bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Sensing and Argo Float Observations Reveal Physical Processes Initiating a Winter-Spring Phytoplankton Bloom South of the Kuroshio Current Near Shikoku
Authors: Wang Tongyu , Chen Fajin , Zhang Shuwen , Pan Jiayi , Devlin Adam Thomas , Ning Hao , Zeng Weiqiang
Published: December 2020

BIO-Argo float (chlorophyll a (Chl-a), temperature, and salinity profiles) and remote sensing data (Chl-a, photosynthetic available radiation (PAR), and wind) located south of the Kuroshio current near Shikoku from September 2018 to May 2019 were used to study phytoplankton bloom and their mechanisms of development in open oceans. Results show that higher (lower) Chl-a concentrations are correlated with a deeper (shallower) mixed layer (RPearson = 0.77, Rcrit = 0.12 (alpha = 0.05, n = 263)) compared to the average of Chl-a and mixed layer depth (0.13 mg/m3 and 105 m). The average net accumulation rates (r) of phytoplankton were close to 0.08 d−1. An increasing r corresponds to a gradually increasing surface Chl-a (S (Chl-a): 0–20 m average Chl-a) and integrated Chl-a inventory (I (Chl-a): integrated Chl-a from surface to euphotic depth). These phenomena indicate that the mechanism of winter-spring phytoplankton blooms is consistent with the dilution-recoupling hypotheses (DRH). During the bloom formation, winter deep mixing and eddy-wind Ekman pumping are enhanced by a strong winter monsoon. The enhancement may disturb predator–prey interactions and dilute zooplankton in deep mixed layers. Moreover, winter deep mixing and eddy-wind Ekman pumping can cause the nutrients to be transported into the euphotic layer, which can promote the growth of phytoplankton and increase grazing. During the bloom extinction, the stratification strengthens and the intensity of light increases; this increases grazing and nutrient consumption, and decreases the phytoplankton bloom significantly (S (Chl-a) and I (Chl-a) increase by 0.3 mg/m3 and 27 mg/m2, respectively). The output from a biogeochemistry model shows that nutrients are consistent with the temporal distribution of S (Chl-a) and I (Chl-a). Our results suggest that physical processes (deep winter mixing and eddy-wind Ekman pumping) under the DHR framework are critical factors for winter-spring blooms in open oceans with an anticyclone eddy.
Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling
Authors: Hui Jinyu , Wu Yiping , Zhao Fubo , Lei Xiaohui , Sun Pengcheng , Singh Shailesh Kumar , Liao Weihong , Qiu Linjing , Li Jiguang
Published: December 2020

Hydrological modeling has experienced rapid development and played a significant role in water resource management in recent decades. However, modeling uncertainties, which are propagated throughout model runs, may affect the credibility of simulation results and mislead management decisions. Therefore, analyzing and reducing uncertainty is of significant importance in providing greater confidence in hydrological simulations. To reduce and quantify parameter uncertainty, in this study, we attempted to introduce additional remotely sensed data (such as evapotranspiration (ET)) into a common parameter estimation procedure that uses observed streamflow only. We undertook a case study of an application of the Soil Water Assessment Tool in the Guijiang River Basin (GRB) in China. We also compared the effects of different combinations of parameter estimation algorithms (e.g., Sequential Uncertainty Fitting version 2, particle swarm optimization) on reduction in parameter uncertainty and improvement in modeling precision improvement. The results indicated that combining Sequential Uncertainty Fitting version 2 (SUFI-2) and particle swarm optimization (PSO) can substantially reduce the modeling uncertainty (reduction in the R-factor from 0.9 to 0.1) in terms of the convergence of parameter ranges and the aggregation of parameters, in addition to iterative optimization. Furthermore, the combined approaches ensured the rationality of the parameters’ physical meanings and reduced the complexity of the model calibration procedure. We also found the simulation accuracy of ET improved substantially after adding remotely sensed ET data. The parameter ranges and optimal parameter sets obtained by multi-objective calibration (using streamflow plus ET) were more reasonable and the Nash–Sutcliffe coefficient (NSE) improved more rapidly using multiple objectives, indicating a more efficient parameter optimization procedure. Overall, the selected combined approach with multiple objectives can help reduce modeling uncertainty and attain a reliable hydrological simulation. The presented procedure can be applied to any hydrological model.
Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach
Authors: Ellsäßer Florian , Röll Alexander , Ahongshangbam Joyson , Waite Pierre-André , Hendrayanto  , Schuldt Bernhard , Hölscher Dirk
Published: December 2020

Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r2 = 0.87 for trees and r2 = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration.
Study of the Effect of Aerosol Vertical Profile on Microphysical Properties Using GRASP Code with Sun/Sky Photometer and Multiwavelength Lidar Measurements
Authors: Molero Francisco , Pujadas Manuel , Artíñano Begoña
Published: December 2020

In this paper, we study the effect of the vertical distribution of aerosols on the inversion process to obtain microphysical properties of aerosols. The GRASP code is used to retrieve the aerosol size distribution from two different schemes. Firstly, only sun/sky photometer measurements of aerosol optical depth and sky radiances are used as input to the retrieval code, and then, both this information and the range-corrected signals from an advanced lidar system are provided to the code. Measurements taken at the Madrid EARLINET station, complemented with those from the nearby AERONET station, have been analyzed for the 2016–2019 time range. The effect found of the measured vertical profile on the inversion is a shift to smaller radius of the fine mode with average differences of 0.05 ± 0.02 µm, without noticeable effects for the coarse mode radius. This coarse mode is sometimes split into two modes, related to large AOD or elevated aerosol-rich layers. The first scheme´s retrieved size distributions are also compared with those provided by AERONET, observing the unusual persistence of a large mode centered at 5 µm. These changes in the size distributions affect slightly the radiative forcing calculated also by the GRASP code. A stronger forcing, dependent on the AOD, is observed in the second scheme. The shift in the fine mode and the effect on the radiative forcing indicate the importance of considering the vertical profile of aerosols on the retrieval of microphysical properties by remote sensing.
Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative
Authors: Szatmári Gábor , Bakacsi Zsófia , Laborczi Annamária , Petrik Ottó , Pataki Róbert , Tóth Tibor , Pásztor László
Published: December 2020

Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.
Toward an Early Warning System for Health Issues Related to Particulate Matter Exposure in Brazil: The Feasibility of Using Global PM2.5 Concentration Forecast Products
Authors: Roux Emmanuel , Ignotti Eliane , Bègue Nelson , Bencherif Hassan , Catry Thibault , Dessay Nadine , Gracie Renata , Gurgel Helen , de Sousa Hacon Sandra , de A. F. M. Magalhães Mônica , Vieira Monteiro Antônio Miguel , Revillion Christophe , Antunes Maciel Villela Daniel , Xavier Diego , Barcellos Christovam
Published: December 2020

PM2.5 severely affects human health. Remotely sensed (RS) data can be used to estimate PM2.5 concentrations and population exposure, and therefore to explain acute respiratory disorders. However, available global PM2.5 concentration forecast products derived from models assimilating RS data have not yet been exploited to generate early alerts for respiratory problems in Brazil. We investigated the feasibility of building such an early warning system. For this, PM2.5 concentrations on a 4-day horizon forecast were provided by the Copernicus Atmosphere Monitoring Service (CAMS) and compared with the number of severe acute respiratory disease (SARD) cases. Confounding effects of the meteorological conditions were considered by selecting the best linear regression models in terms of Akaike Information Criterion (AIC), with meteorological features and their two-way interactions as explanatory variables and PM2.5 concentrations and SARD cases, taken separately, as response variables. Pearson and Spearman correlation coefficients were then computed between the residuals of the models for PM2.5 concentration and SARD cases. The results show a clear tendency to positive correlations between PM2.5 and SARD in all regions of Brazil but the South one, with Spearman’s correlation coefficient reaching 0.52 (p < 0.01). Positive significant correlations were also found in the South region by previously correcting the effects of viral infections on the SARD case dynamics. The possibility of using CAMS global PM2.5 concentration forecast products to build an early warning system for pollution-related effects on human health in Brazil was therefore established. Further investigations should be performed to determine alert threshold(s) and possibly build combined risk indicators involving other risk factors for human respiratory diseases. This is of particular interest in Brazil, where the COVID-19 pandemic and biomass burning are occurring concomitantly, to help minimize the effects of PM emissions and implement mitigation actions within populations.
Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification
Authors: Wang Lei , Xu Xin , Gui Rong , Yang Rui , Pu Fangling
Published: December 2020

Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired “POA-spatio” samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods.
Application of Reflectance Ratios on High-Resolution Satellite Imagery to Remotely Identify Eucalypt Vegetation
Authors: Baranowski Kelsee , Taylor Teairah , Lambert Brian , Bharti Nita
Published: December 2020

The scale and accessibility of passive global surveillance have rapidly increased over time. This provides an opportunity to calibrate the performance of models, algorithms, and reflectance ratios between remote-sensing devices. Here, we test the sensitivity and specificity of the Eucalypt chlorophyll-a reflectance ratio (ECARR) and Eucalypt chlorophyll-b reflectance ratio (ECBRR) to remotely identify eucalypt vegetation in Queensland, Australia. We compare the reflectance ratio values from Sentinel-2 and Planet imagery across four sites of known vegetation composition. All imagery was transformed to reflectance values, and Planet imagery was additionally scaled to harmonize across Planet scenes. ECARR can identify eucalypt vegetation remotely with high sensitivity but shows low specificity and is impacted by the density of the vegetation. ECBRR reflectance ratios show similar sensitivity and specificity when identifying eucalypt vegetation but with values an order of magnitude smaller than ECARR. We find that ECARR was better at identifying eucalypt vegetation in the Sentinel-2 imagery than Planet imagery. ECARR can serve as a general chlorophyll indicator but is not a specific index to identify Eucalyptus vegetation with certainty.
Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
Authors: Kavats Olena , Khramov Dmitriy , Sergieieva Kateryna , Vasyliev Volodymyr
Published: December 2020

The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area.
Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy–Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium
Authors: Corbari Chiara , Skokovic Jovanovic Drazen , Nardella Luigi , Sobrino Josè , Mancini Marco
Published: December 2020

The feasibility of combining remotely sensed land surface temperature data (LST) and an energy–water balance model for improving evapotranspiration estimates over time distributed in space in the Capitanata irrigation consortium is analysed. The energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parametres with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration. The FEST-EWB model was run at 30 m of spatial resolution for the period between 2013 and 2018. Absolute errors of 2.5 °C were obtained for LST estimates against satellite data; while RMSE around 0.06 and 40 Wm−2 are found for ground measured soil moisture and latent heat flux, respectively.
A Novel Approach of Parallel Retina-Like Computational Ghost Imaging
Authors: Cao Jie , Zhou Dong , Zhang Fanghua , Cui Huan , Zhang Yingqiang , Hao Qun
Published: December 2020

Computational ghost imaging (CGI), with the advantages of wide spectrum, low cost, and robustness to light scattering, has been widely used in many applications. The key issue is long time correlations for acceptable imaging quality. To overcome the issue, we propose parallel retina-like computational ghost imaging (PRGI) method to improve the performance of CGI. In the PRGI scheme, sampling and reconstruction are carried out by using the patterns which are divided into blocks from designed retina-like patterns. Then, the reconstructed image of each block is stitched into the entire image corresponding to the object. The simulations demonstrate that the proposed PRGI method can obtain a sharper image while greatly reducing the time cost than CGI based on compressive sensing (CSGI), parallel architecture (PGI), and retina-like structure (RGI), thereby improving the performance of CGI. The proposed method with reasonable structure design and variable selection may lead to improve performance for similar imaging methods and provide a novel technique for real-time imaging applications.
Multi-Variant TLS and SfM Photogrammetric Measurements Affected by Different Factors for Determining the Surface Shape of a Thin-Walled Dome
Authors: Lenda Grzegorz , Siwiec Jakub , Kudrys Jacek
Published: December 2020

The paper presents a combined analysis of terrestrial laser scanning (TLS) and structure from motion (SfM) photogrammetric measurement to determine an accurate model of the surface shape of a thin-walled dome. The analysis takes into account several factors that may affect the accuracy of measurement. In TLS measurements, these are related to scattering of the beam and its penetration into the structure of objects. Penetration of the beam into a synthetic structure changes the measured length. Shell moisture, caused by rainfall or dew effect, similarly affects the measured length, but the changes are dispersed. In the first case, it will change the size of the object, and in the second one, it generates measurement noise. SfM photogrammetric problems, such as object gloss and ambient reflection, lack of detail, and different results from software creating point clouds were also analyzed. An interesting observation was the significant influence of atmospheric pollution, sedimented on the lower half of the glossy dome, on increasing the accuracy of photogrammetric measurement. The analyses contain a number of cases that take into account the complex problems of obtaining and processing data of such facilities: periodic measurement, TLS and SfM photogrammetric measurement, measurement outside and inside the object, determination of the wall thickness, comparison with the project and free-sphere fitting, and use of dome rotation during TLS.
Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References
Authors: Han Xiongzhe , Thomasson J. Alex , Swaminathan Vaishali , Wang Tianyi , Siegfried Jeffrey , Raman Rahul , Rajan Nithya , Neely Haly
Published: December 2020

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.
Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization
Authors: Kim Byeonghwi , Hariyani Yuli-Sun , Cho Young-Ho , Park Cheolsoo
Published: December 2020

White blood cells (WBCs) are essential components of the immune system in the human body. Various invasive and noninvasive methods to monitor the condition of the WBCs have been developed. Among them, a noninvasive method exploits an optical characteristic of WBCs in a nailfold capillary image, as they appear as visual gaps. This method is inexpensive and could possibly be implemented on a portable device. However, recent studies on this method use a manual or semimanual image segmentation, which depends on recognizable features and the intervention of experts, hindering its scalability and applicability. We address and solve this problem with proposing an automated method for detecting and counting WBCs that appear as visual gaps on nailfold capillary images. The proposed method consists of an automatic capillary segmentation method using deep learning, video stabilization, and WBC event detection algorithms. Performances of the three segmentation algorithms (manual, conventional, and deep learning) with/without video stabilization were benchmarks. Experimental results demonstrate that the proposed method improves the performance of the WBC event counting and outperforms conventional approaches.
A New Ammonium Smart Sensor with Interference Rejection
Authors: Capella Juan V. , Bonastre Alberto , Campelo José C. , Ors Rafael , Peris Miguel
Published: December 2020

In many water samples, it is important to determine the ammonium concentration in order to obtain an overall picture of the environmental impact of pollutants and human actions, as well as to detect the stage of eutrophization. Ion selective electrodes (ISEs) have been commonly utilized for this purpose, although the presence of interfering ions (potassium and sodium in the case of NH4+-ISE) represents a handicap in terms of the measurement quality. Furthermore, random malfunctions may give rise to incorrect measurements. Bearing all of that in mind, a smart ammonium sensor with enhanced features has been developed and tested in water samples, as demonstrated and commented on in detail following the presentation of the complete set of experimental measurements that have been successfully carried out. This has been achieved through the implementation of an expert system that supervises a set of ISEs in order to (a) avoid random failures and (b) reject interferences. Our approach may also be suitable for in-line monitoring of the water quality through the implementation of wireless sensor networks.
Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration
Authors: Wu Shiyu , Yang Dongkai , Zhu Yunlong , Wang Feng
Published: December 2020

The Global Navigation Satellite System (GNSS)-based Bistatic Synthetic Aperture Radar (SAR) is getting more and more attention in remote sensing for its all-weather and real-time global observation capability. Its low range resolution results from the narrow signal bandwidth limits in its development. The configuration difference caused by the illumination angle and movement direction of the different satellites makes it possible to improve resolution by multi-satellite fusion. However, this also introduces new problems with the resolution-enhancing efficiency and increased computation brought about by the fusion. In this paper, we aim at effectively improving the resolution of the multi-satellite fusion system. To this purpose, firstly, the Point Spread Function (PSF) of the multi-satellite fusion system is analyzed, and focusing on the relationship between the fusion resolution and the geometric configuration and the number of satellites. Numerical simulation results show that, compared with multi-satellite fusion, dual-satellite fusion is a combination with higher resolution enhancement efficiency. Secondly, a method for dual-satellite fusion imaging based on optimized satellite selection is proposed. With the greedy algorithm, the selection is divided into two steps: in the first step, according to geometry configuration, the single-satellite with the optimal 2-D resolution is selected as the reference satellite; in the second step, the angles between the azimuthal vector of the reference satellite and the azimuthal vector of the other satellites were calculated by the traversal method, the satellite corresponding to the intersection angle which is closest to 90° is selected as the auxiliary satellite. The fused image was obtained by non-coherent addition of the images generated by the reference satellite and the auxiliary satellite, respectively. Finally, the GPS L1 real orbit multi-target simulation and experimental validation were conducted, respectively. The simulation results show that the 2-D resolution of the images produced by our proposed method is globally optimal 15 times and suboptimal 8 times out of 24 data sets. The experimental results show that the 2-D resolution of our proposed method is optimal in the scene, and the area of the resolution unit is reduced by 70.1% compared to the single-satellite’s images. In the experiment, there are three navigation satellites for imaging, the time taken to the proposed method was 66.6% that of the traversal method. Simulations and experiments fully demonstrate the feasibility of the method.
Intelligent Perception System of Robot Visual Servo for Complex Industrial Environment
Authors: Luo Yongchao , Li Shipeng , Li Di
Published: December 2020

Robot control based on visual information perception is a hot topic in the industrial robot domain and makes robots capable of doing more things in a complex environment. However, complex visual background in an industrial environment brings great difficulties in recognizing the target image, especially when a target is small or far from the sensor. Therefore, target recognition is the first problem that should be addressed in a visual servo system. This paper considers common complex constraints in industrial environments and proposes a You Only Look Once Version 2 Region of Interest (YOLO-v2-ROI) neural network image processing algorithm based on machine learning. The proposed algorithm combines the advantages of YOLO (You Only Look Once) rapid detection with effective identification of ROI (Region of Interest) pooling structure, which can quickly locate and identify different objects in different fields of view. This method can also lead the robot vision system to recognize and classify a target object automatically, improve robot vision system efficiency, avoid blind movement, and reduce the calculation load. The proposed algorithm is verified by experiments. The experimental result shows that the learning algorithm constructed in this paper has real-time image-detection speed and demonstrates strong adaptability and recognition ability when processing images with complex backgrounds, such as different backgrounds, lighting, or perspectives. In addition, this algorithm can also effectively identify and locate visual targets, which improves the environmental adaptability of a visual servo system
Statistical Distribution Analysis of Navigation Positioning System Errors—Issue of the Empirical Sample Size
Authors: Specht Mariusz
Published: December 2020

Positioning systems are used to determine position coordinates in navigation (air, land, and marine). Statistical analysis of their accuracy assumes that the position errors (latitude—δφ and longitude—δλ) are random and that their distributions are consistent with the normal distribution. However, in practice, these errors do not appear in a random way, since the position determination in navigation systems is done with an iterative method. It causes so-called “Position Random Walk”, similar to the term “Random Walk” known from statistics. It results in the empirical distribution of δφ and δλ being inconsistent with the normal distribution, even for samples of up to several thousand measurements. This phenomenon results in a significant overestimation of the accuracy of position determination calculated from such a short series of measurements, causing these tests to lose their representativeness. This paper attempts to determine the length of a measurement session (number of measurements) that is representative of the positioning system. This will be a measurement session of such a length that the position error statistics (δφ and δλ) represented by the standard deviation values are close to the real values and the calculated mean values (φ¯ and λ¯) are also close to the real values. Special attention will also be paid to the selection of an appropriate (statistically reliable) number of measurements to be tested statistically to verify the hypothesis that the δφ and δλ distributions are consistent with the normal distribution. Empirical measurement data are taken from different positioning systems: Global Positioning System (GPS) (168′286 fixes), Differential Global Positioning System (DGPS) (864′000 fixes), European Geostationary Navigation Overlay Service (EGNOS) (928′492 fixes), and Decca Navigator system (4052 fixes). The analyses showed that all researched positioning systems (GPS, DGPS, EGNOS and Decca Navigator) are characterized by the Position Random Walk (PRW), which resulted in that the empirical distribution of δφ and δλ being inconsistent with the normal distribution. The size of the PRW depends on the nominal accuracy of position determination by the system. It was found that measurement sessions consisting of 1000 fixes (for the GPS system) overestimate the accuracy analysis results by 109.1% and cannot be considered representative. Furthermore, when analyzing the results of long measurement campaigns (GPS and DGPS), it was found that the representative length of the measurement session differs for each positioning system and should be determined for each of them individually.
Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model
Authors: Tang Shengnan , Zhu Yong , Yuan Shouqi , Li Guangpeng
Published: December 2020

As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.
On Driver Behavior Recognition for Increased Safety: A Roadmap
Authors: Davoli Luca , Martalò Marco , Cilfone Antonio , Belli Laura , Ferrari Gianluigi , Presta Roberta , Montanari Roberto , Mengoni Maura , Giraldi Luca , Amparore Elvio G. , Botta Marco , Drago Idilio , Carbonara Giuseppe , Castellano Andrea , Plomp Johan
Published: December 2020

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.
Statistics and Machine Learning Experiments in English and Romanian Poetry
Authors: Calin Ovidiu
Published: December 2020

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.
Supra-Versus Submaximal Cycle Ergometer Verification of VO2max in Males and Females
Authors: Sawyer Brandon J. , McMahon Nicholas , Thornhill Kirsten L. , Baughman Brett R. , Mahoney Jenny M. , Pattison Kai L. , Freeberg Kaitlin A. , Botts Ryan T.
Published: December 2020

This study was designed to determine the optimal intensity for verification phase testing (VP) in healthy, young adults. Thirty one young, active participants (16 females) completed a cycle ergometer graded exercise test (GXT) VO2max test and 4 VP tests at 80, 90, 100, and 105% of the maximum wattage achieved during the GXT. GXT and VP VO2max values showed a significant test x sex interaction (p = 0.02). The males elicited significantly higher VO2max values during the GXT, 80%, and 90% when compared to the 105%, (105 vs. GXT: p = 0.05; 105% vs. 80%: p < 0.01; 105% vs. 90%: p = 0.02). There were no significant differences in VO2max across the tests in the females (p > 0.05); 80% of the males achieved their highest VP VO2max during a submaximal VP test compared to only 37.5% of the females. A secondary study conducted showed excellent reliability (ICCs > 0.90) and low variation (CVs < 3%) for the 90% VP. Our findings show that a submaximal verification phase intensity is ideal for young healthy males to elicit the highest VO2max during cycle ergometer testing. For females, a range of intensities (80–105%) produce similar VO2max values. However, the 80% VP yields an unnecessarily high time to exhaustion.
The Impact of Essential Oil Feed Supplementation on Enteric Gas Emissions and Production Parameters from Dairy Cattle
Authors: Carrazco Angelica V. , Peterson Carlyn B. , Zhao Yongjing , Pan Yuee , McGlone John J. , DePeters Edward J. , Mitloehner Frank M.
Published: December 2020

Societal pressure to reduce enteric methane emissions from cattle continues to increase. The present study evaluated the efficacy of the commercial essential oil feed additive Agolin® Ruminant on reducing enteric gas emissions and improving milk parameters in dairy cattle. Twenty mid-lactation Holstein cows, blocked by parity and days in milk, were randomly assigned to a top dress treatment with Agolin or an un-supplemented control for a 56-day trial. Cows were group housed and individually fed twice daily. Enteric gas emissions, including methane, carbon dioxide, ammonia, and nitrous oxide, were sampled every 14 days for a 12 h period via head chambers connected to a mobile air quality laboratory. Cows supplemented with Agolin versus the control had less methane intensity (g/period/kg energy-corrected milk (ECM); p = 0.025). Ammonia was the most affected gas, with lower ammonia production (mg/period; p = 0.028), and ammonia intensity (mg/period/kg ECM; p = 0.011) in Agolin-fed versus control-fed cows. All cow performance variables, including dry matter intake, ECM, milk fat, milk protein, or feed efficiency were similar between treatments. Further research should evaluate how Agolin impacts ruminal flora, focusing on mechanistic impacts to fermentation.
Basin Water Sensitivity and Its Impact on Spatial Expansion: A Case Study of the Taihu Basin, China
Authors: Sun Wei , Wang Yi , Chen Wen , He Bin , Liu Chong-Gang
Published: December 2020

Water is the most important and sensitive factor controlling urban development in basins. This paper developed a novel approach to construct a Basin Water Sensitivity Index (BWSI) from the perspective of integrated basin management. The correlation between the spatial expansion of development and water sensitivity over the past 30 years in the Taihu Basin was analyzed using BWSI and urban development data. The analysis found that, (1) from 1985 to 1995 the spatial expansion of developed areas in the Taihu Basin was unrelated to BWSI; (2) from 1995 to 2007 the expansion intensity decreased with an increase in BWSI; and (3) from 2007 to 2017 there was a significant increase in the spatial development expansion intensity and the area of expansion was concentrated in low BWSI areas, while the constraints of water sensitivity on spatial expansion were more obvious than in previous years. The analysis illustrated how BWSI affected spatial growth in a basin and this research can be used to help formulate a spatial growth management policy that includes water sensitivity and provides a scientific basis for coordinating the relationship between economic and social development with water resources and the environment.
Cost-Benefit Analysis of Mixing Gray and Green Infrastructures to Adapt to Sea Level Rise in the Vietnamese Mekong River Delta
Authors: Oanh Pham Thi , Tamura Makoto , Kumano Naoko , Nguyen Quang Van
Published: December 2020

This study evaluated inundation impacts and the economic damage resulting from sea level rise (SLR) in the Vietnamese Mekong River Delta (VMRD), and identified the effectiveness of mixing gray and green infrastructures using cost-benefit analysis. The results showed that the VMRD could potentially be heavily affected by SLR. Without the implementation of proper adaptations, more than 90% of this area could be below sea level and nearly 20 million people could be affected by inundation by the end of the 21st century. The total economic damage could reach more than 22,000 billion US$ (2010 real value) by 2100 with no discount rate. These threats may increase the pressure on the region to ensure well-being, equity, and progress towards achieving sustainable development goals. However, achieving these goals will require the implementation of adaptations for upgrading and restoring in the region. This study assessed the effectiveness of adaptations and demonstrated that mixing gray and green infrastructures could benefit coastal inhabitants at a cost of 12 to 19 billion US$.
Development of a Management Framework for Applying Green Roof Policy in Urban China: A Preliminary Study
Authors: Dong Jing , Zuo Jin , Luo Jiancheng
Published: December 2020

Increased consumption of energy and resources by urbanization has caused increasing concerns regarding the sustainability of building practices around the world. Green roofs (GRs) are attracting increasing attention as an important means to overcome environmental challenges related to urbanization. Although GRs have been widely applied in some areas throughout the world, their development is lagging in China. This paper reviews international GR policies with the aim to develop a framework for applying GR policies in suitable areas of China. After summarizing the typical environmental characteristics and GR policies in China, policies that can expedite GR implementation were recommended. By analyzing the national conditions (climate conditions, economic development, urban construction and policy circumstances), 23 GR policies were identified and divided into three categories (i.e., mandatory policies, incentive policies and assistance policies). Based on the attributes, the policies were developed into a framework that combined the three development stages of GRs (i.e., the pilot exploration stage, the progressive promotion stage and the overall application stage). These findings will help to formulate a GR strategy in China and provide important insights for other countries.
Calculating a Drop in Carbon Emissions in the Strait of Gibraltar (Spain) from Domestic Shipping Traffic Caused by the COVID-19 Crisis
Authors: Durán-Grados Vanessa , Amado-Sánchez Yolanda , Calderay-Cayetano Fátima , Rodríguez-Moreno Rubén , Pájaro-Velázquez Emilio , Ramírez-Sánchez Antonio , Sousa Sofia I. V. , Nunes Rafael A. O. , Alvim-Ferraz Maria C. M. , Moreno-Gutiérrez Juan
Published: December 2020

As a consequence of the COVID-19 pandemic, the Spanish government declared a State of Emergency, and domestic passenger ship traffic was restricted in Spanish ports. This manuscript presents scenarios of emissions from domestic shipping traffic in the seas of the Strait of Gibraltar (Spain) over three months of the COVID-19 pandemic. Emissions were estimated for only 90 days of the pandemic, and two scenarios were studied: emissions while vessels were berthed at the Algeciras Port and emissions as a consequence of the interruption of passenger ship transportation in the Strait of Gibraltar. To this end, the authors’ own model was used, which has near zero uncertainties. This model was used for the first time in this study and takes into account both meteorological and sea condition parameters, as well as the efficiency of the propulsion system. The manuscript concentrates on the emissions of greenhouse gases (GHGs), nitrogen oxides (NOx), sulphur oxides (SOx), carbon dioxide (CO2), and particulate matter (PM) from six Ro-Pax ships that ceased to operate. The main finding is that as a consequence of the pandemic, reductions of up to 12% were found in the Strait of Gibraltar in all the pollutants and GHGs when taking into account all international traffic, while the decrease in emissions from domestic traffic only reached 51%.
Testing Entrepreneurial Intention Determinants in Post-Transition Economies
Authors: Vodă Ana Iolanda , Haller Alina-Petronela , Anichiti Alexandru , Butnaru Gina Ionela
Published: December 2020

The paper aims to analyze the entrepreneurial intention determinants in nine post-transition economies of European member states. To achieve our stated goal, the study focused on the influence of fear of failure and networking on individuals’ inclination towards entrepreneurship. Additionally, gender, income, education, and work status were also considered control variables. The data were collected using the Global Entrepreneurship Monitor (GEM) database. We included responses collected from nine former transition economies, giving us a total of 13,494 observations, for 2015. Logistic regression models were employed to measure the influence of perceptions on the propenisty of individuals to create a new venture. The results indicated that fear of failure is significantly and negatively correlated with entrepreneurial intentions for all groups of countries. The results also indicated that people who have entrepreneurs in the family have a higher propensity for entrepreneurial intentions than people who do not have family members with such a status.