A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

X-ray image distortion correction based on SVR

X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Development of micro wire electrical discharge machining platform and its application

A machining platform of micro wire electrical discharge machining (MWEDM) was developed. The key technology of MWEDM mainly includes granite basement, micro energy pulse generator, detection and servo control system, constant tension winding system and V-block guide wire mechanism. Utilizing micro wire electrode with 30μm in diameter, the MWEDM can machine the micro slot with the minimum size of 38μm wide, and the surface roughness is smaller than 0.1μm, the machining precision is less than 0.5μm, the white layer is no more than 2μm with main cut. All kinds of complex micro parts, such as micro gear, micro bearing bracket and micro shaped holes, can also be machined by using this platform.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

个性化机加工底板托槽的抗剪切强度研究

目的:采用两种临床常用粘接剂,对个性化机加工底板托槽、新型网底托槽、双向倒钩底板托槽和传统网底托槽的抗剪切强度(shear bond strength,SBS)进行比较研究,为临床个性化机加工托槽的粘接提供参考。方法:收集48颗新鲜人类前磨牙,随机分为8组。A、B组选用个性化机加工底板托槽,C、D组选用新型网底托槽,E、F组选用双向倒钩底板托槽,G、H组选用传统网底托槽;A、C、E、G组使用化学固化粘接剂,B、D、F、H组使用光固化粘接剂。在粘接实验前,在所有托槽中,每种托槽随机选1颗,对其底板在放大40倍、100倍的扫描电镜下进行观察并拍照。使用万能材料实验机,对每组托槽进行SBS的测定,并进行粘接剂残留量(adhesive remnant index,ARI)计分。结果:使用化学固化粘接剂时,其他3种托槽相比,双向倒钩底板托槽粘接强度较低,差异有统计学意义(P<0.05)。使用光固化粘接剂时,双向倒钩底板托槽粘接强度最低,新型网底托槽较低,传统网底托槽和个性化机加工底板托槽粘接强度最高,差异有统计学意义(P<0.05),传统网底托槽和个性化机加工底板托槽之间粘接强度无明显差异(P>0.05)。各组ARI计分差异有统计学意义(P<0.05),进一步比较可得:H组ARI计分最小,且H组与A组和E组的差异有统计学意义(P<0.05)。结论:个性化机加工底板托槽使用光固化粘接剂或使用化学固化粘接剂对其粘接强度无明显影响,粘接强度均能满足正畸临床粘接的要求。H组脱粘接断裂部位相对更接近牙釉质,其余各组牙釉质在脱粘接过程中损伤的风险较小。

带式转载机连接托架的力学特性分析及优化设计

为了充分提高带式转载机机械结构强度,设计了一种带式转载机连接托架结构模型。运用有限元分析软件,充分考虑连接托架受到的应力、应变,从静力学属性角度对连接托架进行研究,均衡连接托架的应力分布,减少应力集中区域,优化连接托架结构设计,达到机械强度进一步提高的目的。

床垫折叠装置的设计与仿真分析

为了解决床垫体积庞大不便于运输的问题,介绍一种床垫折叠装置,其能够缩小床垫卷包后的长度,同时对托架旋转晃动问题提出了解决方案。托架刹车过程的瞬态动力学分析结果显示,改进后的托架最大变形量减小至合理范围。研究表明,旋转托架晃动问题的分析思路和改进设计可行,能够为解决同类问题提供参考。

Characterization of the Convoluted 3D Internetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning

Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys.In this study,we used synchrotron X-ray tomography to study the true 3D morphologies of the Ferich phases,Al_(2)Cu phases and casting defects in an ascast Al-5Cu-1.5Fe-1Si alloy.Machine learning-based image processing approach was used to recognize and segment the diff erent phases in the 3D tomography image stacks.In the studied condition,theβ-Al_(9)Fe_(2)Si_(2)andω-Al_(7)Cu_(2)Fe are found to be the main Fe-rich intermetallic phases.Theβ-Al_(9)Fe_(2)Si_(2)phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al_(2)Cu phases and the shrinkage cavities.The Al_(3)Fe phases formed at the early stage of solidification aff ect to a large extent the structure and morphology of the subsequently formed Fe-rich intermetallic phases.The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work.

沙特大型光伏电站平单轴支架中不脱开桥架匹配自动清扫机器人设计实践

[目的]沙特阿尔舒巴赫大型光伏电站,坐落在沙特某沙漠地带,装机容量为2.6 GW,沙特“2030愿景”发布以来,新能源成为其计划中的重要组成部分,该项目在技术上采用了当前最先进的N型双面光伏组件和平单轴自动跟踪式支架,是全球单体项目容量最大的光伏电站。该项目中国光伏树立了名片,为光伏装备“走出去”的标杆。[方法]由于沙漠环境对光伏组件的清洁维护提出了更高的要求,为了提高光伏发电效率,降低运维成本,光伏清扫机器人开始使用。[结果]光伏清扫机器人具有多种形式,自动清扫机器人最适用于大型光伏电站,但在平单轴支架的实际应用中,需要进行针对性的设计优化,已实现平稳运行。[结论]文章针对该光伏电站项目平单轴支架不脱开桥架的优化设计,匹配智能清扫机器的应用实践进行深入研究,为沙漠地区光伏电站智能清洁维护的设计提供参考。

Radiography Image Classification Using Deep Convolutional Neural Networks

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.

大采高煤矿液压支架自动化控制设计及应用

针对当前传统液压支架跟机控制系统精度低下、效率不高和安全性差的问题,某矿以自身实际的地质和大采高工作情况为基础,通过工艺分区的方式,设计了一种液压支架自动化跟机控制系统。着重分析液压支架跟机模型和调斜调偏控制模型设计,并在实际应用中进行可行性验证。结果表明,该控制系统能够高效安全地完成液压支架的跟机工作,支护高度控制科学,能够保持7.16s平均移架速度,将井下的移架效率提升近21%。

液压支架液压系统动态特性分析

为实现乳化液泵站的实时、稳压控制,快速推进煤矿工作面,建立了支架液压系统整体模型,分析其动态特征,单台液压支架完成升柱、降柱及拉架的平均速度依次是0.11m/s、0.058m/s和0.083m/s,时间依次是8s、3.12s和4.5s,液压支架移架工作时间是15.62s,满足液压支架跟机移架时间;并进一步分析当两台支架插架移架时,可明显提升支架的移架速度、升柱时的支护除撑力。

液压支架故障诊断方法与实践研究

针对现代液压支架故障诊断速率较低的问题,以ZY5600/28/56型液压支架为例,介绍一种液压支架故障诊断方法。该方法主要由三大步骤构成,采集到液压支架各方面运行数据后,利用因子分析的方式对数据降维处理,然后以此为基础,通过支持向量机算法对故障分类,利用贝叶斯网络算法诊断出故障的引发原因,以此为故障快速解决提供支持,具有一定应用价值。

光伏发电系统安装施工技术的应用分析

对光伏发电系统安装施工技术的应用进行了系统分析,并提出优化策略。主要研究内容包括光伏支架和组件的安装技术、机房与电缆的布置、汇流箱和配电柜的安装、蓄电池与接地装置的配置。研究结果表明,科学合理的安装施工技术不仅能提高光伏发电系统的发电效率和稳定性,还能有效延长系统的使用寿命,降低运维成本。

E35型条并卷联合机升降气缸改造实践

为解决E35型条并卷联合机升降气缸损坏后更换进口原装备件费用高的问题,介绍升降气缸工作原理及工作流程,通过优选国产无杆气缸,并改造气缸进气口、制作气缸底座支架、制作纱管提升器、制造光电接引器等实现替代进口气缸。指出:改造后的国产气缸装入E35型条并卷联合机使用效果好、备管输送顺利;做好设备的定期维护保养以及持续的创新改革,才能充分发挥设备潜能、为企业降本增效。

汽车挡风玻璃定位与支架粘合视觉检测系统的设计与实现

针对企业提出的汽车前挡风玻璃自动制造需求,设计了一套基于机器视觉的前挡风玻璃定位与支架粘合检测系统。阐述了系统的整体设计方案,详细介绍了电气控制部分、机器人控制部分和机器视觉部分。系统采用底部背光与双侧面条形光相结合的光学控制策略。针对产品的特征,提出了自适应阈值图像分割算法和基于改进的随机霍夫变换特征提取算法,实现汽车挡风玻璃定位和支架粘合质量检测。实验结果表明,设计的系统能够很好地降低粘合支架的次品率,次品率仅为4%,满足了企业生产现场的精度和时间要求,实现粘合精度小于0.5 mm,智能制造系统生产效率为人工的4.5倍,大大提高企业的生产效率。

TD2-43型锚杆机托架的静力学分析

锚杆机常作为连续采煤机的配套设备,在连续采煤机完成掘进和装运工序后,进入工作面钻锚杆眼和安装锚杆,对裸露的顶板及时支护,保证巷道的安全。通过对TD2-43型锚杆机托架进行静力学研究和有限元分析,为TD2-43型锚杆机托架的安全使用提供了理论依据。