- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley
摘要： Precision nutrient management requires accurate assessment of crop nutrient status. This is common for assessing N status, but much less so for other nutrients. Because fluorescence can indicate crop stress, the robustness of different fluorescence indices was assessed to predict crop nutrient status (K, Mg and Ca). The hypothesis was that crop nutrition limitations, especially K, can be detected using fluorescence proximal sensing to quantify crop response with a high degree of spatial resolution. A factorial experiment was imposed with four treatment factors: crop, K fertilizer rate, lime and row management. The soil at the experimental site was K deficient and the crop variables showed significant treatment effects (e.g. yield, protein). Fluorescence sensing identified a significant positive K response for three chlorophyll related indices (SFR_G, SFR_R and CHL), but not for FLAV; while wheat was significantly different from barley. Using a k-fold cross-validation method promising predictive relationships were found. The strongest predictions were for SFR_R to predict crop biomass, for SFR_G to predict crop K content of inter-row wheat, for CHL to predict crop Ca content of inter-row wheat and for FLAV with barley grain protein in the windrow treatment. The fluorescence indices produced more significant crop variable predictions than measuring NDVI using an active sensor. This study illustrates the utility of fluorescence sensing to measure chlorophyll related signals for capturing the nutritional status of barley and wheat crops. These results show encouraging potential to rapidly detect crop nutrient status for non-N nutrients using fluorescence sensing.
关键词： Biomass,Wheat,Fluorescence indices,Chlorophyll,Grain quality prediction,Barley
更新于2025-09-23 15:22:29
-
Sky Image-Based Solar Irradiance Prediction Methodologies Using Artificial Neural Networks
摘要： In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques.
关键词： Artificial Neural Network,Photovoltaic Power Generation,Solar Energy,Global Horizontal Irradiance Prediction,Sky Image
更新于2025-09-23 15:21:21
-
[IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu, China (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - A Vector Parabolic Equation Method for Propagation Predictions Over 3-D Irregular Terrains
摘要： In this paper, a vector parabolic equation (PE) method based on perfectly absorbing thin screen is applied to predicting the propagation of electromagnetic waves over three-dimensional (3-D) irregular terrains. Under the assumption of forward propagation, the 3-D PE is obtained and the split-step Fourier transform algorithm is adopted to march the potentials from one aperture plane to the next. Terrains are equivalent to a series of perfectly absorbing thin screens arranged along the direction of propagation and the Tukey window is used to attenuate the fields smoothly at the upper boundary without reflections. Finally, in order to validate the proposed method, several numerical simulations are made and the results are compared with the two-dimensional PE method. As a result, good agreements are observed and the proposed method was confirmed to take the effect of lateral terrains into account.
关键词： split-step Fourier transform,vector parabolic equation,propagation prediction,irregular terrain
更新于2025-09-23 15:21:21
-
A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm
摘要： In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.
关键词： interspectral prediction,compressive sensing,spatial-spectral adaptation,hyperspectral images
更新于2025-09-23 15:21:21
-
[IEEE 2018 VII. Lighting Conference of the Visegrad Countries (Lumen V4) - Trebic, Czech Republic (2018.9.18-2018.9.20)] 2018 VII. Lighting Conference of the Visegrad Countries (Lumen V4) - Analytical Estimation of Optical Efficiency of Cylindrical Light-Tubes Under Various CIE Sky Types
摘要： The light pipes represent a common device for enhancement of daylight amount in buildings with complex architecture and insufficient lighting by vertical windows or in underground spaces. The advantage of straight vertical light pipes is that they transport light into interior spaces from the whole sky vault independently on solar azimuth. So mainly at higher solar altitudes and under clear sky conditions they can significantly contribute in buildings. The optical efficiency of a light pipe strongly depends on number of reflections of the light beams between the particular interfaces. There are available some numerical tools based on ray-tracing algorithms that enable to calculate the efficiency with high precision under given conditions. However, such calculations are adequately time-consuming and so non- attractive for routine (mass) modelling. This contribution presents an analytical method for prediction of the optical efficiency of straight light-pipes which is applicable on 15 types of sky luminance defined by CIE. Due to the analytical approach, the calculation is very fast. The method is validated by accurate numerical simulations for all the sky types and at various pipe aspect ratios. Mainly in the extreme cases of clear and overcast sky, the results are in good agreement.
关键词： light pipe,analytical prediction,optical efficiency,CIE sky models
更新于2025-09-23 15:21:21
-
Ina??season potato yield prediction with active optical sensors
摘要： Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha?1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha?1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
关键词： sensor technology,petiole sampling,potato,prediction models,multiple regression analysis,Yield prediction,nitrogen loss
更新于2025-09-23 15:21:01
-
A delayed-feedback filter with negative group delay
摘要： A filter with delay-induced negative group delay is presented. The filter consists of multiple time-delayed feedback terms, which lead to a negative group delay for frequencies in the baseband. It can be used for the real-time prediction of band-limited signals. The filter is universal as it does not rely on a specific model of the signal. Specifically, as long as the signal to be predicted is band-limited with a known cutoff frequency, the filter predicts the signal in real time up to a prediction horizon that depends on the cutoff frequency. How signal prediction arises from the negative group delay of the filter is worked out in detail. Its properties, including stability, are derived analytically and demonstrated by numerical simulations. For chaotic systems, the filter is predictive during phases of high predictability.
关键词： real-time prediction,signal prediction,chaotic systems,negative group delay,band-limited signals
更新于2025-09-23 15:21:01
-
Performance evaluation of a MPPT controller with model predictive control for a photovoltaic system
摘要： Efficiency has been a major factor in the growth of photovoltaic (PV) systems. Different control techniques have been explored to extract maximum power from PV systems under varying environmental conditions. This paper evaluates the performance of a new improved control technique known as model predictive control (MPC) in power extraction from PV systems. Exploiting the ability of MPC to predict future state of controlled variables, MPC has been implemented for tacking of maximum power point (MPP) of a PV system. Application of MPC for maximum power point tracking (MPPT) has been found to result into faster tracking of MPP under continuously varying atmospheric conditions providing an efficient system. It helps in reducing unwanted oscillations with an increase in tracking speed. A detailed step by step process of designing a model predictive controller has been discussed. Here, MPC has been applied in conjunction with conventional perturb and observe (P&O) method for controlling the dc-dc boost converter switching, harvesting maximum power from a PV array. The results of MPC controller has been compared with two widely used conventional methods of MPPT, viz. incremental conductance method and P&O method. The MPC controller scheme has been designed, implemented and tested in MATLAB/Simulink environment and has also been experimentally validated using a laboratory prototype of a PV system.
关键词： maximum power point tracking (MPPT),prediction model,Model predictive control (MPC),cost function,photovoltaic (PV),renewable energy
更新于2025-09-23 15:21:01
-
[IEEE 2019 International Conference on Sustainable Information Engineering and Technology (SIET) - Lombok, Indonesia (2019.9.28-2019.9.30)] 2019 International Conference on Sustainable Information Engineering and Technology (SIET) - ANFIS Design Based on Prediction Models for The Photovoltaic System
摘要： Photovoltaic system has uncertain output in generating electrical energy, as it is intensely influenced by different weather condition. This modeling system applies an Adaptive Neuro-Fuzzy Inference System (ANFIS) technique to gain data of power prediction, voltage, current, and temperature. The mathematical representation of the photovoltaic using Matlab/Simulink setting has been developed and presented by using the photovoltaic basic solar irradiation effect and temperature changes. This model is divided into two systems run by ANFIS; ANFIS 1 and ANFIS 2. The design of ANFIS is expected to update its parameter to determine errors between output and target. MAPE (Mean Absolute Percentage Error) value for ANFIS 1 test of open circuit output voltage was 0.0104. This MAPE score is found to be excellent predictive data with less than 10% MAPE value. For the ANFIS 2 test, the AC output voltage was 0.026%, output current of 1.3035%, and 0.0046% of frequency. Based on the MAPE scores, very suitable data prediction has been produced with less than 10% MAPE value. Briefly, this study reveals that the ANFIS technique yields load prediction results that can improve the accuracy and rapidness of prediction as well as very minimum errors.
关键词： MAPE,prediction,photovoltaic,ANFIS,design
更新于2025-09-23 15:21:01
-
Customizable fabrication for auxetic graphene assembled macrofilms with high conductivity and flexibility
摘要： Auxetic materials with negative Poisson's ratios unusually exhibit intuitive mechanical behaviors, such as cross-section expansion instead of contraction during tension. Such behaviors are interesting because they may enhance unusual mechanical properties. However, controllable preparation of materials with negative Poisson's ratio is still a major challenge. In this study, we report the synthesis of a flexible auxetic graphene assembled macrofilm (GAMF) from graphene oxide nanosheets by a thermal annealing and press assistant method. The obtained materials exhibit good flexibility and significantly wide tunable negative Poisson’s ratios ranging from -0.11 to -0.53. We also develop a reconstruction model for characterization the uniaxial tension of GAMF based on X-ray tomographic images. The tensile simulation result predicts the function relationship between Poisson's ratio and critical thickness of pore channels, which is in good agreement with the experimental data. As a result, an effective tunable way is proposed for customizable fabrication of GAMF with tunable negative Poisson's ratios, and the GAMF materials with good flexibility, high electrical conductivity and superior auxetic behavior looks promising for future development of wearable electronics.
关键词： prediction of tunable Poisson’s ratio,porous graphene film,Auxetic materials,negative Poisson’s ratio
更新于2025-09-23 15:21:01