Five-phase Synchronous Reluctance Machines Equipped with a Novel Type of Fractional Slot Winding

Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are well known because of short end winding length,simple structure,field weakening sufficiency,fault tolerant capability and higher slot fill factor.The five-phase machines equipped with FSCW,are very good candidates for the purpose of designing motors for high reliable applications,like electric cars,major transporting buses,high speed trains and massive trucks.But,in comparison to the general distributed windings,the FSCWs contain high magnetomotive force(MMF)space harmonic contents,which cause unwanted effects on the machine ability,such as localized iron saturation and core losses.This manuscript introduces several new five-phase fractional slot winding layouts,by the means of slot shifting concept in order to design the new types of synchronous reluctance motors(SynRels).In order to examine the proposed winding’s performances,three sample machines are designed as case studies,and analytical study and finite element analysis(FEA)is used for validation.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Utility and Application of a Versatile Analytical Method for MMF Calculation in AC Machines

A versatile analytical method(VAM) for calculating the harmonic components of the magnetomotive force(MMF) generated by diverse armature windings in AC machines has been proposed, and the versatility of this method has been established in early literature. However, its practical applications and significance in advancing the analysis of AC machines need further elaboration. This paper aims to complement VAM by augmenting its theory, offering additional insights into its conclusions, as well as demonstrating its utility in assessing armature windings and its application of calculating torque for permanent magnet synchronous machines(PMSM). This work contributes to advancing the analysis of AC machines and underscores the potential for improved design and performance optimization.

Electromagnetic Performance Analysis of Variable Flux Memory Machines with Series-magnetic-circuit and Different Rotor Topologies

In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.

Recent Advances in Video Coding for Machines Standard and Technologies

To improve the performance of video compression for machine vision analysis tasks,a video coding for machines(VCM)standard working group was established to promote standardization procedures.In this paper,recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases,requirements,evaluation frameworks and corresponding metrics of the VCM standard are given.Then the existing methods are presented,introducing the existing proposals by category and the research progress of the latest VCM conference.Finally,we give conclusions.

A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Industrial sewing machines:Maximizing productivity in clothing manufacturing

In the fast-paced world of clothing manufacturing,productivity and efficiency are crucial for staying competitive.Industrial sewing machines play a vital role in this context,offering advanced features and capabilities that significantly enhance production output and quality.This article explores the various aspects of industrial sewing machines,their impact on productivity,and the emerging trends that are shaping the future of the clothing manufacturing industry.

高光谱预处理方法与多模型在分类判别中的对比研究

高光谱技术能够快速、无损地获取丰富的信息,在植物研究和监测中已成为一种广泛应用的工具。茄科植物作为一种重要的经济农作物,与高光谱技术结合进行研究具有巨大的应用潜力。本研究采用高光谱技术对茄科植物的初烤后不同部位叶片进行分类研究。采用Field Spec 3光谱辐射仪对293份不同部位的茄科植物粉末样本进行高光谱采样,采用S-G平滑以及一阶导数和二阶导数的方法对数据进行预处理,用于信息增强和去除噪声,并通过偏最小二乘法对数据进行降维,以减少冗余特征。基于降维数据,采样支持向量机、逻辑回归、K近邻、决策树、随机森林和梯度提升决策树这六种机器学习算法建立分类模型。结果显示,在分类任务中,经过一阶导数处理后,支持向量机模型最佳,在训练集和测试集上分别实现了100.0%和84.7%的准确率。经网格参数优化后确定最优参数为:最大深度不限制,最小样本分割数为4,估计器数量为200。参数优化后五折交叉验证准确率为88.1%,训练集准确率为100%,测试集准确率为86.4%。研究结果表明,预处理方法结合降维方法能够增强数据信息使得分类模型能够更好地捕捉茄科植物样本的特征。该研究对于快速、准确、无损地区分茄科植物的部位具有重要意义。

基于机器视觉的海鲜花螺分类研究

针对目前人工分选海鲜花螺劳动强度大、人工成本高的问题,研究提出一种DPO-SVM海鲜花螺公母分类模型。通过灰度共生矩阵分析提取海鲜花螺外壳间隔纹理特征量,采用SVM作为公母分类模型基体,对不同纹理特征量组合进行分类效果对比,得出使用能量、熵、对比度3种特征量分类效果最好的结论。针对SVM优化问题,以PSO和WOA算法为基础提出DPO算法对SVM的重要参数c、g进行优化;对DPO-SVM性能进行测试,将测试结果与SVM、PSO-SVM、WOA-SVM测试结果对比。相比于其他3种SVM模型,DPOSVM分类准确率大幅度提升,相比于SVM,分类总准确率由85%上升至100%,上升了15%;DPO算法提高了单种群优化算法的寻优性能,相比于PSO算法,DPO算法将最佳适应度从95.26提升至98.68,提升幅度为3.47%。此外,达到最佳适应度的迭代次数由14次减少至6次,下降57.14%,显著优化了收敛速度。研究结果可为自动分拣装置中海鲜花螺公母分类提供技术参考。

自然语言处理研究综述

近年来,自然语言处理因在分析与建模人类语言任务领域取得诸多成果而备受关注。当前,大规模预训练语言模型展现出强大的对话问答和文本生成能力,带来自然语言处理研究的新一轮热潮。自然语言处理在机器翻译、文本摘要、信息抽取等领域应用广泛。文本首先讨论自然语言处理针对语言学四个不同层次文本信息的分析手段,对自然语言处理的基本任务组成进行概述;其次,讨论自然语言处理在具体下游任务中的应用现状,包括自然语言处理在具体任务中的应用历史、当前的研究趋势以及面临的挑战;最后,在大规模预训练语言模型研究对数据集提出更高要求的背景下,对自然语言处理领域已有的数据集及评测基准集等进行讨论。

面向新型人机关系的社会临场感

社会临场感(Social Presence,SP)又称社会存在,是一种与他人在一起的共在感。社会临场感作为面向新型人机关系态度形成机制的经典与主流中介变量,亟待对多学科相关文献进行系统性梳理与理论体系的整体性构建。本文以人机关系演进为背景,从人与计算机交互(Human-Computer Interaction,HCI)和人与机器人交互(Human-Robot Interaction,HRI)的历史视角对社会临场的概念内涵与适用边界进行界定。再以拟人化为前置变量,个体因素为调节变量,认知、情感、行为的态度为结果变量,构建了以社会临场感为中介变量的整合性理论框架,解构面向新型人机关系的心理机制。最后,对人机关系调整与机器社会心理、社会临场感的概念内涵外延的扩展、拟人化与社会临场感三方面进行了展望。

基于离心泵数字孪生流场云图的叶轮故障诊断方法与应用

随着工业技术的发展,离心泵的健康诊断与维护需求日益迫切,结合数字孪生和机器视觉技术,提出一种基于数字孪生流场云图的离心泵叶轮机械故障智能诊断方法。借助离心泵数字孪生模型来模拟叶轮叶片随机断裂故障的演化发展,生成具有不同故障特征的叶轮流场压力及速度云图;基于对Yolov5算法的学习训练,获得了压力和速度云图两类机器视觉模型,并结合统计分析实现了叶轮故障的初步诊断;进而考虑两类检测模型的优势互补特性,基于堆叠集成的思想将二者融合,以提升叶轮故障诊断的准确性。经实验验证,针对叶轮叶片的随机断裂故障,所提方法可达到0.99以上的诊断准确度,开发的离心泵叶轮机械故障智能诊断系统使所提方法得以落地应用。

社交网络节点重要性识别研究进展

准确识别社交网络中的节点重要性对于促进或抑制信息传播、遏制疾病传播具有重要意义,同时在精准营销和社会治理等领域也具有重要理论意义和应用价值。该文从4个角度对节点影响力识别算法进行总结和梳理,具体包括:基于微观局部结构、中观的社团结构、宏观全局结构及基于机器学习的算法。详细介绍了其中的代表性算法,并从不同层面分析了不同算法的优缺点。此外还总结了常用的传播动力学模型和评价指标。最后提炼了仍需解决的问题和未来可能的研究方向。

老挝铜资源成矿规律与基于机器学习的远景预测

老挝处于特提斯成矿域南东段,具有丰富的矿产资源,但其地质工作基础薄弱,厘定矿产资源成矿规律并开展远景区预测是老挝在重点区实现找矿突破的有效途径。老挝1∶1000000国家尺度地球化学填图由中老双方合作完成,为其矿产资源和环境评价提供了高质量的地球化学基础数据和图件。本文主要利用国家尺度地球化学填图数据,结合老挝已发现矿产成矿规律,利用机器学习技术,开展铜资源远景区预测。研究结果表明:(1)老挝铜矿床的形成明显受到构造-岩浆-沉积作用控制,铜矿床主要类型有斑岩型、夕卡岩型、热液型和砂岩型。(2)老挝全国水系沉积物中铜含量为1.20~459.00μg/g,平均值为21.96μg/g,中位值为16.50μg/g,在7个三级大地构造单元中,长山地块和哀牢山—马江等3个缝合带的平均值高于其他几个构造单元,地球化学图显示铜在老挝分布不均匀,存在多个大面积分布的高背景区和异常区。(3)构建了包括单元素异常、矿化元素组合异常、指示中酸性岩体元素组合、控矿构造分布、碳酸盐岩和碎屑岩分布等要素的老挝铜矿多源信息定量信息预测模型。(4)利用随机森林成矿预测方法,共圈定9个成矿远景区,具有寻找斑岩型和夕卡岩型等类型铜矿找矿前景。

基于电子商务环境的农机管理系统设计

介绍了电子商务环境的背景和现状,详细描述了农机管理系统的实际需求和业务流程,并根据系统设计原则设计了系统总体架构,最后从人机管理、作业管理及综合信息等3个方面实现了农机管理系统。系统在测试过程中取得了较好的应用效果,是一种具有广泛应用前景的高效农机管理系统。

建筑机械加工轴承表面缺陷光学识别模型

建筑机械加工轴承表面缺陷识别技术的表面缺陷识别效果不佳,影响工业生产的安全性。为了解决这一问题,提出建筑机械加工轴承表面缺陷光学识别模型构建方法。获取多角度机械加工轴承表面图像,将建筑机械加工轴承表面展开,再进行二维图像拼接,获得建筑机械加工轴承表面没有重复且完整的二维图像;通过局部与全部相结合的平滑策略构建光流误差轨迹模型,在光流求解策略的基础上,构建建筑机械加工轴承表面缺陷光学识别模型,计算建筑机械加工轴承表面二维图像的光流值,根据计算结果对建筑机械加工轴承表面缺陷情况进行判定,实现建筑机械加工轴承表面缺陷的识别。实验结果表明,所提方法的轴承表面缺陷识别率高、识别用时明显提升。

基于GPT-4的专利权利要求书自动生成及其评估研究

[目的/意义]本研究旨在利用GPT-4模型自动生成权利要求书,降低研发人员的撰写难度,提升专利审查的工作效率和质量。[方法/过程]文章构建了适合自动生成权利要求书的Prompt,并实施了Zero-Shot、Exact-Drafting、Stepwise-Claim和Exact-Step Claim 4种提示策略。将专利说明书和技术交底书输入GPT-4模型,使用Prompt指导其输出,实现权利要求书的自动化生成。同时,使用ROUGE和BERTScore评估指标来评价文本质量,并从权利要求数量、文本长度、高频词、关键词和常用搭配等多个维度,分析生成文本与参考文本的异同。最后,请专家分别从清晰度、一致性、相关性、专业性和完整性5个方面,评估生成的权利要求书的质量。[结果/结论]实证研究结果显示,Exact-Step Claim提示策略能显著提升权利要求书的生成质量。此外,基于专利说明书生成的权利要求书,在权利要求数量和文本长度上与参考文本更为匹配,表明GPT-4模型在自然语言理解和生成领域的应用效果与输入文本质量密切相关。本研究提供了一种高效智能的辅助方法,有助于推动专利文本撰写和审查领域的发展,同时也存在挑战,需要进一步提升模型在理解复杂技术术语和遵循专利法规范中的精确度,并探索如何优化模型对权利要求数量和文本长度的判断能力。