Ultrahigh performance passive radiative cooling by hybrid polar dielectric metasurface thermal emitters

Real-world passive radiative cooling requires highly emissive,selective,and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling power.Despite various selective thermal emitters have been demonstrated,it is still challenging to achieve these conditions sim-ultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in multidimension.Here we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design,en-abling a high emissivity of~0.92 within the atmospheric transparency window 8-13μm,a large spectral selectivity of~1.8 and a wide emission angle up to 80 degrees,simultaneously.This selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as~15.4°C under strong solar irradiation of~800 W/m2,signific-antly surpassing the state-of-the-art results.The designed structures also show great potential in tackling the urban heat island effect,with modelling results suggesting a large energy saving and deployment area reduction.This research will make significant impact on passive radiative cooling,thermal energy photonics and tackling global climate change.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

Phonon-assisted upconversion photoluminescence of quantum emitters

Quantum emitters are widely used in quantum networks,quantum information processing,and quantum sensing due to their excellent optical properties.Compared with Stokes excitation,quantum emitters under anti-Stokes excitation exhibit better performance.In addition to laser cooling and nanoscale thermometry,anti-Stokes excitation can improve the coherence of single-photon sources for advanced quantum technologies.In this review,we follow the recent advances in phononassisted upconversion photoluminescence of quantum emitters and discuss the upconversion mechanisms,applications,and prospects for quantum emitters with anti-Stokes excitation.

Non-Cooperative Game of Coordinated Scheduling of Parallel Machine Production and Transportation in Shared Manufacturing

Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry,the coordinated scheduling problem of parallel machine production and transportation is investigated.The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance.A non-cooperative game model is established,considering the competition and self-interest behavior of jobs from different customers for machine resources.The job from different customers is mapped to the players in the game model,the corresponding optional processing machine and location are mapped to the strategy set,and the makespan of the job is mapped to the payoff.Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model.A Nash equilibrium solution algorithm based on the genetic algorithm(NEGA)is designed,and the effective solution of approximate Nash equilibrium for the game model is realized.The fitness function,single-point crossover operator,and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium.Rules are also designed to avoid the generation of invalid offspring chromosomes.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.Compared with other algorithms such as heuristic algorithms(FCFS,SPT,and LPT),the simulated annealing algorithm(SA),and the particle swarm optimization algorithm(PSO),experimental results show that the proposed NE-GA algorithm has obvious performance advantages.

Coordinated Scheduling of Two-Agent Production and Transportation Based on Non-Cooperative Game

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs of two agents compete for the processing position on a machine,and after the pro-cessed,they compete for the transport position on a transport vehicle to be trans-ported to two agents.The two agents have different objective functions.The objective function of the first agent is the sum of the makespan and the total trans-portation time,whereas the objective function of the second agent is the sum of the total completion time and the total transportation time.Given the competition between two agents for machine resources and transportation resources,a non-cooperative game model with agents as game players is established.The job pro-cessing position and transportation position corresponding to the two agents are mapped as strategies,and the corresponding objective function is the utility func-tion.To solve the game model,an approximate Nash equilibrium solution algo-rithm based on an improved genetic algorithm(NE-IGA)is proposed.The genetic operation based on processing sequence and transportation sequence,as well as the fitness function based on Nash equilibrium definition,are designed based on the features of the two-agent production and transportation coordination scheduling problem.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.When compared to heuristic rules such as the Longest Processing Time first(LPT)and the Shortest Processing Time first(SPT),the objective function values of the two agents are reduced by 4.3%and 2.6% on average.

Emitter discharge characteristics of vertical tube irrigation affected by various factors

To examine the working principle of vertical tube irrigation, variations in vertical tube emitter discharge and their causes were analyzed in the laboratory experiment. The effects of the pressure head, initial soil water content, and tube diameter on the emitter discharge of the vertical tube were studied. The results show that quantitative relationship between the time and cumulative infiltration and emitter discharge of the vertical tube is obtained, and R 2 is more than 0.98. Emitter discharge exhibits a positive and negative correlation with the pressure head and soil water content, respectively. Tube dia- meter has a nonsignificant effect on the emitter discharge. Changes of the soil water content around the emitter water outlet are the main causes of emitter discharge variations. In the experiments, the range of vertical tube emitter discharge is 0.056-1.102 L/h. The emitter of vertical tube irrigation automatically adjusts the soil water content and maintains the root zone soil water content within an appropriate range, which achieves continuous irrigation, and further achieves the effect of water-saving.

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

Tomato Yield and Quality and Emitter Clogging as Affected by Chlorination Schemes of Drip Irrigation Systems Applying Sewage Effluent

Chlorination has been recognized as an efficient and economically favorable method for treating clogging in drip emitters caused by biological growth during sewage application. Further important criteria for determining an optimal chlorination scheme are the different responses of crops to the chloride added into the soil through chlorination. During two seasons in 2008 and 2009, field experiments were conducted in a solar-heated greenhouse with drip irrigation systems applying secondary sewage effluent to tomato plants to investigate the influences of chlorine injection intervals and levels on plant growth, yield, fruit quality, and emitter clogging. Injection intervals ranging from 2 to 8 wk and injection concentrations ranging 2-50 mg L-1 of free chlorine residual at the end of the laterals were used. For the 2008 experiments, the yield from the treatments of sewage application with chlorination was 7.5% lower than the yield from the treatment of sewage application without chlorination, while the yields for the treatments with and without chlorination were similar for the 2009 experiments. The statistical tests indicated that neither the chlorine injection intervals and concentrations nor the interactions between the two significantly influenced plant height, leaf area, or tomato yield for both years. The qualities of the fruit in response to chlorination were parameter-dependent. Chlorination did not significantly influence the quality of ascorbic acid, soluble sugar, or soluble acids, but the interaction between the chlorine injection interval and the chlorine concentration significantly influenced the levels of soluble solids. It was also confirmed that chlorination was an effective method for reducing biological clogging. These results suggested that chlorination is safe for a crop that has a moderate sensitivity to chlorine, like tomato, and can maintain a high level of performance in drip irrigation systems applying sewage effluent.

Flow Behaviour Analysis and Experimental Investigation for Emitter Micro-channels

The existing research of the flow behavior in emitter micro-channels mainly focuses on the single-phase flow behavior.And the recent micro-particle image velocimetry（PIV） experimental research on the flow characteristics in various micro-channels mainly focuses on the single-phase fluid flow.However,using an original-size emitter prototype to perform the experiments on the two-phase flow characteristics of the labyrinth channels is seldom reported.In this paper,the practical flow of water,mixed with sand escaped from filtering,in the labyrinth channel,is investigated.And some research work on the clogging mechanism of the labyrinth channel＇s structure is conducted.Computational fluid dynamics（CFD） analysis has been performed on liquid-solid two-phase flow in labyrinth-channel emitters.Based on flow visualization technology-micro-PIV,the flow in labyrinth channel has been photographed and recorded.The path line graph and velocity vector graph are obtained through the post-treatment of experimental results.The graphs agree well with CFD analysis results,so CFD analysis can be used in optimal design of labyrinth-channel emitters.And the optimized anti-clogging structures of the rectangular channel and zigzag channel have been designed here.The CFD numerical simulation and the micro-PIV experiments analysis on labyrinth-channel emitter,make the ＂black box＂ of the flow behavior in the emitter channel broken.Furthermore,the proposed research promotes an advanced method to evaluate the emitter＇s performance and can be used to conducting the optimal design of the labyrinth-channel emitters.

Effects of emitter discharge rates on soil salinity distribution and cotton(Gossypium hirsutum L.) yield under drip irrigation with plastic mulch in an arid region of Northwest China

A field experiment was carried out to investigate the effects of different emitter discharge rates under drip irrigation on soil salinity distribution and cotton yield in an extreme arid region of Tarim River catchment in Northwest China. Four treatments of emitter discharge rates, i.e. 1.8, 2.2, 2.6 and 3.2 L/h, were designed under drip irrigation with plastic mulch in this paper. The salt distribution in the range of 70-cm horizontal distance and 100-cm vertical distance from the emitter was measured and analyzed during the cotton growing season. The soil salinity is expressed in terms of electrical conductivity （dS/m） of the saturated soil extract （ECe）, which was measured using Time Domain Reflector （TDR） 20 times a year, including 5 irrigation events and 4 measured times before/after an irrigation event. All the treatments were repeated 3 times. The groundwater depth was observed by SEBA MDS Dipper 3 automatically at three experimental sites. The results showed that the order of reduction in averaged soil salinity was 2.6 L/h 〉 2.2 L/h 〉 1.8 L/h 〉 3.2 L/h after the completion of irrigation for the 3-year cotton growing season. Therefore, the choice of emitter discharge rate is considerably important in arid silt loam. Usually, the ideal emitter discharge rate is 2.4-3.0 L/h for soil desalinization with plastic mulch, which is advisable mainly because of the favorable salt leaching of silt loam and the climatic conditions in the studied arid area. Maximum cotton yield was achieved at the emitter discharge rate of 2.6 L/h under drip irrigation with plastic mulch in silty soil at the study site. Hence, the emitter discharge rate of 2.6 L/h is recommended for drip irrigation with plastiic mulch applied in silty soil in arid regions.

Radar Emitter Signal Recognition Based on Complexity Features

Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.

Optimization of Metal Coverage on the Emitter in n-Type Interdigitated Back Contact Solar Cells Using a PC2D Simulation

In interdigitated back contact(IBC)solar cells,the metal-electrode coverage on a p-type emitter is optimized by a PC2D simulation.The result shows that the variation of the metal coverage ratio(MCR)will affect both the surface passivation and the electrode-contact properties for the p-type emitter in IBC solar cells.We find that when R_(c) ranges from 0.08 to 0.16Ω·cm^(2),the MCR is optimized with a value of 25%and 33%,resulting in a highest energy-conversion efficiency.The dependences of both Voc and fill factor on MCR are simulated in order to explore the mechanism of the IBC solar cells.

Biofilm structure and its influence on clogging in drip irrigation emitters distributing reclaimed wastewater

Using reclaimed wastewater for crop irrigation is a practical alternative to discharge wastewater treatment plant effluents into surface waters.However,biofouling has been identified as a major contributor to emitter clogging in drip irrigation systems distributing reclaimed wastewater.Little is known about the biofilm structure and its influence on clogging in the drip emitter flow path.This study was first to investigate the microbial characteristics of mature biofilms present in the emitters and the effect of flow path structures on the biofilm microbial communities.The analysis of biofilm matrix structure using a scanning electron microscopy（SEM） revealed that particles in the matrix of the biofilm coupled extracellular polysaccharides（EPS） and formed sediment in the emitter flow path.Analysis of biofilm mass including protein,polysaccharide,and phospholipid fatty acids（PLFAs） showed that emitter flow path style influenced biofilm community structure and diversity.The correlations of biofilm biomass and discharge reduction after 360 h irrigation were computed and suggest that PFLAs provide the best correlation coeffcient.Comparatively,the emitter with the unsymmetrical dentate structure and shorter flow path（Emitter C） had the best anti-clogging capability.By optimizing the dentate structure,the internal flow pattern within the flow path could be enhanced as an important method to control the biofilm within emitter flow path.This study established electron microscope techniques and biochemical microbial analysis methods that may provide a framework for future emitter biofilm studies.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

Radar emitter multi-label recognition based on residual network

In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform(STFT).The time-frequency distribution image is then denoised using a deep normalized convolutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

Effect of drip irrigation method, nitrogen source, and flushing schedule on emitter clogging

Field experiments were carried out at the National Research Center farm, Nubaria area, Behura Governorate, Egypt, to study the effect of nitrogen source, flushing schedule and irrigation method on emitter clogging. Peanut Giza 5 variety (Arachishy pogaea L.) was planted in sandy soil during two successive growing seasons (2010-2011) in the 1st week of May and harvested after 130 days. Treatments used are: 1) two irrigation methods: surface drip irrigation and sub-surface drip irrigation (SDI;SSDI), 2) nitrogen source (NS):NH4NO3, (NH4)2SO4 and Ca(NO3)2 (NS1, NS2 and NS3) and 3) flushing number (FL) 0, 1 and 4 (FL1, FL2;FL3). The experiments design was split-split plot and three replicates were used. Data obtained were subjected to statistical analysis. The main effects of treatments used on clogging per cent could be written the following ascending orders: SDI 3 2 1, NS1 2 3. Concerning the 1st interaction the following ascending orders denote their effects on clogging percent: SDI × FL3 2 1, SDI × NS1 2 3, SSDI × FL3 2 1, SSDI × NS1 2 3, FL1 × NS1 1 × NS2 1 × NS3, FL2 × NS1 2 × NS2 2 × NS3 and FL3 × NS1 3 × NS2 3 × NS3. The differences between any two treatments and/or any two interactions in clogging percent were significant at the 5% level. The effect of the 2nd interaction on clogging percent was significant at the 5% level. The maximum value of clogging (20.18%) and the lowest one (3.9%) were archived in the interactions: SSDI × FL1 × NS3 and SDI × FL3 × NS1, respectively.