Simplified Perovskite Solar Cell with 4.1% Efficiency Employing Inorganic CsPbBr3 as Light Absorber

Perovskite solar cells with cost‐effectiveness, high power conversion efficiency, and improved stability are promising solutions to the energy crisis and environmental pollution. However, a wide‐bandgap inorganic–semiconductor electron‐transporting layer such as TiO₂ can harvest ultraviolet light to photodegrade perovskite halides, and the high cost of a state‐of‐the‐art hole‐transporting layer is an economic burden for commercialization. Here, the building of a simplified cesium lead bromide (CsPbBr₃) perovskite solar cell with fluorine‐doped tin oxide (FTO)/CsPbBr₃/carbon architecture by a multistep solution‐processed deposition technology is demonstrated, achieving an efficiency as high as 4.1% and improved stability upon interfacial modification by graphene quantum dots and CsPbBrI₂ quantum dots. This work provides new opportunities of building next‐generation solar cells with significantly simplified processes and reduced production costs.     

基于LMD的PWM整流电路故障特征提取新方法

脉冲宽度调制(PWM)整流电路结构日益复杂,对其可靠运行提出了更高的要求;对局域均值分解(LMD)用于PWM整流电路的故障特征提取进行研究,提出一种基于LMD和加权频带能量法的特征提取新方法;该方法通过逐步抽取调频调幅成分将故障信号在频域上展开,然后基于信号能量的频带分布特点,充分考虑各频带成分与故障的相关性,构造故障特征向量,实现特征提取;最后以PWM整流电路为例进行仿真,相电压380V,仿真时间0.5s,0.1s时注入故障;结果表明,该方法能有效地提取故障信号的特征,并降低特征向量的维数。     

基于YOLOV4的工件表面质量在线检测方法研究

目的 提升自动化产线上工件表面微小缺陷的检测精度和检测速度。方法 首先,在预处理阶段提出采用CutMix的数据增强方法,增加训练样本的多样性,提高模型的鲁棒性和泛化能力,避免训练模型产生过拟合;使用K–means++聚类算法生成边界候选框,以适应不同尺寸的缺陷,并较早地筛选出更精细的特征。其次,借助CSP Darknet53网络及SPP模块提取输入原始图像的特征,通过训练获得针对工件表面质量的在线检测模型,提升YOLOV4缺陷位置检测及识别的精度。结果 实验结果表明,文中所提出的基于YOLOV4的工件表面质量在线监测方法的预测精度达到97.5%,检测速度达到32.8帧/s,均优于同类的深度学习算法。以贵州某航空工业产品的自动化产线作为实验平台验证了所提方法的可行性和有效性。结论 该方法具备结构简单清晰、自适应性强等优点,检测精度和速度均满足工业场景需求,可以将其用于产品表面质量的在线检测。     

食品包装材质对消费者绿色购买意愿影响的眼动研究

目的 探讨食品包装材质对消费者注意加工流程和绿色购买意愿影响的内在机制。方法 采用包装材质2(环保/普通)×食品类型2(实用品/享乐品)双因素实验设计,在货架购物场景下采用Tobii Glasses 2眼镜式眼动仪,采集被试者浏览货架不同食品时的眼动数据(平均注视时间、注视次数、热点图),并结合行为数据综合分析。结果 在平均注视时间上,包装材质与食品类型的交互作用显著,注视次数交互作用不显著;环保材质包装的食品获得更多关注,被试者偏好于选择环保包装的享乐食品。结论 包装材质与食品类型共同影响消费者的视觉感知效果与加工过程,环保包装对青年消费者的绿色购买意愿有正向促进作用。     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

中立型时滞反馈扭转控制系统的稳定性分析

研究了中立型时滞动力吸振器抑制扭转系统的振动问题.针对一类强迫扭转振动系统,采用动力系统的稳定性理论分析了动力吸振器与扭转振动系统的稳定性问题,以时滞为参数,分别得到了动力吸振器与扭转振动系统的稳定区域,并研究时滞变化时动力吸振器对于主振动系统振动的吸收效果,通过与时滞动力吸振器对比,得到了更大的吸振器和减振系统时滞稳定工作区域,通过数值模拟验证了结论的正确性.     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

Effect of nozzle type on pulse-jet cleaning performance of ceramic filter tube

The dust-cleaning efficiency of the ceramic filter tube is the key factor to determine the long-term stable operation of the dust removal system, and the type of nozzle significantly influences the performance. In this study, four types of nozzles (injection hole, sudden enlargement nozzle, tubular nozzle, and induction nozzle) are used to conduct comparative experimental study on the dust-cleaning effect of the ceramic filter tube. Using the schlieren technique find that the tubular and induction nozzles can improve the deviation angle of pulse-jet airflow. The injection parameters were set as an injection pressure of 200 kPa, an injection distance of 120 mm and a pulse width of 80 ms in the experiment. Using the sidewall pressure measurement system indicates that the integrated pressure of the sudden enlargement nozzle is the largest, but the uniformity is poor. The comprehensive pressure of the sudden enlargement nozzle is increased by 64.86 %, and the non-uniformity is reduced by 28.62 % compared with those of the induction nozzle. Using the visualization filtration cycle test system implies that the degree of damage and stripping state of the dust layer are directly proportional to the sidewall pressure. Moreover, it is found that the residual pressure drop changes exponentially with the dust removal quality under different nozzle types. These conclusions can help explain the mechanism of dust stripping and provide a basis for the selection of nozzles.     

A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction

We analyze a variational approach to image segmentation that is based on a strictly convex non-quadratic cost functional. The smoothness term combines a standard first-order measure for image regions with a total-variation based measure for signal transitions. Accordingly, the costs associated with discontinuities are given by the length of level lines and local image contrast. For real images, this provides a reasonable approximation of the variational model of Mumford and Shah that has been suggested as a generic approach to image segmentation.The global properties of the convex variational model are favorable to applications: Uniqueness of the solution, continuous dependence of the solution on both data and parameters, consistent and efficient numerical approximation of the solution with the FEM-method.Various global and local properties of the convex variational model are analyzed and illustrated with numerical examples. Apart from the favorable global properties, the approach is shown to provide a sound mathematical model of a useful locally adaptive smoothing process. A comparison is carried out with results of a region-growing technique related to the Mumford-Shah model.