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Outline
The traditional development of Child Restraint System (CRS) safety relies on simulation and physical test results, which suffers from high costs, low efficiency and obvious limitations. The paper constructes a simplified high-precision coupled model under standard working conditions to efficiently generate training datasets. By fusing the time-domain, frequency-domain and second-order waveform fitting features of crash pulses, an intelligent prediction model based on deep multilayer perceptron is adopted to rapidly predict 4 core safety indicators, namely the Head Injury Criterion (HIC), head acceleration, axial neck force and chest acceleration. Validation results on new vehicle models show that the prediction model achieves an average accuracy of 93.3%, which can support the rapid development of CRS safety and demonstrates excellent engineering applicability.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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