Approaches on the Calibration of Laser Diode (LD) Tester

Zhan, Zhi Qiang; Xia, Jun; Lei, Yu

doi:10.4028/www.scientific.net/amm.568-570.1292

Cited by 3 publications

(1 citation statement)

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“…The laser calibration circuit is completed by laser diodes [13], which are devices that use semiconductor materials to produce stimulated emission effect [14]. The laser diode used in this study is LB-00922 / DX, with its specific parameters as follows: ground wire light shape and continuous laser, with a laser wavelength of 650 and 675 mm and a laser power of between 0.5 and 0.9 mW; 2.5-12V operating voltage; <10-25 mA operating current, taking European power as standard; storage and operating temperature between −35 °C and 65 °C; <1.5-2.5 mm light panel mode.…”

Section: Laser Calibration Circuitmentioning

confidence: 99%

Application of Laser Sensor in Urban Vehicle Type Detection

Liu

2018

Teh. vjesn.

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With the development of urban transportation, more and more traffic accidents happened, most of which were caused by ultra-high vehicles, which not only damaged the road facilities, but also endangered the safety of the masses. In this study, a vehicle height detection system was designed and applied to urban transportation to prevent such accidents. The system utilized the laser light curtain principle of the laser sensor to accurately detect the height of vehicles, equipped with an optical signal anti -interference system and an anti -collision alarm system. The experimental results showed that the system could effectively detect the unqualified vehicles and give alarms, which provided a reference for the application of laser sensor in urban road traffic detection.

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Section: Laser Calibration Circuitmentioning

confidence: 99%

Application of Laser Sensor in Urban Vehicle Type Detection

Liu

2018

Teh. vjesn.

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Constrained optimization with stochastic feasibility regions applied to vehicle path planning

2015

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In real-time trajectory planning for unmanned vehicles, on-board sensors, radars and other instruments are used to collect information on possible obstacles to be avoided and pathways to be followed. Since, in practice, observations of the sensors have measurement errors, the stochasticity of the data has to be incorporated into the models. In this paper, we consider using a genetic algorithm for the constrained optimization problem of finding the trajectory with minimum length between two locations, avoiding the obstacles on the way. To incorporate the variability of the sensor readings, we propose a modified genetic algorithm, addressing the stochasticity of the feasible regions. In this way, the probability that a possible solution in the search space, say x, is feasible can be derived from the random observations of obstacles and pathways, creating a real-time data learning algorithm. By building a confidence region from the observed data such that its border intersects with the solution point x, the level of the confidence region defines the probability that x is feasible. We propose using a smooth penalty function based on the Gaussian distribution, facilitating the borders of the feasible regions to be reached by the algorithm.

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