Prediction of Static Characteristic Parameters of an Insulated Gate Bipolar Transistor Using Artificial Neural Network

Breakdown voltage (BV), on-state voltage (Von), static latch-up voltage (Vlu), static latch-up current density (Jlu), and threshold voltage (Vth), etc., are critical static characteristic parameters of an IGBT for researchers. Von and Vth can characterize the conduction capability of the device, while BV, Vlu, and Jlu can help designers analyze the safe operating area (SOA) of the device and its reliability. In this paper, we propose a multi-layer artificial neural network (ANN) framework to predict these characteristic parameters. The proposed scheme can accurately fit the relationship between structural parameters and static characteristic parameters. Given the structural parameters of the device, characteristic parameters can be generated accurately and efficiently. Compared with technology computer-aided design (TCAD) simulation, the average errors of our scheme for each characteristic parameter are within 8%, especially for BV and Vth, while the errors are controlled within 1%, and the evaluation speed is improved more than 107 times. In addition, since the prediction process is mathematically a matrix operation process, there is no convergence problem, which there is in a TCAD simulation.

Local bond strength prediction of deformed reinforcing bars in concrete considering heterogeneity at interface regions

An analytical model is proposed to predict local bond strength (τf) by incorporating heterogeneity at interface regions for deformed reinforcing bars centrally anchored in concrete. The rib width on the bar surface is introduced as an interfacial characteristic parameter G in the proposed model; this accounts for the heterogeneity. Both τf and the local interfacial fracture energy (GIIf) of each specimen were found to be linked to G and can be determined analytically from the maximum pull-out loads (Fmax) from tests. It was found that the predicted τf was larger than the maximum average bond stress (τavg-max); the discrepancy between the two values reduced with an increase in L/G. Moreover, with an increase in L/G, the predicted τf showed a certain decrease, with the reduction decreasing with stronger interfacial homogeneity. The predicted GIIf was found to be significantly increased because of the weaker boundary effect. The validity of the proposed model was verified using comparisons of predicted Fmax (using the determined values of τf and GIIf) and the experimental Fmax, with the only failure mode being bar pull-out. Moreover, the model can be applied to steel or fibre-reinforced polymer bars and the concrete refers to all types of cementitious materials.

Characteristic Parameter Optimization of Wireless Flaw Sensing System Based on Active Interference Suppression Algorithm

Based on the active interference suppression algorithm, this study combines the radar working mode and the interference type and realizes the effective detection of the flaw detection signal by successively processing the radar receiving signal and the filtering processing. Firstly, this article builds a simulation platform similar to the actual situation to verify the existing conventional active interference suppression algorithms. Secondly, for the detection of chirp active deception jamming signals entering from the main lobe, a radar active deception jamming detection method based on the characteristic parameter matching of the harmonic components of active deception jamming signals is proposed. After that, the spectral characteristics of the harmonic components of the deception interference signal are analyzed, and the center frequency and the tuning frequency of the real target echo are obtained. Finally, by establishing a frequency modulation parameter library for possible interference harmonic signal components, the acquisition phase of the radar gate by the jammer matched analysis with the preestablished frequency modulation parameter library is implemented to achieve active deception interference detection. This method can effectively detect active deception jamming signals in a complex tunnel environment. The interference suppression algorithms verified by simulation include noise FM interference suppression algorithm based on cancellation and distance false target interference suppression algorithm based on LFM radar summary processing. Through actual measurement data processing and analysis, the effectiveness of the method is verified and the idea of interference suppression is expanded. The construction of the simulation platform is obtained by appropriately modifying the actual parameters, a certain type of suppression jammer, and a certain type of deception jammer used in a certain countermeasure field test at a radar station.

The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

The uncertainty and fluctuations in graphene characteristic parameters are inevitable issues in both of experimental measurements and numerical investigations. In this paper, the correlations between characteristic parameters (Young’s modulus, Poisson’s ratio and thickness of graphene) and resonant frequencies are analyzed by the Monte Carlo based stochastic finite element model. Based on the Monte Carlo stochastic sampling procedure, the uncertainty in the characteristic parameters are properly propagated and quantified. The displacements and rotation modes of graphene under the resonant vibration computed by the finite element method are verified. Furthermore, the result robustness of stochastic samples is discussed based on the statistic records and probability density distributions. In addition, both the Pearson and Spearman correlation coefficients of the corresponding characteristic parameters are calculated and compared. The work in this paper provides a feasible and highly efficient method for the characteristic parameter correlation discussion by taking uncertainty into consideration.

Fault Dynamic Modeling and Characteristic Parameter Simulation of Rolling Bearing with Inner Ring Local Defects

To further study the fault mechanism and fault features of rolling bearings, a two-DOF rolling bearing fault dynamic model with inner ring local defects considering the bearing radial clearance and time-varying displacement excitation is established based on Hertz contact theory. By comparing the simulated fault signal with bearing fault test data in the time domain and frequency domain, the accuracy of the established fault dynamic model is verified. Finally, the change rules of the characteristic parameters of the bearing inner ring fault signal, including effective value, absolute mean value, square root amplitude, peak value, kurtosis factor, pulse factor, peak factor, and shape factor, are simulated by the fault dynamic model. The results highlight that the proposed fault dynamic model is in good agreement with the experimental results. The model can simulate the fault signal characteristic parameters with the change of defect width, external load, and rotating speed effectively. The study in this paper is of engineering application value for bearing condition monitoring and fault diagnosis.

New Magnitude Proxy for Earthquake Early Warning Based on Initial Time Series and Frequency

A new characteristic parameter Sdτ is proposed to improve the performance of magnitude estimation in earthquake early warning (EEW). Sdτ is the product of summation of absolute displacement multiplied by the maximum predominant period (τmaxP) for the first arriving seconds of a seismic wave. About 30,725 underground records at borehole stations for 3645 earthquakes with magnitude between 4.0 and 9.0 from the Japanese KiK-net were used to compare the magnitude proxy performance based on the proposed Sdτ with that based on either τmaxP or peak displacement Pd. The comparison results show that for a magnitude between 4.0 and 7.3, Sdτ has a better correlation with magnitude and higher estimated accuracy than either τmaxP or Pd. Hypocentral distance is not required when using Sdτ, but it can be used to further improve the accuracy of magnitude estimate. These results confirm that Sdτ can significantly improve the accuracy and timeliness of continuous magnitude estimation in an EEW system.

The Wear Detection of Mill-Grinding Tool Based on Acoustic Emission Sensor

The monitoring of tool wear plays an important role in improving the processing efficiency and reducing the production cost of enterprises. This paper is focused on the detection of electroplated diamond mill-grinding tools by using the acoustic emission sensor. The wear stages of mill-grinding tools are divided into three parts, namely initial wear stage, normal wear stage, and severe wear stage. The characteristic parameter method and the waveform analysis method are applied to analyze the acoustic emission signals. The wear characteristics of the tool and workpiece in different wear stages are observed and analyzed. The results indicate that the acoustic emission waveform is relatively stable in the initial wear stage, and the continuous acoustic emission signal is dominated. Moreover, the diamond abrasive grains are mainly worn and slightly broken in the normal wear stage, and there are some pits on the machined workpiece surface after the initial wear stage. In the severe wear stage, most of the abrasive grains are broken or broken in a large area, and there are burst acoustic emission signals in the waveform.

Viscoelastic Mechanical Model of Asphalt Concrete Considering the Influence of Characteristic Parameter of Fiber Content

This paper carries out bending creep tests on polyester fiber-reinforced asphalt concrete beams, and investigates how the volume ratio and aspect ratio of the fibers influence the parameters of viscoelastic mechanical model and the viscoelastic performance of the asphalt concrete. The results show that: with the growing volume ratio and aspect ratio of the fibers, the midspan deflection and bottom flexural-tensile strain of asphalt concrete beams first dropped and then rose over time; the characteristic parameter of fiber content (FCCP) could reflect the overall effects of the volume ratio and aspect ratio of the fibers. On this basis, a viscoelastic mechanical model was established for the asphalt concrete in the light of the influence of FCCP. The test and theoretical results show that, in our tests, the optimal volume ratio of fibers, optimal aspect ratio of fibers, and optimal FCCP are 0.348, 324, and 1.128 for polyester fiber-reinforced asphalt concrete.

Establishing the Method to Predict the Limited Roll Angle of the Vehicle Based on the Basic Dimensions

The roll angle of the vehicle φ is a characteristic parameter for the vehicle's instability. This value appears when the vehicle steers. If the vehicle’s body is tilted, the value of the vertical force at the wheels Fzij will also change. When the value of Fzij reaches zero, the wheel will be lifted off the road, the rollover phenomenon can occur. At this time, the roll angle of the vehicle will reach the maximum value φmax. Previous researches have often used only the vehicle dynamics model to determine the limits of this phenomenon. However, the calculation and simulation process are quite complicated. Therefore, this research has proposed a novel method that can calculate the limit of the rollover phenomenon more easily. In this research, the Rollover State Function (RSF) was established to calculate the limited roll angle of the vehicle. According to the content of the paper, this function depends only on the basic dimensions of the vehicle such as the height of center of the gravity, the track width, etc. Besides, it has relatively high accuracy, even when the vehicle's mass changes, its difference is not large. Therefore, the results of the paper can be applied to later studies to predict the rollover phenomenon.