基于电信号特征分析的电机故障诊断技术

介绍了电机故障诊断技术平台的组成,分析了电机常见故障下电信号的特征。利用宜昌和景洪工厂两台电机进行了实际对比测试,宜昌工厂电机定子电流特征频率在49.2Hz时表现出峰值-36.08dB,在50.8Hz时表现峰值则为-37.23dB,表明电机存在转子断条故障;景洪工厂电机在转子特征频率为113.51Hz时,出现峰值-58.47dB,表明电机存在静态偏心故障。经电机现场抽芯检查,验证了电机故障快速诊断平台给出的电机健康状态评估结果,证实了基于电信号特征分析的电机故障诊断技术的准确性。     

基于Massive MIMO及频谱叠加的5G SA网络上行优化方法

大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)可以有效提升5G SA网络的上行链路数据传输速率以及可靠性。针对5G SA网络上行链路速率和覆盖不均衡的情况,提出了基于大规模MIMO的分组算法,将发送信号矢量进行分组,组内采用最大似然检测,组外采用基于正交三角分解(QR分解)的干扰消除检测,并且结合5G频谱的叠加策略,在降低算法复杂度的同时,有效提升网络覆盖和速率。通过5G SA现网实测,通过MIMO降低分组数量能够提升分组检测性能,结合上行低频段频谱叠加策略能够有效提升5G SA网络上行覆盖30%,提升5G SA网络上行平均速率40%~80%,特别是弱覆盖边缘的网络速率,最高可达600%。     

基于VR技术的智能变电站运行仿真培训系统

为了更加真实、直观地呈现智能变电站的运行整体结构,构建基于VR技术的智能变电站运行仿真培训系统。该系统通过5个模块协同完成智能变电站运行仿真培训,结合实际变电站的设备情况,采用二维纹理映射方法完成变电站设备仿真建模;系统利用虚拟现实交互模块呈现培训场景,参与培训人员能够通过人机交互设备,实现场景漫游和相关培训内容操作。测试结果表明:该系统能够较好地模拟出智能变电站的实际运行情况,参与培训的人员可以身临其境地完成整个变电站的巡视,更真实、直观地呈现智能变电站的整体状态。     

Memory-based dynamic event-triggered control for networked interval type-2 fuzzy systems subject to DoS attacks

In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov–Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.     

Influence of pressure and dwell time on pressure-assisted sintering of calcium cobaltite

Calcium cobaltite Ca₃Co₄O₉, abbreviated Co349, is a promising thermoelectric material for high-temperature applications in air. Its anisotropic properties can be assigned to polycrystalline parts by texturing. Tape casting and pressure-assisted sintering (PAS) are a possible future way for a cost-effective mass-production of thermoelectric generators. This study examines the influence of pressure and dwell time during PAS at 900°C of tape-cast Co349 on texture and thermoelectric properties. Tape casting aligns lentoid Co349. PAS results in a textured Co349 microstructure with the thermoelectrically favorable ab-direction perpendicular to the pressing direction. By pressure variation during sintering, the microstructure of Co349 can be tailored either toward a maximum figure of merit as required for energy harvesting or toward a maximum power factor as required for energy harvesting. Moderate pressure of 2.5 MPa results in 25% porosity and a textured microstructure with a figure of merit of 0.13 at 700°C, two times higher than the dry-pressed, pressureless-sintered reference. A pressure of 7.5 MPa leads to 94% density and a high power factor of 326 µW/mK² at 800°C, which is 11 times higher than the dry-pressed reference (30 MPa) from the same powder.     

激光在线测厚振动分析与精度优化

激光测厚具有安全可靠、测量精度高、测量范围大等优点,广泛应用于纸张、电池极片等薄膜类材料厚度的在线测量。带材宽幅方向扫描测厚时由于扫描架往复运动会产生机械振动,影响在线测厚精度。针对该问题,以锂离子电池极片厚度测量为例,使用双激光差动式测厚平台对电池极片和铜箔分别进行厚度测量,然后对测厚数据进行频谱分析,探究其振动规律的相似性,并基于频谱分析结果采用滑动带阻滤波方式对测厚数据进行处理,滤波后极片和铜箔的厚度极差分别降低了33.4%和73.8%,有效过滤了机械振动导致的测量误差,可满足极片和铜箔厚度测量的精度要求。     

Saliency4ASD: Challenge,dataset and tools for visual attention modeling for autism spectrum disorder

The recent studies showing that gaze features can be useful in the identification of Autism Spectrum Disorder (ASD), have opened a new domain where Visual Attention (VA) modeling could be of great help. In this sense, this paper presents a report of the Grand Challenge “Saliency4ASD: Visual attention modeling for Autism Spectrum Disorder”, organized at IEEE ICME’19, aiming at supporting the research on VA modeling towards this healthcare societal challenge. In particular, this paper describes the workflow, obtained results, and datasets and tools that were used within this activity, in order to help on the development and evaluation of two types of VA models: (1) to predict saliency maps that fit gaze behavior of people with ASD, and (2) to identify individuals with ASD from typical development.     

A customized low-rank prior model for structured cartoon–texture image decomposition

The mathematical characterization of the texture component plays an instrumental role in image decomposition. In this paper, we are concerned with a low-rank texture prior based cartoon–texture image decomposition model, which utilizes a total variation norm and a global nuclear norm to characterize the cartoon and texture components, respectively. It is promising that our decomposition model is not only extremely simple, but also works perfectly for globally well-patterned images in the sense that the model can recover cleaner texture (or details) than the other novel models. Moreover, such a model can be easily reformulated as a separable convex optimization problem, thereby enjoying a splitting nature so that we can employ a partially parallel splitting method (PPSM) to solve it efficiently. A series of numerical experiments on image restoration demonstrate that PPSM can recover slightly higher quality images than some existing algorithms in terms of taking less iterations or computing time in many cases.     

Extrinsic parameter's adjustment and potential implications in Plasmodium falciparum malaria diagnosis

Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the “p” value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.