Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.

Power System Flow Adjustment and Sample Generation Based on Deep Reinforcement Learning

With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.

DETERMINATION OF ANTIMONY IN WATER SAMPLES BY FLOW-INJECTION HYDRIDE GENERATION ATOMIC ABSORPTION SPECTROMETRY WITH ON-LINE ION-EXCHANGE COLUMN PRECONCENTRATION

On-line ion-exchange separation and preconcentration were combined with flow-injection hydride generation atomic absorption spectrometry (HGAAS) to determine ultra-trace amounts of antimony in water samples. Antimony(Ⅲ) was preconcentrated on a micro-column packed with CPG-8Q chelating ion-exchanger using time-based sample loading and eluted by 4 mol l^(-1) HCl directly into the hydride generation AAS system. A detection limit (3σ) of 0.0015μg l^(-1) Sb(Ⅲ) was obtained on the basis of a 20 fold enrichment and with a sampling frequency of 60h^(-1). The precision was 1.0% r.s.d.(n=11) at the 0.5μg l^(-1) Sb(Ⅲ) level. Recoveries for the analysis of antimony in tap water, snow water and sea water samples were in the range 97-102%.

基于主动学习机制GAN的MSWI过程二噁英排放风险预警模型

针对构建城市固废焚烧(municipal solid waste incineration,MSWI)过程剧毒污染物二噁英(dioxin,DXN)排放风险预警模型的样本极为稀少的问题,提出一种基于主动学习机制生成对抗网络(generative adversarial network,GAN)的DXN排放风险预警建模方法.首先,以DXN风险等级作为条件信息使得GAN生成候选虚拟样本;然后,利用基于最大均值差异和多视角可视化分布信息的主动学习机制进行虚拟样本的初筛和评估,以获得期望虚拟样本;最后,基于混合样本构建DXN排放风险预警模型.通过基准数据集和MSWI过程数据集验证了所提方法的有效性.基于主动学习机制GAN的DXN排放风险预警建模方法可以有效解决样本稀少的问题,提高模型精度.

A monodisperse-aerosol generation system： Design, fabrication and performance

Monodisperse aerosols are essential in many applications, such as filter testing, aerosol instrument calibration, and experiments for validating models. This paper describes the design principle, construction, and performance of a monodisperse-aerosol generation system that comprises an atomizer, virtual impactor, microcontroller-based isokinetic probe, wind tunnel, and velocity measurement device. The size distribution of the produced monodisperse aerosols was determined by an optical particle counter. The effects of atomizer characteristics, the rates of minor and major flow, and solution criteria were investigated. It was found that all these parameters affect the generation of monodisperse aerosol. Finally, the expected geometric standard deviation （〈1.25） of monodisperse aerosol particles was obtained with the most suitable atomizer for 10% oleic acid in ethyl alcohol solution with 5%-15% minor flow, where the ratio between the nozzle-to-probe distance and acceleration-nozzle-exit diameter was 0.66. The con- structed monodisperse-aerosol-generation system can be used for instrumental calibration and aerosol research.