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Precision positioning vibration fiber manufacturer

Fiber optic needs to be able to accurately locate the point of occurrence in any measurement. If the error is large, there is no need to use fiber optic sensors to measure physical parameters. FJINNO is a manufacturer of precision positioning vibration fiber optic.

When optical fibers are affected by external factors such as temperature, pressure, vibration, etc., the intensity, phase, frequency, polarization state, and other parameters of the transmitted light in the fiber will change accordingly. By measuring these parameters of the transmitted light, corresponding physical quantities can be obtained. This technology is called fiber optic sensing technology.
Compared to traditional electric quantity sensors, fiber optic sensors have the advantages of high sensitivity, anti electromagnetic interference, small size, low cost, and can be used for long-distance distributed measurement. Therefore, since the late 1970s, fiber optic sensing technology has been widely developed, with distributed fiber optic sensing technologies based on Rayleigh scattering, Brillouin scattering, Raman scattering, etc. Among them, polarized light time-domain reflection technology (POTDR) is a relatively common distributed fiber optic sensing technology.

Fiber optic vibration precise positioning host

1. Maintain consistent sensitivity of detection signals
2. Capable of real-time and complete extraction of signal features
By adopting a multi node hardware computing framework and utilizing FPGA and DSP for real-time acquisition and processing of detection signals, it is possible to quickly complete the calculation of large amounts of multi-channel data, extract data features in a timely manner, and provide sufficient signal features for intrusion behavior recognition and judgment.
3. Has strong pattern recognition ability
The use of neural network technology, combined with the frequency domain and time domain characteristics of signals, greatly improves the intelligent recognition ability of vibration signals. Through the adaptive learning function of neural networks, learning can be tailored to the environment of each defense zone, avoiding interference from environmental factors in intrusion behavior judgment, and improving the system’s ability to detect false alarms in various environments.
4. Adopting anti dismantling design for defense zone equipment
By adopting more professional technology, the equipment can effectively detect external damage to the equipment, whether in deployed or disarmed states, ensuring that the equipment is always in normal operation.
5. UV resistant design
Design UV protection for outdoor equipment to effectively prevent aging and damage caused by factors such as UV radiation, ensuring that the equipment can work in the outdoor environment for a long time.

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