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01 January 2018 - 30 August 2021
My main areas of expertise are in Light Field Imaging, Combustion Diagnostics, Sensors, Instrumentation, Measurement, Condition Monitoring, Digital Image Processing, Deep Learning, Optical Tomography, and Solid Oxide Fuel Cells.
Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can eventually increase the quality of the output voltage along with the efficiency of the MLI. The performance of the algorithm is evaluated with three different case studies involving 7, 11, and 17-level three-phase MLIs. The results are verified using both simulation and experimental studies. The results showed substantial improvement and superiority compared to other available algorithms both in terms of the harmonics reduction of harmonics and finding the correct solutions.
Jahedul Islam; Sheikh Tanzim Meraj; Ammar Masaoud; Apel Mahmud; Amril Nazir; Muhammad Ashad Kabir; Moinul Hossain; Farhan Mumtaz. Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters. IEEE Access 2021, 9, 103610 -103626.
AMA StyleJahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Moinul Hossain, Farhan Mumtaz. Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters. IEEE Access. 2021; 9 ():103610-103626.
Chicago/Turabian StyleJahedul Islam; Sheikh Tanzim Meraj; Ammar Masaoud; Apel Mahmud; Amril Nazir; Muhammad Ashad Kabir; Moinul Hossain; Farhan Mumtaz. 2021. "Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters." IEEE Access 9, no. : 103610-103626.
Flame temperature measurement through a light field camera shows an attractive research interest due to its capabilities of obtaining spatial and angular rays' information by a single exposure. However, the sampling information collected by the light field camera is vast and most of them are redundant. The reconstruction process occupies a larger computing memory and time-consuming. We propose a novel approach i.e., feature rays under-sampling (FRUS) to reduce the light field sampling redundancy and thus improve the reconstruction efficiency. The proposed approach is evaluated through numerical and experimental studies. Effects of under-sampling methods, flame dividing voxels, noise levels and light field camera parameters are investigated. It has been observed that the proposed approach provides better anti-noise ability and reconstruction efficiency. It can be valuable not only for the flame temperature reconstruction but also for other applications such as particle image velocimetry and light field microscope.
Qi Qi; Moinul Hossain; Jin-Jian Li; Biao Zhang; Jian Li; Chuan-Long Xu. Approach to reduce light field sampling redundancy for flame temperature reconstruction. Optics Express 2021, 29, 13094 -13114.
AMA StyleQi Qi, Moinul Hossain, Jin-Jian Li, Biao Zhang, Jian Li, Chuan-Long Xu. Approach to reduce light field sampling redundancy for flame temperature reconstruction. Optics Express. 2021; 29 (9):13094-13114.
Chicago/Turabian StyleQi Qi; Moinul Hossain; Jin-Jian Li; Biao Zhang; Jian Li; Chuan-Long Xu. 2021. "Approach to reduce light field sampling redundancy for flame temperature reconstruction." Optics Express 29, no. 9: 13094-13114.
This study presents a novel multilevel inverter structure that can operate in both switched capacitor and asymmetric DC source modes. In the first mode, it can produce seven-level output voltage employing two switched capacitors and one single DC supply. The five-level output voltage is produced while operating the second mode. The voltage ratio between the input and output voltage for the capacitor mode is 1:3 (triple voltage gain). During the first mode, the capacitor of the inverter is self -balanced whereas the inverter can produce higher voltage output in the DC source mode. The proposed inverter reduces the total standing voltage in both modes of operations as it can generate the output voltage without requiring any additional H-bridge circuit. The feasibility and predominate features of the proposed inverter have been established by comparing with existing topologies in terms of power components count. Results obtained from this study are validated using simulation employing sinusoidal pulse width modulation (SPWM). A hardware prototype has also been developed for further validation.
Sheikh Tanzim Meraj; Mohammad Kamrul Hasan; Jahedul Islam; Yousef A. Baker El-Ebiary; Jamel Nebhen; Moinul Hossain; Khorshed Alam; Nguyen Vo. A Diamond Shaped Multilevel Inverter With Dual Mode of Operation. IEEE Access 2021, 9, 59873 -59887.
AMA StyleSheikh Tanzim Meraj, Mohammad Kamrul Hasan, Jahedul Islam, Yousef A. Baker El-Ebiary, Jamel Nebhen, Moinul Hossain, Khorshed Alam, Nguyen Vo. A Diamond Shaped Multilevel Inverter With Dual Mode of Operation. IEEE Access. 2021; 9 (99):59873-59887.
Chicago/Turabian StyleSheikh Tanzim Meraj; Mohammad Kamrul Hasan; Jahedul Islam; Yousef A. Baker El-Ebiary; Jamel Nebhen; Moinul Hossain; Khorshed Alam; Nguyen Vo. 2021. "A Diamond Shaped Multilevel Inverter With Dual Mode of Operation." IEEE Access 9, no. 99: 59873-59887.
The light field sectioning pyrometry (LFSP) has proven a significant advancement for in-situ measurement of flame temperature through a single light field camera. However, the spatial resolution of LFSP is limited, which severely inhibits the measurement accuracy. This paper aims to evaluate the spatial resolution of LFSP for flame temperature measurement quantitatively. A theoretical model of the spatial resolution is established based on optical parameters and point spread function of the light field camera. The spatial resolution is then numerically analyzed with different parameters of light field cameras. Based on the theoretical model, a novel cage-typed light field camera with a higher spatial resolution of LFSP is developed and experimentally evaluated. A significant improvement of spatial resolution about 17% and 50% in lateral and depth directions, respectively, is achieved. Results show that the spatial resolution is in good agreement with the theoretical model. The LFSP is then evaluated under different combustion cases and their temperatures are reconstructed.
Yudong Liu; Mingjuan Zhu; Tianxiang Wang; Gang Lei; Moinul Hossain; Biao Zhang; Jian Li; Chuanlong Xu. Spatial resolution of light field sectioning pyrometry for flame temperature measurement. Optics and Lasers in Engineering 2021, 140, 106545 .
AMA StyleYudong Liu, Mingjuan Zhu, Tianxiang Wang, Gang Lei, Moinul Hossain, Biao Zhang, Jian Li, Chuanlong Xu. Spatial resolution of light field sectioning pyrometry for flame temperature measurement. Optics and Lasers in Engineering. 2021; 140 ():106545.
Chicago/Turabian StyleYudong Liu; Mingjuan Zhu; Tianxiang Wang; Gang Lei; Moinul Hossain; Biao Zhang; Jian Li; Chuanlong Xu. 2021. "Spatial resolution of light field sectioning pyrometry for flame temperature measurement." Optics and Lasers in Engineering 140, no. : 106545.
State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowdsourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation's cost (time, click, interaction, etc.) while preserving or even improving the annotation's quality. The annotation quality of non-experts has been investigated using IoU (intersection over union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.
Saber Mirzaee Bafti; Chee Siang Ang; Moinul Hossain; Gianluca Marcelli; Marc Alemany-Fornes; Anastasios D. Tsaousis. A crowdsourcing semi-automatic image segmentation platform for cell biology. Computers in Biology and Medicine 2021, 130, 104204 .
AMA StyleSaber Mirzaee Bafti, Chee Siang Ang, Moinul Hossain, Gianluca Marcelli, Marc Alemany-Fornes, Anastasios D. Tsaousis. A crowdsourcing semi-automatic image segmentation platform for cell biology. Computers in Biology and Medicine. 2021; 130 ():104204.
Chicago/Turabian StyleSaber Mirzaee Bafti; Chee Siang Ang; Moinul Hossain; Gianluca Marcelli; Marc Alemany-Fornes; Anastasios D. Tsaousis. 2021. "A crowdsourcing semi-automatic image segmentation platform for cell biology." Computers in Biology and Medicine 130, no. : 104204.
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-of-sight (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.
Lixia Cao; Biao Zhang; Moinul Hossain; Jian Li; Chuanlong Xu. Tomographic reconstruction of light field PIV based on a backward ray-tracing technique. Measurement Science and Technology 2020, 32, 044007 .
AMA StyleLixia Cao, Biao Zhang, Moinul Hossain, Jian Li, Chuanlong Xu. Tomographic reconstruction of light field PIV based on a backward ray-tracing technique. Measurement Science and Technology. 2020; 32 (4):044007.
Chicago/Turabian StyleLixia Cao; Biao Zhang; Moinul Hossain; Jian Li; Chuanlong Xu. 2020. "Tomographic reconstruction of light field PIV based on a backward ray-tracing technique." Measurement Science and Technology 32, no. 4: 044007.
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 eggs.
Wasif Shafaet Chowdhury; Gang Lu; Md Moinul Hossain. Three-dimensional Reconstruction and Measurement of Avian Eggs through Digital Imaging. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) 2020, 1 -5.
AMA StyleWasif Shafaet Chowdhury, Gang Lu, Md Moinul Hossain. Three-dimensional Reconstruction and Measurement of Avian Eggs through Digital Imaging. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). 2020; ():1-5.
Chicago/Turabian StyleWasif Shafaet Chowdhury; Gang Lu; Md Moinul Hossain. 2020. "Three-dimensional Reconstruction and Measurement of Avian Eggs through Digital Imaging." 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) , no. : 1-5.
Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1% and 99.7% in terms of accuracy and 90.4% and 99.4% in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches.
Anastasios Stamoulakatos; Javier Cardona; Chris McCaig; David Murray; Hein Filius; Robert Atkinson; Xavier Bellekens; Craig Michie; Ivan Andonovic; Pavlos Lazaridis; Andrew Hamilton; Moinul Hossain; Gaetano Di Caterina; Christos Tachtatzis. Automatic Annotation of Subsea Pipelines Using Deep Learning. Sensors 2020, 20, 674 .
AMA StyleAnastasios Stamoulakatos, Javier Cardona, Chris McCaig, David Murray, Hein Filius, Robert Atkinson, Xavier Bellekens, Craig Michie, Ivan Andonovic, Pavlos Lazaridis, Andrew Hamilton, Moinul Hossain, Gaetano Di Caterina, Christos Tachtatzis. Automatic Annotation of Subsea Pipelines Using Deep Learning. Sensors. 2020; 20 (3):674.
Chicago/Turabian StyleAnastasios Stamoulakatos; Javier Cardona; Chris McCaig; David Murray; Hein Filius; Robert Atkinson; Xavier Bellekens; Craig Michie; Ivan Andonovic; Pavlos Lazaridis; Andrew Hamilton; Moinul Hossain; Gaetano Di Caterina; Christos Tachtatzis. 2020. "Automatic Annotation of Subsea Pipelines Using Deep Learning." Sensors 20, no. 3: 674.
Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately.
Zhezhe Han; Moinul Hossain; Yuwei Wang; Jian Li; Chuanlong Xu. Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network. Applied Energy 2019, 259, 114159 .
AMA StyleZhezhe Han, Moinul Hossain, Yuwei Wang, Jian Li, Chuanlong Xu. Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network. Applied Energy. 2019; 259 ():114159.
Chicago/Turabian StyleZhezhe Han; Moinul Hossain; Yuwei Wang; Jian Li; Chuanlong Xu. 2019. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network." Applied Energy 259, no. : 114159.
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.
Qi Qi; Moinul Hossain; Biao Zhang; Tianxiang Ling; Chuanlong Xu. Flame temperature reconstruction through a multi-plenoptic camera technique. Measurement Science and Technology 2019, 30, 124002 .
AMA StyleQi Qi, Moinul Hossain, Biao Zhang, Tianxiang Ling, Chuanlong Xu. Flame temperature reconstruction through a multi-plenoptic camera technique. Measurement Science and Technology. 2019; 30 (12):124002.
Chicago/Turabian StyleQi Qi; Moinul Hossain; Biao Zhang; Tianxiang Ling; Chuanlong Xu. 2019. "Flame temperature reconstruction through a multi-plenoptic camera technique." Measurement Science and Technology 30, no. 12: 124002.
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.
Jian Li; Moinul Hossain; Jun Sun; Yudong Liu; Biao Zhang; Christos Tachtatzis; Chuanlong Xu. Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques. Applied Thermal Engineering 2019, 154, 711 -725.
AMA StyleJian Li, Moinul Hossain, Jun Sun, Yudong Liu, Biao Zhang, Christos Tachtatzis, Chuanlong Xu. Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques. Applied Thermal Engineering. 2019; 154 ():711-725.
Chicago/Turabian StyleJian Li; Moinul Hossain; Jun Sun; Yudong Liu; Biao Zhang; Christos Tachtatzis; Chuanlong Xu. 2019. "Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques." Applied Thermal Engineering 154, no. : 711-725.
Yudong Liu; Md Moinul Hossain; Jun Sun; Biao Zhang; Chuanlong Xu. Investigation and optimization of sampling characteristics of light field camera for flame temperature measurement. Chinese Physics B 2019, 28, 1 .
AMA StyleYudong Liu, Md Moinul Hossain, Jun Sun, Biao Zhang, Chuanlong Xu. Investigation and optimization of sampling characteristics of light field camera for flame temperature measurement. Chinese Physics B. 2019; 28 (3):1.
Chicago/Turabian StyleYudong Liu; Md Moinul Hossain; Jun Sun; Biao Zhang; Chuanlong Xu. 2019. "Investigation and optimization of sampling characteristics of light field camera for flame temperature measurement." Chinese Physics B 28, no. 3: 1.
The paper presents the development of an instrumentation system for the visualisation and measurement of flames in a gas-fired multi-burner boiler based on digital imaging and spectrometric techniques. The system consists of a rigid optical probe and an optical fibre, a digital camera, a spectrometer and an embedded computer with associated application software. The characteristic parameters of the flame, including size, temperature and oscillation frequency are quantitatively determined based on flame images obtained. The spectral characteristics of the flame are analysed over the spectral range from the ultraviolet to near infrared. The system was evaluated on a gas-fired heat recovery boiler under different operation conditions. Results obtained suggest the promising correlation between computed flame parameters and operation conditions.
J Cugley; G Lu; M Hossain; Y Yan; I Searle. Visualisation and measurement of flames in a gas-fired multi-burner boiler. Journal of Physics: Conference Series 2018, 1065, 202009 .
AMA StyleJ Cugley, G Lu, M Hossain, Y Yan, I Searle. Visualisation and measurement of flames in a gas-fired multi-burner boiler. Journal of Physics: Conference Series. 2018; 1065 (20):202009.
Chicago/Turabian StyleJ Cugley; G Lu; M Hossain; Y Yan; I Searle. 2018. "Visualisation and measurement of flames in a gas-fired multi-burner boiler." Journal of Physics: Conference Series 1065, no. 20: 202009.
Different light field cameras (i.e., traditional and focused) can be used for the flame temperature measurement. But it is crucial to investigate which light field camera can provide better reconstruction accuracy for the flame temperature. In this study, numerical simulations were carried out to investigate the reconstruction accuracy of the flame temperature for the different light field cameras. The effects of flame radiation sampling of the light field cameras were described and evaluated. A novel concept of sampling region and sampling angle of the light field camera was proposed to assess the directional accuracy of the sampled rays of each pixel on the photosensor. It has been observed that the traditional light field camera sampled more rays for each pixel, hence the sampled rays of each pixel are approached less accurately from a single direction. The representative sampled ray was defined to obtain the direction of flame radiation. The radiation intensity of each pixel was calculated and indicated that the traditional light field camera sampled less radiation information than the focused light field camera. A non-negative least square (NNLS) algorithm was used to reconstruct the flame temperature. The reconstruction accuracy was also evaluated for the different distances from microlens array (MLA) to the photosensor. The results obtained from the simulations suggested that the focused light field camera performed better in comparison to the traditional light field camera. Experiments were also carried out to reconstruct the temperature distribution of ethylene diffusion flames based on the light field imaging, and to validate the proposed model.
Jun Sun; Moinul Hossain; Chuanlong Xu; Biao Zhang. Investigation of flame radiation sampling and temperature measurement through light field camera. International Journal of Heat and Mass Transfer 2018, 121, 1281 -1296.
AMA StyleJun Sun, Moinul Hossain, Chuanlong Xu, Biao Zhang. Investigation of flame radiation sampling and temperature measurement through light field camera. International Journal of Heat and Mass Transfer. 2018; 121 ():1281-1296.
Chicago/Turabian StyleJun Sun; Moinul Hossain; Chuanlong Xu; Biao Zhang. 2018. "Investigation of flame radiation sampling and temperature measurement through light field camera." International Journal of Heat and Mass Transfer 121, no. : 1281-1296.
Combustion systems need to be operated under a range of different conditions to meet fluctuating energy demands. Reliable monitoring of the combustion process is crucial for combustion control and optimisation under such variable conditions. In this paper, a monitoring method for variable combustion conditions is proposed by combining digital imaging, PCA-RWN (Principal Component Analysis and Random Weight Network) techniques. Based on flame images acquired using a digital imaging system, the mean intensity values of RGB (Red, Green, and Blue) image components and texture descriptors computed based on the grey-level co-occurrence matrix are used as the colour and texture features of flame images. These features are treated as the input variables of the proposed PCA-RWN model for multi-mode process monitoring. In the proposed model, the PCA is used to extract the principal component features of input vectors. By establishing the RWN model for an appropriate principal component subspace, the computing load of recognising combustion operation conditions is significantly reduced. In addition, Hotelling's T 2 and SPE (Squared Prediction Error) statistics of the corresponding operation conditions are calculated to identify the abnormalities of the combustion. The proposed approach is evaluated using flame image datasets obtained on the PACT 250kW Air/Oxy-fuel Combustion Test Facility (PACT 250kW Air/Oxy-fuel CTF). Variable operation conditions were achieved by changing the primary air and SA/TA (Secondary Air to Territory Air) splits. The results demonstrate that, for the operation conditions examined, the condition recognition success rate of the proposed PCA-RWN model is over 91%, which outperforms other machine learning classifiers with a reduced training time. The results also show that the abnormal conditions exhibit different oscillation frequencies from the normal conditions, and the T 2 and SPE statistics are capable of detecting such abnormalities
Xiaojing Bai; Gang Lu; Moinul Hossain; Janos Szuhánszki; Syed Sheraz Daood; William Nimmo; Yong Yan; Mohamed Pourkashanian. Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques. Fuel 2017, 202, 656 -664.
AMA StyleXiaojing Bai, Gang Lu, Moinul Hossain, Janos Szuhánszki, Syed Sheraz Daood, William Nimmo, Yong Yan, Mohamed Pourkashanian. Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques. Fuel. 2017; 202 ():656-664.
Chicago/Turabian StyleXiaojing Bai; Gang Lu; Moinul Hossain; Janos Szuhánszki; Syed Sheraz Daood; William Nimmo; Yong Yan; Mohamed Pourkashanian. 2017. "Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques." Fuel 202, no. : 656-664.
Jun Sun; Moinul Hossain; Chuan-Long Xu; Biao Zhang; Shi-Min Wang. A novel calibration method of focused light field camera for 3-D reconstruction of flame temperature. Optics Communications 2017, 390, 7 -15.
AMA StyleJun Sun, Moinul Hossain, Chuan-Long Xu, Biao Zhang, Shi-Min Wang. A novel calibration method of focused light field camera for 3-D reconstruction of flame temperature. Optics Communications. 2017; 390 ():7-15.
Chicago/Turabian StyleJun Sun; Moinul Hossain; Chuan-Long Xu; Biao Zhang; Shi-Min Wang. 2017. "A novel calibration method of focused light field camera for 3-D reconstruction of flame temperature." Optics Communications 390, no. : 7-15.
Syed Sheraz Daood; Marc Ottolini; Scott Taylor; Ola Ogunyinka; Moinul Hossain; Gang Lu; Yong Yan; William Nimmo; Md Moinul Hossain. Pollutant and Corrosion Control Technology and Efficient Coal Combustion. Energy & Fuels 2017, 31, 5581 -5596.
AMA StyleSyed Sheraz Daood, Marc Ottolini, Scott Taylor, Ola Ogunyinka, Moinul Hossain, Gang Lu, Yong Yan, William Nimmo, Md Moinul Hossain. Pollutant and Corrosion Control Technology and Efficient Coal Combustion. Energy & Fuels. 2017; 31 (5):5581-5596.
Chicago/Turabian StyleSyed Sheraz Daood; Marc Ottolini; Scott Taylor; Ola Ogunyinka; Moinul Hossain; Gang Lu; Yong Yan; William Nimmo; Md Moinul Hossain. 2017. "Pollutant and Corrosion Control Technology and Efficient Coal Combustion." Energy & Fuels 31, no. 5: 5581-5596.
This article presents a method for the multimode monitoring of combustion stability under different oxy-gas fired conditions based on flame imaging, principal component analysis (PCA), and kernel support vector machine (KSVM) techniques. The images of oxy-gas flames are segmented into premixed and diffused regions through the watershed transform method. The weighted color and texture features of the diffused and premixed regions are extracted and projected into two subspaces using the PCA to reduce the data dimensions and noises. The multi-class KSVM model is finally built based on the flame features in the principal component subspace to identify the operation condition. Two classic multivariate statistic indices, for example, Hotelling’s T2 and squared prediction error, are used to assess the normal and abnormal states for the corresponding operation condition. The experimental results obtained on a lab-scale oxy-gas rig show that the weighted color and texture features of the defined diffused and premixed regions are effective for detecting the combustion state and that the proposed PCA-KSVM model is feasible and effective to monitor a combustion process under variable operation conditions.
Xiaojing Bai; Gang Lu; Moinul Hossain; Yong Yan; Shi Liu; Md Moinul Hossain. Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine. Combustion Science and Technology 2016, 189, 776 -792.
AMA StyleXiaojing Bai, Gang Lu, Moinul Hossain, Yong Yan, Shi Liu, Md Moinul Hossain. Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine. Combustion Science and Technology. 2016; 189 (5):776-792.
Chicago/Turabian StyleXiaojing Bai; Gang Lu; Moinul Hossain; Yong Yan; Shi Liu; Md Moinul Hossain. 2016. "Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine." Combustion Science and Technology 189, no. 5: 776-792.
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.
Jun Sun; Chuanlong Xu; Biao Zhang; Shimin Wang; Moinul Hossain; Hong Qi; Heping Tan; Md Moinul Hossain. Geometric calibration of focused light field camera for 3-D flame temperature measurement. 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings 2016, 1 -6.
AMA StyleJun Sun, Chuanlong Xu, Biao Zhang, Shimin Wang, Moinul Hossain, Hong Qi, Heping Tan, Md Moinul Hossain. Geometric calibration of focused light field camera for 3-D flame temperature measurement. 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings. 2016; ():1-6.
Chicago/Turabian StyleJun Sun; Chuanlong Xu; Biao Zhang; Shimin Wang; Moinul Hossain; Hong Qi; Heping Tan; Md Moinul Hossain. 2016. "Geometric calibration of focused light field camera for 3-D flame temperature measurement." 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings , no. : 1-6.
This paper presents the 3-D (three-dimensional) temperature measurement of swirling flames of a well-characterized tangential swirl burner using a RGB (red, green and blue) CMOS (Complementary metal-oxide-semiconductor) camera associated with four flexible imaging fiber bundles for flame image acquisition. Optical tomographic algorithms were used to reconstruct the 3-D model of grey-level intensity of the flame and the two-color pyrometric technique was applied for computing the flame temperature based on the reconstructed 3-D model. Three R-type thermocouples were also employed to measure the flame temperature which was then used as a reference for validating the temperature derived from the flame images. Experimental results obtained show that the proposed technique is capable of determining flame temperature profiles, and consequently can be an effective means of characterizing the 3-D swirling flame behaviors, including stability limits such as flame blow-off/flashback, thus reducing the event probability by changing inlet conditions.
Moinul Hossain; Gang Lu; Fares A. Hatem; Agustin Valera-Medina; Richard Marsh; Yong Yan; Md Moinul Hossain. Temperature measurement of gas turbine swirling flames using tomographic imaging techniques. 2015 IEEE International Conference on Imaging Systems and Techniques (IST) 2015, 1 -5.
AMA StyleMoinul Hossain, Gang Lu, Fares A. Hatem, Agustin Valera-Medina, Richard Marsh, Yong Yan, Md Moinul Hossain. Temperature measurement of gas turbine swirling flames using tomographic imaging techniques. 2015 IEEE International Conference on Imaging Systems and Techniques (IST). 2015; ():1-5.
Chicago/Turabian StyleMoinul Hossain; Gang Lu; Fares A. Hatem; Agustin Valera-Medina; Richard Marsh; Yong Yan; Md Moinul Hossain. 2015. "Temperature measurement of gas turbine swirling flames using tomographic imaging techniques." 2015 IEEE International Conference on Imaging Systems and Techniques (IST) , no. : 1-5.