Handwritten Character Recognition Using Unsupervised Feature Selection and Multi Support Vector Machine Classifier

doi:10.22266/ijies2021.1231.26

Cited by 1 publication

(1 citation statement)

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“…The parameters of the model were determined using the PSO algorithm. Many results show that DL has strong scalability and generalisation capability compared to previously used machine learning algorithms (ML) such as Logistic Regression (LR), k-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) and does not require manual feature extraction [14][15][16]. However, DL-enabled methods still have some limitations, such as: (1) source and target domains are evenly distributed; (2) the target domain has enough error data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Transfer Learning-Based Network for Diagnosing Minor Faults in the Production of Wireless Chargers

Wang,

Li,

Zhu

2023

Applied Sciences

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Wireless charger production is critical to energy storage, and effective fault diagnosis of bearings and gears is essential to ensure wireless charging performance with high efficiency, high tolerance to misalignment, and thermal safety. As minor faults are usually difficult to detect, timely diagnosis and detection of minor faults can prevent the fault from worsening and ensure the safety of wireless charging systems. Diagnosing minor faults in bearings and gears with data is a useful but difficult task. To achieve a satisfactory diagnosis of minor faults in the production of wireless charging systems related to the mechanical system that produces wireless charging devices, such as robot arms, this paper proposes a deep learning network based on CNN and LSTM (DTLCL). The method uses deep learning network, model-based transfer learning and range adaptation technology. First, a deep neural network is built to extract significant fault features. Second, the deep transfer network is initialised using model-based transfer learning with a good starting point. Finally, range adaptation using the maximum mean discrepancy between the features learned from the source and target ranges is realised by a multi-layer adaptive technology. The effectiveness of the method was verified using actual measurement data. The training time is 19 s, and the accuracy exceeds 94.5%. The explanation results show that the proposed DTLCL method provides higher accuracy and robust identification of smaller errors compared to the current combination of integrated and single non-transmission models. Due to its data-driven nature, the DTLCL method could be used for fault diagnosis of bearings and gears, which would further promote the application process of wireless charging.

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