bluuebunny/crossref_metadata_2025 huggingface dataset:
Correction: Al-Kharusi, E.S., et al. Large-Scale Retrieval of Coloured Dissolved Organic Matter in Northern Lakes Using Sentinel-2 Data. Remote Sensing 2020, 12(1), p.157
Authors: Al-Kharusi Enass , Tenenbaum David , Abdi Abdulhakim , Kutser Tiit , Karlsson Jan , Bergström Ann-Kristin , Berggren Martin
Published: March 2020

The authors wish to make the following correction to Table 7 in this paper [...]
Ground Deformation of the Chongming East Shoal Reclamation Area in Shanghai Based on SBAS-InSAR and Laboratory Tests
Authors: Yu Qingbo , Wang Qing , Yan Xuexin , Yang Tianliang , Song Shengyuan , Yao Meng , Zhou Kai , Huang Xinlei
Published: March 2020

With the development of the economy, land reclamation, as a result of dredged soil, has become an effective measure to alleviate land scarcity in many coastal cities around the world. Chongming East Shoal (CES), a typical reclamation area in Shanghai that is formed by multi-phase reclamation projects, was selected as the study area. The small baseline subset–interferometry synthetic aperture radar (SBAS-InSAR) method was applied to derive the map of velocity distribution and accumulated deformation with 70 Sentinel-1 synthetic aperture radar (SAR) images collected from 22 March 2015 to 2 December 2019. In addition, 25 undisturbed soil samples, including dredger fill and underlying soil layers, were collected from five boreholes (maximum depth 55 m) through a field investigation. Laboratory tests were then performed on all soil samples in order to facilitate an understanding of geological features, including the measurement of basic physical properties, cation exchange capacity, compressibility, microscale structure, and pores. The present results show that the whole CES was undergoing differential ground deformation, with a velocity ranging from −47.5 to 34.6 mm/y. Fast (−3.4 mm/y) to slow (−0.3 mm/y) mean subsidence velocities were detected in multi-phase reclamation areas from inland areas to the coastline, and were controlled by building load and geological features of soil layers. Urbanization is the main factor that triggers accelerated subsidence and should receive special attention for reclamation areas that have been finished for a long time (over 20 years in this study). The geological features indicated that poor drainage conditions in offshore soil layers resulted in slow subsidence. The field investigation and laboratory test can be powerful explanatory tools to monitor the results from a mechanical perspective.
Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling
Authors: Zhao Yan , Potgieter Andries B , Zhang Miao , Wu Bingfang , Hammer Graeme L
Published: March 2020

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.
Changes in Atmospheric, Meteorological, and Ocean Parameters Associated with the 12 January 2020 Taal Volcanic Eruption
Authors: Jing Feng , Chauhan Akshansha , P Singh Ramesh , Dash Prasanjit
Published: March 2020

The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because the airmass of the Taal volcano spreads westward according to the analysis of airmass trajectories and wind directions. The presence of finer particles has been observed by analyzing aerosol properties that can be attributed to the volcanic plume after the eruption. We have also observed an enhancement in SO2, CO, and water vapor, and a decrease in Ozone after a few days of the eruption. The unusual variations in salinity, sea temperature, and surface latent heat flux have been observed as a result of the ash from the Taal volcano in the south-west and south-east over the ocean. Our results demonstrate that the observations combining satellite with ground data could provide important information about the changes in the atmosphere, meteorology, and ocean parameters associated with the Taal volcanic eruption.
Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands
Authors: Yang Aixia , Zhong Bo , Hu Longfei , Wu Shanlong , Xu Zhaopeng , Wu Hongbo , Wu Junjun , Gong Xueshuang , Wang Haibo , Liu Qinhuo
Published: March 2020

GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration.
Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture
Authors: Wang Lei , Fang Shibo , Pei Zhifang , Zhu Yongchao , Khoi Dao Nguyen , Han Wei
Published: March 2020

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.
UAV and Structure from Motion Approach to Monitor the Maierato Landslide Evolution
Authors: Godone Danilo , Allasia Paolo , Borrelli Luigi , Gullà Giovanni
Published: March 2020

In February 2010 a large landslide affected the Maierato municipality (Calabria, Italy). The landslide, mainly caused by a period of prolonged and intense rainfalls, produced a mass displacement of about 5 million m³ and several damages to farmlands, houses and infrastructures. In the aftermath several conventional monitoring actions were carried out. In the current post emergency phase, the monitoring was resumed by carrying out unmanned aerial vehicles (UAV) flights in order to describe the recent behavior of the landslide and to assess residual risk. Thanks to the potentialities of the structure from motion algorithms and the availability of post emergency reconnaissance photos and a previous 3D dataset, the three-dimensional evolution of the area was computed. Moreover, an experimental multispectral flight was carried out and its results supported the interpretation of local phenomena. The dataset allowed to quantify the elevation losses and raises in several peculiar sectors of the landslide. The obtained results confirm that the UAV monitoring and the structure from motion approach can effectively contribute to manage residual risk in the medium and long term within an integrated geotechnical monitoring network.
Copernicus Global Land Cover Layers—Collection 2
Authors: Buchhorn Marcel , Lesiv Myroslava , Tsendbazar Nandin-Erdene , Herold Martin , Bertels Luc , Smets Bruno
Published: March 2020

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation.
Rainfall Monitoring Based on Next-Generation Millimeter-Wave Backhaul Technologies in a Dense Urban Environment
Authors: Han Congzheng , Huo Juan , Gao Qingquan , Su Guiyang , Wang Hao
Published: March 2020

High-resolution and accurate rainfall monitoring is of great importance to many applications, including meteorology, hydrology, and flood monitoring. In recent years, microwave backhaul links from wireless communication networks have been suggested for rainfall monitoring purposes, complementing the existing monitoring systems. With the advances in microwave technology, new microwave backhaul solutions have been proposed and applied for 5G networks. Examples of the latest microwave technology include E-band (71–76 and 81–86 GHz) links, multi-band boosters, and line-of-sight multiple-input multiple-output (LOS-MIMO) backhaul links. They all rely on millimeter-wave (mmWave) technology, which is the fastest small-cell backhaul solution. In this paper, we will study the rain attenuation characteristics of these new microwave backhaul techniques at different mmWave frequencies and link lengths. We will also study the potential of using these new microwave solutions for rainfall monitoring. Preliminary results indicate that all the test mmWave links can be very effective for estimating the path-averaged rain rates. The correlation between the mmWave link measurement-derived rain rate and the local rain gauge is in the range of 0.8 to 0.9, showing a great potential to use these links for precipitation and flood monitoring in urban areas.
SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion
Authors: Chen Jie , He Fen , Zhang Yi , Sun Geng , Deng Min
Published: March 2020

The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the intactness and boundary accuracy of a detected building. Our method achieves impressive results on two 2D semantic labeling datasets, which outperform some competing weakly supervised methods and are close to the result of the fully supervised method.
Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement
Authors: Ajdaroski Mirel , Tadakala Ruchika , Nichols Lorraine , Esquivel Amanda
Published: March 2020

Participation in sports has risen in the United States over the last few years, increasing the risk of injuries such as tears to the anterior cruciate ligament (ACL) in the knee. Previous studies have shown a correlation between knee kinematics when landing from a jump and this injury. The purpose of this study was to validate the ability of a commercially available inertial measurement units (IMUs) to accurately measure knee joint angles during a dynamic movement. Eight healthy subjects participated in the study. Validation was performed by comparing the angles measured by the wearable device to those obtained through the gold standard motion capture system when landing from a jump. Root mean square, linear regression analysis, and Bland–Altman plots were performed/constructed. The mean difference between the wearable device and the motion capture data was 8.4° (flexion/extension), 4.9° (ab/adduction), and 3.9° (rotation). In addition, the device was more accurate at smaller knee angles. In our study, a commercially available wearable IMU was able to perform fairly well under certain conditions and was less accurate in other conditions.
Grip Stabilization through Independent Finger Tactile Feedback Control
Authors: Veiga Filipe , Edin Benoni , Peters Jan
Published: March 2020

Grip force control during robotic in-hand manipulation is usually modeled as a monolithic task, where complex controllers consider the placement of all fingers and the contact states between each finger and the gripped object in order to compute the necessary forces to be applied by each finger. Such approaches normally rely on object and contact models and do not generalize well to novel manipulation tasks. Here, we propose a modular grip stabilization method based on a proposition that explains how humans achieve grasp stability. In this biomimetic approach, independent tactile grip stabilization controllers ensure that slip does not occur locally at the engaged robot fingers. Local slip is predicted from the tactile signals of each fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable grasps emerge without any form of central communication when such independent controllers are engaged in the control of multi-digit robotic hands. The resulting grasps are resistant to external perturbations while ensuring stable grips on a wide variety of objects.
Nondestructive Testing of the Miter Gates Using Various Measurement Methods
Authors: Binczyk Mikolaj , Kalitowski Przemyslaw , Szulwic Jakub , Tysiac Pawel
Published: March 2020

When any problems related to civil engineering structures appear, identifying the issue through the usage of only one measuring method is difficult. Therefore, comprehensive tests are required to identify the main source. The strains and displacement measurements, as well as modal identification, are widely used in the nondestructive testing of structures. However, measurements are usually carried out at several points and confirm or exclude only one of many potential causes of the problem. The main aim of this paper is to identify the causes of miter gates’ excessive vibration. The research includes displacement measurements using a tachometer and a laser scanner, acceleration measurements connected with modal analysis, and calculations with the finite element method (FEM) model. The numerical model underwent verification regarding test results. Particular attention was paid to evaluate the practical use of a laser scanner for diagnosing miter gates. Unlike classical methods, it measures many points. The analysis eliminated a number of potential causes of excessive vibration and highlighted the field of excessive deformation. The identified anomaly could be associated with bearings’ misalignment after closing the door. This construction part should be subjected to further research using classical methods. The laser scanning has been proven to be a method that can only generally present the deformation of the structure.
Integration and Investigation of Selected On-Board Devices for Development of the Newly Designed Miniature UAV
Authors: Olejnik Aleksander , Rogólski Robert , Kiszkowiak Łukasz , Szcześniak Michał
Published: March 2020

The article is a development of the topic generally presented in the conference proceedings issued after the 2019 IEEE International Workshop on Metrology for Aerospace. In contrast to topics presented in the conference, the article describes in detail avionic equipment and on-board systems integration process and their in-flight adjustment in regard to the newly designed miniature unmanned aerial vehicle (mini-UAV). The mini-airplane was constructed and assembled in the course of the research project, the purpose of which was to show implementation of a totally new mini-UAV design. The intention of the work was to develop a new unmanned system including an originally constructed small airplane with elements purchased from open market. Such approach should allow to construct a new aerial unmanned system, which technologically would not be very advanced but should be easy to use and relatively inexpensive. The demonstrator mini-airplane has equipment typical for such an object, i.e., electric propulsion, autopilot system, camera head and parachute device for recovery. The key efforts in the project were taken to elaborate an original but easy to use system, to integrate subsystem elements and test them so to prove their functionality and reliability.
Advancing the State of the Fog Computing to Enable 5G Network Technologies
Authors: Meng Yahui , Naeem Muhammad Ali , Almagrabi Alaa Omran , Ali Rashid , Kim Hyung Seok
Published: March 2020

Fog Computing (FC) is promising to Internet architecture for the emerging of modern technological approaches such as Fifth Generation (5G) networks and the Internet of Things (IoT). These are the advanced technologies that enable Internet architecture to enhance the data dissemination services based on numerous sensors generating continuous sensory information. It is tough for the current Internet architecture to meet up with the growing demands of the users for such a massive amount of information. Therefore, it needs to adopt modern technologies for efficient data dissemination services across the Internet. Thus, the FC and 5G are updating the data transmission using new technological approaches that are intelligently processing data to provide enhanced communications. This study proposes necessary measures to boost the growth of FC to 5G network usage. It is done by taking an extensive review of how 5G operates as well as studying its taxonomy, the idea of IoT, reviewed projects on IoT applicability, comparison of computing technologies, and the importance of FC. Moreover, it elaborates dynamic issues of computing network technologies, and information is provided on how to remedy these for future recommendations in the field of research and computing network technologies. This paper heavily focuses on the applications of FC as an enabler to the 5G network by identifying the necessary services and network-oriented features that are needed to be used in the place for an improved future enterprise network technology.
Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control
Authors: Jung Dae-Hyun , Kim Hak-Jin , Kim Joon Yong , Lee Taek Sung , Park Soo Hyun
Published: March 2020

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R2 of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.
A Fast Analysis Method for Blue-Green Laser Transmission through the Sea Surface
Authors: Dong Liwei , Li Ni , Xie Xinhao , Bao Chenying , Li Xiaolu , Li Duan
Published: March 2020

The fast estimation of blue-green laser transmission characteristics through the fluctuating sea surface, such as refraction angles and transmittance, is very important to correct operating parameters, detection depth and anti-detection warning in airborne Light Detection and Ranging (LiDAR) applications. However, the geometry of the sea surface is changed by complex environment factors, such as wind and wave, which significantly affect the rapid acquisition of the blue-green laser transmission characteristics. To address this problem, a fast analysis method is provided to rapidly compute the blue-green laser transmittance and refraction angles through the fluctuating sea surface driven by different wind directions and speeds. In the method, a three-dimensional wave model driven by the wind was built to describe the wave spatial distribution varying with time. Using the wave model, the propagation path of the scanning laser footprint was analyzed using the proposed meshing method, thus the transmittance and refraction angles of the optical path can be fast obtained by using parallel computing. The simulation results imply that the proposed method can reduce the time consumption by 70% compared with the traditional analytical method with sequential computing. This paper provides some statistical laws of refraction angles and transmittance through the fluctuating sea surface under different wind conditions, which may serve as a basic for fast computation of airborne LiDAR transmission characteristics in complex environments.
Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry
Authors: Terán José , Navarro Loraine , Quintero M. Christian G. , Pardo Mauricio
Published: March 2020

Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented.
Research and Implementation of Vehicle Target Detection and Information Recognition Technology Based on NI myRIO
Authors: Wang Hongliang , He Shuang , Yu Jiashan , Wang Luyao , Liu Tao
Published: March 2020

A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy.
Development of Response Surface Model of Endurance Time and Structural Parameter Optimization for a Tailsitter UAV
Authors: Yao Xiaomin , Liu Wenshuai , Han Wenting , Li Guang , Ma Qian
Published: March 2020

This study designed a vertical take-off and landing tailsitter unmanned aerial vehicle (UAV) with a long endurance time. Nine parameters of the tailsitter UAV were investigated. Using a 2k full factorial test, 512 experiments on the nine parameters were conducted at their maximum and minimum values. The time coefficient and air resistance were calculated using the computational fluid dynamics (CFD) method under different parameter combinations. The analysis of variance determined that the specific factors influencing the time coefficient and air resistance were the root chord, wingtip chord, wingspan, and sweep angle. By carrying out a central composite design (CCD) test, 25 sample points of the four particular factors were constructed. The time coefficient and air resistance were simulated under different structural parameter combinations using the CFD method. CFD simulation was verified by carrying out a wind tunnel test, and the results revealed that the aerodynamic coefficient error was less than 5%, while the air resistance error was less than 6%. The response surface methodology (RSM) for the time coefficient and air resistance was established using a genetic aggregation method. A multi-objective genetic algorithm (MOGA) was used to optimize the parameters with regard to the maximum time coefficient and minimum air resistance. The optimal structural parameters were wing root chord length at 315 mm, wingtip chord length at 182 mm, wingspan length at 1198 mm, and sweep angle at 16°. Compared with the original layout and size, the time coefficient of the new design of the tailsitter UAV improved by 19.5%, while the air resistance reduced by 34.78%. The results obtained by this study are significant for the design of tailsitter UAVs.
Modern Soft-Sensing Modeling Methods for Fermentation Processes
Authors: Zhu Xianglin , Rehman Khalil Ur , Wang Bo , Shahzad Muhammad
Published: March 2020

For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.
Robust Time-Delay Feedback Control of Vehicular CACC Systems with Uncertain Dynamics
Authors: Song Xiulan , Chen Li , Wang Ke , He Defeng
Published: March 2020

This paper proposes a new, robust time-delay cooperative adaptive cruise control (CACC) approach for vehicle platooning systems with uncertain dynamics and varying communication delay. The uncertain CACC models with perturbed parameters are used to describe the uncertain dynamics of the vehicle platooning system. By combining the constant time headway strategy and predecessor-following communication topology, a set of robust delay feedback controllers is designed for the uncertain vehicle platoon with varying communication delay. Then, the set of CACC controllers is computed by solving some linear matrix inequalities, which further establish the robust (string) stability of the uncertain platooning system with the varying communication delay. The co-simulation experiment of CarSim and Simulink with a group of a seven-car platoons and varying velocity is used to demonstrate the effectiveness of the presented method.
Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
Authors: Zhang Hongjia , Liu Yanjuan , Wang Chang , Fu Rui , Sun Qinyu , Li Zhen
Published: March 2020

Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles.
Using Rough Sets to Improve Activity Recognition Based on Sensor Data
Authors: Guesgen Hans W.
Published: March 2020

Activity recognition plays a central role in many sensor-based applications, such as smart homes for instance. Given a stream of sensor data, the goal is to determine the activities that triggered the sensor data. This article shows how spatial information can be used to improve the process of recognizing activities in smart homes. The sensors that are used in smart homes are in most cases installed in fixed locations, which means that when a particular sensor is triggered, we know approximately where the activity takes place. However, since different sensors may be involved in different occurrences of the same type of activity, the set of sensors associated with a particular activity is not precisely defined. In this article, we use rough sets rather than standard sets to denote the sensors involved in an activity to model, which enables us to deal with this imprecision. Using publicly available data sets, we will demonstrate that rough sets can adequately capture useful information to assist with the activity recognition process. We will also show that rough sets lend themselves to creating Explainable Artificial Intelligence (XAI).
Fabrication and Characteristics of a Three-Axis Accelerometer with Double L-Shaped Beams
Authors: Wang Ying , Zhao Xiaofeng , Wen Dianzhong
Published: March 2020

A three-axis accelerometer with a double L-shaped beams structure was designed and fabricated in this paper, consisting of a supporting body, four double L-shaped beams and intermediate double beams connected to two mass blocks. When applying acceleration to the accelerometer chip, according to the output voltage changes of three Wheatstone bridges constituted by twelve piezoresistors on the roots of the beams, the corresponding acceleration along three axes can be measured based on the elastic force theory and piezoresistive effect. To improve the characteristics of the three-axis accelerometer, we simulated how the width of the intermediate double beams affected the characteristics. Through optimizing the structure size, six chips with different widths of intermediate double beams were fabricated on silicon-on-insulator (SOI) wafers using micro-electromechanical systems (MEMS) technology and were packaged on printed circuit boards (PCB) by using an electrostatic bonding process and inner lead bonding technology. At room temperature and VDD = 5.0 V, the resulting accelerometer with an optimized size (w = 500 μm) realized sensitivities of 0.302 mV/g, 0.235 mV/g and 0.347 mV/g along three axes, with a low cross-axis sensitivity. This result provides a new strategy to further improve the characteristics of the three-axis accelerometer.
Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection
Authors: Zhang Zi , Pan Hong , Wang Xingyu , Lin Zhibin
Published: March 2020

Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.
Application of Microfluidic Chip Technology in Food Safety Sensing
Authors: Gao Hongwei , Yan Chunlei , Wu Wei , Li Juan
Published: March 2020

Food safety analysis is an important procedure to control food contamination and supervision. It is urgently needed to construct effective methods for on-site, fast, accurate and popular food safety sensing. Among them, microfluidic chip technology exhibits distinguish advantages in detection, including less sample consumption, fast detection, simple operation, multi-functional integration, small size, multiplex detection and portability. In this review, we introduce the classification, material, processing and application of the microfluidic chip in food safety sensing, in order to provide a good guide for food safety monitoring.
Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things
Authors: Nakamura Yuta , Zhang Yuanyu , Sasabe Masahiro , Kasahara Shoji
Published: March 2020

Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost.
Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions
Authors: Zhu Chuanmei , Zhang Zipeng , Wang Hongwei , Wang Jingzhe , Yang Shengtian
Published: March 2020

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.
A Heterogeneous Network Modeling Method Based on Public Goods Game Theory to Explore Cooperative Behavior in VANETs
Authors: Wang Qiuhua , Liu Hao , Jin Xing , Wang Zhen
Published: March 2020

Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only extracts part attributes of vehicles and does not distinguish the differences between strategy and attribute. In this paper, we focus on the heterogeneous network model based on the public goods game theory for VANET. Then we propose a Dynamic Altruism Public Goods Game (DAPGG) model consisting of rational nodes, altruistic nodes, and zealots to more realistically characterize the real VANET. Rational nodes only care about their own benefits, altruistic nodes comprehensively consider the payoffs in the neighborhood, while zealots insist on behaving cooperatively. Finally, we explore the impacts of these attributes on the evolution of cooperation under different network conditions. The simulation results show that only adding altruistic nodes can effectively improve the proportion of cooperators, but it may cause conflicts between individual benefits and neighborhood benefits. Altruistic nodes together with zealots can better improve the proportion of cooperators, even if the network conditions are not suitable for the spread of cooperative behavior.
A Closed-form Expression to Estimate the Uncertainty of THD Starting from the LPIT Accuracy Class
Authors: Mingotti Alessandro , Peretto Lorenzo , Tinarelli Roberto
Published: March 2020

Power quality is a wide-ranging and current topic that involves a huge effort from the scientific community. Power quality issues have to be avoided or solved in order to preserve the integrity of the network and its assets. To this purpose, several power quality indexes and measurement techniques have been developed and used by experts. This paper aims at solving the issue of having an uncertainty associated to the total harmonic distortion (THD) measurements. The idea is to obtain a close-form expression, which only requires the knowledge of the instrument transformer accuracy class, to estimate the mean value and the variance of THD. After the development of such an expression, it has been tested and stressed to confirm its effectiveness and applicability in a variety of conditions, and for harmonics up to 25th (of 50 Hz), defined by the standards.