Classification and discrimination of real and fake blood based on photoacoustic spectroscopy combined with particle swarm optimized wavelet neural networks

Ren, Zhong; Liu, Tao; Liu, Guodong

doi:10.1016/j.pacs.2021.100278

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…The timefrequency window can expand the research scope in the time domain and frequency domain at the same time, which is convenient for better problem discovery. This property makes it applicable to nonlinear sciences such as differential equations, pattern recognition, and computer vision [9][10][11].…”

Section: Wnnmentioning

confidence: 99%

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

2022

IJOMAM

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The frequency conversion speed regulation system powered by inverter is widely used in various fields. It is particularly important that the inverter that is most prone to failure can quickly and accurately locate the fault location and analyze the cause of the fault. The Wavelet Neural Network (WNN) is used as the entry point, and the fault diagnosis of the inverter is studied. Firstly, the Back Propagation (BP) algorithm commonly used in WNN is analyzed. A variable learning rate factor is introduced, and a variable learning rate variable weighted recursive least squares algorithm is proposed. Then, the relevant parameters are set. The inverter fault diagnosis simulation experiment is designed. Experiments show that the improved Learning Rate and Variable Weight Recursive Least Squares (LWRLS) algorithm completes fitting 1200-1300 steps faster than the BP algorithm in terms of operation speed. In terms of network error, the error value of the WNN of the BP algorithm can only reach 104. The error of the WNN of the LWRLS algorithm reaches 106. Therefore, when the algorithm is improved, the network error of WNN is reduced a lot. In the fault diagnosis accuracy, the average accuracy of the BP algorithm is 98.37%, and the standard deviation is 1.2571. The average accuracy of the LWRLS algorithm is 99.56% with a standard deviation of 0.3920. The fault recognition rate after the improved algorithm is significantly better, and the stability is better. Theoretical analysis and simulation results show that the improved LWRLS algorithm in WNN has the advantages of fast operation speed, small network error and high diagnostic accuracy in the inverter fault diagnosis method. The proposed improved WNN algorithm provides a reference for the application of inverter fault diagnosis.

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Section: Wnnmentioning

confidence: 99%

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

2022

IJOMAM

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“…College students' psychological state fluctuates greatly and may not be fully matured (Huang Z. et al, 2021 ; Huang Y. et al, 2021 ). The ever-changing social environment may incentivize college students' psychological instability, so various factors of college students' psychological evaluation should be coordinated (Ren et al, 2021 ). As a saying goes that change itself is the only constant.…”

Section: College Students' Entrepreneurial Psychologymentioning

confidence: 99%

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Zhang

2021

Front. Psychol.

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With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

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“…The main chromophores are the number of erythrocytes and hemoglobin which are reflected in the features of the PA signal. For example, the PA effect has been employed to analyze erythrocyte osmolarity using microfluidic devices [ 23 ], to characterize the viscosity of in vivo blood samples [ 24 ], to detect blood glucose [ 25 ], and to classify real and fake blood using wavelet neural networks [ 26 ]. Nonetheless, many of these studies use animal samples or human blood extracted from a single donor, and the blood is chemically treated to mimic some morphological changes in the RBCs [ 27 – 30 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling of photoacoustic sensor data to classify human blood samples

Pérez-Pacheco,

Ramírez-Chavarría,

Quispe-Siccha

et al. 2023

Med Biol Eng Comput

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The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios. Graphical abstract

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Classification and discrimination of real and fake blood based on photoacoustic spectroscopy combined with particle swarm optimized wavelet neural networks

Abstract: Graphical abstract

Cited by 8 publications

References 23 publications

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Dynamic modeling of photoacoustic sensor data to classify human blood samples

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