为了能够对多种混合气体进行精确的识别和浓度测量,根据被检测气体的红外光谱的吸收特性,利用阵列式量子级联激光器设计了高精度的气体检测仪。在激光器驱动电源的设计上采用了时分复用的方法,通过高速脉冲控制和分段拟合的方法实现了...为了能够对多种混合气体进行精确的识别和浓度测量,根据被检测气体的红外光谱的吸收特性,利用阵列式量子级联激光器设计了高精度的气体检测仪。在激光器驱动电源的设计上采用了时分复用的方法,通过高速脉冲控制和分段拟合的方法实现了对电源输出的精准控制。同时,对红外探测器输出的微弱信号进行处理,大大提高了测量精度。测试结果表明:驱动电流的最大偏差仅为0.65 m A,稳定度优于4×10-5A,并对4种气体在10个浓度等级上进行了测试,最大平均测量误差仅为0.80%。展开更多
The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted pa...The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.展开更多
文摘为解决当前常用煤矿氧气检测仪器易受交叉气体干扰且功耗大的问题,基于GD32F303RCT6微控制器和ADN8834热电冷却控制器,设计了一种软启动开关电路控制的垂直腔面发射激光器(Vertical-cavity Surface-emitting Laser,VCSEL)高精度驱动及温控电路。驱动电路中,高频正弦波信号和低频锯齿波信号叠加的二进制数据由微控制器产生,经信号发生电路、电压电流转换电路转化成VCSEL高精度驱动电流信号;温控电路中,设计基于比例积分微分(Proportional Integral Differential,PID)补偿电路和数模转换控制器(Digital to Analog Converter,DAC)目标温度控制电路实现激光器温度自动调节。测试结果表明:驱动电路的电流输出区间为0.680~1.360 mA;锯齿波频率误差小于0.5%,正弦波频率误差小于0.1%;氧气吸收峰扫描精度高达0.07 pm,对应电流扫描精度为0.12μA;温控电路的温度控制精度为±0.012℃。满足了可调谐半导体激光吸收光谱(Tunable Diode Laser Absorption Spectroscopy,TDLAS)煤矿氧气检测应用需求。
文摘为了能够对多种混合气体进行精确的识别和浓度测量,根据被检测气体的红外光谱的吸收特性,利用阵列式量子级联激光器设计了高精度的气体检测仪。在激光器驱动电源的设计上采用了时分复用的方法,通过高速脉冲控制和分段拟合的方法实现了对电源输出的精准控制。同时,对红外探测器输出的微弱信号进行处理,大大提高了测量精度。测试结果表明:驱动电流的最大偏差仅为0.65 m A,稳定度优于4×10-5A,并对4种气体在10个浓度等级上进行了测试,最大平均测量误差仅为0.80%。
基金financially supported by the Important National Science and Technology Specific Project of China (Grant No. 2016ZX05047-002)
文摘The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.
