Carrier Stored Layer Density Effect Analysis of Radiated Noise at Turn-On Switching via Gabor Wavelet Transform

Tadakuma, Toshiya; Rogers, Michael J.; Nishi, Koichi; Joko, Motonobu; Shoyama, Masahito

doi:10.1109/ted.2021.3061492

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…(3) For each particle individual, compare the fitness of the individual extreme value (pBest) with the current fitness of the individual. If the current fitness is good, then pBest is replaced, and similarly, the global extreme value (gBest) is also updated with the same judgment (4) According to formulas ( 6) and ( 7), update the speed and position of the particle within the allowable range to generate the next generation of particles (5) Increase the number of iterations by 1, go to step (2), until the maximum number of iterations is satisfied; the algorithm ends and the global optimal solution gBest is obtained, and the optimal solution is mapped to the initial weight and threshold of the BP neural network [15,16].…”

Section: Particle Swarmmentioning

confidence: 99%

[Retracted] Key Technologies of Face Sensor Recognition Entry System for New Energy Vehicles Based on Particle Swarm Neural Network

Li¹,

Zhao³

et al. 2023

Journal of Sensors

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In order to solve the problem of high dimensionality and low recognition rate caused by complex calculation in face recognition, the author proposes a face recognition algorithm based on weighted DWT and DCT based on particle swarm neural network applied to new energy vehicles. The algorithm first decomposes the face image with wavelet transform, removes the influence of the diagonal component, the weighted low-frequency and high-frequency discrete cosine transform coefficients are extracted as feature vectors, and finally, the particle swarm optimization BP neural network is used for classification and identification. Experimental results show that when the wavelet weights take a 0 = 0.9 , a 1 = 0.05 , and a 2 = 0.05 , the recognition rate reaches the highest. Regardless of whether the low-frequency component continues to increase or decrease, and the high-frequency component continues to decrease or increase, the recognition rate will decrease. When the eigenvector dimension is around 60, the recognition rate difference between the weighted wavelet algorithm and the general low-frequency wavelet algorithm reaches the maximum. The recognition rate of the proposed algorithm is much higher than the other two traditional algorithms. Conclusion. The effectiveness and feasibility of the algorithm are verified on the ORL face database.

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Section: Particle Swarmmentioning

confidence: 99%

[Retracted] Key Technologies of Face Sensor Recognition Entry System for New Energy Vehicles Based on Particle Swarm Neural Network

Li¹,

Zhao³

et al. 2023

Journal of Sensors

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“…[15][16][17][18][19] In designing for overall system performance, the IGBT must be optimized with not only low loss but also high reliability and low noise. [20][21][22][23][24][25][26] In previous studies, although turn-off switching characteristics have been analyzed for each new generation device to clarify switching behavior with carrier dynamics, the design direction to address both low loss and low surge voltage has not been sufficiently discussed.…”

Section: Introductionmentioning

confidence: 99%

Turn-off switching voltage surge analysis with dependence on IGBT cell design

Fujimoto,

Nishizawa,

Saito

2024

Jpn. J. Appl. Phys.

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Surge voltage at IGBT turn-off switching was analyzed with dependence on cell design parameters. Although drift layer thinning is effective to improve trade-off characteristics between turn-off loss Eoff and on-state voltage Von, voltage surge is induced due to quick expanding depletion layer. Therefore, the surge voltage Vsurge has also trade-off relationship with the Von at the same Eoff condition. The origin of voltage surge was analyzed using TCAD simulation, and total amount of remained holes in the drift layer during turn-off switching is a key factor for the Vsurge. Narrow mesa structure and thick buffer layer are effective for improvement of trade-off characteristics between Vsurge and Von. However, the optimum buffer layer thickness depends on the voltage-class due to the speed for punch through.

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“…Actually, several time‐domain or frequency‐domain or time‐frequency domain analysis methods such as spectral analysis [8, 9], cyclostationary approach [10], fast Fourier transform [11, 12], wavelet transform [13] and so on are used to extract useful features from signals. However, since the vibration signal itself is a noisy non‐linear signal, these methods are prone to the problems of high time complexity and low noise filtering performance when processing these signals [14, 15]. In order to better solve the problem of signal noise, Melih Kuncan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Actually, several time-domain or frequency-domain or time-frequency domain analysis methods such as spectral analysis [8,9], cyclostationary approach [10], fast Fourier transform [11,12], wavelet transform [13] and so on are used to extract useful features from signals. However, since the vibration signal itself is a noisy nonlinear signal, these methods are prone to the problems of high time complexity and low noise filtering performance when processing these signals [14,15]. In order to better solve the problem of signal noise, Melih Kuncan et al proposed a new vibration signal feature extraction method named onedimensional ternary pattern (1D-TP) [16] which could fully describe all the characteristics of the obtained signals and extract effectively in real time if the characteristics of fault signals change.…”

Section: Introductionmentioning

confidence: 99%

An improved bearing fault detection strategy based on artificial bee colony algorithm

Wang

Yue

et al. 2022

CAAI Trans on Intel Tech

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The operating state of bearing affects the performance of rotating machinery; thus, how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical. In this study, the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error. Then XGBoost is used to recognise the faults from the obtained features, and artificial bee colony algorithm (ABC) is introduced to optimise the parameters of XGBoost. Moreover, for improving the performance of intelligent algorithm, an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed. The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%, which is much higher than the ones corresponding to traditional extraction strategies. And with the help of improved ABC algorithm, the performance of XGBoost classifier could be optimised; the accuracy could be improved from 97.02% to 98.60% compared with the traditional classification strategy.

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Carrier Stored Layer Density Effect Analysis of Radiated Noise at Turn-On Switching via Gabor Wavelet Transform

Cited by 8 publications

References 35 publications

[Retracted] Key Technologies of Face Sensor Recognition Entry System for New Energy Vehicles Based on Particle Swarm Neural Network

[Retracted] Key Technologies of Face Sensor Recognition Entry System for New Energy Vehicles Based on Particle Swarm Neural Network

Turn-off switching voltage surge analysis with dependence on IGBT cell design

An improved bearing fault detection strategy based on artificial bee colony algorithm

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