This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model...This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.展开更多
Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are explorin...Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-...Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.展开更多
Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert p...Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.展开更多
Medical image processing technology plays an indispensable role in the field of modern medicine.By processing and analyzing medical images,it provides doctors with more comprehensive and accurate medical information,t...Medical image processing technology plays an indispensable role in the field of modern medicine.By processing and analyzing medical images,it provides doctors with more comprehensive and accurate medical information,thereby effectively aiding them in generating higher-quality treatment plans.In recent years,with the rapid development of deep learning technology,medical image processing techniques has been powered by providing more accurate information for diagnosis of disease.展开更多
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor...The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.展开更多
Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradi...Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.展开更多
This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle(UAV)imagery by integrating the Visible Atmospherically Resistant Index(VARI)with deep learning models.The primary chall...This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle(UAV)imagery by integrating the Visible Atmospherically Resistant Index(VARI)with deep learning models.The primary challenge addressed is the detection of bananas interplanted with betel nuts,a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap.The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector(SSD),You Only Look Once version 3(YOLOv3),and Faster Region-Based Convolutional Neural Network(Faster RCNN)—using Red,Green,Blue(RGB)and VARI images for banana detection.Results show that VARI significantly improves detection accuracy,with YOLOv3 achieving the best performance,achieving a precision of 73.77%,recall of 100%,and reduced training time by 95 seconds.Additionally,the average Intersection over Union(IoU)increased by 4%–25%across models with VARI-enhanced images.This study confirms that incorporating VARI improves the performance of deep learning models,offering a promising solution for precise crop detection in complex agricultural environments.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For ...The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).展开更多
The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and u...The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.展开更多
Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this ...The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).展开更多
In recent years,machine learning(ML)techniques have demonstrated a strong ability to solve highly complex and non-linear problems by analyzing large datasets and learning their intrinsic patterns and relationships.Par...In recent years,machine learning(ML)techniques have demonstrated a strong ability to solve highly complex and non-linear problems by analyzing large datasets and learning their intrinsic patterns and relationships.Particularly in chemical engineering and materials science,ML can be used to discover microstructural composition,optimize chemical processes,and create novel synthetic pathways.Electrochemical processes offer the advantages of precise process control,environmental friendliness,high energy conversion efficiency and low cost.This review article provides the first systematic summary of ML in the application of electrochemical oxidation,including pollutant removal,battery remediation,substance synthesis and material characterization prediction.Hot trends at the intersection of ML and electrochemical oxidation were analyzed through bibliometrics.Common ML models were outlined.The role of ML in improving removal efficiency,optimizing experimental conditions,aiding battery diagnosis and predictive maintenance,and revealing material characterization was highlighted.In addition,current issues and future perspectives were presented in relation to the strengths and weaknesses of ML algorithms applied to electrochemical oxidation.In order to further support the sustainable growth of electrochemistry from basic research to useful applications,this review attempts to make it easier to integrate ML into electrochemical oxidation.展开更多
This study employed convolutional neural networks(CNNs)for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces.The image dataset covers 40 distinc...This study employed convolutional neural networks(CNNs)for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces.The image dataset covers 40 distinct rock mineral-types.Three CNN architectures(Simple model,SqueezeNet,and Xception)were evaluated to compare their performance and feature extraction capabilities.Gradient-weighted Class Activation Mapping(Grad-CAM)was employed to visualize the features influencing model predictions,providing insights into how each model distinguishes between mineral classes.Key discriminative attributes included texture,grain size,pattern,and color variations.Texture and grain boundaries were identified as the most critical features,as they were strongly activated regions by the best model.Patterns such as banding and chromatic contrasts further enhanced classification accuracy.Performance analysis revealed that the Simple model had limited ability to isolate fine-grained details,producing broad and less specific activations(0.84 test accuracy).SqueezeNet demonstrated improved localization of discriminative features but occasionally missed finer textural details(0.95 test accuracy).The Xception model outperformed the others,achieving the highest classification accuracy(0.98 test accuracy)by exhibiting precise and tightly focused activations,capturing intricate textures and subtle chromatic variations.Its superior performance can be attributed to its deep architecture and efficient depth-wise separable convolutions,which enabled hierarchical and detailed feature extraction.Results underscores the importance of texture,pattern,and chromatic features in accurate mineral classification and highlights the suitability of deep,efficient architectures like Xception for such tasks.These findings demonstrate the potential of CNNs in geoscience research,offering a framework for automated mineral identification in industrial and scientific applications.展开更多
BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective pr...BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.展开更多
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly pr...The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(Qr)).A Gaussian process regression model in machine learning was developed to predict V_(RA),and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fo)to accurately predict V_(RA) in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very effcient as it found the"optimum"Qr with the maximum V_(RA) using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts V_(RA) more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.展开更多
基金2024 Anqing Normal University University-Level Key Project(ZK2024062D)。
文摘This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.
文摘Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
基金supported by the Gas Hydrate R&D Organization and the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-010)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1C1C1004460)Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korean government(MOTIE)(20214000000500,Training Program of CCUS for Green Growth).
文摘Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.
基金supported by the Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan within the framework of grant AP23489899“Applying Deep Learning and Neuroimaging Methods for Brain Stroke Diagnosis”.
文摘Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.
文摘Medical image processing technology plays an indispensable role in the field of modern medicine.By processing and analyzing medical images,it provides doctors with more comprehensive and accurate medical information,thereby effectively aiding them in generating higher-quality treatment plans.In recent years,with the rapid development of deep learning technology,medical image processing techniques has been powered by providing more accurate information for diagnosis of disease.
文摘The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftungfunding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。
文摘Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.
文摘This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle(UAV)imagery by integrating the Visible Atmospherically Resistant Index(VARI)with deep learning models.The primary challenge addressed is the detection of bananas interplanted with betel nuts,a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap.The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector(SSD),You Only Look Once version 3(YOLOv3),and Faster Region-Based Convolutional Neural Network(Faster RCNN)—using Red,Green,Blue(RGB)and VARI images for banana detection.Results show that VARI significantly improves detection accuracy,with YOLOv3 achieving the best performance,achieving a precision of 73.77%,recall of 100%,and reduced training time by 95 seconds.Additionally,the average Intersection over Union(IoU)increased by 4%–25%across models with VARI-enhanced images.This study confirms that incorporating VARI improves the performance of deep learning models,offering a promising solution for precise crop detection in complex agricultural environments.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金The support provided by the Natural Science Foundation of Hubei Province(Grant No.2021CFA081)the National Natural Science Foundation of China(Grant No.42277160)the fellowship of China Postdoctoral Science Foundation(Grant No.2022TQ0241)is gratefully acknowledged.
文摘The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).
基金supported by the National Natural Science Foundation of China(22408227,22238005)the Postdoctoral Research Foundation of China(GZC20231576).
文摘The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
基金support from the National Natural Science Foundation of China(22108052).
文摘The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).
基金funding from the National Natural Science Foundation of China(Nos.22122606,22076142,62276190)National Key Basic Research Program of China(No.2017YFA0403402)+2 种基金National Natural Science Foundation of China(No.U1932119)the Science&Technology Commission of Shanghai Municipality(No.14DZ2261100)the Fundamental Research Funds for the Central Universities。
文摘In recent years,machine learning(ML)techniques have demonstrated a strong ability to solve highly complex and non-linear problems by analyzing large datasets and learning their intrinsic patterns and relationships.Particularly in chemical engineering and materials science,ML can be used to discover microstructural composition,optimize chemical processes,and create novel synthetic pathways.Electrochemical processes offer the advantages of precise process control,environmental friendliness,high energy conversion efficiency and low cost.This review article provides the first systematic summary of ML in the application of electrochemical oxidation,including pollutant removal,battery remediation,substance synthesis and material characterization prediction.Hot trends at the intersection of ML and electrochemical oxidation were analyzed through bibliometrics.Common ML models were outlined.The role of ML in improving removal efficiency,optimizing experimental conditions,aiding battery diagnosis and predictive maintenance,and revealing material characterization was highlighted.In addition,current issues and future perspectives were presented in relation to the strengths and weaknesses of ML algorithms applied to electrochemical oxidation.In order to further support the sustainable growth of electrochemistry from basic research to useful applications,this review attempts to make it easier to integrate ML into electrochemical oxidation.
基金support provided by Imam Abdulrah-man Bin Faisal University,Dammam,KSA,in carrying out this research.
文摘This study employed convolutional neural networks(CNNs)for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces.The image dataset covers 40 distinct rock mineral-types.Three CNN architectures(Simple model,SqueezeNet,and Xception)were evaluated to compare their performance and feature extraction capabilities.Gradient-weighted Class Activation Mapping(Grad-CAM)was employed to visualize the features influencing model predictions,providing insights into how each model distinguishes between mineral classes.Key discriminative attributes included texture,grain size,pattern,and color variations.Texture and grain boundaries were identified as the most critical features,as they were strongly activated regions by the best model.Patterns such as banding and chromatic contrasts further enhanced classification accuracy.Performance analysis revealed that the Simple model had limited ability to isolate fine-grained details,producing broad and less specific activations(0.84 test accuracy).SqueezeNet demonstrated improved localization of discriminative features but occasionally missed finer textural details(0.95 test accuracy).The Xception model outperformed the others,achieving the highest classification accuracy(0.98 test accuracy)by exhibiting precise and tightly focused activations,capturing intricate textures and subtle chromatic variations.Its superior performance can be attributed to its deep architecture and efficient depth-wise separable convolutions,which enabled hierarchical and detailed feature extraction.Results underscores the importance of texture,pattern,and chromatic features in accurate mineral classification and highlights the suitability of deep,efficient architectures like Xception for such tasks.These findings demonstrate the potential of CNNs in geoscience research,offering a framework for automated mineral identification in industrial and scientific applications.
基金Supported by Science and Technology Support Program of Qiandongnan Prefecture,No.Qiandongnan Sci-Tech Support[2021]12Guizhou Province High-Level Innovative Talent Training Program,No.Qiannan Thousand Talents[2022]201701.
文摘BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
基金The authors acknowledge financial support from the National Natural Science Foundation of China(Grant Nos.51771114 and 51371117).
文摘The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(Qr)).A Gaussian process regression model in machine learning was developed to predict V_(RA),and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fo)to accurately predict V_(RA) in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very effcient as it found the"optimum"Qr with the maximum V_(RA) using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts V_(RA) more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.
