Huijie Wang

Identification of two-phase flow regimes based on support vector machine and electrical capacitance tomography

It is important to identify two-phase flow regimes for the accuracy measurement of other flow parameters. Electrical capacitance tomography (ECT) is often used to identify two-phase/multi-phase flow regimes. The support vector machine (SVM) is a machine-learning algorithm based on the statistical learning theory, which has desirable classification ability with fewer training samples, and can be used for flow regime identification. The capacitance measurement data obtained from an ECT system contain flow regime information. The principal component analysis method has been used to reduce the dimension of the capacitance measurements. Simulation was carried out using the SVM method. The results show its feasibility. Static and dynamic experiments were also done for typical flow regimes, and the results indicate that this method is fast in speed and can identify these flow regimes correctly

A new normalization method based on electrical field lines for electrical capacitance tomography

Electrical capacitance tomography (ECT) is considered to be one of the most promising process tomography techniques. The image reconstruction for ECT is an inverse problem to find the spatially distributed permittivities in a pipe. Usually, the capacitance measurements obtained from the ECT system are normalized at the high and low permittivity for image reconstruction. The parallel normalization model is commonly used during the normalization process, which assumes the distribution of materials in parallel. Thus, the normalized capacitance is a linear function of measured capacitance. A recently used model is a series normalization model which results in the normalized capacitance as a nonlinear function of measured capacitance. The newest presented model is based on electrical field centre lines (EFCL), and is a mixture of two normalization models. The multi-threshold method of this model is presented in this paper. The sensitivity matrices based on different normalization models were obtained, and image reconstruction was carried out accordingly. Simulation results indicate that reconstructed images with higher quality can be obtained based on the presented model.

3D modelling of the human thorax for ventilation distribution measured through electrical impedance tomography

Thoracic electrical impedance tomography (EIT) aims to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. It is a non-invasive, radiation-free monitoring technique. In this paper, true 3D thorax models with conductivity distribution or complex conductivity distribution under different ARDS conditions are built up in comparison with the 2.5D ones, and EIT-derived numeric indices are also employed for evaluation of the lung ventilation. The purpose of this paper is to study different effects of different thorax models with either conductivity or complex conductivity on the reconstructed images and ventilation indices.

Online monitoring of oil film using electrical capacitance tomography and level set method

In the application of oil-air lubrication system, electrical capacitance tomography (ECT) provides a promising way for monitoring oil film in the pipelines by reconstructing cross sectional oil distributions in real time. While in the case of small diameter pipe and thin oil film, the thickness of the oil film is hard to be observed visually since the interface of oil and air is not obvious in the reconstructed images. And the existence of artifacts in the reconstructions has seriously influenced the effectiveness of image segmentation techniques such as level set method. Besides, level set method is also unavailable for online monitoring due to its low computation speed. To address these problems, a modified level set method is developed: a distance regularized level set evolution formulation is extended to image two-phase flow online using an ECT system, a narrowband image filter is defined to eliminate the influence of artifacts, and considering the continuity of the oil distribution variation, the detected oil-air interface of a former image can be used as the initial contour for the detection of the subsequent frame; thus, the propagation from the initial contour to the boundary can be greatly accelerated, making it possible for real time tracking. To testify the feasibility of the proposed method, an oil-air lubrication facility with 4 mm inner diameter pipe is measured in normal operation using an 8-electrode ECT system. Both simulation and experiment results indicate that the modified level set method is capable of visualizing the oil-air interface accurately online.

Speeding up Raman spectral imaging by the three-dimensional low rank estimation method

Raman spectral imaging has been widely used as a very important analytical tool in various fields. For obtaining the high spectral signal-to-noise ratio Raman images, the long integration time is necessary, which is placing a limit on the application of Raman spectral imaging. We introduce a simple and feasible numerical method of the Three-dimensional Low Rank Estimation (3D-LRE), which can speed up the data acquisition process of the Raman spectral imaging. The spectral signal-to-noise ratio of the Raman images can be increased by over 75 times and the speed of the data acquisition can be improved by over 30 times. By combining with line-scan or multifocus-scan techniques, the Raman images can be obtained in a few seconds.

Improving the resolution and the throughput of spectrometers by a digital projection slit

The contradiction between spectral resolution and throughput for the optical spectrometers is still a problem that needs to be solved. We introduce a simple and feasible method of the digital projection slit (DPS), which can improve both the spectral resolution and throughput of the optical spectrometer. The DPS spectrum is accurate and reliable without using the optical transfer function (OTF) of the optical spectrometer. The method has been successfully applied in the fiber spectrometer and the Raman spectrometer. The resolution of the recovered spectra can be increased by ~3 times and the throughput can be increased by ~5 times.

Enhancing the signal-to-noise ratio of FTIR spectrometers by a digital J-Stop

Fourier transform infrared (FTIR) spectrometers have been widely used as very important analytical tools in various fields. Owing to the Jacquinot Stop (J-Stop), high throughput is a widely recognized advantage inherent to Fourier transform interferometers. However, there is a fundamental trade-off between the throughput and spectral resolution, which is primarily affected by the size of the J-Stop. So far, no effective optimization methods have been provided to break the trade-off. In this paper, we introduce a numeric technique of the digital J-Stop, which has been experimentally validated using the FTIR spectra collected from a commercial spectrometer. The result shows that the throughput can be increased by ~12 times, while the spectral resolution is also improved. In this way, the signal-to-noise ratio (SNR) gets improved by ~3 times.

Note: A unibody NIR transmission probe for in situ liquid detection

The transmission probe is widely used for in situ spectroscopic detection in various fields. Conventional transmission probes are always assembled from parts, which require accurate assembly and good sealing. In this paper, a universal and reliable near-infrared (NIR) transmission probe is proposed, which is simply made up of a unibody fused silica rod. The proposed NIR transmission probe has been successfully used to measure the alcohol by volume of the Chinese spirit for quality control. This unibody NIR transmission probe has great potential for the detection of corrosive substances, owing to the good chemical resistance.

Raman Spectroscopy for Pharmaceutical Quantitative Analysis by Low-Rank Estimation

Raman spectroscopy has been widely used for quantitative analysis in biomedical and pharmaceutical applications. However, the signal-to-noise ratio (SNR) of Raman spectra is always poor due to weak Raman scattering. The noise in Raman spectral dataset will limit the accuracy of quantitative analysis. Because of high correlations in the spectral signatures, Raman spectra have the low-rank property, which can be used as a constraint to improve Raman spectral SNR. In this paper, a simple and feasible Raman spectroscopic analysis method by Low-Rank Estimation (LRE) is proposed. The Frank-Wolfe (FW) algorithm is applied in the LRE method to seek the optimal solution. The proposed method is used for the quantitative analysis of pharmaceutical mixtures. The accuracy and robustness of Partial Least Squares (PLS) and Support Vector Machine (SVM) chemometric models can be improved by the LRE method.

Note: A NDIR instrument for multicomponent gas detection using the galvanometer modulation

The non-dispersive infrared (NDIR) instruments are widely applied to multicomponent gas detection in many fields. In this paper, a NDIR instrument using the galvanometer modulation is proposed, which has a common optical path for multicomponent gas detection. Compared with the conventional NDIR instrument using the chopper modulation, the proposed instrument with a simpler configuration has better detection efficiency and stability. The proposed instrument has been successfully used to measure the main gas components of vehicle emissions. The NDIR instrument using the galvanometer modulation has great potential for remote sensing application, where multicomponent gas is always spatially inhomogeneous.