Novel Semi-supervised Fault Diagnosis Method Combining Tri-training and Deep Belief Network for Charging Equipment of Electric Vehicle

Gao, De-Xin; Lin, Xihao; Zheng, Xiaoyu; Yang, Qing

doi:10.1007/s12239-022-0150-7

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…The results of this system are as follows: (1) A personal network hard disk system based on the DBN image recognition algorithm is designed, which can automatically identify and classify the uploaded images and save them to the corresponding folders, thus facilitating the management and searching of images by users. (2) The DBN image recognition algorithm has been improved and optimized to achieve better accuracy and results in classifying and recognizing images. The DBN image recognition algorithm was much more accurate than the traditional neural networkbased classification algorithm through experimental comparison.…”

Section: Discussionmentioning

confidence: 99%

“…This topic is based on the DBN image recognition algorithm to design and implement a personal net disk system. In addition to implementing the basic file functions of the net disk, the most important thing is to perform image recognition classification based on images [2] . The classification design can facilitate the management of image files by users.…”

Section: Introductionmentioning

confidence: 99%

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Design and implementation of personal online disk system based on DBN image recognition algorithm

Wen,

Chen,

Tang

et al. 2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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With the growth of the Internet and the age of information explosion, more and more people need network drives to store and share the resources they find online. While downloading files to a local hard drive can ensure data security, this also brings with it many inconveniences, such as the need to share with others using third-party websites or communication software, or the need to transfer files using a removable hard drive. As a result, personal network drives have emerged on the market to make data sharing easier by transferring files to a server. This project will retain the basic functions of a traditional network drive system, such as file uploading, downloading, and deletion, while taking into account the security of the data and using deep learning-based DBN algorithms for image recognition, using artificial intelligence and image recognition techniques to detect pirated and illegal image material. Through the combined application of the above technologies, an efficient personal web-disk system is designed and implemented.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and implementation of personal online disk system based on DBN image recognition algorithm

Wen,

Chen,

Tang

et al. 2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

View full text Add to dashboard Cite

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Performance Analysis of MixMatch-Based Semi-Supervised Learning for Defect Detection in Manufacturing Processes

Kim,

Jeong,

Kim

2023

J. Soc. Korea Ind. Syst. Eng.

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Sensor fault diagnosis modeling of lithium-ion batteries for electric vehicles

Li¹,

Fan²

2023

mat express

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Electric Vehicles (EVs) are considered the replacement for gas-emitting and environmentally polluting fuel vehicles. The development of EVs relies on electronic devices and communication circuits for ease of access. The heart of EVs is the battery that requires post-run-time charging and precise maintenance. Electronic sensors attached to the batteries monitor their health, discharging, and charging rate for user notification and prolonged functioning. Therefore, the operation of the sensors is to be monitored promptly for effective battery maintenance. This article introduces a Fault Detection Method (FDM) using Operational Data (OD) accumulated from the EV. This data is first split into active and passive that references the run-time and idle time of the vehicle. The sensor operations during the idle time are considered similar due to minimal or no sensing function. Contrarily the run-time observation shows up variations that are not huge compared to the previous history. In this data verification process, recurrent learning is employed for identifying variations in the active and passive states of the EVs. If variations are continuous regardless of the active and passive states, then the sensor is identified as faulty post the manual battery life assessment. The learning is trained using the current and previous sensor observations and batter health for identifying the variations.

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Novel Semi-supervised Fault Diagnosis Method Combining Tri-training and Deep Belief Network for Charging Equipment of Electric Vehicle

Cited by 3 publications

References 24 publications

Design and implementation of personal online disk system based on DBN image recognition algorithm

Design and implementation of personal online disk system based on DBN image recognition algorithm

Performance Analysis of MixMatch-Based Semi-Supervised Learning for Defect Detection in Manufacturing Processes

Sensor fault diagnosis modeling of lithium-ion batteries for electric vehicles

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