Soot volume fraction profiling of asymmetric diffusion flames through tomographic imaging

This paper presents the 3-D (three-dimensional) reconstruction of soot volume fraction of diffusion flames based on tomographic imaging and image processing techniques. Eight flexible imaging fiber bundles and two RGB (Red, Green and Blue) CCD (Charge-coupled Device) cameras are used to obtain concurrently the 2-D (two-dimensional) image projections of the flame from eight different angles of view around the burner. Algorithms which combine the tomographic and two-color pyrometric techniques are utilized to reconstruct the soot volume fraction distributions on both cross- and longitudinal-sections of the flame. A series of experiments were carried out on a gas-fired combustion rig for the determination of soot volume fraction using the algorithms proposed. Test results demonstrate the effectiveness of the developed algorithms

Fast and accurate flow measurement through dual-camera light field particle image velocimetry and ordered-subset algorithm

Light field particle image velocimetry (LF-PIV) can measure the three-dimensional (3D) flow field via a single perspective and hence is very attractive for applications with limited optical access. However, the flow velocity measurement via single-camera LF-PIV shows poor accuracy in the depth direction due to the particle reconstruction elongation effect. This study proposes a solution based on a dual-camera LF-PIV system along with an ordered-subset simultaneous algebraic reconstruction technique (OS-SART). The proposed system improves the spatial resolution in the depth direction and reduces the reconstruction elongation. The OS-SART also reduces the computational time brought by the dual-camera LF-PIV. Numerical reconstructions of the particle fields and Gaussian ring vortex field are first performed to evaluate the reconstruction accuracy and efficiency of the proposed system. Experiments on a circular jet flow are conducted to further validate the velocity measurement accuracy. Results indicate that the particle reconstruction elongation is reduced more than 10 times compared to the single-camera LF-PIV and the reconstruction efficiency is improved at least twice compared to the conventional SART. The accuracy is improved significantly for the ring vortex and 3D jet flow fields compared to the single-camera system. It is therefore demonstrated that the proposed system is capable of measuring the 3D flow field fast and accurately

Improving Global Neighborhood Structure Map Denoising Approach for Digital Images

This paper proposes a new noise reduction model for digital images. In the proposed model, the intensity similarity between the center pixel and its neighboring pixels within a certain window for constructing a Global Neighborhood Structure (GNS) using Dominant Neighborhood Structure (DNS) maps of central pixels has been measured. The intensity similarity was calculated by using the Canberra Distance measurement equation; where the conventional GNS map approach used the Euclidean distance principle. To evaluate the performance of the proposed model, several noise attacks were imposed on two public image datasets and experimental results demonstrated that the proposed model outperforms the conventional GNS map based denoising technique by exhibiting higher PSNR and SNR values

Simulation of flame temperature reconstruction through multi-plenoptic camera techniques

Due to the variety of burner structure and fuel mixing, the flame temperature distribution is not only manifold but also complex. Therefore, it is necessary to develop an advanced temperature measurement technique, which can provide not only the adequate flame radiative information but also reconstruct the complex temperature accurately. This paper presents a comprehensive simulation of flame temperature reconstruction through multi-plenoptic camera techniques. A novel multi-plenoptic camera imaging technique is proposed which is able to provide adequate flame radiative information only from two different directions and to reconstruct the three dimensional (3D) temperature of a flame. An inverse algorithm i.e., Non-negative Least Squares is used to reconstruct the flame temperature. To verify the reconstruction algorithm, two different temperature distributions such as unimodal axisymmetric and bimodal asymmetric are used. Numerical simulations are carried out to evaluate the performance of the technique. It has been observed that the reconstruction accuracy decreases with the increasing of signal-to-noise ratios. However, compared with the single plenoptic and conventional multi-camera techniques, the proposed method has the advantages of lower relative error and better reconstruction quality and stability even with the higher SNRs for both temperature distributions. Therefore, the proposed multi-plenoptic camera imaging technique is capable of reconstructing the complex 3-D temperature fields more accurately

Combustion Condition Monitoring Through Deep Learning Networks

Combustion condition monitoring is essential in a power plant for maintaining stable operations and operational safety. Therefore it is crucial to develop an intelligent combustion monitoring system. Existing traditional methods not only need a large quantity of labeled data but also require rebuilding monitoring model for new conditions. Aiming these problems, the present study proposes a novel approach combining denoising auto-encoder (DAE) and generative adversarial network (GAN) to monitor combustion condition. By using the learning mechanism of the GAN, the robust feature extraction ability of DAE as a generator is improved. These features are then fed into the Gaussian process classifier (GPC) for condition identification. Especially, newly occurring conditions can be correctly classified by simply training the GPC, rather than training from scratch. Experiments performed on a gaseous combustor indicate that the proposed approach can extract representative features accurately and achieve high performance in combustion condition monitoring with the accuracy of 98.5% for original conditions and 97.8% for the new conditions

High-resolution microscale velocity field measurement using light field particle image-tracking velocimetry

Light field microparticle image velocimetry (LF-μPIV) can realize the three-dimensional (3D) microscale velocity field measurement, but the spatial resolution of the velocity field is low. Therefore, this study proposes a high-resolution LF particle image-tracking velocimetry (PIV–PTV) in combination with a cross-validation matching (CVM) algorithm. The proposed method performs motion compensation for the distribution of particle center position based on the low-resolution velocity field achieved by PIV and then conducts the CVM on tracer particles with the nearest neighbor method. The motion compensation reduces the particle displacement during the matching, while the CVM reduces the impact of missing particles on the matching accuracy. Thus, the proposed method enables precise tracking of individual particles at higher particle concentrations and improves the spatial resolution of the velocity field. Numerical simulations were conducted on the 3D displacement field reconstruction. The influence of interrogation window size, particle diameter, and concentration was analyzed. Experiments were conducted on the microscale 3D velocity field within the microchannel with right-angle bends. Results indicate that the proposed method provides the high-resolution measurement of the microscale 3D velocity field and improves the precision of the velocity field compared to the PTV at higher particle concentrations. It demonstrates that the proposed method outperforms PIV by 26% in resolution and PTV by 76% in precision at a higher particle concentration of 1.5 particles per microlens

Three-dimensional Reconstruction and Measurement of Avian Eggs through Digital Imaging

This paper presents a computer vision-based method for the 3-D (three-dimensional) reconstruction and characterization of avian eggs. Two low-cost cameras are used to acquire images of eggs from top and side views, respectively. The image segmentation is performed using the image binarization technique. The contour-slice based method is employed for the 3-D reconstruction. The geometrical parameters of avian eggs, such as length, breadth, volume and surface area, are then computed based on the reconstructed model. The performance of the system is evaluated using eggs from different breeds and sizes. Comparative results between the physical measurement and the proposed approach suggest that the digital imaging approach has an overall accuracy of 98% for the geometrical parameter measurement of avian egg

Comments on Information Erasure in Black Hole

We analyze the Kim, Lee & Lee model of information erasure by black holes and find contradictions with standard physical laws. We demonstrate that the erasure model leads to arbitrarily fast information erasure; the proposed physical interpretation of information freezing at the event horizon as observed by an asymptotic observer is problematic; and information erasure, whatever the process may be, near the black hole horizon leads to contradictions with quantum mechanics if Landauer's principle is assumed. The later part of the work demonstrates the significance of the "erasure entropy." We show that the erasure entropy is the mutual information between two subsystems.Comment: 13 pages, clarified some issues in detai