Voltage sag compensation with minimum energy injection by use of a micro‐SMES

This paper presents a method to compensate voltage sags with minimum energy injection for a series‐connected voltage restorer using a micro‐SMES. A circuit for extracting the fundamental symmetrical components from sag voltages and a minimum energy injection algorithm is described. Simulations of voltage sag compensation have been carried out using PSCAD/EMTDC for various faults. The simulation results confirm the validity of the proposed method and show the possibility of reducing the size of energy storage devices. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 141(3): 70–80, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10047     

三峡工地高压供电线路故障判断方法的探讨

高压供电设备故障特别是输电线路的高压接地故障对工地的影响是相当大的。由于接地故障为隐性故障，没有明显的物理现象，且若是长期接地可能会发展成较为严重的两相或三相短路故障，使系统解列运行直至崩溃；由于工地流动人员较多，还易造成电击事故。利用高压一相接地时在线路中产生零序电压这一特性来迅速判断和切除接地的用电设备，可以保证各项施工的顺利进行。     

Enumerating maximal bicliques in bipartite graphs with favorable degree sequences

We propose an output-sensitive algorithm for the enumeration of all maximal bicliques in a bipartite graph, tailored to the case when the degree distribution in one partite set is very skewed. We accomplish a worst-case bound better than previously known general bounds if, e.g., the degree sequence follows a power law.     

M序列的对称性变换及对称群

n级M序列是由n级非线性回馈移位寄存器产生的周期为2n的二元序列,也称为de Bruijn序列。该文将讨论M序列的对称性变换,提出了一类新的对称性变换——"与/或"逻辑对称性变换,并研究了基于对称性变换的对称群。     

软起动器和变频器的时分多机复用技术及其应用

本文提出了时分多机复用的概念，并对其技术和应用加以具体说明，展现了该技术良好的应用前景。     

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.     

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.