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Xuerui Zhang
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400042, China

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Journal article
Published: 23 April 2021 in Actuators
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Gear reliability assessment of vehicle transmission has been a challenging issue of determining vehicle safety in the transmission industry due to a significant amount of classification errors with high-coupling gear parameters and insufficient high-density data. In terms of the preprocessing of gear reliability assessment, this paper presents a representation generation approach based on generative adversarial networks (GAN) to advance the performance of reliability evaluation as a classification problem. First, with no need for complex modeling and massive calculations, a conditional generative adversarial net (CGAN) based model is established to generate gear representations through discovering inherent mapping between features with gear parameters and gear reliability. Instead of producing intact samples like other GAN techniques, the CGAN based model is designed to learn features of gear data. In this model, to raise the diversity of produced features, a mini-batch strategy of randomly sampling from the combination of raw and generated representations is used in the discriminator, instead of using all of the data features. Second, in order to overcome the unlabeled ability of CGAN, a Wasserstein labeling (WL) scheme is proposed to tag the created representations from our model for classification. Lastly, original and produced representations are fused to train classifiers. Experiments on real-world gear data from the industry indicate that the proposed approach outperforms other techniques on operational metrics.

ACS Style

Jie Li; Boyu Zhao; Kai Wu; Zhicheng Dong; Xuerui Zhang; Zhihao Zheng. A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network. Actuators 2021, 10, 86 .

AMA Style

Jie Li, Boyu Zhao, Kai Wu, Zhicheng Dong, Xuerui Zhang, Zhihao Zheng. A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network. Actuators. 2021; 10 (5):86.

Chicago/Turabian Style

Jie Li; Boyu Zhao; Kai Wu; Zhicheng Dong; Xuerui Zhang; Zhihao Zheng. 2021. "A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network." Actuators 10, no. 5: 86.

Review
Published: 06 January 2015 in Environmental Science and Pollution Research
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The smart water quality monitoring, regarded as the future water quality monitoring technology, catalyzes progress in the capabilities of data collection, communication, data analysis, and early warning. In this article, we survey the literature till 2014 on the enabling technologies for the Smart Water Quality Monitoring System. We explore three major subsystems, namely the data collection subsystem, the data transmission subsystem, and the data management subsystem from the view of data acquiring, data transmission, and data analysis. Specifically, for the data collection subsystem, we explore selection of water quality parameters, existing technology of online water quality monitoring, identification of the locations of sampling stations, and determination of the sampling frequencies. For the data transmission system, we explore data transmission network architecture and data communication management. For the data management subsystem, we explore water quality analysis and prediction, water quality evaluation, and water quality data storage. We also propose possible challenges and future directions for each subsystem.

ACS Style

Jianhua Dong; Guoyin Wang; Huyong Yan; Ji Xu; Xuerui Zhang. A survey of smart water quality monitoring system. Environmental Science and Pollution Research 2015, 22, 4893 -4906.

AMA Style

Jianhua Dong, Guoyin Wang, Huyong Yan, Ji Xu, Xuerui Zhang. A survey of smart water quality monitoring system. Environmental Science and Pollution Research. 2015; 22 (7):4893-4906.

Chicago/Turabian Style

Jianhua Dong; Guoyin Wang; Huyong Yan; Ji Xu; Xuerui Zhang. 2015. "A survey of smart water quality monitoring system." Environmental Science and Pollution Research 22, no. 7: 4893-4906.