Polymer Optical Fiber Liquid Level Sensor: A Review

He, Runjie; Teng, Chuanxin; Kumar, Santosh; Marques, Carlos; Min, Rui

doi:10.1109/jsen.2021.3132098

Cited by 88 publications

(18 citation statements)

References 109 publications

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“…The fusion of POF has resulted in a tenfold increase in temperature sensitivity. The main parameters of commercially available POF are large core diameter (from 500 to 1000 µm), the high value of numerical aperture (from 0.19 to 0.5), and high attenuation (from 0.19 to 0.25 dB/m for the wavelength of 650 nm) [66] comparing to 8.2 µm, 0.14 and 0.02 dB/km for 1525-1575 nm [67] for conventional silica glass optical fiber. It must be noted that even though the POF have acceptable attenuation in the visible range it is impossible to use them as a transmission medium in the telecommunication wavelength because of the very high attenuation of light [68] reaching 1000 dB/km for 1000 nm in case of PMMA POF (Figure 3).…”

Section: Polymer Fiber Optic Sensorsmentioning

confidence: 99%

Temperature Sensors based on Polymer Fiber Optic Interferometer

Jędrzejewska‐Szczerska¹

2022

Preprint

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Temperature measurements are of great importance in many fields of human activities, including industry, technology, and science. For example, obtaining a certain temperature value or a sudden change in it can be the primary control marker of a chemical process. Fiber optic sensors have remarkable properties giving a broad range of applications. They enable continuous real-time temperature control in difficult-to-reach areas, in hazardous working environments (air pollution, chemical or ionizing contamination), and the presence of electromagnetic disturbances. The use of fiber optic temperature sensors in polymer technology can significantly reduce the cost of their production. Moreover, the installation process and usage would be simplified. As a result, these types of sensors would becoming increasingly popular in industrial solutions. This review provides a critical overview of the latest development of fiber optic temperature sensors based on Fabry-Perot interferometer made with polymer technology.

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Section: Polymer Fiber Optic Sensorsmentioning

confidence: 99%

Temperature Sensors based on Polymer Fiber Optic Interferometer

Jędrzejewska‐Szczerska¹

2022

Preprint

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“…OFSs are classified as intrinsic or extrinsic, the former one of which uses the optical fiber as a liquid level sensor probe, enabling the optical signal to be measured and modulated while the latter only employs optical fiber as the signal transmission medium and an external modulation device as the sensor. Various FOSs configurations for monitoring liquid levels, such as long-period gratings (LPG), Fabry–Perot cavities, fiber Bragg gratings (FBG), and multimode interference were presented in recent years [ 8 , 9 , 10 , 11 , 12 ]. In [ 10 ], etched chirped fiber Bragg grating (CFBG) is proposed as a liquid level sensor.…”

Section: Introductionmentioning

confidence: 99%

High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network

Dejband

Manie

Deng

et al. 2023

Sensors

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In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance.

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“…Water level monitoring is an important part of hydrological monitoring; continuous and reliable water level monitoring is of great significance for water safety guarantee, water resource management, and water environment and water ecology protection. At present, water level measurement approaches include manual reading, automatic water level measurement, and optical fiber-based liquid level measurement technology [ 1 ]. Manual reading is used to observe the water gauge with the naked eye; however, the reading frequency is low and it has a certain degree of subjectivity.…”

Section: Introductionmentioning

confidence: 99%

A Water Level Measurement Approach Based on YOLOv5s

Qiao

Yang

Wang

2022

Sensors

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Existing water gauge reading approaches based on image analysis have problems such as poor scene adaptability and weak robustness. Here, we proposed a novel water level measurement method based on deep learning (YOLOv5s, convolutional neural network) to overcome these problems. The proposed method uses the YOLOv5s to extract the water gauge area and all scale character areas in the original video image, uses image processing technology to identify the position of the water surface line, and then calculates the actual water level elevation. The proposed method is validated with a video monitoring station on a river in Beijing, and the results show that the systematic error of the proposed method is only 7.7 mm, the error is within 1 cm/the error is between 1 cm and 3 cm, and the proportion of the number of images is 95%/5% (daylight), 98%/2% (infrared lighting at night), 97%/2% (strong light), 45%/44% (transparent water body), 91%/9% (rainfall), and 90%/10% (water gauge is slightly dirty). The results demonstrate that the proposed method shows good performance in different scenes, and its effectiveness has been confirmed. At the same time, it has a strong robustness and provides a certain reference for the application of deep learning in the field of hydrological monitoring.

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Polymer Optical Fiber Liquid Level Sensor: A Review

Cited by 88 publications

References 109 publications

Temperature Sensors based on Polymer Fiber Optic Interferometer

Temperature Sensors based on Polymer Fiber Optic Interferometer

High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network

A Water Level Measurement Approach Based on YOLOv5s

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