Spectral radiance reconstruction from trichromatic camera responses based on orthogonal test and regularized algorithm

Reconstruction of spectral information based on multi‐channel image system is a significant problem in color reproduction, detection, and recognition. A spectral radiance reconstruction from trichromatic digital camera responses is researched in this article. The mapping relationship between the trichromatic imaging system response and the incident spectral radiance is analyzed. Then, in order to remove the ill‐posedness of the problem, a regularized constraint solution model of spectral radiance reconstruction matrix is established. And the spectral radiance can be reconstructed by spectral radiance reconstruction matrices and trichromatic imaging system response. Finally, the spectral radiance reconstruction matrix is estimated by the system radiometric calibration experiment. The input radiance is offered by a LCD display. A 3‐factor and 9‐level orthogonal test is designed for the calibration experiment, and a test set of 24 colors is used for precision analysis. The results show that the average relative mean error of our method is 8.69%, it is lower than that of Wiener filtering method by 2.84%. The method can reconstruct spectral radiance information effectively.     

A simulation study of protons heated by left/right-handed Alfvén waves generated by electromagnetic proton-proton instability

Most protons in the solar wind belong to one of two different populations,the less dense beam protons and the denser core protons.The beam protons,with a velocity of(1-2)VA(VA is the local Alfvén speed),always drift relative to the core protons;this kind of distribution is unstable and stimulates several kinds of wave mode.In this study,using a 2D hybrid simulation model,we find that the original right-handed elliptically polarized Alfvén waves become linearly polarized,and eventually become right-handed and circularly polarized.Given that linearly polarized waves are a superposition of left-handed and right-handed waves,cyclotron resonance in the right-handed/left-handed component heats beam/core protons perpendicularly.The resonance between beam protons and right-handed polarized waves is stronger when the beam relative density is lower,resulting in more dramatic perpendicular heating of beam protons,whereas the situation is reversed when the beam relative density is larger.     

Boosted 2D graphene nanosheets by organic-inorganic hybrid cross-linker for an efficient and stable supercapacitor

Using covalent graphene derivatives in energy storage applications is promising. From this view, covalently cross-linked graphene oxide (GO) nanosheets are designed using polyoligomeric silsesquioxanes-propyl-NH₂ (POPN). Then, by incorporating cobalt sulfide nanoparticles into the porous scaffold, a high-value nanocomposite is formed. In a typical three-electrode cell, this nanocomposite declared substantial specific capacity of 454 and 438 Fg^-1 using cyclic voltammetry (CV) and charge-discharge (GCD) assessments. The device is assembled via two identical electrodes containing RGO-SiO₃-NH2-poss-NH2-SiO₃-RGO/cobalt sulfide (RGO-Si-POPN-Si-RGO/CoS₂). Utilizing CV and GCD methods, specific capacitances of 328 and 315 Fg^-1 are realized at a sweep rate and current density of 2 mVs^?1 and 0.5 Ag^-1, respectively. The device presents desirable energy density of 18.5 Whkg^?1 at the power density of 325 Wkg^-1. More impressively, around 97.9% of the specific capacitance is retained after 5000 charge-discharge cycles. The results confirm exceptional capacitive capabilities and super stability of the nanocomposite suitable for practical systems.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

Numerical investigation of a convective hybrid nanofluids around a rotating sheet

Hybrid nanofluids are formulated with various kinds of base fluids. They are designed to provide good heat transfer performance. They can achieve this by dispersing various kinds of nanoparticles in the base materials. This new technology of formulating hybrid nanofluids has a wide range of applications in various industries such as solar energy, medical equipment, and aerospace. Keeping these applications in view, this study provides an insight into the effects of convective heat transport on a Hybrid nanofluid, across a rotating sheet with a variable heat source. In this investigation, the governing boundary layer partial differential equations were modified into the ordinary differential equations, by using the proper similarity transformations. Later, they were solved numerically, with the support of the Lobatto IIIA technique in MATLAB. The influence of the Richardson number on flow parameters was studied, and it was discovered that increasing Ri increases the velocity while decreasing temperature and concentration profiles. The impact of various other flow parameters on the flow fields and also on the behavior of Nusselt number, coefficient skin friction, and Sherwood number were studied and represented graphically. The outcomes were found to be in excellent accord when compared with quoted studies.     

Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.     

Engineering biphasic hybrid phosphide nanowires as efficient electrocatalyst for hydrogen evolution reaction: Experimental and theoretical insights

Development of highly efficient and cheap electrocatalysts towards the hydrogen evolution reaction (HER) is of great importance for electrochemical water splitting. Herein, hybrid Cu/NiMo-P nanowires on the copper foam were successfully fabricated via a simple two-step method. The hierarchically structured Cu/NiMo-P exhibits large surface areas and rapid electron transfer ability, leading to enhanced catalytic activity. The as-prepared Cu/NiMo-P electrodes need overpotentials of 34 mV and 130 mV to obtain 10 mA cm^?2 for HER in acidic and alkaline solutions, respectively. Density functional theory (DFT) calculations reveal that the Cu/NiMo-P hybrid has a more thermo-neutral hydrogen adsorption free energy and enhanced charge transfer ability as well.