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671 results on '"Li, Guoxiang"'

101. Tubule System of Earliest Shells as a Defense Against Microboring

Author

Li, luoyang, primary, Cui, Linhao, additional, Topper, Timothy, additional, Betts, Marissa, additional, Altanshagai, Gundsambuu, additional, Enkhbaatar, Batktuyag, additional, Li, Guoxiang, additional, Li, Sanzhong, additional, Skovsted, Christian, additional, and Zhang, Xingliang, additional

Published
2023
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112. Simulation and Control Strategy Study of the Hydrogen Supply System of a Fuel Cell Engine.

Author

Zhao, Ming, Wang, Wenbin, Zhu, Xiaochun, Cao, Mengxue, Gao, Zhengyuan, Sun, Ke, Bai, Shuzhan, and Li, Guoxiang

Subjects
FUEL systems, FUEL cells, HYDROGEN, ENGINES
Abstract

The hydrogen supply system is one of the important components of a hydrogen fuel cell engine, and its performance has an important impact on the economy and power of the engine system. In this paper, a hydrogen supply system based on cyclic mode is designed for a hydrogen fuel cell stack with a full load power of 150 kW, and the corresponding hydrogen fuel cell engine simulation model is built and validated. The control strategy of the fuel cell hydrogen supply system is developed, and its effect is verified through bench tests. The results show that the developed control strategy can keep the volume fraction of nitrogen below 6%, the hydrogen excess ratio does not exceed 1.5 under medium and high operating conditions, the anode pressure is relatively stable, and the stack can operate efficiently and reliably. [ABSTRACT FROM AUTHOR]

Published
2023
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135. Remote sensing image classification based on DenseNet feature hashing.

Author

LI Guoxiang, XIA Guo' en, BAI Liming, and MA Wenbin

Subjects
IMAGE recognition (Computer vision), CLASSIFICATION algorithms, REMOTE sensing, SUPPORT vector machines, BINARY codes, DESCRIPTOR systems
Abstract

To achieve accurate remote sensing scene classification, this study proposed a classification algorithm based on DenseNet feature hashing. First, dimension reduction was conducted for high - level semantic features output by a DenseNet through a fully connected layer. Then, normalized feature vectors were generated as the input of the classification layer using an activation function, and an end - to - end classification network was formed. Using the trained network as a feature extractor, the features of the activation layer of test data were mapped into binary hash codes. Finally, the remote sensing scene classification was conducted using support vector machine. The new algorithm was validated on public data sets UC Merced, WHU, and NWPU - RESISC45, and its classification effect was compared with that of multifile algorithms at three levels, namely the conventional local feature descriptor, transfer learning, and depth feature coding. The experimental results are as follows. The new algorithm had significantly higher classification accuracy than conventional algorithms based on mid - and low -level semantic features. Compared with the algorithm based on transfer learning, the proposed algorithm has fine -scale DenseNet feature mapping and accumulates elements used to determine core categories of images and, thus, is more suitable for the feature distribution of remote sensing images. Compared with the depth feature coding algorithm, the new algorithm has a simple feature structure, high classification accuracy, and strong transferability and extensibility and, thus, can meet the classification requirements of different remote sensing scenarios. [ABSTRACT FROM AUTHOR]

Published
2023
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136. Occurrence of Tannuolina in the early Cambrian siltstones of the Hazara Basin, North Pakistan, and its biostratigraphical significance.

Author

Pan, Bing, Khan, Malik Muhammad Saud Sajid, Li, Guoxiang, Ahmed, Shehryar, Qasim, Muhammad, Yin, Zongjun, Jadoon, Ishtiaq A. K., and Zhu, Maoyan

Subjects
BIOSTRATIGRAPHY, CARBONATE rocks, LITHOFACIES, SUBCONTINENTS, FOSSILS
Abstract

The tommotiids are an important group of Cambrian small shelly fossils, primarily retrieved from carbonate rock by acid dissolution. In this study, abundant isolated sclerites of Tannuolina are recovered from the siltstone-dominated upper part of the Hazira Formation in the eastern Hazara Basin, North Pakistan. This discovery of tommotiids preserved in the siltstones not only suggests their occurrence in a variety of sedimentary lithofacies, but also provides the opportunity to look for the sclerites or scleritomes (or even soft tissue) of tommotiids in much wider taphonomic windows. The sclerites include two morphs (i.e. mitral and sellate types). Through morphological comparison, they can be identified as Tannuolina zhangwentangi Qian and Bengtson, 1989. The large mitral and sellate sclerites (about 1 cm) illustrated in the present study manifest a relatively consistent morphology during size increase. The sellate sclerites may contain two sub-types, a larger sellate sclerite with sella on the sellate side and a smaller convex triangular sclerite without sella on either side. In the original scleritome, the two sub-types of sellate sclerites probably combine as a composite with the duplicatural side of the smaller one attached on the sella area of the large one. For the first time, T. zhangwentangi, previously unknown outside South China, has been recovered from the Indian subcontinent. The middle and upper part of the Hazira Formation exclusively bearing T. zhangwentangi can directly invite correlation with the S. flabelliformis–T. zhangwentangi Assemblage Zone of South China representing the uppermost Cambrian Stage 2. This new find not only signifies the utility of T. zhangwentangi for intercontinental biostratigraphical correlation, but also suggests that the Terreneuvian small shelly fossil (SSF) biostratigraphy between the Indian subcontinent and South China is consistent, comprising three (at least two) correlative SSF Assemblage Zones (Zone I, possibly Zone III, and Zone IV of South China). Additionally, our result may also support a relatively close palaeogeographical linkage between these two regions in the early Cambrian. Thematic collection: This article is part of the Advances in the Cambrian Explosion collection available at: https://www.lyellcollection.org/topic/collections/advances-in-the-cambrian-explosion [ABSTRACT FROM AUTHOR]

Published
2023
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149. A Novel k-MPSO Clustering Algorithm for the Construction of Typical Driving Cycles

Author

Wei Yan, Mei-Jing Li, Qu Chunyan, Li Guoxiang, and Yong-Chang Zhong

Subjects
particle swarm optimization, General Computer Science, Computer science, 020209 energy, General Engineering, Particle swarm optimization, Swarm behaviour, 02 engineering and technology, k-MPSO clustering algorithm, driving cycle, Data set, Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, General Materials Science, lcsh:Electrical engineering. Electronics. Nuclear engineering, Cluster analysis, lcsh:TK1-9971, Algorithm, Artificial fish swarm algorithm
Abstract

The practical driving cycle is of great significance in studying the control strategy of vehicles, and effective clustering of micro-trips is the key to obtaining the typical driving cycle. A novel and efficient method for constructing typical driving cycles is presented in this paper. First, by combining the preying behavior and random behavior of the artificial fish swarm algorithm (AFSA) with particle swarm optimization (PSO), a modified particle swarm optimization (MPSO) is proposed. By comparing the means and standard deviations of the optima, MPSO is verified as much more accurate and stable than PSO, AFSA, select particle swarm optimization (SPSO) and cross particle swarm optimization (CPSO) in the optimization calculation of four typical multi-modal benchmark functions. Second, by applying MPSO to optimize the k-means algorithm, the k-MPSO clustering algorithm is obtained. In the case of clustering the Iris standard data set, the average error rates of the k-means algorithm and k-MPSO clustering algorithm are 11.6% and 7.8%, respectively, which means that the k-MPSO clustering algorithm has a stronger searching ability. Finally, with the ECAN Tools software, real-world driving data that include thousands of micro-trips in Jinan are collected, and 19 representative characteristic parameters are selected to fully describe the driving conditions. After principal component analysis (PCA), the k-MPSO clustering algorithm method is applied to cluster the micro-trips into three classes and construct the typical driving cycle in Jinan.

Published
2020

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