZD090)。
文摘Conversion of dryland to paddy fields(CDPF)is an effective way to transition from rain-fed to irrigated agricul ture,helping to mitigate the effects of climate change on agriculture and increase yields to meet growing food demand.However,the suitability of CDPF is spatio-temporally dynamic but has often been neglected in previous studies.To fill this knowledge gap,this research developed a novel method for quantifying the suitability of CDPF,based on the MaxEnt model for application in Northeast China.We explored the spatiotemporal characteristics of the suitability of CDPF under the baseline scenario(2010-2020),and future projections(2030-2090)coupled with climate change and socioeconomic development scenarios(SSP126,SSP245,and SSP585),and revealed the driving factors behind it.Based on this,we identified potential priority areas for future CDPF implementation.The results show that the suitability of CDPF projects implemented in the past ten years is relatively high.Com pared with the baseline scenario,the suitability of CDPF under the future scenarios will decline overall,with the lightest decrease in the RCP585 and the most severe decrease in the RCP245.The key drivers affecting the suitability of CDPF are elevation,slope,population count,total nitrogen,soil organic carbon content,and precip itation seasonality.The potential priority areas for the future CDPF range from 6,284.61 km^(2)to 37,006.02 km^(2).These findings demonstrate the challenges of CDPF in adapting to climate change and food security,and provide insights for food-producing regions around the world facing climate crises.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.
基金funded by the National Natural Science Foundation of China (41807285)。
文摘The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
基金supported by grants from the National Natural Science Foundation of China(31771243)the Fok Ying Tong Education Foundation(141113)to Aiguo Chen.
文摘In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.
基金Supported by the National Natural Science Foundation of China(21076179)the National Basic Research Program of China(2012CB720500)
文摘This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
基金This project is supported by National Natural Science Foundation of China(No.50375153).
文摘Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.
基金Project supported by the National Natural Science Foundation of China(Nos.12101072 and 11421101)the National Key Research and Development Program of China(No.2018YFF0300104)+1 种基金the Beijing Municipal Science and Technology Project(No.Z201100005820002)the Open Research Fund of Shenzhen Research Institute of Big Data(No.2019ORF01001)。
文摘Fine-grained weather forecasting data,i.e.,the grid data with high-resolution,have attracted increasing attention in recent years,especially for some specific applications such as the Winter Olympic Games.Although European Centre for Medium-Range Weather Forecasts(ECMWF)provides grid prediction up to 240 hours,the coarse data are unable to meet high requirements of these major events.In this paper,we propose a method,called model residual machine learning(MRML),to generate grid prediction with high-resolution based on high-precision stations forecasting.MRML applies model output machine learning(MOML)for stations forecasting.Subsequently,MRML utilizes these forecasts to improve the quality of the grid data by fitting a machine learning(ML)model to the residuals.We demonstrate that MRML achieves high capability at diverse meteorological elements,specifically,temperature,relative humidity,and wind speed.In addition,MRML could be easily extended to other post-processing methods by invoking different techniques.In our experiments,MRML outperforms the traditional downscaling methods such as piecewise linear interpolation(PLI)on the testing data.
