Stereoscopic video quality assessment (SVQA) is a challenging problem. It has not been well investigated on how to measure depth perception quality independently under different distortion categories ...and degrees, especially exploit the depth perception to assist the overall quality assessment of 3D videos. In this paper, we propose a new depth perception quality metric (DPQM) and verify that it outperforms existing metrics on our published 3D video extension of High Efficiency Video Coding (3D-HEVC) video database. Furthermore, we validate its effectiveness by applying the crucial part of the DPQM to a novel blind stereoscopic video quality evaluator (BSVQE) for overall 3D video quality assessment. In the DPQM, we introduce the feature of auto-regressive prediction-based disparity entropy (ARDE) measurement and the feature of energy weighted video content measurement, which are inspired by the free-energy principle and the binocular vision mechanism. In the BSVQE, the binocular summation and difference operations are integrated together with the fusion natural scene statistic measurement and the ARDE measurement to reveal the key influence from texture and disparity. Experimental results on three stereoscopic video databases demonstrate that our method outperforms state-of-theart SVQA algorithms for both symmetrically and asymmetrically distorted stereoscopic video pairs of various distortion types.
The exploitation of a low-cost catalyst is desirable for hydrogen generation from electrolysis or photoelectrolysis. In this study we have demonstrated that nickel phosphide (Ni12P5) nanoparticles ...have efficient and stable catalytic activity for the hydrogen evolution reaction. The catalytic performance of Ni12P5 nanoparticles is favorably comparable to those of recently reported efficient nonprecious catalysts. The optimal overpotential required for 20 mA/cm2 current density is 143 ± 3 mV in acidic solution (H2SO4, 0.5 M). The catalytic activity of Ni12P5 is likely to be correlated with the charged natures of Ni and P. Ni12P5 nanoparticles were introduced to silicon nanowires, and the power conversion efficiency of the resulting composite is larger than that of silicon nanowires decorated with platinum particles. This result demonstrates the promising application potential of metal phosphide in photoelectrochemical hydrogen generation.
Light field (LF) photography is an emerging paradigm for capturing more immersive representations of the real world. However, arising from the inherent tradeoff between the angular and spatial ...dimensions, the spatial resolution of LF images captured by commercial micro-lens-based LF cameras is significantly constrained. In this paper, we propose effective and efficient end-to-end convolutional neural network models for spatially super-resolving LF images. Specifically, the proposed models have an hourglass shape, which allows feature extraction to be performed at the low-resolution level to save both the computational and memory costs. To fully make use of the 4D structure information of LF data in both the spatial and angular domains, we propose to use 4D convolution to characterize the relationship among pixels. Moreover, as an approximation of 4D convolution, we also propose to use spatial-angular separable (SAS) convolutions for more computationally and memory-efficient extraction of spatial-angular joint features. Extensive experimental results on 57 test LF images with various challenging natural scenes show significant advantages from the proposed models over the state-of-the-art methods. That is, an average PSNR gain of more than 3.0 dB and better visual quality are achieved, and our methods preserve the LF structure of the super-resolved LF images better, which is highly desirable for subsequent applications. In addition, the SAS convolution-based model can achieve three times speed up with only negligible reconstruction quality decrease when compared with the 4D convolution-based one. The source code of our method is available online.
Image retargeting aims to adjust the resolution and aspect ratio to an arbitrary size while preserving important content of the image. Usually multi-operator image retargeting demonstrates better ...generalization than single operator scheme due to heterogeneous characteristics of different regions in the image. Most existing multi-operator retargeting methods search the optimal operator at each step with exponential complexity and with the possibility of falling into local optimum. Therefore, in order to produce better results with lower computational costs, we formulate the multi-operator retargeting as a Markov decision-making process and apply Reinforcement Learning (RL) to achieve global optimum. Instead of using traditional image-level measures, we design a high-level semantic and aesthetic reward function to better match human visual perception. With the priori in reward, we further propose a weakly supervised Semantics and Aesthetics aware Multi-operator Image Retargeting (SAMIR) framework. Particularly, the semantic part of the reward helps to constrain the severe deformations that may occur during retargeting process, while the aesthetic part guarantees the sensory quality, which can effectively measure the perceptual effects of different operators on various image content. The operator of each step is learned in an end-to-end manner. In addition, retargeting can be performed in arbitrary target size, step size, and direction. The experiment results on both representative aesthetic datasets and retargeting datasets consistently show that our model outperforms the state-of-the-art methods.
Cobalt phosphide (Co2P) nanorods are found to exhibit efficient catalytic activity for the hydrogen evolution reaction (HER), with the overpotential required for the current density of 20mA/cm2 as ...small as 167mV in acidic solution and 171mV in basic solution. In addition, the Co2P nanorods can work stably in both acidic and basic solution during hydrogen production. This performance can be favorably compared to typical high efficient non-precious catalysts, and suggests the promising application potential of Co2P nanorods in the field of hydrogen production. The HER process follows a Volmer–Heyrovsky mechanism, and the rates of the discharge step and desorption step appear to be comparable during the HER process. The similarity of charged natures of Co and P in the Co2P nanorods to those of the hydride-acceptor and proton-acceptor in highly efficient Ni2P catalysts, NiFe hydrogenase, and its analogues implies that the HER catalytic activity of the Co2P nanorods might be correlated with the charged natures of Co and P.
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•A convenient method is used to fabricate cobalt phosphide (Co2P) nanorods.•Co2P nanorods exhibit efficient electrocatalytic activity in hydrogen evolution reaction.•Co2P nanorods work stably in both acidic and basic solutions.
Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are ...also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a bi-directional natural salient scene distortion model (BNSSD) including image natural scene statistics (NSS) measurement, salient global structure distortion measurement, and bi-directional salient information loss measurement. First, we propose a new NSS model in log-Gabor domain and verify its effectiveness in reflecting nature scene statistical distortions introduced during the retargeting process. Second, the concept of salient global structure distortion is proposed to measure the global structure uniformity in the corresponding salient regions between original and retargeted images. Finally, we propose a bidirectional salient information loss metric to measure the information loss between salient areas in original image and retargeted image. The effectiveness of the BNSSD model is verified on two widely recognized public databases, and the experimental results show that our method outperforms the state-of-the-art algorithms under different statistical assessment criteria.
Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of ...the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating pseudo changes. Transformers have an efficient global spatio-temporal modelling capability, which is beneficial for the feature representation of changes of interest. However, the lack of detailed information may cause the transformer to locate the boundaries of changed regions inaccurately. Therefore, in this article, a hybrid CNN-transformer architecture named CTCANet, combining the strengths of convolutional networks, transformer, and attention mechanisms, is proposed for high-resolution bi-temporal remote sensing image change detection. To obtain high-level feature representations that reveal changes of interest, CTCANet utilizes tokenizer to embed the features of each image extracted by convolutional network into a sequence of tokens, and the transformer module to model global spatio-temporal context in token space. The optimal bi-temporal information fusion approach is explored here. Subsequently, the reconstructed features carrying deep abstract information are fed to the cascaded decoder to aggregate with features containing shallow fine-grained information, through skip connections. Such an aggregation empowers our model to maintain the completeness of changes and accurately locate small targets. Moreover, the integration of the convolutional block attention module enables the smoothing of semantic gaps between heterogeneous features and the accentuation of relevant changes in both the channel and spatial domains, resulting in more impressive outcomes. The performance of the proposed CTCANet surpasses that of recent certain state-of-the-art methods, as evidenced by experimental results on two publicly accessible datasets, LEVIR-CD and SYSU-CD.
High‐frequency resonance in traction power supply systems (TPSSs) is of great concern because of its potential to cause great damage to the safety and stable operation of railroad systems; it has ...been demonstrated that when a locomotive generates a harmonic current with the resonant frequency of the series impedance on the traction network, a large amount of high‐frequency harmonic voltage is generated. This paper proposes a high‐frequency harmonic suppression method based on synchronous sampled carrier phase‐shift PWM modulation strategy (SSCPS‐PWM), which has the advantage of not only considering the consistency of sampling the analogue signal by multiple controllers, but also further optimising the precise synchronization between multiple converter pulses. This method generates lower high‐frequency harmonic content and is particularly suitable for the field of high‐power, low switching frequency, multi‐controller locomotive traction converters. The proposed method was simulated and compared with the traditional method for the harmonics injected by the locomotive, and a real railway line with a harsh power supply condition was selected and field tests were conducted in four sections by a 7200kW 6‐axle electric locomotive. The test results showed that the proposed method effectively reduced the high‐frequency harmonic current of the locomotive and reduced the high‐frequency resonant overvoltage in the TPSS.
This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for ...battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy.
•A new tunnel model which considers the dislocation and rotational deformation is adopted.•The variational method is used to analyze the responses of existing tunnel due to upper excavation.•Tunnel ...deformation and reaction force can be presented in finite series, and no discretization process is required.•The validity of the proposed method is verified through comparison with filed data and other theoretical methods.
This paper aims to provide an analytical solution for evaluating the deformation of a segmental tunnel induced by upper basement excavation. To this end, a model considering shear dislocation and rotation of a segmental tunnel is proposed and the interaction between tunnel and ground is modeled using a two-parameter Pasternak foundation model. The vertical displacement of the tunnel is then approximated by the finite Fourier series. Energy balance equations for the tunnel and soil are obtained via energy method and then the governing equation is derived based on the principle of minimum potential energy. With the variational approach, expressions for the tunnel heave, relative vertical deformation, rotational angle and shear force between two tunnel rings are derived. The feasibility of the proposed model is validated against the field data and the results obtained using Euler-Bernoulli beam and Winkler beam, indicating that the proposed method can be used to effectively estimate the responses of a segmental tunnel to upper excavation. Finally, a parametric analysis is performed to examine the effects of different factors on the responses of existing shield tunnel to upper deep excavation.
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