目的:探究防护型急救车底盘抵御爆炸性武器手雷、地雷的毁伤效果。方法:在满足防护型急救车防护要求的前提下,采用12 mm厚聚乙烯(polyethylene,PE)防护板和复合陶瓷PE防护板(1 mm铝板+6 mm B4C陶瓷+12 mm高分子量聚乙烯纤维)对急救车底...目的:探究防护型急救车底盘抵御爆炸性武器手雷、地雷的毁伤效果。方法:在满足防护型急救车防护要求的前提下,采用12 mm厚聚乙烯(polyethylene,PE)防护板和复合陶瓷PE防护板(1 mm铝板+6 mm B4C陶瓷+12 mm高分子量聚乙烯纤维)对急救车底盘进行防护。通过理论计算和仿真模拟,分别分析某62 g TNT手雷对PE防护板和某6000 g TNT当量地雷对复合陶瓷PE防护板的爆炸毁伤效果。结果:PE防护板和复合陶瓷PE防护板能够分别抵御某手雷和某地雷在1.1 m炸距下的爆炸毁伤作用,且均未出现明显破坏。结论:PE防护板和复合陶瓷PE防护板均能满足抵御爆炸性武器手雷、地雷毁伤作用及机动性、轻量化要求,该研究可为防护型急救车底盘防护设计提供参考。展开更多
A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in th...A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.展开更多
文摘目的:探究防护型急救车底盘抵御爆炸性武器手雷、地雷的毁伤效果。方法:在满足防护型急救车防护要求的前提下,采用12 mm厚聚乙烯(polyethylene,PE)防护板和复合陶瓷PE防护板(1 mm铝板+6 mm B4C陶瓷+12 mm高分子量聚乙烯纤维)对急救车底盘进行防护。通过理论计算和仿真模拟,分别分析某62 g TNT手雷对PE防护板和某6000 g TNT当量地雷对复合陶瓷PE防护板的爆炸毁伤效果。结果:PE防护板和复合陶瓷PE防护板能够分别抵御某手雷和某地雷在1.1 m炸距下的爆炸毁伤作用,且均未出现明显破坏。结论:PE防护板和复合陶瓷PE防护板均能满足抵御爆炸性武器手雷、地雷毁伤作用及机动性、轻量化要求,该研究可为防护型急救车底盘防护设计提供参考。
文摘A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
