Refinement Technique Research Articles

Zinc ferrite ZnxFe3–xO4 nanocomposites as efficient photo catalysts: a study of synthesis, properties and catalytic activity

Zinc ferrite nanocomposites ZnxFe3−xO4 (x = 0, 0.5, 0.7, 0.8, 0.9, and 1) were successfully synthesized using the sol-gel co-precipitation technique. The physicochemical properties of the resulting nanocomposites were investigated using X-ray diffraction (XRD), Rietveld refinement technique (GSASII), scanning electron microscopy (SEM), Fourier transforms infrared (FTIR) spectrometry, UV/vis spectroscopy, and a vibrating sample magnetometer (VSM). The predominant phase in all of the samples was found to be ZnFe2O4 spinel, with other phases such as Fe2O3 and Fe3O4 also present. The crystallite size ranged from 6.87 to 10.88 nm, and the resulting spinel phase powder had grain sizes between 100 and 220 nm. The lattice constants and coercivity of the samples increased with increasing Zn concentration, while saturation magnetization decreased. The photocatalytic activity of the nanocomposites for the photo-degradation of methylene blue (MB) aqueous solution was investigated, and the ZnFe2O4 nanocomposites showed the highest MB degradation rate and efficiency (94%) within 60 min of UV and visible light irradiation. These results demonstrate the potential of ZnFe2O4 nanocomposites synthesized via the sol-gel co-precipitation technique for photocatalytic applications.

Three-Dimensional Modeling of Marine Controlled Source Electromagnetic Using a New High-Order Finite Element Method With an Absorption Boundary Condition

The marine controlled source electromagnetic method (MCSEM) is widely used in marine oil, gas exploration and deep structures investigation because of its low cost and high efficiency. In this paper, a high-precision forward algorithm for 3D modeling for MCSEM is proposed. Unstructured meshes are used to discretize the model domain, and local mesh refinement techniques are applied, which is conducive to simulating complex terrains and targets. The application of electric dipole discrete technology can load wire excitation sources with arbitrarily complex spatial shapes, which can simulate more realistic electromagnetic field distribution of marine controllable sources electromagnetic in actual exploration. Absorption boundary conditions based on real number and exponential stretching techniques are introduced to improve the numerical solution accuracy. To improve the efficiency of solving, the open-source massively parallel solver (MUMPS) based on the multifrontal algorithm is used to solve the finite element equations. Finally, some typical geoelectric models are designed to verify the correctness and effectiveness of the proposed algorithm. The calculation results show that higher order finite elements bring about higher accuracy. In addition, the loading of absorption boundary conditions is simple and effective than conventional Dirichlet boundary conditions.

Efficient Dynamic Mesh Refinement Technique for Simulation of HPM Breakdown-Induced Plasma Pattern Formation

Numerical simulation of the complex plasma dynamics associated with high power, high-frequency microwave breakdown at high pressures, leading to the formation of filamentary plasma structures such as self-organized plasma arrays, is a computationally challenging problem. The widely used 2-D electromagnetic (EM)–plasma fluid model, which accurately captures the experimental observations, requires a runtime of several days to months to simulate standard problems due to stringent numerical requirements in terms of cell size and time step. This article presents a self-aware mesh refinement (MR) algorithm that uses a coarse mesh and a fine mesh that dynamically expand based on the plasma profile topology to resolve the sharp gradients in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -fields and plasma density in the breakdown region. The dynamic MR (DMR) technique is explained in detail, and its performance has been evaluated using a standard benchmark microwave breakdown problem. We observe a speedup of 8 (of the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(r^{3})$ </tex-math></inline-formula> , when the refinement factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> ) is 2) compared with a traditional single uniform fine-mesh-based simulation. The technique is scalable and performs better when the problem size increases. We also present a comprehensive spatio-temporal visual analysis to explain the complex physics of high-power microwave (HPM) breakdown, leading to self-organized plasma filaments as an application of the DMR technique.

A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

Autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results.

Dual Cross-Attention for Video Object Segmentation via Uncertainty Refinement

In this paper, we propose a novel approach to video object segmentation where dual streams consisting of a shared network and a special network are designed to constitute the feature memory of history frames. Cues of spatial position and time stamp are explicitly explored to learn the context for each frame in the video sequence. Self-attention and cross-attention are simultaneously exploited to extract more powerful features for segmentation. In contrast to STM and its variants, the proposed dual cross-attention performs in both appearance space and semantic space such that the derived features are more distinctive and then robust to similar overlapping objects. During decoding for segmentation, a local refinement technique is designed for the uncertain boundaries to obtain more precise and smooth object contours. Experimental results on the challenging benchmark datasets DAVIS-2016, DAVIS-2017, and YouTube-VOS demonstrate the effectiveness of our proposed approach to video object segmentation.

High-Quality Hypergraph Partitioning

Hypergraphs are a generalization of graphs where edges (aka nets ) are allowed to connect more than two vertices. They have a similarly wide range of applications as graphs. This article considers the fundamental and intensively studied problem of balanced hypergraph partitioning (BHP) , which asks for partitioning the vertices into k disjoint blocks of bounded size while minimizing an objective function over the hyperedges. Here, we consider the two most commonly used objectives: the cut-net metric and the connectivity metric . We describe our open-source hypergraph partitioner KaHyPar which is based on the successful multi-level approach—driving it to the extreme of using one level for (almost) every vertex. Using carefully designed data structures and dynamic update techniques, this approach turns out to have a very good time–quality tradeoff. We present two preprocessing techniques— pin sparsification using locality-sensitive hashing (LSH) and community detection based on the Louvain algorithm . The community structure is used to guide the coarsening process that incrementally contracts vertices. Portfolio-based partitioning of the contracted hypergraph then already achieves a good initial solution. While reversing the contraction process, a combination of several refinement techniques achieves a good final partitioning. In particular, we support a highly-localized local search that can directly produce a k -way partitioning and complement this with flow-based techniques that take a more global view. Optionally, a memetic algorithm evolves a pool of solution candidates to an overall good solution. We evaluate KaHyPar for a large set of instances from a wide range of application domains. With respect to quality, KaHyPar outperforms all previously considered systems that can handle large hypergraphs such as hMETIS, PaToH, Mondriaan, or Zoltan. Somewhat surprisingly, to some extend, this even extends to graph partitioners such as KaHIP when considering the special case of graphs. KaHyPar is also faster than most of these systems except for PaToH which represents a different speed–quality tradeoff.

Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function

The energy management system (EMS) of hybridization and electrification plays a pivotal role in improving the stability and cost-effectiveness of future vehicles. Existing efforts mainly concentrate on specific optimization targets, like fuel consumption, without sufficiently taking into account the degradation of on-board power sources. In this context, a novel multi-objective energy management strategy based on deep reinforcement learning is proposed for a hybrid electric vehicle (HEV), explicitly conscious of lithium-ion battery (LIB) wear. To be specific, this paper mainly contributes to three points. Firstly, a non-parametric reward function is introduced, for the first time, into the twin-delayed deep deterministic policy gradient (TD3) strategy, to facilitate the optimality and adaptability of the proposed energy management strategy and to mitigate the effort of parameter tuning. Then, to cope with the problem of state redundancy, state space refinement techniques are included in the proposed strategy. Finally, battery health is incorporated into this multi-objective energy management strategy. The efficacy of this framework is validated, in terms of training efficiency, optimality and adaptability, under various standard driving tests.

A new approach for road extraction using data augmentation and semantic segmentation

Accurate road extraction from remote sensing images is a challenging task. Several methods of extraction have been developed but the precision of extraction is still limited for the unpaved and small-width roads. This paper proposes an accurate road extraction approach called DAA-SSEG since it uses data augmentation architecture (DAA) and semantic segmentation model (SSEG). The proposed approach DAA-SSEG is based on a modified full convolutional neural network that overcomes the vanishing gradient and the training saturation issues. It recognizes roads at the pixel level. Furthermore, The DAA-SSEG approach uses a new plan of data augmentation based on geometric transformation and images refinement techniques. It allows getting a richer dataset thus better training and an accurate extraction. The experiment denotes that the proposed approach DAA-SSEG, that combine data augmentation architecture and semantic segmentation method, outperforms some state-of-the-art methods in terms of F-measures. The results demonstrate that it ensures accurate extraction of unpaved and small-width roads, in urban and rural areas. Moreover, the proposed approach distinguishes between roads and trails and can extract some roads not labeled beforehand.

Low-Complexity Sparse Array Synthesis Based on Off-Grid Compressive Sensing

In this letter, a novel sparse array synthesis method for nonuniform planar arrays is proposed, which belongs to compressive sensing (CS) based synthesis. Particularly, we propose an off-grid refinement technique to simultaneously optimize the antenna element positions and excitations with a low complexity, in response to the antenna position optimization problem that is difficult for standard CS. More importantly, we take into account the minimum interelement spacing constraint for ensuring the physically realizable solution. Specifically, the off-grid orthogonal match pursuit algorithm is first proposed with low complexity and then off-grid look ahead orthogonal match pursuit is designed with better synthesis performance but higher complexity. In addition, simulation results have shown that the proposed schemes have more advantages in computational complexity and synthesis performances compared with the related method.

Ab initio simulation of amorphous GeSe[formula omitted] and GeSe[formula omitted

The structural, vibrational, and electronic properties of glassy GeSe4 were studied, in conjunction with GeSe3, using an approach combining Reverse Monte Carlo with density functional theory total energies and forces. The models generated using the force enhanced atomic refinement (FEAR) technique showed close agreement with both X-ray and neutron diffraction data while sitting in a deep enough energy minimum defined by accurate interatomic interactions. These models produced important structural features of the system like the Ge–Ge correlations and the first sharp diffraction peak (FSDP) and is in better agreement to the experiment compared to the melt-quench (MQ) model.

Implications changing of the CdS window layer thickness on photovoltaic characteristics of n-CdS/i-AgSe/p-CdTe solar cells

Rietveld refinement techniques have been used to investigate the structural characteristics of CdS window layers at various thicknesses in the current study. The structural parameters were improved as the thickness of the CdS-layer was raised, according to XRD patterns. This, in turn, was owing to the increase in the crystal's size for the studied thin layers. For the Ni/n-CdS/i-AgSe/p-CdTe/Pt heterojunction that was successfully fabricated employing an AgSe buffer layer deposited directly on the p-CdTe absorber layer and then the CdS window layer deposited on these mentioned layers, the photovoltaic properties were determined under the dark and illuminated conditions. In dark conditions, from the forward and reverse (current-voltage) data, the essential behavior related to the fabricated devices has been determined. In addition, the heterojunction resistance, the shunt resistance, the series resistance and the rectification rate were all determined. As well, in the illumination case, the open-circuit voltage, the short-circuit current, the fill factor, the power conversion efficiency, (PCE), the photoresponsivity, the quantum efficiency, the dependence of generated photocurrent on the light intensity, the dependence of generated photocurrent on wavelength (λ) for the studied solar cells have been computed and discussed.

Crystallographic and Physicochemical Analysis of Bovine and Human Teeth Using X-ray Diffraction and Solid-State Nuclear Magnetic Resonance.

Dental research often uses bovine teeth as a substitute for human teeth. The aim of this study was to evaluate differences in the crystalline nanostructures of enamel and dentin between bovine and human teeth, using X-ray diffraction (XRD) and solid-state nuclear magnetic resonance (NMR). The crystallite size (crystallinity) and microstrains were analyzed using XRD with the Rietveld refinement technique and the Halder-Wagner method. The 31P and 1H NMR chemical environments were analyzed by two-dimensional (2D) 1H-31P heteronuclear-correlation (HETCOR) magic-angle spinning (MAS) NMR spectroscopy. Enamel had a greater crystallite size and fewer microstrains than dentin for both bovine and human teeth. When compared between the species, the bovine apatite had a smaller crystallite size with more microstrains than the human apatite for both dentin and enamel. The 2D HETCOR spectra demonstrated that a water-rich layer and inorganic HPO4- ions were abundant in dentin; meanwhile, the hydroxyl group in the lattice site was more dominant in enamel. A greater intensity of the hydroxyl group was detected in human than in bovine for both dentin and enamel. For 31P projections, bovine dentin and bovine enamel have wider linewidths than human dentin and human enamel, respectively. There are differences in the crystallite profile between human and bovine. The results of dental research should be interpreted with caution when bovine teeth are substituted for human teeth.

Microscopic Structure of Liquid Nitric Oxide.

The microscopic structure of nitric oxide is investigated using neutron scattering experiments. The measurements are performed at various temperatures between 120 and 144 K and at pressures between 1.1 and 9 bar. Using the technique of empirical potential structure refinement (EPSR), our results show that the dimer is the main form, around 80%, of nitric oxide in the liquid phase at 120 K, but the degree of dissociation to monomers increases with increasing temperature. The reported degree of dissociation of dimers, and its trend with increasing temperature, is consistent with earlier measurements and studies. It is also shown that nonplanar dimers are not inconsistent with the diffraction data and that the possibility of nitric oxide molecules forming longer oligomers, consisting of bonded nitrogen atoms along the backbone, cannot be ruled out in the liquid. A molecular dynamics simulation is used to compare the present EPSR simulations with an earlier proposed intermolecular potential for the liquid.

A Survey Study on Automatic Subtitle Synchronization and Positioning System for Deaf and Hearing Impaired People

In this study, we provide a subtitle synchronisation and placement system intended to improve deaf and hearing-impaired individuals' access to multimedia content. The paper's main contributions are a novel synchronisation algorithm that can reliably align the closed caption with the audio transcript without any human involvement and a timestamp refinement technique that can modify the duration of the subtitle segments in accordance with audiovisual recommendations. Regardless of the kind of video, the experimental evaluation of the strategy on a sizable dataset of 30 films pulled from the French national television verifies the method with average accuracy scores above 90%. The success of our strategy is demonstrated by the subjective assessment of the suggested subtitle synchronization and location system, carried out with real hearing challenged persons.

A switched system approach for the direct solution of singular optimal control problems

Optimal control problems where the control variable appears linearly in the formulation lead to singular control arcs that introduce analytical and numerical complications during the solution process. In this work, the switching behavior (switching between singular and nonsingular control arcs) is exploited to solve singular optimal control problems using a direct method. The problem is reformulated as a switched system optimal control problem where each control arc defines a different subsystem, i.e., lower and upper limits and a singular arc. The switching between control arcs is handled using binary functions that depend on time. The switched system is embedded into a larger family by relaxing the binary functions to avoid integer variables. The variable switching times at which the control switches between arcs are mapped to fixed points in a new time domain using a time-scaling transformation. A direct transcription method along with control parametrization is applied to transform the embedded problem into a nonlinear programming formulation that contains few variables and constraints. A penalty term is introduced to obtain binary solutions for the optimal selection of control arcs. The proposed reformulation avoids the use of mesh refinement techniques and continuation methods, and allows to solve challenging problems in very short CPU times with high accuracy.

Profit factors in real estate projects: A comparison of academic studies and expert opinions

Primary goal of private real estate investment projects (REIPs) is to make profit. Therefore, REIP profitability is a critical performance indicator. This study identifies and compares the determinants of profit in REIPs in the academic literature and the industry. Firstly, 191 profitability factors are identified and categorized by an in-depth review of the literature over the period of 2000-2019 in the selected databases and journals on construction, real estate and economics. Secondly, a final set of 66 factors from the literature are constructed based on the frequency analysis and using the normative refinement technique. Finally, face to face interviews are conducted with 15 senior managers working in the Turkish real estate and construction sector to validate the factors identified with the comprehensive literature review. Consequently, 96 REIP profit factors were obtained from the industry. The comparative analysis of the literature review and expert opinions shows that beside the prevalent similarities, experts focus more on design related factors, previous project records, value engineering, testing and commissioning, and the work environment. The benefits of this study are twofold: (i) helping professionals to understand the factors required to control profitability in REIPs, (ii) providing an extensive list of profitability factors for future researchers.

A parallel adaptive finite-element approach for 3-D realistic controlled-source electromagnetic problems using hierarchical tetrahedral grids

SUMMARY To effectively and efficiently interpret or invert controlled-source electromagnetic (CSEM) data which are recorded in areas with the kind of complex geological environments and arbitrary topography that are typical, 3-D CSEM forward modelling software that can quickly solve large-scale problems, provide accurate electromagnetic responses for complex geo-electrical models and can be easily incorporated into inversion algorithms are required. We have developed a parallel goal-oriented adaptive mesh refinement finite-element approach for frequency-domain 3-D CSEM forward modelling with hierarchical tetrahedral grids that can offer accurate electromagnetic responses for large-scale complex models and that can efficiently serve for inversion. The approach uses the goal-oriented adaptive vector finite element method to solve the total electric field vector equation. The geo-electrical model is discretized by unstructured tetrahedral grids which can deal with complex underground geological models with arbitrary surface topography. Different from previous adaptive finite element software working on unstructured tetrahedral grids, we have utilized a novel mesh refinement technique named the longest edge bisection method to generate hierarchically refined grids. As the refined grids are nested into the coarse grids, the refinement technique can precisely map the electrical parameters of inversion grids onto the forward modelling grids so that the extra numerical errors generated by the inconsistency of electrical parameters between inversion grids and forward modelling grids are eliminated. In addition, we use the parallel domain-decomposition technique to further accelerate the computations, and the flexible generalized minimum residual solver (FGMRES) with an auxiliary Maxwell solver pre-conditioner to solve the final large-scale system of linear equations. In the end, we validate the performance of the proposed scheme using two synthetic models and one realistic model. We demonstrate that accurate electromagnetic fields can be obtained by comparison with the analytic solutions and that the code is highly scalable for large-scale problems with millions or even hundreds of millions of unknowns. For the synthetic 3-D model and the realistic model with complex geometry, our solutions match well with the results calculated by an existing 3-D CSEM forward modelling code. Both synthetic and realistic examples demonstrate that our newly developed code is an effective, efficient forward modelling engine for interpreting CSEM field data acquired in areas of complex geology and topography.

Short-range repulsive force model for near-contact interactions of bubbles.

We introduce a short-range repulsive force model to tackle near-contact interactions when the collision occurs between bubbles. In contrast to the previous numerical method adopting the adaptive mesh refinement technique, such a mesoscale model can be applied to a relatively coarse mesh, which can prevent the nonphysical coalescence between bubbles without excessive mesh refinement. We assume that the repulsive force is inversely proportional to the third power of distance, as a reasonable approximation to the short-range phase interactions. The model is validated against two different experiments. In both experiments, two identical bubbles rising side by side were considered. First, the experiment performed in a water-glycerol mixture helps determine the model parameter K. Second, three typical combinations of bubble size and initial separation distance are simulated, presenting different types of interactions, i.e., coalescence, bouncing coalescence, and bouncing separation, agreeing well with the second experiment, which was performed in pure water. Owing to its simplicity, this model can be easily implemented into existing codes, and it can be extended to the case with multiple bubbles or droplets.

Multi-level adaptive mesh refinement technique for phase-field method

In order to solve the contradiction between the calculation efficiency and accuracy of the phase-field method (PFM), a multi-level adaptive mesh refinement technique suitable for PFM was developed and improved. A multi-level mesh refinement method of 2-D quadrilateral elements including three-layer transition elements without hanging nodes is designed, and the automatic refinement crack tip elements is realized by combining the phase field value. The local element coarsening technique is adopted to solve the problem of rapid elements number growth and reduce elements number in the calculation process. Combined with backtracking algorithm and “tree structure” node information storage technique, crack propagation is simulated. Through the benchmark example, the accuracy of the multi-level adaptive mesh refinement technique and the applicability to non-orthogonal mesh are verified, and the efficiency is greatly improved. For more complex crack propagation, the simulation results are also in good agreement with the experimental results. The critical gradient method proposed at last can find the refinement range more accurately and reduce elements number, which shows a great prospect in engineering applications.

Deep refinement of tellurium: equipment and process improvement through process simulation

Simulation data have been presented on a process of deep refinement of tellurium based on Authors-developed refinement technique implemented through analysis of the process unit thermodynamical condition using Flow Simulation software, from SolidWorks software product. The technique put forward herein has been implemented in a plant comprising a vertical air-tight reaction chamber arranged inside a multi-zone thermal unit and executing a sequence of refinement stages which use different techniques and are integrated in a single process. The experimental data which have been the basis for calculations have allowed one to determine the boundary conditions of the mathematical model taking into account previous operation experience of the software product used. Temperature profile calculation has been carried out taking into account all the types of heat transfer in the system, the weight / dimensions parameters of system units and the physicochemical properties of refined tellurium, materials of equipment fittings and reactor media. The temperature modes of the process stages have been accepted as the boundary conditions for the thermal calculations, with temperatures being measured at equipment fitting locations at which temperature gages connected with a PID controller have been installed. The simulation of specific refinement process conditions allowed process modes and equipment fitting component design to be corrected. We have developed and produced test models of process and imitation equipment. Analysis of the thermal fields for the final model has shown good agreement with the mathematical model. Equipment upgrading and process parameter improvement on the basis of the simulation results have allowed T-u Grade tellurium (99.95 wt.%) refinement to a 99.99992 wt.% purity by 30 main impurities in the course of physical experiments, the product yield being at least 60%.