Segmentation of Fuzzy and Touching Cells Based on Modified Minimum Spanning Tree and Concave Point Detection

In order to segment fuzzy and touching cell images accurately, an improved algorithm is proposed based on minimum spanning tree (MST) and concave point detection. First, the cell images are smoothed and enhanced by a Gaussian filter. Then, the improved minimum spanning tree algorithm is used to segment the cell images. The MST algorithm is modified from three aspects, namely, weight function of edges, difference function of internal and inter region, and threshold function and parameter k. Furthermore, the problem of cell touching is solved by means of concave point detection. According to the rugged topography of touching cells, the concave points are found from the concave regions in the touching cell images, which are used to find the separation points quickly and accurately. Experimental results indicate that the new algorithm is ideal and effective

Mineral Froth Image Classification and Segmentation

Accurate segmentation of froth images is always a problem in the research of floating modeling based on Machine Vision. Since a froth image is with the characteristic of complexity and diversity, it is a feasible research idea for the workflow of which the froth image is firstly classified and then segmented by the image segmentation algorithm designed for each type of froth images. This study proposes a new froth image classification algorithm. The texture feature is extracted to complete the classification. Meanwhile, an improved method based on the original valley‐edge detection algorithm is also proposed in the study. Firstly, the fractional differential is introduced to design the new valley‐edge detection templates which can extract more information on bubble edges after the enhancement of the weak edges, and finally the close bubble boundaries are obtained by carrying out the improved deburring and gap connection algorithms. Experimental results show that the new classification method can be used to distinguish the types of small, middle and large bubble images. The improved image segmentation algorithm can well reduce the problems of over‐segmentation and under‐segmentation, and it is in higher adaptability

A Fusion Scheme of Local Manifold Learning Methods

Spectral analysis‐based dimensionality reduction algorithms, especially the local manifold learning methods, have become popular recently because their optimizations do not involve local minima and scale well to large, high‐dimensional data sets. Despite their attractive properties, these algorithms are developed based on different geometric intuitions, and only partial information from the true geometric structure of the underlying manifold is learned by each method. In order to discover the underlying manifold structure more faithfully, we introduce a novel method to fuse the geometric information learned from different local manifold learning algorithms in this chapter. First, we employ local tangent coordinates to compute the local objects from different local algorithms. Then, we utilize the truncation function from differential manifold to connect the local objects with a global functional and finally develop an alternating optimization‐based algorithm to discover the low‐dimensional embedding. Experiments on synthetic as well as real data sets demonstrate the effectiveness of our proposed method

Study on Employee Satisfaction in Enterprises-- Based on the Empirical Analysis of Ningbo Foreign Trade Enterprises

By improving employee satisfaction and fully mobilize the enthusiasm of the employees improve the core competence of enterprises has become one of the important factors, this article through to ningbo home and foreign trade enterprise employee satisfaction survey, the empirical analysis of the influence factors of employee satisfaction, and puts forward relevant suggestions

Commercial Janus Fabrics as Reusable Facemask Materials: A Balance of Water Repellency, Filtration Efficiency, Breathability, and Reusability

Facemasks as personal protective equipment play a significant role in helping prevent the spread of viruses during the COVID-19 pandemic. A desired reusable fabric facemask should strike a balance of water repellency, good filtration efficiency (FE), breathability, and mechanical robustness against washing cycles. Despite significant efforts in testing various commercial fabric materials for filtration efficiency, few have investigated fabric performance as a function of the fiber/yarn morphology and wettability of the fabric itself. In this study, we examine commercial fabrics with Janus-like behaviors to determine the best reusable fabric facemask materials by understanding the roles of morphology, porosity, and wettability of the fabric on its overall performance. We find that the outer layer of the diaper fabric consisted of laminated polyurethane, which is hydrophobic, has low porosity (∼5%) and tightly woven yarn structures, and shows the highest overall FE (up to 54%) in the submicron particle size range (0.03-0.6 μm) among the fabrics tested. Fabric layers with higher porosity lead to lower-pressure drops, indicating higher breathability but lower FE. Tightly woven waterproof rainwear fabrics perform the best after 10 washing cycles, remaining intact morphologically with only a 2-5% drop in the overall FE in the submicron particle size range, whereas other knitted fabric layers become loosened and the laminated polyurethane thin film on the diaper fabric is wrinkled. In comparison, the surgical masks and N95 respirators made from nonwoven polypropylene (PP) fibers see over a 30% decline in the overall FE after 10 washing cycles. Overall, we find that tightly woven Janus fabrics consisting of a low porosity, a hydrophobic outer layer, and a high porosity and hydrophilic inner layer offer the best performance among the fabrics tested as they can generate a high overall FE, achieve good breathability, and maintain fabric morphology and performance over multiple washing cycles

Analysis of wide-band oscillation of hybrid MMC interfacing weak AC power system

The wide-band oscillation of the hybrid MMC induced by excessive power infeed under weak AC power system integration is analyzed in this paper. A closed-loop state-space-based time-domain small-signal model is firstly established to investigate the instability problem. Different from the findings in two-level VSCs or half-bridge MMCs, the root locus analysis and participation factor analysis in this paper reveals that the oscillation frequency and involved control loops are highly related to the operation status. When hybrid MMC operates as a rectifier, a low-frequency oscillation is observed with the d-channel control loop mainly participated. In contrast, a high-frequency oscillation occurs with a q-channel control loop mainly involved when the hybrid MMC operates as an inverter. This wide-band oscillation phenomenon is explored with the aid of two simplified loop-gain-based s-domain models, which are derived referring to the selective modal analysis approach. To suppress the oscillation, sensitivity analysis regarding the impact of parameters on the phase margin is conducted to recognize effective parameter adjustment methods. The analysis results are validated by detailed electromagnetic simulations