452 research outputs found
Geometric Structure Extraction and Reconstruction
No full textGeometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results
Data for: Accelerating and Stabilizing the Vapor-Liquid Equilibrium (VLE) Calculation in Compositional Simulation of Unconventional Reservoirs Using Deep Learning Based Flash Calculation
No full textThe folder contains the detailed models and the trained weights of a deep-learning based flash calculation module.The H5 files can be directly loaded into Keras.The weights file can be extracted for practical usage.classfication_model.h5 network structure of phase classificationclassfication_weights.h5 weights of phase classificationconcentration_model.h5 network structure of concentration determinationconcentration_weights.h5 weights of concentration determinationReference: Shihao Wang, Nicolas Sobecki, Didier Ding, Lingchen Zhu, Yu-Shu Wu Accelerating and Stabilizing the Vapor-Liquid Equilibrium (VLE) Calculation in Compositional Simulation of Unconventional Reservoirs Using Deep Learning Based Flash Calculatio
Mineralogy and Geochemistry of High-Sulfur Coals from the M8 Coal Seam, Shihao Mine, Songzao Coalfield, Chongqing, Southwestern China
No full textMineral matter, including minerals and non-mineral elements, in coal is of great significance for geological evolution, high-value coal utilization, and environment protection. The minerals and elemental geochemistry of Late Permian coals from the M8 coal seam, Shihao mine, Songzao coalfield in Chongqing, were analyzed to evaluate the sediment source, sedimentary environment, hydrothermal fluids, and utilization prospects of critical metals. The average total sulfur (4.21%) was high in coals, which mainly exists in the forms of pyritic sulfur. Kaolinite, pyrite, calcite, quartz, illite and illite/smectite (I/S) mixed layers, and anatase predominated in coals, with trace amounts of chlorite, ankerite, and siderite. Epigenetic cell- and fracture-filling pyrite, veined calcite, and ankerite were related to hydrothermal fluids and/or pore water after the diagenesis stage. Compared to the world’s hard coals, As and Cd are enriched in the Shihao M8 coals, and Li, Cr, Co, Zr, Mo, Pb, and Tb are slightly enriched. These high contents of sulfophile elements may be related to seawater intrusion. The terrigenous clastics of the Shihao M8 coals originated from the felsic–intermediate rocks atop the Emeishan Large Igneous Provinces (ELIP) (Kangdian Upland), while the roof and floor samples were derived from Emeishan high-Ti basalt. Through the combination of sulfur contents and indicator parameters of Fe2O3 + CaO + MgO/SiO2 + Al2O3, Sr/Ba and Y/Ho, the depositional environment of peat swamp was found to be influenced by seawater. Although the critical elements in coal or coal ash did not reach the cut-off grade for beneficial recovery, the concentration of Li and Zr were high enough in coal ash
Geometric Structure Extraction and Reconstruction
No full textGeometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results
Investigating eutectic behavior and material relocation in B4C-stainless steel composites using the improved MPS method
No full textIn nuclear severe accidents, eutectic reactions induce early melting of stainless steel (SS) cladding and boron carbide (B4C), leading to control rod failure and eutectic melt relocation. To accurately simulate eutectic melting, we modified the standard Moving Particle Semi-implicit (MPS) method. The conventional MPS model is inadequate due to its simplistic treatment of surface tension, and viscosity. By revising these parameters and incorporating mass diffusion and eutectic reaction criteria based on the Fe-B phase diagram, the enhanced MPS method can effectively capture the complex behaviors of eutectic melting in both 2D and 3D simulations. The study aims to measure boron concentration through the unidirectional diffusion of boron within the stainless steel (SS) layers while evaluating the updated model's ability to replicate melt relocation behavior and geometry. In the current MPS simulations, one scenario employed dummy walls as heat sources, while another scenario used SS surface particles as heat sources to avoid interference with the melt flow as it reached the bottom of the specimen. The results indicate that upon eutectic reaction, boron diffuses into the SS wall, initiating melting at the B4C-SS interface and leading to melt flow following SS cladding penetration. Also, we observed that as temperature increases, there is a proportional rise in boron concentration within the melt due to enhanced unidirectional diffusion of boron atoms into SS cladding. Additionally, the effect of gravity on boron transport has been assessed, revealing its impact on the diffusion rate. The primary focus of this study lies in assessing the eutectic reaction model in the updated MPS code, particularly examining the formation of the eutectic melt, the concentration of B4C within it, and the resemblance of the final formed melt to the experimental observations
Model development for oxidation and degradation behavior of accident tolerant Cr coating on Zr alloy cladding under high temperature steam atmosphere
No full textRETRACTED ARTICLE: Flot2 targeted by miR-449 acts as a prognostic biomarker in glioma
No full textWe, the Editors and Publisher of the journal Artificial Cells, Nanomedicine, and Biotechnology, have retracted the following article:Shaosong Huang, Shihao Zheng, Shengyue Huang, Hui Cheng, Ying Lin, Yuxing Wen & Wei Lin (2019) Flot2 targeted by miR-449 acts as a prognostic biomarker in glioma. Artificial Cells, Nanomedicine, and Biotechnology, 47(1), 250–255, DOI: 10.1080/21691401.2018.1549062Since publication, concerns have been raised about the integrity of the data in the article. When approached for an explanation, the authors have been unresponsive, and we have been unable to verify their original data. We are therefore retracting this article and the corresponding author listed in this publication has been informed.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”
Data for: Accelerating and Stabilizing the Vapor-Liquid Equilibrium (VLE) Calculation in Compositional Simulation of Unconventional Reservoirs Using Deep Learning Based Flash Calculation
No full textThe folder contains the detailed models and the trained weights of a deep-learning based flash calculation module.The H5 files can be directly loaded into Keras.The weights file can be extracted for practical usage.classfication_model.h5 network structure of phase classificationclassfication_weights.h5 weights of phase classificationconcentration_model.h5 network structure of concentration determinationconcentration_weights.h5 weights of concentration determinationReference: Shihao Wang, Nicolas Sobecki, Didier Ding, Lingchen Zhu, Yu-Shu Wu Accelerating and Stabilizing the Vapor-Liquid Equilibrium (VLE) Calculation in Compositional Simulation of Unconventional Reservoirs Using Deep Learning Based Flash CalculationTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
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