DIN EN 60749-27:2013-04, Halbleiterbauelemente_- Mechanische und klimatische Prüfverfahren_- Teil_27: Prüfung der Empfindlichkeit gegen elektrostatische Entladungen (ESD)_- Machine Model (MM) (IEC_60749-27:2006_+ A1:2012); Deutsche Fassung EN_60749-27:2006_+ A1:2012

2013 ◽  
Keyword(s):  
Machine Model

The Turing Machine Model of Sensing, Manipulation, and Control

1989 ◽  
Author(s):  
Constantine Tsikos ◽  
Tom Chmielewski ◽  
Brian Frederick
Keyword(s):  
Turing Machine ◽  
Machine Model ◽  
And Control ◽  
Manipulation And Control

The Study of ESD Destructive Mechanism for PN-Junction

1996 ◽  
Author(s):  
N. Wakai ◽  
M. TsuTsumi ◽  
T. Setoya
Keyword(s):  
Energy Density ◽  
Thermal Diffusion ◽  
Electric Discharge ◽  
Intermediate Region ◽  
Human Body Model ◽  
Machine Model ◽  
Pn Junction ◽  
Special Cases ◽  
Equilibrium Region

Abstract Mechanism of destruction caused by electrostatic discharge of PN junction was examined from two viewpoints; classification of destruction mode with consideration to destructive energy density, and comparison of destruction shape. Destructive energy density of PN junction was calculated based on Speakman model, and destruction mode was classified by Wunsch-Bell plot. As a result of Wunsch-Bell plot, electric discharge which occur at low resistance, for example machine model (MM: C∙R = 200pF ∙ 0Ω), resulted in adiabatic destruction that does not involve thermal diffusion. With electric discharge at high resistance, for example human body model (HBM: C∙R = 100pF ∙ 1500Ω), excessive destruction in intermediate region that involves thermal diffusion, and depending on the device, destruction at equilibrium region were proven to be reproducible. In case of MM, (adiabatic region destruction) destruction was confirmed in a wide extent of the joint part, but in case of HBM (intermediate region destruction) destruction was confirmed near the center of the joint part. From this fact, it was found that by verifying the places of destruction and their shapes, although in special cases, it is possible to know the destruction mode when destruction occurs.

Community Power and Population Increase: An Empirical Test of the Growth Machine Model

1981 ◽  
Vol 86 (6) ◽  
pp. 1387-1400 ◽  
Author(s):  
Larry Lyon ◽  
Lawrence G. Felice ◽  
M. Ray Perryman ◽  
E. Stephen Parker
Keyword(s):  
Empirical Test ◽  
Population Increase ◽  
Machine Model ◽  
Growth Machine ◽  
Community Power

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

scholarly journals A Novel High Performance Discrete Flux Integrator for Control Algorithms of Fast Rotating AC Machines

2021 ◽  
Vol 11 (5) ◽  
pp. 2150
Author(s):  
Claudio Rossi ◽  
Alessio Pilati ◽  
Marco Bertoldi
Keyword(s):  
High Performance ◽  
Permanent Magnets ◽  
Machine Model ◽  
Ac Machines ◽  
Fast Calculation ◽  
Calculation Time ◽  
Digital Implementation ◽  
Rotating Speed ◽  
Interior Permanent Magnets ◽  
Accuracy Performance

This paper deals with the digital implementation of a motor control algorithm based on a unified machine model, thus usable with every traditional electric machine type (induction, brushless with interior permanent magnets, surface permanent magnets or pure reluctance). Starting from the machine equations in matrix form in continuous time, the paper exposes their discrete time transformation, suitable for digital implementation. Since the solution of these equations requires integration, the virtual division of the calculation time in sub-intervals is proposed to make the calculations more accurate. Optimization of this solver enables faster runs and higher precision especially when high rotating speed requires fast calculation time. The proposed solver is presented at different implementation levels, and its speed and accuracy performance are compared with standard solvers.

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