Compressively sampled light field reconstruction using orthogonal frequency selection and refinement

This paper considers the compressive sensing framework as a way of overcoming the spatio-angular trade-off inherent to light field acquisition devices. We present a novel method to reconstruct a full 4D light field from a sparse set of data samples or measurements. The approach relies on the assumption that sparse models in the 4D Fourier domain can efficiently represent light fields. The proposed algorithm reconstructs light fields by selecting the frequencies of the Fourier basis functions that best approximate the available samples in 4D hyper-blocks. The performance of the reconstruction algorithm is further improved by enforcing orthogonality of the approximation residue at each iteration, i.e. for each selected basis function. Since sparsity is better preserved in the continuous Fourier domain, we propose to refine the selected frequencies by searching for neighboring non-integer frequency values. Experiments show that the proposed algorithm yields performance improvements of more than 1 dB compared to state-of-the-art compressive light field reconstruction methods. The frequency refinement step also significantly enhances the visual quality of reconstruction results of our method by a 1.8 dB average.     

Simulation of membrane chemical degradation in a proton exchange membrane fuel cell by computational fluid dynamics

Membrane chemical degradation is a major contributor to the still limited lifetime of proton exchange membrane (PEM) fuel cells. In the present work, this phenomenon is simulated by computational fluid dynamics (CFD). The main advantage of the CFD model is that it can provide the degradation profile across the cell active area. Results reveal that degradation accelerates when voltage, temperature and pressure are increased and when reactants humidity and membrane thickness are decreased. Moreover, membrane deterioration is found to be more severe where oxygen pressure is higher, and more heterogeneous when oxygen distribution is less uniform. Generally, conditions that increase current production and thus oxygen depletion along the cell increase degradation heterogeneity. The flow field design is also found to influence the membrane degradation spatial profile. The modeling strategy here applied, the incorporation of a degradation sub-model into a general-purpose CFD code, can be used to include other degradation mechanisms.     

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. In this paper, a new hybrid technique for predicting the settlement of GRSF has been proposed based on the combination of evolutionary algorithm, that is, grey-wolf optimisation (GWO) and artificial neural network (ANN), abbreviated as ANN-GWO model. For this purpose, the reliable pertinent data were generated through numerical simulations conducted on validated large-scale 3-D finite element model. The predictive power of the model was assessed using various well-established statistical indices, and also validated against several independent scientific studies as reported in literature. Furthermore, the sensitivity analysis was conducted to examine the robustness and reliability of the model. The results as obtained have indicated that the developed hybrid ANN-GWO model can estimate the maximum settlement of GRSF under service loads in a reliable and intelligent way, and thus, can be deployed as a predictive tool for the preliminary design of GRSF. Finally, the model was translated into functional relationship which can be executed without the need of any expensive computer-based program.     

Factors influencing the use of artificial intelligence in government: Evidence from China

Some studies have discussed the potential and challenges related to the use of artificial intelligence (AI) in government. However, there are few empirical studies that have examined factors that influence the use of AI in government. By collecting policy documents and empirical data from the government, IT enterprises, and the public in China, we identified the influencing factors in the three stages of government adoption, implementation, and decision-making. The research results show that the influencing factors of government application of AI are different at different stages and with different stakeholders’ backgrounds.     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

Local scour at offshore windfarm monopile foundations: A review

In this article, current research findings of local scour at offshore windfarm monopile foundations are presented. The scour mechanisms and scour depth prediction formulas under different hydrodynamic conditions are summarized, including the current-only condition, wave-only condition, combined wave-current condition, and complex dynamic condition. Furthermore, this article analyzes the influencing factors on the basis of classical equations for predicting the equilibrium scour depth under specific conditions. The weakness of existing researches and future prospects are also discussed. It is suggested that future research shall focus on physical experiments under unsteady tidal currents or other complex loadings. The computational fluid dynamics-discrete element method and artificial intelligence technique are suggested being adopted to study the scour at offshore windfarm foundations.     

基于时序数据的云网协同平台人工智能运维体系

云计算在企业应用中的拓展不但表现为平台规模的拓展，也表现为平台应用的延伸。“云网协同”和“微服务化”是当前企业云平台演进的重要趋势。随着企业信息化建设重要性的持续提升，微服务化云网协同平台的运行维护面临极大挑战。首先分析了平台运维面临的挑战，梳理了平台人工智能运维需求，提出了基于时序数据分析的平台人工智能运维技术体系，并给出了云网协同平台人工智能运维子系统参考设计。所提技术体系和系统设计具有实用性和推广性，可以作为企业云平台建设和优化的技术途径参考。     

Numerical simulation of the effect of multiple obstacles inside the tube on the spontaneous ignition of high-pressure hydrogen release

Self-ignition may occur during hydrogen storage and transportation if high-pressure hydrogen is suddenly released into the downstream pipelines, and the presence of obstacles inside the pipeline may affect the ignition mechanism of high-pressure hydrogen. In this work, the effects of multiple obstacles inside the tube on the shock wave propagation and self-ignition during high-pressure hydrogen release are investigated by numerical simulation. The RNG k-ε turbulence model, EDC combustion model, and 19-step detailed hydrogen combustion mechanism are employed. After verifying the reliability of the model with experimental data, the self-ignition process of high-pressure hydrogen release into tubes with obstacles with different locations, spacings, shapes, and blockage ratios is numerically investigated. The results show that obstacles with different locations, spacings, shapes and blockage ratios will generate reflected shock waves with different sizes and propagation trends. The closer the location of obstacles to the burst disk, the smaller the spacing, and the larger the blockage ratio will cause the greater the pressure of the reflected shock wave it produces. Compared with the tubes with rectangular-shaped, semi-circular-shaped and triangular-shaped obstacles, self-ignition is preferred to occur in tube with triangular-shaped obstacles.     

Experimental and computational analysis of the coolant distribution considering the viscosity of the cutting fluid during machining with helical deep hole drills

An experimental analysis regarding the distribution of the cutting fluid is very difficult due to the inaccessibility of the contact zone within the bore hole. Therefore, suitable simulation models are necessary to evaluate new tool designs and optimize drilling processes. In this paper the coolant distribution during helical deep hole drilling is analyzed with high-speed microscopy. Micro particles are added to the cutting fluid circuit by a developed high-pressure mixing vessel. After the evaluation of suitable particle size, particle concentration and coolant pressure, a computational fluid dynamics (CFD) simulation is validated with the experimental results. The comparison shows a very good model quality with a marginal difference for the flow velocity of 1.57% between simulation and experiment. The simulation considers the kinematic viscosity of the fluid. The results show that the fluid velocity in the chip flutes is low compared to the fluid velocity at the exit of the coolant channels of the tool and drops even further between the guide chamfers. The flow velocity and the flow pressure directly at the cutting edge decrease to such an extent that the fluid cannot generate a sufficient cooling or lubrication. With the CFD simulation a deeper understanding of the behavior and interactions of the cutting fluid is achieved. Based on these results further research activities to improve the coolant supply can be carried out with great potential to evaluate new tool geometries and optimize the machining process.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00383-w     

Modeling and simulation of steam methane reforming and methane combustion over continuous and segmented catalyst beds in autothermal reactor

In this paper, a numerical analysis of the production of hydrogen via autothermal (ATR) steam methane reforming (SMR) is presented. The combustion reaction occurs over a Pt/Al₂O₃ catalyst, and the reforming reaction is operated using a Ni/Al₂O₃ catalyst inside the same cylindrical channel. A novel configuration with18 catalytic-bed macro-patterns alternately mounted, referred to as SDB, is designed and compared with the catalytic dual-bed reactor (conventional configuration), referred to as CDB, at the same operating temperature and pressure conditions of 900 °C and 14 bars, respectively. The results showed that hydrogen yield was improved by 4.5% compared to the conventional configuration, while a decrease of 67 °C of the highest temperature was noticed. Meanwhile, the methane conversion was 63.73% and 65.44% for the CDB and SDB configurations, respectively. Furthermore, the length of the reactor can be decreased by 27%, keeping the same hydrogen yield at the outlet of the conventional reactor, indicating a potential reduction in hydrogen cost.