Mr. Chunhua Yang Profile

Chunhua Yang

at Central South Univ.

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Publications (4)

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Proceedings Article | 16 October 2023 Paper

Axial section multi-reflection cavity-like enhanced oxygen detection in pharmaceutical vial

Chen Hu, Yonggang Li, Qiwu Luo, Chunhua Yang, Weihua Gui

Proceedings Volume 12792, 127921R (2023) https://doi.org/10.1117/12.2691531

KEYWORDS: Oxygen, Reflection, Beam path

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The pharmaceutical industry extensively employs glass vials for the packaging of sterile preparations. Air invasion resulted from vial leakage leads to preparation quality deterioration. Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been established as an effective non-contact method for assessing seal quality by detecting residual oxygen concentration in vial headspace. However, definitely unlike that the scheme of cavity-enhanced absorption spectroscopy (CEAS) has a sufficiently long optical path, headspace oxygen detection should be realized within the short inner diameter length of vials, while the external optical path is longer and with rich oxygen in the open production environment. Innovatively, we attempt to make full use of the cavity-like geometric nature of the glass vial to increase the inner absorption optical path length, by coating a high-reflectance silver ring film on the outer wall of vials. This novel scheme enables the incident laser to achieve Axial Section Multiple Reflection (ASMR) within space-limited vials (using ‘n-ASMR’ denotes the mode with ‘n’ times of reflections), extending the absorption path effectively without equipping any additional absorption cavity, we name it Cavity-Like Enhanced Absorption Spectroscopy (CLEAS), which breakthroughs the limitations of the conventional Direct Transmission (DT) method only along the diameter direction. In the Allan variance analysis tests, compare with the detection limit 0.226% with an integration time 33.8s of the DT method, our 2/4/6/8-ASMR methods achieve the detection limits 0.058%, 0.054%, 0.058% and 0.046% with integration time 28.9s, 14.6s, 4.76s and 5.60s, respectively, which indicate a brand-new roadmap has been discovered by the CLEAS scheme to extend absorption path in space-limited glass vial without increasing any hardware facilities.

Proceedings Article | 4 April 2023 Paper

Dynamic sparse residual oxygen prediction method for encapsulated pharmaceutical vials under TDLAS/WMS framework

Qiwu Luo, Bingxing Zhou, Chunhua Yang

Proceedings Volume 12617, 1261741 (2023) https://doi.org/10.1117/12.2666217

KEYWORDS: Oxygen, Signal detection, Signal processing, Wavelets, Interference (communication), Environmental sensing, Glasses, Technology, Support vector machines, Statistical modeling

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TDLAS/WMS-based oxygen detection has been proven to be an effective method for the sealability verification of encapsulated pharmaceutical vials of sterile preparations. However, under the actual aseptic preparation filling production mill, it is not easy to maintain high oxygen detection precision due to the multiple irritating noises from both the environment (e.g., temperature, moderation, vibration) and the detection system itself (e.g., laser, circuits). This paper proposes a novel dynamic sparse residual oxygen prediction method for pharmaceutical vials under TDLAS/WMS framework. On the one hand, we directly feed the decomposed wavelet sub-components to the prediction model, other than reconstructing them back to the time domain after some filtering measures in the wavelet domain. Then the fine-grained signal descriptive advantage of wavelet transform is preserved, and the rough reconstruction is dropped, instead by the adaptive feature selection managed by the subsequent classification model. On the other hand, to deal with the large data volume gathered from the high-speed production, we introduce the L₁-norm to the target function of LSSVM, where sample-sparsity is added to the prediction model innovatively. Thus, by assigning more weight to valuable samples, the influence of environmental interference on prediction decision-making is weakened. Consequently, sample-sparsity and feature-sparsity are realized simultaneously to track the dynamic environmental variation. Experimental results show that the proposed method yields higher average accuracy than others, and provides a referred choice to suppress the inevitable detection interferences, not only from hardware and optical optimization but also from a signal processing perspective.

Proceedings Article | 4 April 2023 Paper

A fast online chemical oxygen demand method based on time-varying spectrum

Jingxuan Geng, Chunhua Yang, Yonggang Li

Proceedings Volume 12617, 126176N (2023) https://doi.org/10.1117/12.2666725

KEYWORDS: Particles, Oxygen, Turbidity, Water quality, Machine learning, Absorption spectrum, Absorption

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As one of the most predominant Water Quality Parameters (WQPs), Chemical Oxygen Demand (COD) can directly reflect the pollution situation of detected water samples and is widely used to evaluate the wastewater treatment performance of Wastewater Treatment Plants (WWTPs). In this paper, a novel COD detection method called Manifold Time-Varying Spectrum Detection (MTVSD) is proposed, which can accurately determine the COD concentration of wastewater in a short measurement period without the interference of suspended particles. To precisely determine COD components in both dissolved and undissolved states, the core ideas of traditional physical and chemical detection methods are combined in MTVSD, and the procedure of oxidant consumption and the decomposition of suspended particles are captured in a spectrum form (time-varying spectrum). Then, to accurately extract the inner structure of the time-varying spectrum, a novel machine learning method, Structured Manifold Learning (SML), is invented based on the structural sparse representation and manifold learning, which can automatically extract spectrum components related to COD, solving the curse problem of high dimension, enhancing the detection accuracy and reducing the necessary detection speed. Based on the experimental results, our proposed method can more effectively eliminate the interference brought by turbidity and accurately detect the COD value (relative error < 10%) with a far shorter digestion time (within five minutes).

Proceedings Article | 18 March 2021 Paper

Development of an atmospheric CO2 sensor based on TDLAS-WMS

Jingxuan Geng, Lijuan Lan, Qiwu Luo, Chunhua Yang

Proceedings Volume 11780, 117801V (2021) https://doi.org/10.1117/12.2590791

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Greenhouse gases (GHG) have negative impacts on the climate changes. Carbon dioxide (CO₂), one of the dominant GHG, comes mainly from the anthropogenic emissions in urban areas. In this work, we develop a CO₂ sensor based on TDLAS-WMS (tunable diode laser absorption spectroscopy – wavelength modulation spectroscopy) to detect the daily CO₂ concentration in the atmosphere. Firstly, the residual amplitude modulation (RAM) in the first harmonic is eliminated using the phasor decomposition method. Then, the multi-harmonic detection is employed to improve the precision of the sensor. It reveals that the precision can reach 0.02 ppm for CO₂ measurement. The atmospheric CO₂ measurement is conducted in urban area for a whole year. The results indicate that the anthropogenic activities can be observed in the diurnal CO₂ cycles, especially in the wintertime. The developed sensor has a great potential for the air quality monitoring in urban areas.

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