Jianbin Xiong's research while affiliated with Guangdong Polytechnic Normal University and other places

... Many scholars have studied this, Liu et al [3] proposed to introduce ACO to iteratively optimize the ELM to find the optimal input weights and biases faster for fault classification. Li et al [4] proposed a BLS model based on the BP algorithm and incremental learning for bearing fault diagnosis. Liu et al [5] proposed a non-fuzzy solution weighting-based BP-AdaBoost method for rotor fault type and fault condition level identification. ...

... The proposed solution was compared in terms of the number of trainable parameters of the neural network with other AI-based implementations [30,[36][37][38][39][40][41][42][43][44][45][46] for the same purpose, as depicted in Table 2. The smallest number of parameters is necessary to increase the computational efficiency, to increase the generalization capacity of the model, to prevent overfitting, and to increase the interpretability of the model, respectively. ...

... Due to the memory effects of fractional-order derivatives, they can better reflect the influence of the system's historical states on its current state. Therefore, the theory and methods of fractional-order systems have wide applications in various fields such as control theory, signal processing, biomedical engineering, economics, physics, and more [12,13]. Researchers have conducted extensive studies on fractional-order systems, including stability [14], synchronization [15], and state estimation [16]. ...

... Huang et al. decomposed the original vibration signal into multi-scale vibration components using the wavelet packet decomposition, then used the CNN to extract fault features from the multi-scale vibration components for the fault diagnosis of a wind turbine gearbox [24]. Xiong et al. combined a complementary ensemble empirical mode decomposition with multidimensional non-dimensional indicators to extract complementary ensemble multi-dimensional indicators (CEMDIs) from vibration signals, which were then transformed into two-dimensional data as the input for the CNN to perform the fault diagnosis of rotating machinery [25]. Zhang et al. proposed an adaptive multi-dimensional variational mode decomposition to decompose an original signal and used a multi-scale CNN to extract the fault features from the denoised signal for the fault-type recognition of rolling bearings [26]. ...

... Xiong et al [37] combined complementary ensemble empirical mode decomposition (CEEMD) and mutual dimensionless index (MDI) for feature extraction, subsequently proposing a multi-kernel relevance vector machine (MK-RVM) in the building electrical system fault diagnosis. Xiong et al [38] introduced a feature extraction method combining variational mode decomposition (VMD) and MDI for fault diagnosis of building electrical systems, and used a quantum genetic algorithm optimized support vector machine (QGA-SVM) method in the fault classification part. Zhang and Li [39] proposed a ramanujanfourier transform (RFT) noise estimation method for diagnosing analog circuit faults, where the noise of the output response is extracted by RFT and the extracted noise is further analyzed to obtain diagnostic results. ...

... Therefore, the ARMA-FNN prediction model is established to monitor the state of the prediction cell in real time. In addition, the establishment of relevant models according to the process itself can not only optimize the process parameters, but also optimize the model through continuous collection of analysis data [6]. In the aspect of cell voltage setting, the multi-objective optimization model of cell voltage can be established with the minimum difference between the average voltage and the target value and the good state of the cell as the goal, and the production operation requirement as the constraint condition, so as to obtain a set of better operating parameters, so as to achieve the purpose of energy saving and consumption reduction. ...

... With the acceleration of the process of industrial modernization, automated production has an increasing demand for accurate detection of mechanical parts, especially mechanical gears and bearing edge profiles (Goli et al., 2021;Zicari et al., 2021). With the trend of standardization and serialization of production, the high quality and precision of mechanical gears and bearings have become the key to ensure product quality and safety (Zhu et al., 2022). Therefore, comprehensive and high-precision detection of key features such as mechanical gears and bearing edges is particularly important in modern industrial production, which is not only related to product quality, but also the cornerstone of improving production efficiency and safe operation of equipment (Lv et al., 2022). ...

... ELM is a feedforward neural network, which reduces the training error through the setting of weights and the formulation of hidden layers. ELM has been widely used in many fields [2]. Combining PCA with ELM to form an extreme learning machine neural network based on principal component analysis can reduce the workload of data analysis by reducing unnecessary analysis indexes in the case of a large amount of data information, while ELM is used to analyze the extracted data features and complete the analysis of data information features in the process of multiple training iterations. ...

... The eye images are captured by an image sensor, and subsequent image processing is performed to identify the pupil characteristics of each individual's eye. By analyzing these characteristics in real time, the subject's gaze point on the screen can be determined [7]. ...

... In addition to the aforementioned approaches, many other scholars have conducted extensive research in the field of building electrical fault diagnosis. Xiong et al [37] combined complementary ensemble empirical mode decomposition (CEEMD) and mutual dimensionless index (MDI) for feature extraction, subsequently proposing a multi-kernel relevance vector machine (MK-RVM) in the building electrical system fault diagnosis. Xiong et al [38] introduced a feature extraction method combining variational mode decomposition (VMD) and MDI for fault diagnosis of building electrical systems, and used a quantum genetic algorithm optimized support vector machine (QGA-SVM) method in the fault classification part. ...