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832 results on '"Kong, Fanrong"'

10. Experimental Research on Isothermal Relaxation Current Measurement of High Voltage Cable

Author

Liu, Heqian, Shen, Yubo, Kong, Fanrong, Zhang, Peng, Zhang, Dewen, Zhang, Jian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Xie, Kaigui, editor, Hu, Jianlin, editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2023
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29. Micro Multiobjective Evolutionary Algorithm With Piecewise Strategy for Embedded-Processor-Based Industrial Optimization

Author

Peng, Hu, Kong, Fanrong, and Zhang, Qingfu

Abstract

In some industrial applications, it is required to do off-line multiobjective optimization in embedded systems. Due to their limited computing and memory capability, embedded processor may not be able to run conventional multiobjective optimization evolutionary algorithms (MOEAs). This article proposes a micro MOEA with piecewise strategy $(\mu $ MOEA) for industrial optimization in embedded processor. $\mu $ MOEA introduces an improved piecewise strategy based on the MOEA/D framework, which serially optimizes subclusters to be compatible with embedded processor under limited computing power. For the purpose of further enhancing $\mu $ MOEA, a dynamic and flexible weight vector update trigger mechanism is proposed, so that the algorithm can save and utilize the computing resources of the embedded processor as much as possible. Abundant artificial test problems are carrying out to test the performance of $\mu $ MOEA. Through various experiments, it can be found that $\mu $ MOEA has outstanding performance in ZDT, DTLZ, SMOP, and MaF problems. Last and most importantly, $\mu $ MOEA is successfully applied to two specific application scenarios of industrial optimization on embedded processor for simulation, such as two different types of semi-autogenous grinding optimization problems and micro-grid energy optimization problem, which prove the feasibility of applying MOEA to embedded processor.

Published
2024
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31. Advances in Liquid Crystal Epoxy: Molecular Structures, Thermal Conductivity, and Promising Applications in Thermal Management.

Author

Zhou, Wenying, Wang, Yun, Kong, Fanrong, Peng, Weiwei, Wang, Yandong, Yuan, Mengxue, Han, Xiaopeng, Liu, Xiangrong, and Li, Bo

Subjects
LIQUID crystals, MOLECULAR crystals, MOLECULAR structure, THERMAL conductivity, THERMOMECHANICAL properties of metals, CERAMICS
Abstract

Traditional heat conductive epoxy composites often fall short in meeting the escalating heat dissipation demands of large‐power, high‐frequency, and high‐voltage insulating packaging applications, due to the challenge of achieving high thermal conductivity (k), desirable dielectric performance, and robust thermomechanical properties simultaneously. Liquid crystal epoxy (LCE) emerges as a unique epoxy, exhibiting inherently high k achieved through the self‐assembly of mesogenic units into ordered structures. This characteristic enables liquid crystal epoxy to retain all the beneficial physical properties of pristine epoxy, while demonstrating a prominently enhanced k. As such, liquid crystal epoxy materials represent a promising solution for thermal management, with potential to tackle the critical issues and technical bottlenecks impeding the increasing miniaturization of microelectronic devices and electrical equipment. This article provides a comprehensive review on recent advances in liquid crystal epoxy, emphasizing the correlation between liquid crystal epoxy's microscopic arrangement, organized mesoscopic domain, k, and relevant physical properties. The impacts of LC units and curing agents on the development of ordered structure are discussed, alongside the consequent effects on the k, dielectric, thermal, and other properties. External processing factors such as temperature and pressure and their influence on the formation and organization of structured domains are also evaluated. Finally, potential applications that could benefit from the emergence of liquid crystal epoxy are reviewed. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Interfacial insight into elevated dielectric properties in graphite nanosheets reinforced PVDF composites via engineering TiO2 shell as an interlayer.

Author

Tu, Chunchao, Zhang, Fan, Zheng, Jian, Zhang, Yanqing, Liang, Yaodong, Cao, Jing, Kong, Fanrong, Yang, Yating, Lin, Na, Zhang, Nan, Chen, Xiaolong, Wang, Fang, and Zhou, Wenying

Subjects
STRUCTURAL shells, ELECTRIC conductivity, NANOSTRUCTURED materials, ELECTRICAL resistivity, GRAPHITE, DIELECTRIC loss, DIELECTRIC properties
Abstract

Percolating composites present giant dielectric constant (ε′) at the critical filler loading (fc), whereas the accompanied undesirable high loss and leakage current significantly limit their wide applications owing to the deteriorated breakdown strength (Eb). To prohibit the unwished large loss and leakage current of the graphite nanosheets (GNS)/poly(vinylidene fluoride, PVDF) while still keeping a high ε′, the titanium dioxide (TiO2) encapsulated GNS, i.e., GNS@TiO2, were first obtained in this study via a sol-gel technology, and then composited with PVDF to explore the TiO2 shell's impact and modulation effect on the dielectric properties of the nanocomposites. The findings on the relationship between structure and dielectric parameters reveal that the presence of a TiO2 interlayer not only strengthens the interface interactions, but also remarkably relieves the pronounced interfacial mismatch in both ε′ and electric conductivity between the filler and host matrix, thus leading to boosted Eb of the nanocomposites. Moreover, the dielectric loss and conductivity of the nanocomposites can be significantly suppressed and reduced to rather low levels because of the introduced deep traps resulting from the interlayer frustrating the long-distance electron migration. And the integrated dielectric properties of the GNS@TiO2/PVDF can be effectively regulated by adjusting the GNS@TiO2's electric resistivity and optimizing the TiO2 shell thickness. This work offers a beneficial strategy for the design of percolating nanodielectrics with high εr and Eb but suppressed loss for prospective applications in microelectronic devices and power equipment. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments

Author

Helal, Manal, Kong, Fanrong, Chen, Sharon C-A, Zhou, Fei, Dwyer, Dominic E, Potter, John, Sintchenko, Vitali, Helal, Manal, Kong, Fanrong, Chen, Sharon C-A, Zhou, Fei, Dwyer, Dominic E, Potter, John, and Sintchenko, Vitali

Abstract

The aim of this study was to develop a method that would identify the cluster centroids and the optimal number of clusters for a given sensitivity level and could work equally well for the different sequence datasets. A novel method that combines the linear mapping hash function and multiple sequence alignment (MSA) was developed. This method takes advantage of the already sorted by similarity sequences from the MSA output, and identifies the optimal number of clusters, clusters cut-offs, and clusters centroids that can represent reference gene vouchers for the different species. The linear mapping hash function can map an already ordered by similarity distance matrix to indices to reveal gaps in the values around which the optimal cut-offs of the different clusters can be identified. The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods. This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset. The combination of MSA with the linear mapping hash function is a computationally efficient way of gene sequence clustering and can be a valuable tool for the assessment of similarity, clustering of different microbial genomes, identifying reference sequences, and for the study of evolution of bacteria and viruses.

Published
2023

50. Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data

Author

Helal, Manal, Kong, Fanrong, Chen, Sharon C. A., Bain, Michael, Christen, Richard, Sintchenko, Vitali, Helal, Manal, Kong, Fanrong, Chen, Sharon C. A., Bain, Michael, Christen, Richard, and Sintchenko, Vitali

Abstract

The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. Results: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578.

Published
2023
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