Development of a Mid-Infrared Interband Cascade Laser Methane Sensor

Wenxue, 郑文雪 Zheng; Chuantao, 郑传涛 Zheng; Dan, 姚丹 Yao; Shuo, 杨硕 Yang; Peipei, 党佩佩 Dang; Yiding, 王一丁 Wang

doi:10.3788/aos201838.0328013

Cited by 5 publications

(5 citation statements)

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“…Li et al [27] proposed ESCR, a semi-supervised framework, tackles recurring concept drift and evolution in non-stationary data streams using an ensemble of clustering-based classifiers. Huang et al [28] introduces Dynamic Updated Ensemble (DUE), an algorithm for handling imbalanced non-stationary data streams with concept drift efficiently.…”

Section: Related Workmentioning

confidence: 99%

“…Clustering based approach is found in [2] to achieve adaptive means of concept drift detection with unsupervised learning. Supervised learning approaches are found in [4], [8], [9], [15] and [27] where concept drift based solutions are explored to solve problems in different domains. The notion of diversity measure is explored in [10] while ensemble learning approach is preferred in [3], [7], [21] and [28].…”

Section: Introductionmentioning

confidence: 99%

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LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

Trupthi,

CH,

SNEHA

et al. 2024

Preprint

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In the contemporary era, there has been increased collaboration among machines and things due to innovative technologies like Internet of Things (IoT). With use cases of IoT pertaining to industries, there is unprecedented increase in data generation and dissemination resulting in large data streams. In this context, data stream analytics is given paramount importance but it suffers from concept drift issues leading to performance deterioration in many automation applications. There are many existing methods for automatic detection of concept drifts in data streams. However, there is need for an adaptive approach that learns dynamically through machine learning (ML) techniques. Another drawback of existing method is lack of efficient feature selection method that leverages drift detection performance. In this paper, we address these shortcomings by proposing a framework known as Learning based Concept Drift Detection Framework (LCDDF). We also proposed two algorithms, namely Concept Drift-aware Feature Engineering (CDFE) and Learning based Model Selection (LbMS), to realize the framework and improve the state of the art in detection accuracy. The former is used to perform feature engineering which concept drift-aware leading to improving quality of learning process. The latter detects best models for making an ensemble towards improving prediction performance. Our empirical study has revealed that the proposed framework with underlying algorithms outperform many state of the art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

Trupthi,

CH,

SNEHA

et al. 2024

Preprint

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“…Laser gas detection, based on the selective absorption of specific wavelength light by gas molecules, offers advantages such as fast detection speed and the ability to detect low concentrations. Laser direct absorption detection technology is a simple gas detection method that measures the attenuation of a laser beam passing through a certain length of gas to calculate the concentration of the detected gas [4,5]. Tunable laser absorption spectroscopy technology utilizes the wavelength tuning characteristics of semiconductor lasers to obtain the gas absorption spectrum within the tuning range [6,7].…”

Section: Introductionmentioning

confidence: 99%

Raman lidar for remote sensing of gas

Duan,

Li,

Yang

et al. 2024

Advanced Fiber Laser Conference (AFL2023)

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Raman lidar is an active remote sensing technology that has been widely applied in fields such as laser atmospheric transmission, global climate prediction, aerosol radiation effects, and atmospheric environment. Raman lidar has the ability to measure target distances and provide spatial depth resolution. It offers high sensitivity and a long detection range without the need for cooperative targets. In this study, a pulsed laser with a wavelength of 355 nm and a single-pulse energy of 350mJ was used as the light source. The spectrometer system employed a blazed grating and a narrowband filter. Signal acquisition was performed using a 450 mm diameter Cassgrain telescope, and a single-photon detector was utilized to enhance the extraction and detection of Raman signals. Outdoor telemetric measurements of dimethyl methylphosphonate (DMMP) gas were conducted. In the vehicle moving mode, target gases could be detected up to a distance of 1.8 km. In the stationary mode, target gases could be detected up to a distance of 5 km.

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“…In order to determine the concentration of the detected gas, laser direct absorption detection technique measures the attenuation of a certain laser over a set length of gas [4][5][6][7][8]. The tunable diode laser absorption spectroscopy (TDLAS) system uses a wavelengthtunable semiconductor laser to obtain gas absorption spectra within the tuning range.…”

Section: Introductionmentioning

confidence: 99%

CO Detection System Based on TDLAS Using a 4.625 μm Interband Cascaded Laser

Wang

Yuan

et al. 2022

IJERPH

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During industrial operations and in confined places, carbon monoxide (CO) may collect in harmful proportions if ventilation is insufficient or appliances are not properly maintained. When the concentration of CO is too high, it might result in suffocation, coma, or even death. The detection of tiny concentrations of CO plays an important role in safe production. Due to the selective absorption of specific wavelengths of light by gas molecules, lasers have a wide range of applications in the field of gas detection. In this paper, a tunable diode laser absorption spectroscopy (TDLAS) system for CO detection was constructed using an interband cascaded laser (ICL) with a central wavelength of 4.625 μm. The modulated signal generated by the FPGA module was output to the laser controller to modulate the laser. The signal received by the detector was input to the FPGA module. After lock-in amplification, the second harmonic signal of high frequency modulation was output. Several concentrations of CO that were dispersed via static gas distribution were identified. A CO detection system with an open optical path was constructed, and the detection distance was about 8 m. The minimum detectable concentration is around 10.32 ppmm. The concentration of CO in the open optical path was 510.6 ppmm, according to the calibration of the detected concentration. The remote detection system based on TDLAS using an ICL can be used to monitor CO in the open optical path.

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Development of a Mid-Infrared Interband Cascade Laser Methane Sensor

Cited by 5 publications

References 0 publications

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

LCDDF: An Adaptive and Learning based Framework with Feature Selection for Efficient Detection of Concept Drift in Data Streams

Raman lidar for remote sensing of gas

CO Detection System Based on TDLAS Using a 4.625 μm Interband Cascaded Laser

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