Good dielectric performance and broadband dielectric polarization in Ag,Nb co-doped TiO2

Due to the demand of miniaturization and integration for ceramic capacitors in electronic components market, TiO₂-based ceramics with colossal permittivity has become a research hotspot in recent years. In this work, we report that Ag⁺/Nb⁵⁺ co-doped (Ag_1/4Nb_3/4)_xTi_1−xO₂ (ANTOx) ceramics with colossal permittivity over a wide frequency and temperature range were successfully prepared by a traditional solid–state method. Notably, compositions of ANTO0.005 and ANTO0.01 respectively exhibit both low dielectric loss (0.040 and 0.050 at 1 kHz), high dielectric permittivity (9.2 × 10³ and 1.6 × 10⁴ at 1 kHz), and good thermal stability, which satisfy the requirements for the temperature range of application of X9R and X8R ceramic capacitors, respectively. The origin of the dielectric behavior was attributed to five dielectric relaxation phenomena, i.e., localized carriers' hopping, electron–pinned defect–dipoles, interfacial polarization, and oxygen vacancies ionization and diffusion, as suggested by dielectric temperature spectra and valence state analysis via XPS; wherein, electron-pinned defect–dipoles and internal barrier layer capacitance are believed to be the main causes for the giant dielectric permittivity in ANTOx ceramics.     

SLM printing of cermet powders: Inhomogeneity from atomic scale to microstructure

It is very challenging for 3D printing based on the selective laser melting (SLM) technology to obtain cermet bulk materials with high density and homogeneous microstructures. In this work, the SLM process of the cermet powders was studied by both simulations and experiments using the WC-Co cemented carbides as an example. The results indicated that the evolution of the ceramic and metallic phases in the cermet particle during the heating, melting and solidification processes were all significantly inhomogeneous from atomic scale to mesoscale microstructures. As a consequence, the microstructural defects were caused intrinsically in the printed bulk material. The formation and growth of the bonding necks between the particles were mainly completed at the later stage of laser heating and the early stage of solidification. Both simulations and experiments demonstrated that thin amorphous layers formed at the ceramics/metal interfaces. This work disclosed the mechanisms for the evolution from the atomic scale to microstructure during the SLM printing of cermet powders, and discovered the origin of the defects in the printed cermet bulk materials.     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

印刷电路板缺陷检测的实际应用

电路板在我们的日常生活中非常常见,这就使得印刷电路板的缺陷检测显得尤为重要。AOI作为新兴的检测PCB板缺陷的系统,在生产实际中正在被大家熟知并且应用。相较于传统的检测方式,AOI系统比较灵活,无论是在检测时间还是系统运算上,或者是对相关技术人员的要求相较于传统方式都比较有优势,本文就AOI系统在实际中的应用展开讨论,分析并且介绍了在实际应用中的具体细则。     

基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。     

Multiply Charged Conjugated Polyelectrolytes as a Multifunctional Interlayer for Efficient and Scalable Perovskite Solar Cells

A series of anionic conjugated polyelectrolytes (CPEs) is synthesized based on poly(fluorene-co-phenylene) by varying the side-chain ionic density from two to six per repeat units (MPS2-TMA, MPS4-TMA, and MPS6-TMA). The effect of MPS2, 4, 6-TMA as interlayers on top of a hole-extraction layer of poly(bis(4-phenyl)-2,4,6-trimethylphenylamine (PTAA) is investigated in inverted perovskite solar cells (PeSCs). Owing to the improved wettability of perovskites on hydrophobic PTAA with the CPEs, the PeSCs with CPE interlayers demonstrate a significantly enhanced device performance, with negligible device-to-device dependence relative to the reference PeSC without CPEs. By increasing the ionic density in the MPS-TMA interlayers, the wetting, interfacial defect passivation, and crystal growth of the perovskites are significantly improved without increasing the series resistance of the PeSCs. In particular, the open-circuit voltage increases from 1.06 V for the PeSC with MPS2-TMA to 1.11 V for the PeSC with MPS6-TMA. The trap densities of the PeSCs with MPS2,4,6-TMA are further analyzed using frequency-dependent capacitance measurements. Finally, a large-area (1 cm²) PeSC is successfully fabricated with MPS6-TMA, showing a power conversion efficiency of 18.38% with negligible hysteresis and a stable power output under light soaking for 60 s.     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Thermal and transport properties of as-grown Ni-rich TiNi shape memory alloys

Electrical resistivity, Seebeck coefficient, specific heat and thermal conductivity measurements on the Ti_50−xNi_50+x (x = 0.0–1.6 at.%) shape memory alloys are performed to investigate their thermal and transport properties. In this study, anomalous features are observed in both cooling and heating cycles in all measured physical properties of the slightly Ni-rich TiNi alloys (x ≤ 1.0), corresponds to the transformation between the B19′ martensite and B2 austenite phases. Besides, the transition temperature is found to decrease gradually with increasing Ni content, and the driving force for the transition is also found to diminish slowly with the addition of excess Ni, as revealed by specific heat measurements. While the signature of martensitic transformation vanishes for the Ni-rich TiNi alloys with x ≥ 1.3, the characteristics of strain glass transition start to appear. The Seebeck coefficients of these TiNi alloys were found to be positive, suggesting the hole-type carriers dominate the thermoelectric transport. From the high-temperature Seebeck coefficients, the estimated value of Fermi energy ranges from ∼1.5 eV (Ti_48.4Ni_51.6) to ∼2.1 eV (Ti₅₀Ni₅₀), indicating the metallic nature of these alloys. In addition, the thermal conductivity of the slightly Ni-rich TiNi alloys with x ≤ 1.0 shows a distinct anomalous feature at the B19′ → B2 transition, likely due to the variation in lattice thermal conductivity.