Learning Rank Reduced Interpolation with Principal Component Analysis

In computer vision most iterative optimization algorithms, both sparse and dense, rely on a coarse and reliable dense initialization to bootstrap their optimization procedure. For example, dense optical flow algorithms profit massively in speed and robustness if they are initialized well in the basin of convergence of the used loss function. The same holds true for methods as sparse feature tracking when initial flow or depth information for new features at arbitrary positions is needed. This makes it extremely important to have techniques at hand that allow to obtain from only very few available measurements a dense but still approximative sketch of a desired 2D structure (e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded as sample from a 2D random process. The method presented here exploits the complete information given by the principal component analysis (PCA) of that process, the principal basis and its prior distribution. The method is able to determine a dense reconstruction from sparse measurement. When facing situations with only very sparse measurements, typically the number of principal components is further reduced which results in a loss of expressiveness of the basis. We overcome this problem and inject prior knowledge in a maximum a posterior (MAP) approach. We test our approach on the KITTI and the virtual KITTI datasets and focus on the interpolation of depth maps for driving scenes. The evaluation of the results show good agreement to the ground truth and are clearly better than results of interpolation by the nearest neighbor method which disregards statistical information.Comment: Accepted at Intelligent Vehicles Symposium (IV), Los Angeles, USA, June 201

Tuning the Mechanical Behavior of Density-Graded Elastomeric Foam Structures via Interlayer Properties.

The concept of density-graded foams has been proposed to simultaneously enhance strain energy dissipation and the load-bearing capacities at a reduced structural weight. From a practical perspective, the fabrication of density-graded foams is often achieved by stacking different foam densities. Under such conditions, the adhesive interlayer significantly affects the mechanical performance and failure modes of the structure. This work investigates the role of different adhesive layers on the mechanical and energy absorption behaviors of graded flexible foams with distinct density layers. Three adhesive candidates with different chemical, physical, and mechanical characteristics are used to assemble density-graded polyurea foam structures. The mechanical load-bearing and energy absorption performances of the structures are evaluated under quasi-static and dynamic loading conditions. Mechanical tests are accompanied by digital image correlation (DIC) analyses to study the local strain fields developed in the vicinity of the interface. Experimental measurements are also supplemented by model predictions that reveal the interplay between the mechanical properties of an adhesive interlayer and the macroscale mechanical performance of the graded foam structures. The results obtained herein demonstrate that the deformation patterns and macroscale properties of graded foam composites can be tuned by selecting different bonding agents. It is also shown that the proper selection of an adhesive can be a practical way to address the strength-energy dissipation dichotomy in graded structures

Engineering 3D architected metamaterials for enhanced mechanical properties and functionalities.

Compared with conventional materials, architected metamaterials have shown unprecedented mechanical properties and functionalities applications. Featured with controlled introduction of porosity and different composition, architected metamaterials have demonstrated unprecedent properties not found in natural materials. Such design strategies enable researchers to tailor materials and structures with multifunctionalies and satisfy conflicting design requirements, such as high stiffness and toughness; high strength with vibration mitigation properties, etc. Furthermore, with the booming advancement of 3D printing technologies, architected materials with precisely defined complex topologies can be fabricated effortlessly, which in turn promotes the research significantly. The research objectives of this dissertation are to achieve the enhanced mechanical properties and multifunctionalities of architected metamaterials by integrative design, computational modeling, 3D printing, and mechanical testing. Phononic crystal materials are capable of prohibiting the propagation of mechanical waves in certain frequency ranges. This certain frequency ranges are represented by phononic band gaps. Formally, band gaps are formed through two main mechanisms, Bragg scattering and local resonance. Band gaps induced by Bragg scattering are dependent on periodicity and the symmetry of the lattice. However, phononic crystals with Bragg-type band gaps are limited in their application because they do not attenuate vibration at lower frequencies without requiring large geometries. It is not practical to build huge models to achieve low frequency vibration mitigation. Alternatively, band gaps formed by local resonance are due to the excitation of resonant frequencies, and these band gaps are independent of periodicity. Therefore, lower frequency band gaps have been explored mostly through the production of phononic metamaterials that exploit locally resonant masses to absorb vibrational energy. However, despite research advances, the application of phononic metamaterials is sill largely hindered by their limited operation frequency ranges. Designing lightweight phononic metamaterials with low-frequency vibration mitigation capability is still a challenging topic. On the other hand, conventional phononic crystals usually exhibit very poor mechanical properties, such as low stiffness, strength, and energy absorption. This could largely limit their practical applications. Ideally, multifunctional materials and structures with both vibration mitigation property and high mechanical performance are demanded. In this work, we propose architected polymer foam material to overcome the challenges. Beside altering the topological architecture of metamaterials, tailoring the composition of materials is another approach to enhance the mechanical properties and realize multifunctionalities. Natural materials have adopted this strategy for long period of time. Biological structural materials such as nacre, glass sea sponges feature unusual mechanical properties due to the synergistic interplay between hard and soft material phases. These exceptional mechanical performance are highly demanded in engineering applications. As such, intensive efforts have been devoted to developing lightweight structural composites to meet the requirements. Despite the significant advances in research, the design and fabrication of low-cost structural materials with lightweight and superior mechanical performance still represent a challenge. Taking inspiration from cork material, we propose a new type of multilayered cellular composite (MCC) structure composed of hard brittle and soft flexible phases to tackle this challenge. On the other hand, piezoelectric materials with high sensitivity but low energy absorption have largely limited their applications, especially during harsh environment where external load could significantly damage the materials. Enlightened by the multiphase composite concept, we apply this design motif to develop a new interpenetrating-phased piezoelectric materials by combining PZT material as skeleton and PDMS material as matrix. By using a facial camphene-templated freeze-casting method, the co-continuous composites are fabricated with good quality. Through experiment and simulation studies, the proposed composite demonstrates multifunction with exceptional energy absorption and high sensitivity. Based on the above experimental studies, we further propose to use topology optimization framework to obtain the composites with the best performance of multifunctionalities. Specifically, we will use the solid isotropic material with penalization (SIMP) approach to optimize the piezoelectric materials with multi-objectives of 1) energy absorption and 2) electric-mechanical conversion property. The materials for the optimization design will be elastic PZT as skeleton and elatic material PDMS as matrix. To enable the gradient search of objective function efficiently, we will use adjoint method to derive the shape sensitivity analysis

Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing

A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.Comment: 9 pages, 6 figures, accepted to IEEE MASS 201

Influence of sintering temperature on hardness and wear properties of TiN Nano reinforced SAF 2205

Abstract: Conventional duplex stainless steel degrade in wear and mechanical properties at high temperature. Attempts have been made by researchers to solve this problems leading to the dispersion of second phase particles into duplex matrix. Powder metallurgy methods have been used to fabricate dispersion strengthened steels with a challenge of obtaining fully dense composite and grain growth. This could be resolved by appropriate selection of sintering parameters especially temperature. In this research, spark plasma sintering was utilized to fabricate nanostructured duplex stainless steel grade SAF 2205 with 5 wt.% nano TiN addition at different temperatures ranging from 1000 °C to 1200 °C. The effect of sintering temperature on the microstructure, density, hardness and wear of the samples was investigated. The results showed that the densities and grain sizes of the sintered nanocomposites increased with increasing the sintering temperature. The microstructures reveal ferrite and austenite grains with fine precipitates within the ferrite grains. The study of the hardness and wear behaviors, of the samples indicated that the optimum properties were obtained for the sintering temperature of 1150 °C

Adaptive bone re-modelling for optimization of porous structural components

This paper presents a speculative application of adaptive bone-remodelling to generate porous structures for building components using a numerical meshless method. We hypothesize that such porous structures could then be 3d printed to achieve light weight and material efficientbuilding components. The meshless model is built up of particles that are connected by arms to their neighbours within a distance called a horizon. The re-modelling adaption is then based on the ratio of arms strain over average arm strain which is mapped to a third-order polynomial function and used to scale the arm stiffness in a way that mimics the resorption and densification of bone tissue. The method is shown to work rather well in the recreation of the structural patterns found in cross section of a femur bone. The translation to a geometry which can be manufactured with additive techniques is not tackled specifically and suggest a direction for further work

Non-Intrusive Uncertainty Quantification for U3Si2 and UO2 Fuels with SiC/SiC Cladding using BISON for Digital Twin-Enabling Technology

U.S. Nuclear Regulatory Committee (NRC) and U.S. Department of Energy (DOE) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the DOE/ NRC. DTs have the potential to transform the nuclear energy sector in the coming years by incorporating risk-informed decision-making into the Accelerated Fuel Qualification (AFQ) process for ATF. A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. However, novel ATF technology suffers from a couple of challenges, such as (i) Data unavailability; (ii) Lack of data, missing data; and (iii) Model uncertainty. These challenges must be resolved to gain the trust in DT framework development. In addition, DT-enabling technologies consist of three major areas: (i) modeling and simulation (M&S), covering uncertainty quantification (UQ), sensitivity analysis (SA), data analytics through ML/AI, physics-based models, and data-informed modeling, (ii) Advanced sensors/instrumentation, and (iii) Data management. UQ and SA are important segments of DT-enabling technologies to ensure trustworthiness, which need to be implemented to meet the DT requirement. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, UQ and SA are paramount to the success of DT framework in terms of multi-criteria and risk-informed decision-making. In this study, the adaptability of polynomial chaos expansion (PCE) based UQ/SA in a non-intrusive method in BISON was investigated to ensure M&S aspects of the AFQ for ATF. This study introduces the ML-based UQ and SA methods while exhibiting actual applications to the finite element-based nuclear fuel performance code