It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This stu...It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,su...Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite cons...Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.展开更多
To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
Objective:To understand the current awareness and willingness to learn about the use of out-of-hospital automated external defibrillators(AEDs)in Deyang City,providing a basis for improving the success rate of rescue ...Objective:To understand the current awareness and willingness to learn about the use of out-of-hospital automated external defibrillators(AEDs)in Deyang City,providing a basis for improving the success rate of rescue operations.Methods:A questionnaire survey was conducted among residents in Deyang City from January 2025 to October 2025,covering residents’basic information,awareness of AED-related knowledge,and attitudes towards AED usage.Results:A total of 1,886 questionnaires were collected,with 1,823 valid questionnaires,yielding an effective rate of 96.66%.Among the 1,823 respondents,692(37.96%)had received cardiopulmonary resuscitation(CPR)-related learning or training,619(33.96%)could accurately describe the name of an AED,417(22.87%)could clearly describe the function of an AED,and 308(16.89%)could accurately describe how to use an AED.Among them,1,549(84.97%)were willing to provide assistance to patients experiencing cardiac arrest;1,731(94.95%)were willing to provide assistance under the premise of knowing how to use an AED;and 1,750(95.99%)were willing to learn about AED-related knowledge.Among the 91 individuals unwilling to provide rescue,75 responded with reasons.Among them,36 cases(48.00%)were reluctant to rescue due to a lack of relevant first aid knowledge,32 cases(42.67%)expressed concerns about exacerbating the patient’s condition due to improper operation,and 4 cases(5.33%)were unwilling to interact with strangers due to personal reasons.Conclusion:Currently,residents in Deyang City have limited knowledge about AEDs but demonstrate a strong willingness to learn and apply them.Measures need to be taken to enhance their understanding and application of AEDs.展开更多
We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)with...We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)within 10 min.Unlike conventional methods,sTED minimizes cell loss and enhances reproducibility,achieving>90%cell viability in mouse tissues and>60%in human tumors,with 1.5×10^(4)-2.5×10^(4)cells/mg yield from mouse liver.Tailored for biopsies and ultrasmall samples,sTED addresses critical standardization challenges in organoid-based research.展开更多
Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhyt...Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhythm of 100-120 times/min for CC.[1,2]Interruptions of the CC must be as short as possible and are related to ventilation,defibrillation and turnover of the rescuers.Most of the automated external defibrillators(AEDs)require interruptions of the CC to perform rhythm analysis.Among the numerous marketed models of AEDs,some provide real-time feedback about the quality of the CC.展开更多
Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape th...Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape the future through intelligent automation.This is a key milestone on the way to achieving Rieter’s vision 2027-the fully automated spinning mill.展开更多
The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and cen...The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and centrifugal honey separation,expose beekeepers to potential bee stings and frequently compromise honeycomb integrity.To address these limitations,we designed an automated honey-harvesting robot capable of autonomous frame extraction and beeswax removal.The robot mainly consists of a mobile mechanism equipped with image recognition for beehive localization,a magnetic adsorption-based beehive frame handling device(60.8 N maximum suction)coupled with a cross-slide mechanism for precise frame manipulation,and a thermal beeswax layer-melting apparatus,with optimal melting parameters(15 m/s airflow at 90℃ for 30 seconds)determined through rigorous thermal flow simulations utilizing FLUENT/Mechanical software.Field experiments demonstrated beehive frames handling success rate exceeding 85%,beeswax layer removal efficacy over 80% and damage of honeycombs below 30%.The experiment results validate the robot's operational reliability and its capacity to automate critical harvesting procedures.This study significantly reduces the labor intensity for beekeepers,effectively eliminates the risk of direct human-bee contact and improves the removal of beeswax layer,thereby catalyzing the modernization of the beekeeping industry.展开更多
The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.展开更多
BACKGROUND Automated insulin delivery(AID)systems have demonstrated benefits in managing patients with type 2 diabetes(T2D),but data are still limited.Moreover,the efficacy and safety of the AID systems in these patie...BACKGROUND Automated insulin delivery(AID)systems have demonstrated benefits in managing patients with type 2 diabetes(T2D),but data are still limited.Moreover,the efficacy and safety of the AID systems in these patients have been inadequately explored by systematic reviews and meta-analyses.AIM To provide a comprehensive understanding of the optimal use of AID in managing insulin-treated outpatients with T2D.METHODS A systematic search of multiple databases and registries,including MEDLINE,Scopus,Web of Science,Cochrane Library,and ClinicalTrials.gov,was conducted from inception to May 15,2025,to identify studies on AID use for outpatients with T2D.The co-primary outcomes were the change in glycated hemoglobin(HbA1c)and continuous glucose monitoring(CGM)metrics.Statistical analyses were conducted using Review Manager Web software with random-effects models and the inverse variance statistical method.The results were presented as mean differences(MDs)or risk ratios(RRs)with 95%CI.RESULTS A total of 15 studies with 28985 participants were identified,including 6 randomized trials(n=748;3 crossover and 3 parallel-group trials)and 9 single-arm studies.All included randomized trials raised some concerns,and the single-arm studies had serious risks of overall bias.Meta-analysis of randomized trials showed that AID is more effective than the control group in lowering HbA1c(MD:-0.89%,95%CI:-1.32 to-0.46,P<0.0001,I2=82%).Compared to control interventions,AID use was linked to a higher percentage of time in range(MD:19.25%,95%CI:11.43-27.06,P<0.00001,I2=74%)and a lower percentage of time above range>10 mmol/L(MD:-19.48%,95%CI:-27.14 to-11.82,P<0.00001,I2=73%);however,time below range remained similar between the two groups.The mean sensor glucose level was lower in the AID group;however,the coefficient of variation of glucose was the same in both groups.AID use also led to a reduction in insulin dose,but this is not a consistent finding across all study designs.The risks of serious adverse events(AEs)and severe hypoglycemia were similar in both groups;however,AID use raised the risk of device deficiency.Single-arm studies with participants using AID systems also demonstrated reductions in HbA1c(ranging from 0.7%to 2.07%)and improvements in CGM metrics,along with acceptable safety data.CONCLUSION Based on short-term study data,the use of AID systems in outpatients with T2D appears to improve glycemic outcomes and CGM metrics,with no significant AEs.Larger and longer-term randomized controlled trials involving diverse populations,along with a cost-benefit analysis,are needed to guide more informed clinical practice decisions.展开更多
To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal draw...To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal drawing and explores automation and intelligent equipment solutions within the framework of the group coal drawing method.Numerical simulations were performed to investigate the impact of the Number of Drawing Openings(NDO)and rounds on top-coal recovery,coal draw-ing efficiency,and Top Coal Loss(TCL)mechanism.Subsequently,considering the recovery and coal drawing efficiency and by introducing the instantaneous gangue content and cumulative gangue content in simulations,the top-coal recovery,gangue content,and coal loss distribution when considering excessive coal drawing were analyzed.This established a foun-dation for determining the optimal NDO and shutdown timing.Finally,the key technical principle and automated control of a shock vibration and hyperspectral fusion recognition device were detailed,and an intelligent coal drawing control method based on this technology was developed.This technology enabled the precise control of the instantaneous gangue content(35%)during coal drawing.The top-coal recovery at the Tashan Mine 8222 working face increased by 14.78%,and the gangue content was controlled at~9%,consistent with the numerical simulation results.Thus,the reliability of the numerical simulation results was confirmed to a certain extent.Meanwhile,the single-group drawing method significantly enhanced the production capacity of the 8222 working face,achieving an annual output of 15 million tons.展开更多
Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-value...Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.展开更多
BACKGROUND End-stage kidney disease is a growing global health burden with many patients requiring urgent kidney replacement therapy.Urgent-start peritoneal dialysis(PD)has emerged as a viable alternative to hemodialy...BACKGROUND End-stage kidney disease is a growing global health burden with many patients requiring urgent kidney replacement therapy.Urgent-start peritoneal dialysis(PD)has emerged as a viable alternative to hemodialysis particularly in resourcelimited settings.However,concerns remain regarding catheter-related complications associated with early initiation of PD.Automated PD(APD)offers enhanced flexibility and fluid management,but evidence regarding its safety and outcomes in urgent-start scenarios with shortened break-in periods is limited.AIM To evaluate the clinical outcomes and biochemical changes associated with urgent-start APD with a shortened break-in period.METHODS This was a single center,observational study that included 62 patients with endstage kidney disease who required urgent-start dialysis,underwent PD catheter placement,and received APD.Patients were stratified based on catheter opening time(<12 hours vs>12 hours).Catheter-related complications,biochemical parameters,and dialysis efficacy were analyzed.RESULTS The median catheter opening time was 11 h(interquartile range:8-14 hours).No significant differences in catheterrelated complications were observed between groups(P>0.05).Catheter dysfunction,migration,leakage,and replacement occurred in 14.5%,9.7%,12.9%,and 11.3%of patients,respectively.APD led to significant reductions in serum creatinine,blood urea nitrogen,urea,phosphorus,and potassium(P<0.05),alongside correction of metabolic acidosis.No cases of peritonitis or hemoperitoneum were observed.CONCLUSION Urgent-start APD with shortened break-in appears safe with low complication rates and improved biochemical outcomes.展开更多
Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transport...Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transportation capabilities,allowing them tomove in the opposite directionwithout turning around,which helps reduce transportation time.This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals.A bi-level mixed integer programming(MIP)model is proposed,taking into account task assignment,bidirectional conflict-free routing,and battery swapping.The upper model focuses on container task assignment and AGV battery swapping planning,while the lower model ensures conflict-free movement of AGVs.A double-threshold battery swapping strategy is introduced,allowing AGVs to utilize waiting time for loading for battery swapping.An improved differential evolution variable neighborhood search(IDE-VNS)algorithm is developed to solve the bi-level MIP model,aiming to minimize the completion time of all jobs.Experimental results demonstrate that compared to the differential evolution(DE)algorithm and the genetic algorithm(GA),the IDEVNS algorithmreduces fitness values by 44.49% and 45.22%,though it does increase computation time by 56.28% and 62.03%,respectively.Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small.As the container scale increases,the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation.The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.展开更多
The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manua...The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.展开更多
Desulfurization of coke oven gas(COG)is a critical step for achieving green and sustainable development in the coking industry.Ammonium binuclear cobalt phthalocyanine sulfonate(PDS),serving as the core catalyst in th...Desulfurization of coke oven gas(COG)is a critical step for achieving green and sustainable development in the coking industry.Ammonium binuclear cobalt phthalocyanine sulfonate(PDS),serving as the core catalyst in the Hydroquinone-PDSFerrous sulfate(HPF)desulfurization process,requires precise concentration monitoring for process optimization.To address the limitations of traditional detection methods,including insufficient sensitivity,cumbersome manual operations,and weak anti-interference capability,this study developed a fully automated analytical system and method based on spectrophotometry,thereby filling a technological gap in automated PDS detection.Systematic performance validation demonstrated that the autoanalyzer exhibits excellent sensitivity and a wide linear range,with results consistent with industrial standard methods.Leveraging a programmable logic controller(PLC),the system achieves end-to-end automation from sample pretreatment to data feedback,overcoming limitations inherent in manual operations such as delayed sample processing and human-induced errors,which significantly enhances the sensitivity and timeliness of PDS concentration monitoring in complex industrial matrices.The proposed method offers triple benefits of environmental friendliness,cost-effectiveness,and social value,providing key technical support for the coking industry.展开更多
基金supported by the DH2025-TN07-07 project conducted at the Thai Nguyen University of Information and Communication Technology,Thai Nguyen,Vietnam,with additional support from the AI in Software Engineering Lab.
文摘It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
基金the National Key Research and Development Program of China(Grant No.2023YFC3009400).
文摘Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
文摘Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
文摘Objective:To understand the current awareness and willingness to learn about the use of out-of-hospital automated external defibrillators(AEDs)in Deyang City,providing a basis for improving the success rate of rescue operations.Methods:A questionnaire survey was conducted among residents in Deyang City from January 2025 to October 2025,covering residents’basic information,awareness of AED-related knowledge,and attitudes towards AED usage.Results:A total of 1,886 questionnaires were collected,with 1,823 valid questionnaires,yielding an effective rate of 96.66%.Among the 1,823 respondents,692(37.96%)had received cardiopulmonary resuscitation(CPR)-related learning or training,619(33.96%)could accurately describe the name of an AED,417(22.87%)could clearly describe the function of an AED,and 308(16.89%)could accurately describe how to use an AED.Among them,1,549(84.97%)were willing to provide assistance to patients experiencing cardiac arrest;1,731(94.95%)were willing to provide assistance under the premise of knowing how to use an AED;and 1,750(95.99%)were willing to learn about AED-related knowledge.Among the 91 individuals unwilling to provide rescue,75 responded with reasons.Among them,36 cases(48.00%)were reluctant to rescue due to a lack of relevant first aid knowledge,32 cases(42.67%)expressed concerns about exacerbating the patient’s condition due to improper operation,and 4 cases(5.33%)were unwilling to interact with strangers due to personal reasons.Conclusion:Currently,residents in Deyang City have limited knowledge about AEDs but demonstrate a strong willingness to learn and apply them.Measures need to be taken to enhance their understanding and application of AEDs.
基金supported by the National Natural Science Foundation of China(Nos.32371470 and 82341019)the Department of Science and Technology of Guangdong Province(No.2023B0909020003).
文摘We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)within 10 min.Unlike conventional methods,sTED minimizes cell loss and enhances reproducibility,achieving>90%cell viability in mouse tissues and>60%in human tumors,with 1.5×10^(4)-2.5×10^(4)cells/mg yield from mouse liver.Tailored for biopsies and ultrasmall samples,sTED addresses critical standardization challenges in organoid-based research.
文摘Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhythm of 100-120 times/min for CC.[1,2]Interruptions of the CC must be as short as possible and are related to ventilation,defibrillation and turnover of the rescuers.Most of the automated external defibrillators(AEDs)require interruptions of the CC to perform rhythm analysis.Among the numerous marketed models of AEDs,some provide real-time feedback about the quality of the CC.
文摘Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape the future through intelligent automation.This is a key milestone on the way to achieving Rieter’s vision 2027-the fully automated spinning mill.
基金National Natural Science Foundation of China(31700644)Shandong Province Postdoctoral Innovation Project(SDCX-ZG-202400195)。
文摘The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and centrifugal honey separation,expose beekeepers to potential bee stings and frequently compromise honeycomb integrity.To address these limitations,we designed an automated honey-harvesting robot capable of autonomous frame extraction and beeswax removal.The robot mainly consists of a mobile mechanism equipped with image recognition for beehive localization,a magnetic adsorption-based beehive frame handling device(60.8 N maximum suction)coupled with a cross-slide mechanism for precise frame manipulation,and a thermal beeswax layer-melting apparatus,with optimal melting parameters(15 m/s airflow at 90℃ for 30 seconds)determined through rigorous thermal flow simulations utilizing FLUENT/Mechanical software.Field experiments demonstrated beehive frames handling success rate exceeding 85%,beeswax layer removal efficacy over 80% and damage of honeycombs below 30%.The experiment results validate the robot's operational reliability and its capacity to automate critical harvesting procedures.This study significantly reduces the labor intensity for beekeepers,effectively eliminates the risk of direct human-bee contact and improves the removal of beeswax layer,thereby catalyzing the modernization of the beekeeping industry.
文摘The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
文摘BACKGROUND Automated insulin delivery(AID)systems have demonstrated benefits in managing patients with type 2 diabetes(T2D),but data are still limited.Moreover,the efficacy and safety of the AID systems in these patients have been inadequately explored by systematic reviews and meta-analyses.AIM To provide a comprehensive understanding of the optimal use of AID in managing insulin-treated outpatients with T2D.METHODS A systematic search of multiple databases and registries,including MEDLINE,Scopus,Web of Science,Cochrane Library,and ClinicalTrials.gov,was conducted from inception to May 15,2025,to identify studies on AID use for outpatients with T2D.The co-primary outcomes were the change in glycated hemoglobin(HbA1c)and continuous glucose monitoring(CGM)metrics.Statistical analyses were conducted using Review Manager Web software with random-effects models and the inverse variance statistical method.The results were presented as mean differences(MDs)or risk ratios(RRs)with 95%CI.RESULTS A total of 15 studies with 28985 participants were identified,including 6 randomized trials(n=748;3 crossover and 3 parallel-group trials)and 9 single-arm studies.All included randomized trials raised some concerns,and the single-arm studies had serious risks of overall bias.Meta-analysis of randomized trials showed that AID is more effective than the control group in lowering HbA1c(MD:-0.89%,95%CI:-1.32 to-0.46,P<0.0001,I2=82%).Compared to control interventions,AID use was linked to a higher percentage of time in range(MD:19.25%,95%CI:11.43-27.06,P<0.00001,I2=74%)and a lower percentage of time above range>10 mmol/L(MD:-19.48%,95%CI:-27.14 to-11.82,P<0.00001,I2=73%);however,time below range remained similar between the two groups.The mean sensor glucose level was lower in the AID group;however,the coefficient of variation of glucose was the same in both groups.AID use also led to a reduction in insulin dose,but this is not a consistent finding across all study designs.The risks of serious adverse events(AEs)and severe hypoglycemia were similar in both groups;however,AID use raised the risk of device deficiency.Single-arm studies with participants using AID systems also demonstrated reductions in HbA1c(ranging from 0.7%to 2.07%)and improvements in CGM metrics,along with acceptable safety data.CONCLUSION Based on short-term study data,the use of AID systems in outpatients with T2D appears to improve glycemic outcomes and CGM metrics,with no significant AEs.Larger and longer-term randomized controlled trials involving diverse populations,along with a cost-benefit analysis,are needed to guide more informed clinical practice decisions.
基金the Fundamental Research Funds for the Central Universities(2023YQTD02)National Key R&D Program of China(2023YFC2907501)。
文摘To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal drawing and explores automation and intelligent equipment solutions within the framework of the group coal drawing method.Numerical simulations were performed to investigate the impact of the Number of Drawing Openings(NDO)and rounds on top-coal recovery,coal draw-ing efficiency,and Top Coal Loss(TCL)mechanism.Subsequently,considering the recovery and coal drawing efficiency and by introducing the instantaneous gangue content and cumulative gangue content in simulations,the top-coal recovery,gangue content,and coal loss distribution when considering excessive coal drawing were analyzed.This established a foun-dation for determining the optimal NDO and shutdown timing.Finally,the key technical principle and automated control of a shock vibration and hyperspectral fusion recognition device were detailed,and an intelligent coal drawing control method based on this technology was developed.This technology enabled the precise control of the instantaneous gangue content(35%)during coal drawing.The top-coal recovery at the Tashan Mine 8222 working face increased by 14.78%,and the gangue content was controlled at~9%,consistent with the numerical simulation results.Thus,the reliability of the numerical simulation results was confirmed to a certain extent.Meanwhile,the single-group drawing method significantly enhanced the production capacity of the 8222 working face,achieving an annual output of 15 million tons.
基金supported by the National Key Project of the Ministry of Science and Technology of China(No.2022YFC3701200)the National Natural Science Foundation of China(No.42090030).
文摘Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.
文摘BACKGROUND End-stage kidney disease is a growing global health burden with many patients requiring urgent kidney replacement therapy.Urgent-start peritoneal dialysis(PD)has emerged as a viable alternative to hemodialysis particularly in resourcelimited settings.However,concerns remain regarding catheter-related complications associated with early initiation of PD.Automated PD(APD)offers enhanced flexibility and fluid management,but evidence regarding its safety and outcomes in urgent-start scenarios with shortened break-in periods is limited.AIM To evaluate the clinical outcomes and biochemical changes associated with urgent-start APD with a shortened break-in period.METHODS This was a single center,observational study that included 62 patients with endstage kidney disease who required urgent-start dialysis,underwent PD catheter placement,and received APD.Patients were stratified based on catheter opening time(<12 hours vs>12 hours).Catheter-related complications,biochemical parameters,and dialysis efficacy were analyzed.RESULTS The median catheter opening time was 11 h(interquartile range:8-14 hours).No significant differences in catheterrelated complications were observed between groups(P>0.05).Catheter dysfunction,migration,leakage,and replacement occurred in 14.5%,9.7%,12.9%,and 11.3%of patients,respectively.APD led to significant reductions in serum creatinine,blood urea nitrogen,urea,phosphorus,and potassium(P<0.05),alongside correction of metabolic acidosis.No cases of peritonitis or hemoperitoneum were observed.CONCLUSION Urgent-start APD with shortened break-in appears safe with low complication rates and improved biochemical outcomes.
基金supported by National Natural Science Foundation of China(No.62073212)Shanghai Science and Technology Commission(No.23ZR1426600).
文摘Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transportation capabilities,allowing them tomove in the opposite directionwithout turning around,which helps reduce transportation time.This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals.A bi-level mixed integer programming(MIP)model is proposed,taking into account task assignment,bidirectional conflict-free routing,and battery swapping.The upper model focuses on container task assignment and AGV battery swapping planning,while the lower model ensures conflict-free movement of AGVs.A double-threshold battery swapping strategy is introduced,allowing AGVs to utilize waiting time for loading for battery swapping.An improved differential evolution variable neighborhood search(IDE-VNS)algorithm is developed to solve the bi-level MIP model,aiming to minimize the completion time of all jobs.Experimental results demonstrate that compared to the differential evolution(DE)algorithm and the genetic algorithm(GA),the IDEVNS algorithmreduces fitness values by 44.49% and 45.22%,though it does increase computation time by 56.28% and 62.03%,respectively.Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small.As the container scale increases,the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation.The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.
基金supported by the Postdoctoral Fellowship Program of CPSF,China(No.GZC20232015)the China Postdoctoral Science Foundation(No.2024M752499)+3 种基金the Postdoctoral Project of Hubei Province,China(No.2024HBBHCXA076)the Wuhan East Lake New Technology Development Zone Open List Project,China(No.2022KJB128)the National Natural Science Foundation of China(No.51875428)the Fundamental Research Funds for the Central Universities,China(No.104972024RSCbs0013)。
文摘The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.
基金supported by the cross-sectional research project sponsored by Jilin Baoli Science and Technology Co.(2024-2200-0800-0378)
文摘Desulfurization of coke oven gas(COG)is a critical step for achieving green and sustainable development in the coking industry.Ammonium binuclear cobalt phthalocyanine sulfonate(PDS),serving as the core catalyst in the Hydroquinone-PDSFerrous sulfate(HPF)desulfurization process,requires precise concentration monitoring for process optimization.To address the limitations of traditional detection methods,including insufficient sensitivity,cumbersome manual operations,and weak anti-interference capability,this study developed a fully automated analytical system and method based on spectrophotometry,thereby filling a technological gap in automated PDS detection.Systematic performance validation demonstrated that the autoanalyzer exhibits excellent sensitivity and a wide linear range,with results consistent with industrial standard methods.Leveraging a programmable logic controller(PLC),the system achieves end-to-end automation from sample pretreatment to data feedback,overcoming limitations inherent in manual operations such as delayed sample processing and human-induced errors,which significantly enhances the sensitivity and timeliness of PDS concentration monitoring in complex industrial matrices.The proposed method offers triple benefits of environmental friendliness,cost-effectiveness,and social value,providing key technical support for the coking industry.
