Geometric calibration of focused light field camera for 3-D flame temperature measurement

Focused light field camera can be used to measure three-dimensional (3-D) temperature field of a flame because of its ability to record intensity and direction information of each ray from flame simultaneously. This work aims to develop a suitable geometric calibration method of focused light field camera for 3-D flame temperature measurement. A modified method based on Zhang's camera calibration is developed to calibrate the camera and the measurement system. A single focused light-field camera is used to capture images of bespoke calibration board for calibration in this study. Geometric parameters including intrinsic (i.e., camera parameters) and extrinsic (i.e., camera connecting with the calibration board) of the focused light field camera are calibrated to trace the ray projecting onto each pixel on CCD (charge-coupled device) sensor. Instead of using line features, corner point features are directly utilized for the calibration. The characteristics of focused light field camera including one 3-D point corresponding to several image points and matching main lens and microlens f-numbers, are used for calibration. Results with a focused light field camera are presented and discussed. Preliminary 3-D temperature distribution of a flame is also investigated and presented

Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques

It is important to identify boundary constraints in the inverse algorithm for the reconstruction of flame temperature because a negative temperature can be reconstructed with improper boundary constraints. In this study, a hybrid algorithm, a combination of Levenberg-Marquardt with boundary constraint (LMBC) and non-negative least squares (NNLS), was proposed to reconstruct the flame temperature and absorption coefficient simultaneously by sampling the multi-wavelength flame radiation with a colored plenoptic camera. To validate the proposed algorithm, numerical simulations were carried out for both the symmetric and asymmetric distributions of the flame temperature and absorption coefficient. The plenoptic flame images were modeled to investigate the characteristics of flame radiation sampling. Different Gaussian noises were added into the radiation samplings to investigate the noise effects on the reconstruction accuracy. Simulation results showed that the relative errors of the reconstructed temperature and absorption coefficient are less than 10, indicating that accurate and reliable reconstruction can be obtained by the proposed algorithm. The algorithm was further verified by experimental studies, where the reconstructed results were compared with the thermocouple measurements. The simulation and experimental results demonstrated that the proposed algorithm is effective for the simultaneous reconstruction of the flame temperature and absorption coefficient

Boosting Out-of-distribution Detection with Typical Features

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspective of typicality and regard the feature's high-probability region of the deep model as the feature's typical set. We propose to rectify the feature into its typical set and calculate the OOD score with the typical features to achieve reliable uncertainty estimation. The feature rectification can be conducted as a {plug-and-play} module with various OOD scores. We evaluate the superiority of our method on both the commonly used benchmark (CIFAR) and the more challenging high-resolution benchmark with large label space (ImageNet). Notably, our approach outperforms state-of-the-art methods by up to 5.11$\%$ in the average FPR95 on the ImageNet benchmark

Tomographic Reconstruction of Light Field PIV Based on Backward Ray-Tracing Technique

The calculation of the weight matrix is one of the key steps of the tomographic reconstruction in the light field particle image velocimetry (light field PIV) system. At present, the existing calculation method of the weight matrix in light field PIV based on the forward ray-tracing technique (named as Fahringer’s method) is very time-consuming. To improve the computational efficiency of the weight matrix, this paper presents a computational method for the weight matrix based on the backward ray-tracing technique in combination with Gaussian function (named as Gaussian function method). An Expectation-Maximization (EM) algorithm is employed for the reconstruction of the 3D particle field, and a summed line-ofsight (SLOS) estimation is further used to accelerate the reconstruction process. The computational accuracy and efficiency of the weight matrix, the reconstruction quality of the 3D particle field, and the velocity field accuracy by Gaussian function method are numerically investigated. Finally, experiments are carried out to verify the feasibility of the weight matrix by Gaussian function method. Numerical results illustrated that Gaussian function method can improve the computational efficiency of the weight matrix by more than 10 times. SLOS is capable of further accelerating the computational efficiency of the overall reconstruction process including the pre-determination, the calculation of the weight matrix and the reconstruction. The velocity field accuracy by Gaussian function method is almost the same as that by Fahringer’s method. The experimental results of the 3D-3C velocity field of a laminar flow further verify the feasibility of the computational method for the weight matrix based on Gaussian function

Flame temperature reconstruction through multi-plenoptic camera technique

Due to the variety of burner structure and fuel mixing, the flame temperature distribution is not only irregular but also complex. Therefore, it is necessary to develop an advanced temperature measurement technique, which can provide not only adequate flame radiative information but also reconstruct complex flame temperature accurately. In this paper, a novel multi-plenoptic camera imaging technique is proposed which is not only provide adequate flame radiative information from two different directions but also reconstruct the complex flame temperature distribution accurately. An inverse algorithm i.e., Non-Negative Least Squares is used to reconstruct the flame temperature. The bimodal asymmetric temperature distribution is considered to verify the feasibility of the proposed system. Numerical simulations and experiments were carried out to evaluate the performance of the proposed technique. Simulation results demonstrate that the proposed system is able to provide higher reconstruction accuracy although the reconstruction accuracy decreases with the increase of noise levels. Meanwhile, compared with the single plenoptic and conventional multi-camera techniques, the proposed method has the advantages of lower relative error and better reconstruction quality even with higher noise levels. The proposed technique is further verified by experimental studies. The experimental results also demonstrate that the proposed technique is effective and feasible for the reconstruction of flame temperature. Therefore, the proposed multi-plenoptic camera imaging technique is capable of reconstructing the complex flame temperature fields more precisely

An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control and environmental protection. This study proposes a novel ensemble deep learning model for exhaust emissions (NOx and CO2) prediction. In this ensemble learning model, the stacked denoising autoencoder is established to extract the deep features of flame images. Four forecasting engines include artificial neural network, extreme learning machine, support vector machine and least squares support vector machine are employed for preliminary prediction of NOx and CO2 emissions based on the extracted image deep features. After that, these preliminary predictions are combined by Gaussian process regression in a nonlinear manner to achieve a final prediction of the emissions. The effectiveness of the proposed ensemble deep learning model is evaluated through 4.2Â MW heavy oil-fired boiler flame images. Experimental results suggest that the predictions are achieved from the four forecasting engines are inconsistent, however, an accurate prediction accuracy has been achieved through the proposed model. The proposed ensemble deep learning model not only provides accurate point prediction but also generates satisfactory confidence interval

Application of natural polysaccharides and their novel dosage forms in gynecological cancers: therapeutic implications from the diversity potential of natural compounds

Cancer is one of the most lethal diseases. Globally, the number of cancers is nearly 10 million per year. Gynecological cancers (for instance, ovarian, cervical, and endometrial), relying on hidden diseases, misdiagnoses, and high recurrence rates, have seriously affected women’s health. Traditional chemotherapy, hormone therapy, targeted therapy, and immunotherapy effectively improve the prognosis of gynecological cancer patients. However, with the emergence of adverse reactions and drug resistance, leading to the occurrence of complications and poor compliance of patients, we have to focus on the new treatment direction of gynecological cancers. Because of the potential effects of natural drugs in regulating immune function, protecting against oxidative damage, and improving the energy metabolism of the body, natural compounds represented by polysaccharides have also attracted extensive attention in recent years. More and more studies have shown that polysaccharides are effective in the treatment of various tumors and in reducing the burden of metastasis. In this review, we focus on the positive role of natural polysaccharides in the treatment of gynecologic cancer, the molecular mechanisms, and the available evidence, and discuss the potential use of new dosage forms derived from polysaccharides in gynecologic cancer. This study covers the most comprehensive discussion on applying natural polysaccharides and their novel preparations in gynecological cancers. By providing complete and valuable sources of information, we hope to promote more effective treatment solutions for clinical diagnosis and treatment of gynecological cancers

Approach to select optimal cross-correlation parameters for light field particle image velocimetry

The light field particle image velocimetry (LF-PIV) has shown a great potential for three-dimensional (3D) flow measurement in space-constrained applications. Usually, the parameters of the cross-correlation calculation in the LF-PIV are chosen based on empirical analysis or introduced from conventional planar PIV, which lowers the accuracy of 3D velocity field measurement. This study presents an approach to selecting optimal parameters of the cross-correlation calculation and thereby offers systematic guidelines for experiments. The selection criterion of the interrogation volume size is studied based on the analysis of the valid detection probability of the correlation peak. The optimal seeding concentration and the size of tracer particles are then explored through synthetic Gaussian vortex field reconstruction. The optimized parameters are employed in a cylinder wake flow measurement in a confined channel. A comparative study is conducted between the LF-PIV and a planar PIV system. Results indicate that the LF-PIV along with the optimized parameters can measure the 3D flow velocity of the cylinder wakes accurately. It has been observed that the mean and max errors of velocity decrease by 32.6% and 18.8%, respectively compared to the related LF-PIV techniques without consideration of optimal parameters. Therefore, it is suggested that the optimized cross-correlation parameters in the LF-PIV can improve the accuracy of 3D flow measurement

Flame soot absorption coefficient and temperature reconstruction through line-of-sight attenuation and light field imaging

The accuracy and efficiency of 3-D flame temperature field reconstruction using the light field imaging technique heavily depend on soot radiation characteristics. In this study, we employ the line-of-sight attenuation method to reconstruct the soot absorption coefficient distribution in a pure absorbing flame. Utilizing these soot absorption coefficients, the radiative transfer equation is effectively transformed within the framework of the light field imaging technique into a linear inverse problem and also outlines the flame boundary. This proposed strategy reduces the unwanted detection rays significantly, thus eliminating the extensive computational processing. Consequently, the proposed approach substantially enhances the accuracy and efficiency of flame temperature reconstruction. Numerical simulations were carried out on a bimodal asymmetric flame to validate the noise tolerance capabilities, investigate the effects of varying voxel numbers on flame division and carry out a comparative study. Experimental studies were also conducted to reconstruct flame temperature and soot absorption coefficient distributions under different combustion operating conditions. Thermocouple measurements were performed and compared with the reconstructed temperatures. Outcomes achieved from both numerical simulations and experimental studies demonstrate the feasibility, accuracy and robustness of the proposed method