Reference guided image super-resolution via efficient dense warping and adaptive fusion

Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

High H2 selectivity with low coke formation for methanol steam reforming over Cu/Y1.5Ce0.84Ru0.04O4 catalyst in a microchannel plate reactor

The synthesized novel metal oxides Y_xCe_yRu_zO₄ (x = 1.5, y = 0.84, z = 0.04) which was produced by the sol-gel method was used as a support for Cu active metal on the surface of a microchannel plate reactor in the methanol steam reforming (MSR) process. The prepared catalysts were characterized by X-ray powder diffraction (XRD), BET surface area analysis (S_BET), energy-dispersive X-ray analysis (EDX), field-emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), temperature-programmed desorption (NH₃-TPD), and temperature-programmed reduction (H₂-TPR). High methanol conversion (99.5%) and H₂ selectivity (98.7%) and low CO selectivity (1.4%) were achieved for Cu/Y_xCe_yRu_zO₄ coated microchannel reactor at 250 °C. FE-SEM images and TGA curve of the spent catalyst displayed no coke formation on the surface of the catalyst after 32 h on stream at 300 °C. The low reduction temperature of Cu, high BET surface area, and high pore volume of the catalyst are considered imperative factors that cause a better dispersion of copper on the Y_1.5Ce_0.84Ru_0.04O₄ support.     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

Direct high-purity hydrogen production from ammonia by using a membrane reactor combining V-10mol%Fe hydrogen permeable alloy membrane with Ru/Cs2O/Pr6O11 ammonia decomposition catalyst

The hydrogen production capabilities of the membrane reactor combining V-10 mol%Fe hydrogen permeable alloy membrane with Ru/Cs₂O/Pr₆O₁₁ ammonia decomposition catalyst are studied. The ammonia conversion is improved by 1.7 times compared to the Ru/Cs₂O/Pr₆O₁₁ catalyst alone by removing the produced hydrogen through the V-10mol%Fe alloy membrane during the ammonia decomposition. 79% of the hydrogen atoms contained in the ammonia gas are extracted directly as high-purity hydrogen gas. Both the Ru/Cs₂O/Pr₆O₁₁ catalyst and the V-10 mol% Fe alloy membrane are highly durable, and the initial performance of the hydrogen separation rate lasts for more than 3000 h. The produced hydrogen gas conforms to ISO 14687–2:2019 Grade D for fuel cell vehicles because the ammonia and nitrogen concentrations are less than 0.1 ppm and 100 ppm, respectively.     

High quality 3D reconstruction based on fusion of polarization imaging and binocular stereo vision

Polarization imaging can retrieve inaccurate objects’ 3D shapes with fine textures, whereas coarse but accurate depths can be provided by binocular stereo vision. To take full advantage of these two complementary techniques, we investigate a novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality 3D reconstruction. We first generate the polarization surface by correcting the azimuth angle errors on the basis of registered binocular depth, to solve the azimuthal ambiguity in the polarization imaging. Then we propose a joint 3D reconstruction model for depth fusion, including a data fitting term and a robust low-rank matrix factorization constraint. The former is to transfer textures from the polarization surface to the fused depth by assuming their relationship linear, whereas the latter is to utilize the low-frequency part of binocular depth to improve the accuracy of the fused depth considering the influences of missing-entries and outliers. To solve the optimization problem in the proposed model, we adopt an efficient solution based on the alternating direction method of multipliers. Extensive experiments have been conducted to demonstrate the efficiency of the proposed method in comparison with state-of-the-art methods and to exhibit its wide application prospects in 3D reconstruction.     

The operation types and operation window for high-purity hydrogen production for the sorption enhanced steam methane reforming in a fixed-bed reactor

The operation types and operation window for high-purity H₂ production for the sorption enhanced steam methane reforming (SE-SMR) with Ni/Al₂O₃ catalyst and CaO sorbent in a fixed-bed reactor are investigated by an experimentally verified 2D numerical method. Four chemical reactions including steam reforming, water gas shift, global steam reforming, and CO₂ sorption are considered. The operation window is defined as the H₂ and CO molar fractions at outlet satisfying both y_H2,out ≥ 90% and y_CO,out ≤ y_CO,allow (= 1%, 2% or 3%) in dry base. Under the conditions of y_H2,out and y_CO,allow, there are six operation types, of which 2 types are within the operation window and 4 types are not within the operation window as the temperature, weight hourly space velocity (WHSV) and steam to methane (S/C) molar ratio vary. For a common case of S/C = 3, the operation windows for y_CO,allow = 3% at WHSV = 8.5 h^?1 and 42.5 h^?1 are located at 570–670 °C and 640–690 °C respectively, based on the parameters in this work. The operation window of temperature is wider with decreasing WHSV, and it becomes wider remarkably as the S/C ratio increases. The lowest temperature inside the operation window is 550 °C. The effects of the temperature, WHSV and S/C ratio on the operating types, y_H2,out and y_CO,out are also presented and discussed in details.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Analysis of operating limits and combustion state regulation for low-calorific value gases in industrial burners

The effective and efficient utilization of low-calorific value (LCV) gases has gained increasing attention in scientific research and industrial fields. In this study, the combustion characteristics of three LCV gases in practical devices are analyzed by using a nonadiabatic perfectly stirred reactor model. The complete steady-state solution in the temperature-residence time parameter space is obtained with arc-length continuation. The stable operation region is quantified by the eigenvalue analysis. The transition of solution curves is quantified with heat loss coefficient. Five key system parameters are systematically investigated on their effects on stability limits. With the combustion performance being quantified by a combustion state index, a combustion state regulation method is proposed to find the optimal regulation path of system parameters. Active subspace method is further applied to shorten the regulation step by identifying the active direction. The proposed method and findings are useful for optimal regulation of burning LCV gases in industrial burners.