Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation

Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.

An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features

Machine learning combined with density functional theory(DFT)enables rapid exploration of catalyst descriptors space such as adsorption energy,facilitating rapid and effective catalyst screening.However,there is still a lack of models for predicting adsorption energies on oxides,due to the complexity of elemental species and the ambiguous coordination environment.This work proposes an active learning workflow(LeNN)founded on local electronic transfer features(e)and the principle of coordinate rotation invariance.By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures,LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments.As a result,it enables the prediction of^(*)H adsorption energy on binary oxide surfaces with a mean absolute error(MAE)below 0.18 eV.Moreover,we incorporate local coverage(θ_(l))and leverage neutral network ensemble to establish an active learning workflow,attaining a prediction MAE below 0.2 eV for 5419 multi-^(*)H adsorption structures.These findings validate the universality and capability of the proposed features in predicting^(*)H adsorption energy on binary oxide surfaces.

Evolution of pore systems in low-maturity oil shales during thermal upgrading--Quantified by dynamic SEM and machine learning

In-situ upgrading by heating is feasible for low-maturity shale oil,where the pore space dynamically evolves.We characterize this response for a heated substrate concurrently imaged by SEM.We systematically follow the evolution of pore quantity,size(length,width and cross-sectional area),orientation,shape(aspect ratio,roundness and solidity)and their anisotropy—interpreted by machine learning.Results indicate that heating generates new pores in both organic matter and inorganic minerals.However,the newly formed pores are smaller than the original pores and thus reduce average lengths and widths of the bedding-parallel pore system.Conversely,the average pore lengths and widths are increased in the bedding-perpendicular direction.Besides,heating increases the cross-sectional area of pores in low-maturity oil shales,where this growth tendency fluctuates at<300℃ but becomes steady at>300℃.In addition,the orientation and shape of the newly-formed heating-induced pores follow the habit of the original pores and follow the initial probability distributions of pore orientation and shape.Herein,limited anisotropy is detected in pore direction and shape,indicating similar modes of evolution both bedding-parallel and bedding-normal.We propose a straightforward but robust model to describe evolution of pore system in low-maturity oil shales during heating.

A Discrete Multi‑Objective Artificial Bee Colony Algorithm for a Real‑World Electronic Device Testing Machine Allocation Problem

With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.

Analysis and Comparison of Power Electronic Converters for Conventional and Toroidal Switched Reluctance Machines

Different power electronic converter topologies are introduced in this paper for both Conventional Switched Reluctance Machine (CSRM) and Toroidal Switched Reluctance Machine (TSRM) drive systems. Their commutation, switch and diode currents, power losses, and efficiencies under over modulation operation are analyzed and compared for converter characteristics study, performance evaluation and topology selection for CSRM and TSRM drive systems. The switch and diode silicon volumes required for each CSRM and TSRM drives are also compared according to their corresponding currents at the equivalent machine torque versus speed operating points.

Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores

BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

基于深度卷积神经网络的电子玻璃缺陷分类方法

电子玻璃是信息显示产业的关键基础材料之一。近年来,显示产业向大尺寸化、超高清和轻薄化发展,对于电子玻璃基板的质量提出了更高的要求。机器视觉检测具有速度快、精度高、成本低、稳定性好等优点,被广泛应用于各种工业场景中。图像处理算法、识别分类算法是机器视觉检测的关键技术。本文针对基于深度卷积神经网络的整图分类方法在电子玻璃表面缺陷检测领域的应用,从图像数据处理、卷积神经网络构建、训练调参、评价标准等方面介绍其研究进展,并总结部分应用实例,对电子玻璃缺陷分类未来的研究方向进行展望。

偏心拉伸实验的理论分析与实验研究

偏心拉伸实验是一种综合性、研究性的材料力学实验项目。为了增加材料力学实验课程的难度和深度,通过理论分析推导偏心拉伸应力公式,并指出偏心拉伸试样的实际中性层并不再是几何中性层;利用ABAQUS软件模拟了偏心拉伸试样的应力分布;通过自制的一种可以在电子万能试验机上进行偏心拉伸实验的装置并利用电测法测试了试样横截面上各点的应变。结果表明:偏心拉伸实验的理论计算结果、实验结果和模拟结果基本吻合,3种数据之间的偏差较小;而且,应力在试样横截面上几乎呈线性分布,左边受压缩,右边受拉伸,左边的应力最小,右边的应力最大。

飞机电推进系统高效能电机及其驱动控制技术

飞机动力系统的电气化是继机载二次能源逐步统一为电能之后航空电气化发展的重要方向和高级阶段,能够大幅提升飞机动力系统能量利用效率,是航空业绿色发展的重要途径。电机系统是飞机电推进系统的核心机电能量变换环节,高效能电机、电力电子变换及其驱动控制技术是电推进飞机发展的基础支撑。该文从电推进飞机动力系统架构出发,分析电推进飞机电机系统的特征与要求。总结永磁同步电机、异步电机、电励磁无刷同步电机和超导电机4种类型电机在电推进飞机上的研究和应用现状,系统讨论电推进系统中不同类型电机和电机驱动器的关键技术。最后,展望新材料、新器件、新工艺、人工智能和综合热管理等技术对电推进飞机电机及其驱动控制技术发展的推动作用。

冒用花呗行为定性之争:问题、本质及解释

互联网金融领域的刑事犯罪治理难题表现出消极的点面效应。围绕冒用花呗的行为定性,至少存在花呗的法律属性定位、机器能否被骗、机器如何被骗的分析难题。其一,对于花呗的法律属性,研究论证的瑕疵在于客观解释立场的缺失。根据客观解释立场,“其他金融机构”中的“其他”意表除了商业银行以外的可以发行信用卡的金融机构,花呗属于刑法意义上的信用卡。其二,关于机器能否被骗。机器不具有自我意识的认识桎梏不能说明机器不可以被骗,否则只会固化人机关系“二元认识论”的旧观念,故机器不能被骗的立场应当被摒弃。其三,关于机器如何被骗。在探讨人工智能作为诈骗对象时引入预设同意理论已成为学界共识,然而该理论的运用现状过于粗简,其不仅可以说明机器的处分意识来源,更能说明人机关系的一体化。冒用花呗的行为定性中,关键特征是“人机交互的一体关系”,机器是自然人的电子代理人,人所排斥之事项即为机器所排斥之事项。第三方支付对于冒用者的身份要素陷入了错误认识,进而导致被害人财产受损。冒用花呗的行为应当定性为信用卡诈骗罪。

美军认知电子战关键技术发展方向分析

美军认知电子战相关概念发展迅速,也开展了较多的项目研究。为分析其关键技术发展现状并研判其未来发展趋势,从美高校、智库、军事科研机构、厂商4个角度,通过研究论文、报告等公开文献,对美军认知电子战关键技术发展情况,特别是其在机器学习、人工智能方面的探索进行了深入的分析,对美军认知电子战技术领域研究方向、潜在难题及对应的关键技术思路进行了较为全面的梳理,并从数据、模型、平台、应用等方面给出了启示。

新型无损检测技术在番茄品质检测中的研究与应用进展

番茄是我国种植面积最广的蔬菜之一,受到广大消费者的青睐。近年来,随着人们对健康饮食需求的逐步提升,番茄的品质愈发受到关注。番茄形状较为规则,但不同品种间的大小、果型、颜色差异较大,蕴含的营养成分种类繁多、化学结构复杂,导致其品质检测存在一定难度。传统番茄品质检测方法大多存在主观性强、破坏性强、耗时费力的缺点,难以满足大规模品质检测的需求。近年来,随着各类无损检测技术的发展,机器学习、多光谱技术、电子鼻/电子舌等新型检测方法也已逐步应用于番茄品质的快速、无损检测中。本文在传统番茄品质检测技术的基础上,重点总结了基于图像识别的人工智能、电子鼻技术和光谱技术在番茄无损检测方面的发展与应用,为番茄品质检测的研究与发展提供参考。

面向肺癌筛查的呼出气检测电子鼻设计与应用

人体呼出气包含的挥发性有机化合物(Volatile Organic Compounds,VOCs)与人体健康和疾病状态密切相关。电子鼻将气体传感器阵列与模式识别算法结合,可实现气味的识别与分类。针对肺癌患者诊治端口前移需求,开展呼出气检测电子鼻系统的设计与应用研究,基于半导体气体传感器阵列优化电路与气路设计,构建便携式电子鼻系统样机,对129例临床呼出气样本进行检测;通过数据处理与统计分析,研究健康对照(51例)与肺部疾病患者(78例)的分类效果。结果表明,设计的电子鼻对肺部疾病患者的识别准确率达89.1%(敏感性83.3%,特异性98.0%),展现出应用于肺癌早期筛查的潜力,有望成为一种无创、便捷的肺部疾病辅助诊断方法。

近红外光谱融合电子鼻数据对烟叶产地判别研究

基于烟叶近红外光谱、Heracles电子鼻及二者的融合数据,建立了云南、河南、福建和吉林4个省份的烟叶产地识别模型以及河南省内漯河、南阳、平顶山、许昌和驻马店5个地级市的烟叶产地识别模型.对于地理位置相距比较远的不同省份的烟叶,基于单一数据源就可以建立准确率比较高的产地识别模型.对于河南省内5个地级市的烟叶,其地理位置相距近,气候变化小,烟叶相似性高,仅基于单一信息源的数据,该产地识别模型的准确率偏低.为了提高河南省内5个地级市烟叶产地识别的准确率,将烟叶近红外光谱数据与Heracles电子鼻数据进行融合,由于增加了烟叶数据信息量,这5个产地的识别效果明显提升,其留一法内部交叉验证准确率为98.26%,高于数据融合前单一数据源判别模型的86.96%.研究表明Heracles电子鼻数据可以在不同的数据维度上,对近红外光谱数据进行信息量补充,为烟草品种溯源、质量监测、市场监督等方面提供新思路.

基于机器视觉的图书机器人取书路径控制方法研究

针对图书馆图书机器人的路径规划问题,通过结合Cartographer算法和激光视觉雷达技术来构建图书馆栅格地图,并采用改进的A*算法和蚁群优化算法进行路径规划,最后提出了一种新型机器人取书路径规划控制模型;实验结果显示,所提出的模型在权重系数为0.6时,其规划出的最优路径相比于其他模型最大缩短了8 m;其路径规划成功率达95%,且规划的路径拐点数较同类模型最多减少了4个;说明研究成功提出了一种有效的图书机器人取书路径规划模型,提高了取书效率,满足了机器视觉和移动机器人在图书馆取书过程中的应用需求。

Cr和Si含量对重载铁路用75 kg·m^(-1)U77MnCrH钢轨组织与性能的影响

为开发适应重载铁路用高强韧、高硬度耐磨钢轨,按照C-Si-Mn-Cr成分设计体系进行U77MnCrH钢轨钢的成分设计和中试试验;基于中试试验的组织性能,筛选出组织性能较好的试验钢进行75 kg·m^(-1)U77MnCrH钢轨试制试验;使用金相显微镜、场发射扫描电子显微镜、透射电子显微镜、万能拉伸试验机及硬度计对75 kg·m^(-1)U77MnCrH钢轨试样进行组织性能分析。结果表明:随着Cr含量由0.26%增加到0.38%,钢轨钢试样中珠光体片层间距由165 nm减小到146 nm,抗拉强度和硬度均有提高,屈服强度R_(p0.2)与断后伸长率均有降低;随着Si含量由0.25%增加到0.45%,珠光体片层间距由165 nm减小到132 nm,断后伸长率和硬度均有提高,屈服强度R_(p0.2)明显增加,抗拉强度变化不大;0.45%的Si配合0.26%的Cr获得的钢轨珠光体更加均匀细小,片层间距分布更加均匀,力学性能与硬度匹配更好,抗拉强度达到1258 MPa,屈服强度R_(p0.2)达到845 MPa,断后伸长率达到13.0%,轨头顶面硬度达384 HBW,横断面硬度介于36.2~38.8 HRC。75 kg·m^(-1)U77MnCrH钢轨的各项指标对生产实践具有较强的参考价值。

基于细粒度动态特征的摹仿签名书写人识别

电子签名笔迹逐渐取代传统笔迹,电子签名的真伪鉴别成为公安、司法鉴定领域的难题。于是提出了细粒度电子签名笔迹动态特征提取方法,利用K近邻、决策树、随机森林、支持向量机等监督学习方法综合分析摹仿电子签名的动、静态特征,建立摹仿电子签名笔迹书写人识别模型。实验结果显示,基于K近邻算法的书写人识别模型表现最好,正确率0.917,精确率0.906,召回率为0.871,AUC为0.965。实验表明,笔迹动态特征能够显著提升摹仿签名书写人识别模型性能,增加样本类别数或者减低样本数量均会降低模型的识别能力。

煤矿掘进机新型智能电控系统设计与研究

基于先进无线5G技术及液压系统软硬件,搭建掘进机电控系统主框架、CAN通信框架和电控模块,设计了掘进机智能电控系统,运用试验的方法对掘进机智能电控系统控制精度进行研究分析。研究结果表明:设计的掘进机智能电控系统控制精度在40 mm以内,满足巷道掘进100 mm施工要求,该研究为掘进机自动、智能化升级改造及智能电控系统的优化和改善提供技术支持及参考依据。