A modified FDTD algorithm for processing ultra-wide-band response

Finite-difference time-domain (FDTD) is an effective algorithm for resolving Maxwell equations directly in time domain. Although FDTD has obtained sufficient development, there still exists some improvement space for it, such as ultra-wide-band response and frequency-dependent nonlinearity. In order to resolve these troubles, a modified version of FDTD called complex-field frequency-decomposition (CFFD) FDTD method is introduced, in which the complex-field is adopted to eliminate pseudo-frequency components when computing nonlinearity and the frequency-decomposition is adopted to transform an ultra-wide-band response into a series of narrow-band responses when computing the interaction of ultra-short pulse with matters. Its successful applications in several typical situations and comparison with other methods sufficiently verify the uniqueness and superiority in processing ultra-wide-band response and frequency-dependent nonlinearity.Comment: 8 figure

A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network

This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states

Maize-soybean intercropping improved maize growth traits by increasing soil nutrients and reducing plant pathogen abundance

IntroductionMaize (Zea mays L.)–soybean (Glycine max L.) intercropping has been widely utilized in agricultural production due to its effectiveness in improving crop yield and nutrient use efficiency. However, the responses of maize rhizosphere microbial communities and the plant pathogen relative abundance to maize growth traits in maize-soybean intercropping systems with different chemical nitrogen fertilizer application rates remain unclear.MethodsIn this study, a field experiment was conducted, and the bacterial and fungal communities of maize rhizosphere soils in maize-soybean intercropping systems treated with different N fertilization rates were investigated using Illumina NovaSeq sequencing. Maize growth traits, soil physicochemical properties and soil enzyme activities were also examined.Results and discussion:We found that intercropping and N fertilizer treatments strongly influenced soil microbial diversity, structure and function. The PLSPM (partial least squares path modeling) confirmed that soil nutrients directly positively affected maize biomass and that intercropping practices indirectly positively affected maize biomass via soil nutrients, especially NH4+-N. Intercropping agronomic approaches also improved maize growth traits by reducing the plant pathogen abundance, and the relative abundance of the plant pathogen Trichothecium roseum significantly decreased with intercropping treatments compared to monocropping treatments. These results confirmed the benefits of maize-soybean intercropping treatments for agricultural production

A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution

Super-Resolution (SR) has gained increasing research attention over the past few years. With the development of Deep Neural Networks (DNNs), many super-resolution methods based on DNNs have been proposed. Although most of these methods are aimed at ordinary frames, there are few works on super-resolution of omnidirectional frames. In these works, omnidirectional frames are projected from the 3D sphere to a 2D plane by Equi-Rectangular Projection (ERP). Although ERP has been widely used for projection, it has severe projection distortion near poles. Current DNN-based SR methods use 2D convolution modules, which is more suitable for the regular grid. In this paper, we find that different projection methods have great impact on the performance of DNNs. To study this problem, a comprehensive comparison of projections in omnidirectional super-resolution is conducted. We compare the SR results of different projection methods. Experimental results show that Equi-Angular cube map projection (EAC), which has minimal distortion, achieves the best result in terms of WS-PSNR compared with other projections. Code and data will be released.Comment: Accepted to ICASSP202

Speed Control Based on ESO for the Pitching Axis of Satellite Cameras

The pitching axis is the main axis of a satellite camera and is used to control the pitch posture of satellite cameras. A control strategy based on extended state observer (ESO) is designed to obtain a fast response speed and highly accurate pitching axis control system and eliminate disturbances during the adjustment of pitch posture. First, a sufficient condition of stabilization for ESO is obtained by analyzing the steady-state error of the system under step input. Parameter tuning and disturbance compensation are performed by ESO. Second, the ESO of speed loop is designed by the speed equation of the pitching axis of satellite cameras. The ESO parameters are obtained by pole assignment. By ESO, the original state variable observes the motor angular speed and the extended state variable observes the load torque. Therefore, the external load disturbances of the control system are estimated in real time. Finally, simulation experiments are performed for the system on the cases of nonload starting, adding external disturbances on the system suddenly, and the load of system changing suddenly. Simulation results show that the control strategy based on ESO has better stability, adaptability, and robustness than the PI control strategy

Treatment of was tewater from jeans production by hydrolytic acidification /biological contact oxidation /sedimentation

[摘要]：针对牛仔衣染色( 以靛蓝染料为主) 废水色度较高、有毒性及污染严重的特点, 确定采用水解酸化/生物 接触氧化/沉淀的工艺处理该废水。在丽发制衣厂的应用实践表明: 该工艺切实可行, 运行费用低, 处理效果稳定, 出 水水质达到DB 44 /26—2001 中一级排放标准, COD、BOD5、SS、色度去除率分别达到94%、92%、87%、83%。[Abstract]:The wastewater fromjeans production(mainly containing indigo dyeing) is characterized by higher colourity, toxicity and severe pollutant. With the consideration of these special characters, the wastewater is treated by hydrolysis acidification /biological contact oxidation /sedimentation process. The operation in Lifa Clothing Manufacture demonstrates that this process is feasible. Its running cost is low and the treatment results are stable. Finally, it also shows that the treated effluent quality accords with the requirement of Ⅰclass standard of water pollutant discharge extreme(DB 44 /26—2001) in Guangdong province. Its COD, BOD5, SS and colourity removal rate reach 94%, 92%, 87% and 83% respectively.海南省自然科学基金资助项目( 80673

Phytoremediation Potential of Three Species of Macrophytes for Nitrate in Contaminated Water

Abstrac

A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts

Copyright © 2014 American Geophysical UnionThis paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process.National Natural Science Foundation of ChinaHun River cascade hydropower reservoirs development company, Ltd.UK Royal Academy of Engineerin