RESEARCH ON KEY TECHNOLOGIES FOR IMPROVEMENT OF MEASUREMENT ACCURACY OF STEREO DEFLECTOMETRY

Obtaining three-dimensional (3D) shape data of specular surfaces plays an increasingly important role in the quality control and function evaluation of high value-added industry, such as space, automobile, Photovoltaics, integrated circuits and so on. In recent years, stereo deflectometry has been widely studied and applied for obtaining form information of freeform specular surfaces. Theoretically, the global form measurement accuracy of stereo deflectometry can be up to nanometre. However, the sources of errors limit the measurement accuracy of the current stereo deflectometry application at the scale of submicron. To this end, this thesis documents the design and development of the calibration methods, error analysis and compensation in the field of stereo deflectometry. To limit the influence of system distortion, a novel holistic calibration technique utilising iterative distortion compensation algorithm has been designed and developed. A search algorithm with an objective function has been developed to solve the low-accuracy initial value problem caused by image distortion and imaging model error. With the intention of decreasing the impact of the phase error in stereo deflectometry, a novel imaging model has been explored the nexus between phase inaccuracy and gradient error. The period of fringe displayed on displaying screen and pixel size of the screen has been studied to augment measurement accuracy through taking into account their impact on sampling phase inaccuracy and gradient miscalculation. In addition, four geometric parameters of a stereo deflectometry system are analysed and evaluated. These are the distance between the main camera and the measured object surface, the angle between main camera ray and surface normal, the distance between the fringe-displaying screen and object and the angle between the main camera and the reference camera. The influence of the geometric parameters on the measurement accuracy is evaluated. A stereo deflectometry system is designed, optimised and calibrated based on the investigation of this thesis. Two evaluation experiments have been conducted and experimental results indicate the system’s measurement accuracy can achieve tens of nanometres

Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.Comment: 41 pages, 14 figure

A calibration method for non-overlapping cameras based on mirrored phase target

A novel calibration method for non-overlapping cameras is proposed in this paper. A LCD screen is used as a phase target to display two groups of orthogonal phase-shifted sinusoidal patterns during the calibration process. Through a mirror reflection, the phase target is captured by the cameras respectively. The relations between each camera and the phase target can be obtained according the proposed algorithm. Then the relation between the cameras can be calculated by treating the phase target as an intermediate value. The proposed method is more flexible than conventional mirror-based approach, because it do not require the common identification points and is robust to out-of-focus images. Both simulation work and experimental results show the proposed calibration method has a good result in calibrating a non-overlapping cameras system

An iterative distortion compensation algorithm for camera calibration based on phase target

Camera distortion is a critical factor affecting the accuracy of camera calibration. A conventional calibration approach cannot satisfy the requirement of a measurement system demanding high calibration accuracy due to the inaccurate distortion compensation. This paper presents a novel camera calibration method with an iterative distortion compensation algorithm. The initial parameters of the camera are calibrated by full-field camera pixels and the corresponding points on a phase target. An iterative algorithm is proposed to compensate for the distortion. A 2D fitting and interpolation method is also developed to enhance the accuracy of the phase target. Compared to the conventional calibration method, the proposed method does not rely on a distortion mathematical model, and is stable and effective in terms of complex distortion conditions. Both the simulation work and experimental results show that the proposed calibration method is more than 100% more accurate than the conventional calibration method

Poisson-Boltzmann based machine learning (PBML) model for electrostatic analysis

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity , and geometric complexity associated with the PB equation. The present work introduces a PB based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost

Structure maps for MAX phases formability revisited

The extraordinary chemical diversity of MAX phases raises the question of how many and which novel ones are yet to be discovered. The conventional schemes rely either on executions of well designed experiments or elaborately crafted calculations; both of which have been key tactics within the past several decades that have yielded many of important new materials we are studying and using today. However, these approaches are expensive despite the emergence of high throughput automations or evolution of high speed computers. In this work, we have revisited the in prior proposed light duty strategy, i.e. structure mapping, for describing the genomic conditions under which one MAX phase could form; that allow us to make successful formability and non formability separation of MAX phases with a fidelity of 95.5%. Our results suggest that the proposed coordinates, and further the developed structure maps, are able to offer a useful initial guiding principles for systematic screenings of potential MAX phases and provide untapped opportunities for their structure prediction and materials design

Macrophages Phenotype Regulated by IL-6 Are Associated with the Prognosis of Platinum-Resistant Serous Ovarian Cancer: Integrated Analysis of Clinical Trial and Omics

Background. The treatment of platinum-resistant recurrent ovarian cancer (PROC) is a clinical challenge and a hot topic. Tumor microenvironment (TME) as a key factor promoting ovarian cancer progression. Macrophage is a component of TME, and it has been reported that macrophage phenotype is related to the development of PROC. However, the mechanism underlying macrophage polarization and whether macrophage phenotype can be used as a prognostic indicator of PROC remains unclear. Methods. We used ESTIMATE to calculate the number of immune and stromal components in high-grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas database. The differential expression genes (DEGs) were analyzed via protein–protein interaction network, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis to reveal major pathways of DEGs. CD80 was selected for survival analysis. IL-6 was selected for gene set enrichment analysis (GSEA). A subsequent cohort study was performed to confirm the correlation of IL-6 expression with macrophage phenotype in peripheral blood and to explore the clinical utility of macrophage phenotype for the prognosis of PROC patients. Results. A total of 993 intersecting genes were identified as candidates for further survival analysis. Further analysis revealed that CD80 expression was positively correlated with the survival of HGSOC patients. The results of GO and KEGG analysis suggested that macrophage polarization could be regulated via chemokine pathway and cytokine–cytokine receptor interaction. GSEA showed that the genes were mainly enriched in IL-6-STAT-3. Correlation analysis for the proportion of tumor infiltration macrophages revealed that M2 was correlated with IL-6. The results of a cohort study demonstrated that the regulation of macrophage phenotype by IL-6 is bidirectional. The high M1% was a protective factor for progression-free survival. Conclusion. Thus, the macrophage phenotype is a prognostic indicator in PROC patients, possibly via a hyperactive IL-6-related pathway, providing an additional clue for the therapeutic intervention of PROC