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。     

基于CiteSpace的资源型产业研究知识图谱分析

资源型产业发展为推进国家经济增长和工业化进程提供了重要保障。为深入了解资源型产业的研究情况，以CNKI数据库为数据源，搜集2000—2020年关于资源型产业的核心及以上期刊论文，利用CiteSpace软件从发文作者与研究机构分布、关键词共现网络和时区图谱等方面，绘制知识图谱，进行可视化分析。研究发现：资源型产业领域的研究成果愈加丰富，但研究群体间联系合作较少，且现有的合作研究主要集中在所处地域资源富集和具有学科优势的研究机构及学者；资源型产业领域的研究热点可概括为产业发展、资源型城市、产业集群、产业结构、产业链和产业集聚等方面；针对资源型产业领域未来可从资源型产业相关理论研究、创新发展模式和可持续发展等方面深入展开。     

Design of frameworks for self-adaptive service-oriented applications: A systematic analysis

Self-adaptive service-oriented Applications (Self-Apps) must be able to understand themselves or the environment in which they are executed, and propose solutions to meet changing conditions. The development of these applications is not a trivial task, since it encompasses issues from different research areas. Despite the importance of frameworks for Self-Apps, there is a lack of comprehensive analysis of how the design of such applications is performed, and regarding the standardization of concepts and coverage of minimum requirements for Self-Apps. The main contribution of this article is to present this comprehensive analysis, providing the state of the art for this subject. This analysis was built through a Systematic Mapping Study, based on a total of 65 studies, from which we identify the main attributes for Quality of Service (QoS), search strategies, and service management strategies employed in the design of frameworks for Self-Apps. The main aspects of requirements involved in the design of Self-Apps were pointed out to stakeholders. For example, these applications must implement a method for evaluation of QoS based on metrics. We also put forward the S-Frame, a modular solution that brings together the main features for the design of Self-Apps, and describe the main challenges concerning these applications.     

Revealing Nanomechanical Domains and Their Transient Behavior in Mixed-Halide Perovskite Films

Halide perovskites are a versatile class of semiconductors employed for high performance emerging optoelectronic devices, including flexoelectric systems, yet the influence of their ionic nature on their mechanical behavior is still to be understood. Here, a combination of atomic-force, optical, and compositional X-ray microscopy techniques is employed to shed light on the mechanical properties of halide perovskite films at the nanoscale. Mechanical domains within and between morphological grains, enclosed by mechanical boundaries of higher Young's Modulus (YM) than the bulk parent material, are revealed. These mechanical boundaries are associated with the presence of bromide-rich clusters as visualized by nano-X-ray fluorescence mapping. Stiffer regions are specifically selectively modified upon light soaking the sample, resulting in an overall homogenization of the mechanical properties toward the bulk YM. This behavior is attributed to light-induced ion migration processes that homogenize the local chemical distribution, which is accompanied by photobrightening of the photoluminescence within the same region. This work highlights critical links between mechanical, chemical, and optoelectronic characteristics in this family of perovskites, and demonstrates the potential of combinational imaging studies to understand and design halide perovskite films for emerging applications such as photoflexoelectricity.     

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.     

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.     

市售西班牙自然发酵香肠的食用品质特征分析

以销量较高的市售西班牙自然发酵香肠为研究对象，通过感官评价筛选出喜好度较高的发酵香肠，测定香肠水分含量、pH值、色泽、质地等指标，采用顶空固相微萃取-气相色谱-质谱联用（headspace-solid phase microextraction-gas chromatography-mass spectrometry，HS-SPME-GC-MS）法分析香肠挥发性风味成分。结果表明，总体可接受性较高的自然发酵香肠具有以下特征：光泽度高、色泽暗红（亮度值16.028～31.399，红度值13.80～16.31），组织紧密、质地均一、硬度（4 721.859 0～6 360.352 0 g）适中，pH值（5.68～5.89）、水分含量（20.12%～28.09%）较低，咀嚼感好、干燥度适中，果香味、清香味、甜味、乳香味、鲜味明显，咸味、酸味适中，胡椒味较淡。总体接受性最高的香肠（1号香肠）中检出81 种挥发性风味成分，其中乙醛、异戊醛、2,3-丁二酮、月桂烯、3-甲基丁酸乙酯、顺-4-羟基-6-十二烯酸内酯、乙酸为主要风味成分（香气活力值＞50）。     

退火温度对新型锆合金薄板带材析出相和织构的影响

采用扫描电镜和超高分辨透射电镜,对具有良好冲制性能的新型锆合金薄板成品带材进行含晶粒、第二相粒子等在内的显微组织研究,并探索真空退火处理条件下温度对带材显微组织的影响。结果显示:新型锆合金薄板成品带材晶粒平均尺寸2.17 μm,存在{0001}<1010>和{0001}<1120>两种织构,大部分晶粒<1120>平行带材RD方向,较少晶粒<1010>平行带材RD方向;第二相粒子分布在晶粒内部及晶界,平均尺寸114 nm,尺寸较大的为不规则椭圆形的Zr-Nb-Fe相,尺寸较小的为圆形的β-Nb相;热处理退火温度降低,带材晶粒尺寸减小,第二相粒子细小弥散分布;新型锆合金薄板成品带材良好冲制性能主要源于轧制积累应变诱发再结晶过程进行充分,导致晶粒细小及孪晶发生破碎;相对轧制变形,退火对带材冲制性能影响不显著。