MILITAAROBJEKTIDE VALVETEHNOLOOGIA ARENG

Artikkel analüüsib viimastel aastatel maailmas käivitunud infotehno loogilisi arenguhüppeid, mis on toonud olulisi muutusi valvesüsteemide alale. Militaarobjektide valvesüsteeme on käsitletud nii sensorseadmete, süsteemi arhitektuuri kui ka üha rohkem tehisintellekti kasutava andmetöötluse vaates. Käsit luse süstematiseerimiseks on esile toodud neli põhilist alamsüsteemi ning teema sidu miseks maailma arengusuundadega on analüüsitud kaheksa olulise infotehnoloogilise uurimisteema kasvukõveraid. Detailsemalt käsitletud sensorseadmed on järgmised: 1) valvekaamerad nähtava valguse, lähiinfrapuna ja soojuskiirguse diapasoonidele; 2) sensoritega varustatud targad piirdetarad; 3) süsteemid mehitamata õhusõidukite avastamiseks ja jälgi miseks. Kaamerate puhul on analüüsitud kujutise sensorite arengutendentse ja DORI (detekteerimine, jälgimine, eristamine, identifitseerimine) tuvastusstandardit. Artiklis käsitletakse olulisemaid termineid ning arengusuundi, pidades silmas materjali võimalikku kasutamist tehniliste erialade väljaõppes Kaitseväe Akadeemias ja mujal

Temperature-independent Vibration Sensor Based on Fabry-Perot Interferometer using a Fiber Bragg Grating approach

An innovative vibration sensor based on a Fabry-Perot Interferometer (FPI) using Fiber Bragg Grating (FBG) reflectors has been discussed in this work. The sensor has been designed to be compact and easy to fabricate and independent of temperature, to overcome limitations seen in some previous designs, providing an effective correction for temperature effects in FBG-based FPI (FBG-FPI) sensors. In this approach, light from a broadband source in the wavelength range 1500 – 1580 nm has been used to illuminate the FBGs used (having a reflective peak of 1547.42 nm), employing in this way a light source within the FPI optimum wavelength operating range of 1547.15 nm – 1547.80 nm. The sensor developed in this work has been shown able to capture a 3 kHz burst signal from a signal generator, in 1 Hz, 2 Hz, and 3 Hz intervals. In addition, the research carried out has revealed that the sensor could be used to capture sinusoidal signals at frequencies up to 9 kHz, creating a performance comparable with many existing conventional piezoelectric sensors. The ability to operate regardless of any ambient temperature changes (in the range from 26.5°C (room temperature) up until 80°C), opens the way to use such a sensor system over a wide range of engineering applications taking advantage of the next generation of FBG-based FPIs

Intrusion location technology of Sagnac distributed fiber optical sensing system based on deep learning

For distributed fiber optical sensing based on Sagnac effect, the intrusion is usually located by notch frequency. However, the notch spectrum is the comprehensive result of the intrusion, so when multiple disturbances simultaneously intrude from different positions of the sensing fiber, it is impossible to establish a mathematical expression between the intrusion position and the notch frequency, this leads to the problem of multi-point intrusion localization. Therefore, in this paper, deep learning technology is used to locate multiple disturbing points in Sagnac distributed optical fiber sensing system, and the related specific technologies of deep learning applying to sagnac distributed optical fiber sensing are studied. First, according to the characteristics of the system, a network structure based on the regression probability distribution is proposed, second, a loss function is constructed. The results show that the trained model can realize the positioning of multiple and single intrusion points

Large-Dynamic-Range and High-Stability Phase Demodulation Technology for Fiber-Optic Michelson Interferometric Sensors

A large-dynamic-range and high-stability phase demodulation technology for fiber-optic Michelson interferometric sensors is proposed. This technology utilizes two output signals from a 2 × 2 fiber-optic coupler, the interferometric phase difference of which is π. A linear-fitting trigonometric-identity-transformation differential cross-multiplication (LF-TIT-DCM) algorithm is used to interrogate the phase signal from the two output signals from the coupler. The interferometric phase differences from the two output signals from the 2 × 2 fiber-optic couplers with different coupling ratios are all equal to π, which ensures that the LF-TIT-DCM algorithm can be applied perfectly. A fiber-optic Michelson interferometric acoustic sensor is fabricated, and an acoustic signal testing system is built to prove the proposed phase demodulation technology. Experimental results show that excellent linearity is observed from 0.033 rad to 3.2 rad. Moreover, the influence of laser wavelength and optical power is researched, and variation below 0.47 dB is observed at different sound pressure levels (SPLs). Long-term stability over thirty minutes is tested, and fluctuation is less than 0.36 dB. The proposed phase demodulation technology obtains large dynamic range and high stability at rather low cost

COMPARING DISTRIBUTED ACOUSTIC SENSING TO THREE-COMPONENT GEOPHONES IN AN UNDERGROUND MINE

Geophones have become the industry standard for seismic data collection. However, a relatively new method is gaining popularity called Distributed Acoustic Sensing (DAS). DAS uses changes in backscattered light of a fiber-optic cable to detect strain from acoustic energy. The purpose of this project was to make a direct comparison between DAS and three component geophones, specifically in a mining setting. Experiments were done in the Underground Education Mining Center on the campus of Montana Tech. The sources used for this project were vertical sledgehammer shots, oriented shear sledgehammer shots, and blasting caps set off in both unstemmed and stemmed drillholes. Although the explosives performed the best for the geophones, the large amount of energy and its close distance from the fiber seemed to compromise the entire fiber loop. In a one to one comparison, the underground hammer shots seemed to produce data that was a rough match between the DAS traces and the geophone traces. However, the shots on the surface of the mine, specifically the shots oriented inline with the cable, seemed be close to an exact match between trace of the fiber and traces of the geophones. The data suggest that DAS is most useful when the fiber can be oriented in the same direction as particle motion from whatever source is used, whereas the three component geophones can accurately capture data from all sources

Structured light enhanced machine learning for fiber bend sensing

The intricate optical distortions that occur when light interacts with complex media, such as few- or multi-mode optical fiber, often appear random in origin and are a fundamental source of error for communication and sensing systems. We propose the use of orbital angular momentum (OAM) feature extraction to mitigate phase-noise and allow for the use of intermodal-coupling as an effective tool for fiber sensing. OAM feature extraction is achieved by passive all-optical OAM demultiplexing, and we demonstrate fiber bend tracking with 94.1% accuracy. Conversely, an accuracy of only 14% was achieved for determining the same bend positions when using a convolutional-neural-network trained with intensity measurements of the output of the fiber. Further, OAM feature extraction used 120 times less information for training compared to intensity image based measurements. This work indicates that structured light enhanced machine learning could be used in a wide range of future sensing technologies

Machine learning algorithms for monitoring pavement performance

ABSTRACT: This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods

Comparative in situ study of dynamic load generated by gravel piles measured by a fiber-optic interferometer

Currently, all the technology used for seismic monitoring is based on sensors in the electrical domain. There are, however, other physical principles that may enable and fully replace existing devices in the future. This paper introduces one of these approaches, namely the field of fiber optics, which has great potential to be fully applied in the field of vibration measurement. The proposed solution uses a Michelson fiber-optic interferometer designed without polarization fading and with an operationally passive demodulation technique using three mutually phase-shifted optical outputs. Standard instrumentation commonly used in the field of seismic monitoring in geotechnical engineering was used as a reference. Comparative measurements were carried out during the implementation of gravel piles, which represents a significant source of vibration. For the correlation of the data obtained, the linear dependence previously verified in laboratory measurements was used. The presented results show that the correlation is also highly favorable (correlation coefficient in excess of 0.9) from the values measured in situ, with an average deviation for the oscillation velocity amplitude of the optical sensor not exceeding 0.0052.Web of Science2215art. no. 557