bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China
Authors: Zhou Zhaoqiang , Ding Yibo , Liu Suning , Wang Yao , Fu Qiang , Shi Haiyun
Published: July 2022

Vegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai–Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring.
WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
Authors: Costa Gabriel B. , Santos e Silva Cláudio M. , Mendes Keila R. , dos Santos José G. M. , Neves Theomar T. A. T. , Silva Alex S. , Rodrigues Thiago R. , Silva Jonh B. , Dalmagro Higo J. , Mutti Pedro R. , Nunes Hildo G. G. C. , Peres Lucas V. , Santana Raoni A. S. , Viana Losany B. , Almeida Gabriele V. , Bezerra Bergson G. , Marques Thiago V. , Ferreira Rosaria R. , Oliveira Cristiano P. , Gonçalves Weber A. , Campos Suany , Andrade Maria U. G.
Published: July 2022

The analysis of gross primary production (GPP) is crucial to better understand CO2 exchanges between terrestrial ecosystems and the atmosphere, while the quantification of water-use efficiency (WUE) allows for the estimation of the compensation between carbon gained and water lost by the ecosystem. Understanding these dynamics is essential to better comprehend the responses of environments to ongoing climatic changes. The objective of the present study was to analyze, through AMERIFLUX and LBA network measurements, the variability of GPP and WUE in four distinct tropical biomes in Brazil: Pantanal, Amazonia, Caatinga and Cerrado (savanna). Furthermore, data measured by eddy covariance systems were used to assess remotely sensed GPP products (MOD17). We found a distinct seasonality of meteorological variables and energy fluxes with different latent heat controls regarding available energy in each site. Remotely sensed GPP was satisfactorily related with observed data, despite weak correlations in interannual estimates and consistent overestimations and underestimations during certain months. WUE was strongly dependent on water availability, with values of 0.95 gC kg−1 H2O (5.79 gC kg−1 H2O) in the wetter (drier) sites. These values reveal new thresholds that had not been previously reported in the literature. Our findings have crucial implications for ecosystem management and the design of climate policies regarding the conservation of tropical biomes, since WUE is expected to change in the ongoing climate change scenario that indicates an increase in frequency and severity of dry periods.
A Sidelobe Suppression Method for Circular Ground-Based SAR 3D Imaging Based on Sparse Optimization of Radial Phase-Center Distribution
Authors: Zhang Qiming , Sun Jinping , Wang Yanping , Lin Yun
Published: July 2022

Circular ground-based SAR (GBSAR) is a new 3D imaging GBSAR with the potential of acquiring high-quality 3D SAR images and 3D deformation. However, its donut-shaped spectrum and short radius of antenna rotation cause high sidelobes on 3D curved surfaces, resulting in 3D SAR images with poor quality. The multi-phase-center circular GBSAR with full array can effectively suppress sidelobes by filling the donut-shaped spectrum to be the equivalent solid spectrum, but it requires a larger number of phase centers, increasing system cost and engineering difficulties. In this paper, a sidelobe suppression method for circular GBSAR 3D imaging based on sparse optimization of radial phase-center distribution is proposed to suppress high sidelobes at low cost. By deriving the point spread function (PSF) of multi-phase-center circular GBSAR and taking the peak sidelobe level (PSL) and integrated sidelobe level (ISL) of the derived PSF as multi-objective functions, we solve the multi-objective optimization problem to optimize the sparse distribution of radial phase centers. The advantage of the proposed method is that the solved optimal radial phase-center distribution can effectively suppress the 3D sidelobes of circular GBSAR with a limited number of phase centers. Finally, the sidelobe suppression effect of the proposed method is verified via 3D imaging simulations.
Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data
Authors: Yeasin Md , Haldar Dipanwita , Kumar Suresh , Paul Ranjit Kumar , Ghosh Sonaka
Published: July 2022

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.
A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China
Authors: Yuan Xinyue , Liu Chao , Nie Ruihua , Yang Zhengli , Li Weile , Dai Xiaoai , Cheng Junying , Zhang Junmin , Ma Lei , Fu Xiao , Tang Min , Xu Yina , Lu Heng
Published: July 2022

After the “5·12” Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood disasters. Therefore, it is of great significance to build a collapse and landslide susceptibility evaluation model in Wenchuan County for local disaster prevention and mitigation. Taking Wenchuan County as the research object and according to the data of 1081 historical collapse and landslide disaster points, as well as the natural environment, this paper first selects six categories of environmental factors (13 environmental factors in total) including topography (slope, aspect, curvature, terrain relief, TWI), geological structure (lithology, soil type, distance to fault), meteorology and hydrology (rainfall, distance to river), seismic impact (PGA), ecological impact (NDVI), and impact of human activity (land use). It then builds three single models (LR, SVM, RF) and three CF-based hybrid models (CF-LR, CF-SVM, CF-RF), and makes a comparative analysis of the accuracy and reliability of the models, thereby obtaining the optimal model in the research area. Finally, this study discusses the contribution of environmental factors to the collapse and the landslide susceptibility prediction of the optimal model. The research results show that (1) the areas prone to extremely high collapse and landslide predicted by the six models (LR, CF-LR, SVM, CF-SVM, RF and CF-RF) have an area of 730.595 km2, 377.521 km2, 361.772 km2, 372.979 km2, 318.631 km2, and 306.51 km2, respectively, and the frequency ratio precision of collapses and landslides is 0.916, 0.938, 0.955, 0.956, 0.972, and 0.984, respectively; (2) the ranking of the comprehensive index based on the confusion matrix is CF-RF>RF>CF-SVM>CF-LR>SVM>LR and the ranking of the AUC value is CF-RF>RF>CF-SVM>CF-LR>SVM>LR. To a certain extent, the coupling models can improve precision more over the single models. The CF-RF model ranks the highest in all indexes, with a POA value of 257.046 and an AUC value of 0.946; (3) rainfall, soil type, and distance to river are the three most important environmental factors, accounting for 24.216%, 22.309%, and 11.41%, respectively. Therefore, it is necessary to strengthen the monitoring of mountains and rock masses close to rivers in case of rainstorms in Wenchuan county and other similar areas prone to post-earthquake landslides.
Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds
Authors: Henriques Mohamed , Catry Teresa , Belo João Ricardo , Piersma Theunis , Pontes Samuel , Granadeiro José Pedro
Published: July 2022

Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.
UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China
Authors: Xiao Yi , Guo Yahui , Yin Guodong , Zhang Xuan , Shi Yu , Hao Fanghua , Fu Yongshuo
Published: July 2022

Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.
Recognition of Landslide Triggering Mechanisms and Dynamics Using GNSS, UAV Photogrammetry and In Situ Monitoring Data
Authors: Peternel Tina , Janža Mitja , Šegina Ela , Bezak Nejc , Maček Matej
Published: July 2022

Detecting the mechanism of landslides and evaluating their dynamics is challenging, especially concerning composite landslides. For this purpose, several investigation and monitoring techniques should be implemented to obtain reliable information on landslide characteristics (e.g., geological and hydrogeological conditions and type of landslide processes), kinematics (displacement rate), and potential triggering mechanisms (e.g., change in groundwater table and precipitation). The Urbas landslide in northwest Slovenia has been studied for decades through geological, geotechnical, geodetic, and remote sensing investigations. However, due to the complexity of the landslide and the short duration of continuous monitoring, no assessment of its dynamics has been made. To meet this need, this study analysed continuous and periodic monitoring of landslide displacements using data from the global navigation satellite system (GNSS), a wire extensometer, unmanned aerial vehicle (UAV) photogrammetry, and hydrometeorological sensing (groundwater table, precipitation). The results of this study show that the dynamics of the Urbas landslide differ along the landslide area, depending on local geological and hydrogeological conditions. Consequently, certain parts of the landslide are at different evolutionary states and respond differently to the same external triggers.
A System for Autonomous Seaweed Farm Inspection with an Underwater Robot
Authors: Stenius Ivan , Folkesson John , Bhat Sriharsha , Sprague Christopher Iliffe , Ling Li , Özkahraman Özer , Bore Nils , Cong Zheng , Severholt Josefine , Ljung Carl , Arnwald Anna , Torroba Ignacio , Gröndahl Fredrik , Thomas Jean-Baptiste
Published: July 2022

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup.
LPWAN Key Exchange: A Centralised Lightweight Approach
Authors: Pathak Gaurav , Gutierrez Jairo , Ghobakhlou Akbar , Rehman Saeed Ur
Published: July 2022

The Internet of Things (IoT) is one of the fastest emerging technologies in the industry. It includes diverse applications with different requirements to provide services to users. Secure, low-powered, and long-range transmissions are some of the most vital requirements in developing IoT applications. IoT uses several communication technologies to fulfill transmission requirements. However, Low Powered Wide Area Networks (LPWAN) transmission standards have been gaining attention because of their exceptional low-powered and long-distance transmission capabilities. The features of LPWAN transmission standards make them a perfect candidate for IoT applications. However, the current LPWAN standards lack state-of-the-art security mechanism s because of the limitations of the IoT devices in energy and computational capacity. Most of the LPWAN standards, such as Sigfox, NB-IoT, and Weightless, use static keys for node authentication and encryption. LoRaWAN is the only LPWAN technology providing session key mechanisms for better security. However, the session key mechanism is vulnerable to replay attacks. In this paper, we propose a centralized lightweight session key mechanism for LPWAN standards using the Blom–Yang key agreement (BYka) mechanism. The security of the session key mechanism is tested using the security verification tool Scyther. In addition, an energy consumption model is implemented on the LoRaWAN protocol using the NS3 simulator to verify the energy depletion in a LoRaWAN node because of the proposed session key mechanisms. The proposed session key is also verified on the Mininet-WiFi emulator for its correctness. The analysis demonstrates that the proposed session key mechanism uses a fewer number of transmissions than the existing session key mechanisms in LPWAN and provides mechanisms against replay attacks that are possible in current LPWAN session key schemes.
Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia
Authors: Stetzuhn Matthias , Tigges Timo , Pielmus Alexandru Gabriel , Spies Claudia , Middel Charlotte , Klum Michael , Zaunseder Sebastian , Orglmeister Reinhold , Feldheiser Aarne
Published: July 2022

Compensated shock and hypovolaemia are frequent conditions that remain clinically undetected and can quickly cause deterioration of perioperative and critically ill patients. Automated, accurate and non-invasive detection methods are needed to avoid such critical situations. In this experimental study, we aimed to create a prediction model for stroke volume index (SVI) decrease based on electrical cardiometry (EC) measurements. Transthoracic echo served as reference for SVI assessment (SVI-TTE). In 30 healthy male volunteers, central hypovolaemia was simulated using a lower body negative pressure (LBNP) chamber. A machine-learning algorithm based on variables of EC was designed. During LBNP, SVI-TTE declined consecutively, whereas the vital signs (arterial pressures and heart rate) remained within normal ranges. Compared to heart rate (AUC: 0.83 (95% CI: 0.73–0.87)) and systolic arterial pressure (AUC: 0.82 (95% CI: 0.74–0.85)), a model integrating EC variables (AUC: 0.91 (0.83–0.94)) showed a superior ability to predict a decrease in SVI-TTE ≥ 20% (p = 0.013 compared to heart rate, and p = 0.002 compared to systolic blood pressure). Simulated central hypovolaemia was related to a substantial decline in SVI-TTE but only minor changes in vital signs. A model of EC variables based on machine-learning algorithms showed high predictive power to detect a relevant decrease in SVI and may provide an automated, non-invasive method to indicate hypovolaemia and compensated shock.
Measurement of Pipe and Liquid Parameters Using the Beam Steering Capabilities of Array-Based Clamp-On Ultrasonic Flow Meters
Authors: Massaad Jack , van Neer Paul L. M. J. , van Willigen Douwe M. , Pertijs Michiel A. P. , de Jong Nicolaas , Verweij Martin D.
Published: July 2022

Clamp-on ultrasonic flow meters (UFMs) are installed on the outside of the pipe wall. Typically, they consist of two single-element transducers mounted on angled wedges, which are acoustically coupled to the pipe wall. Before flow metering, the transducers are placed at the correct axial position by manually moving one transducer along the pipe wall until the maximum amplitude of the relevant acoustic pulse is obtained. This process is time-consuming and operator-dependent. Next to this, at least five parameters of the pipe and the liquid need to be provided manually to compute the flow speed. In this work, a method is proposed to obtain the five parameters of the pipe and the liquid required to compute the flow speed. The method consists of obtaining the optimal angles for different wave travel paths by varying the steering angle of the emitted acoustic beam systematically. Based on these optimal angles, a system of equations is built and solved to extract the desired parameters. The proposed method was tested experimentally with a custom-made clamp-on UFM consisting of two linear arrays placed on a water-filled stainless steel pipe. The obtained parameters of the pipe and the liquid correspond very well with the expected (nominal) values. Furthermore, the performed experiment also demonstrates that a clamp-on UFM based on transducer arrays can achieve self-alignment without the need to manually move the transducers.
Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
Authors: Bregar Klemen , Krištofelc Tomaž , Depolli Matjaž , Avbelj Viktor , Rashkovska Aleksandra
Published: July 2022

The paper analyses the autonomy of a wireless body sensor that continuously measures the potential difference between two proximal electrodes on the skin, primarily used for measuring an electrocardiogram (ECG) when worn on the torso. The sensor is powered by a small rechargeable battery and is designed for extremely low power use. However, the autonomy of the sensor, regarding its power consumption, depends significantly on the measurement quality selection, which directly influences the amount of data transferred. Therefore, we perform an in-depth analysis of the power consumption sources, particularly those connected with the Bluetooth Low Energy (BLE) communication protocol, in order to model and then tune the autonomy of the wireless low-power body sensor for long-term ECG monitoring. Based on the findings, we propose two analytical models for power consumption: one for power consumption estimation in idle mode and the other one for power estimation in active mode. The proposed models are validated with the measured power consumption of the ECG sensor at different ECG sensor settings, such as sampling rate and transmit power. The proposed models show a good fit to the measured power consumption at different ECG sensor sampling rates. This allows for power consumption analysis and sensor autonomy predictions for different sensor settings. Moreover, the results show that the transmit power has a negligible effect on the sensor autonomy in the case of streaming data with high sampling rates. The most energy can be saved by lowering the sampling rate with suitable connection interval and by packing as much data as possible in a single BLE packet.
SAX and Random Projection Algorithms for the Motif Discovery of Orbital Asteroid Resonance Using Big Data Platforms
Authors: Riza Lala Septem , Fazanadi Muhammad Naufal , Utama Judhistira Aria , Samah Khyrina Airin Fariza Abu , Hidayat Taufiq , Nazir Shah
Published: July 2022

The phenomenon of big data has occurred in many fields of knowledge, one of which is astronomy. One example of a large dataset in astronomy is that of numerically integrated time series asteroid orbital elements from a time span of millions to billions of years. For example, the mean motion resonance (MMR) data of an asteroid are used to find out the duration that the asteroid was in a resonance state with a particular planet. For this reason, this research designs a computational model to obtain the mean motion resonance quickly and effectively by modifying and implementing the Symbolic Aggregate Approximation (SAX) algorithm and the motif discovery random projection algorithm on big data platforms (i.e., Apache Hadoop and Apache Spark). There are five following steps on the model: (i) saving data into the Hadoop Distributed File System (HDFS); (ii) importing files to the Resilient Distributed Datasets (RDD); (iii) preprocessing the data; (iv) calculating the motif discovery by executing the User-Defined Function (UDF) program; and (v) gathering the results from the UDF to the HDFS and the .csv file. The results indicated a very significant reduction in computational time between the use of the standalone method and the use of the big data platform. The proposed computational model obtained an average accuracy of 83%, compared with the SwiftVis software.
SMU Open-Source Platform for Synchronized Measurements
Authors: Guarnieri Calò Carducci Carlo , Pau Marco , Cazal Cesar , Ponci Ferdinanda , Monti Antonello
Published: July 2022

The ramping trend of cheap and performant single board computers (SBC) is growingly offering unprecedented opportunities in various domains, taking advantage of the widespread support and flexibility offered by an operating system (OS) environment. Unfortunately, data acquisition systems implemented in an OS environment are traditionally considered not to be suitable for reliable industrial applications. Such a position is supported by the lack of hardware interrupt handling and deterministic control of timed operations. In this study, the authors fill this gap by proposing an innovative and versatile SBC-based open-source platform for CPU-independent data acquisition. The synchronized measurement unit (SMU) is a high-accuracy device able to perform multichannel simultaneous sampling up to 200 kS/s with sub-microsecond synchronization precision to a GPS time reference. It exhibits very low offset and gain errors, with a minimum bandwidth beyond 20 kHz, SNR levels above 90 dB and THD as low as −110 dB. These features make the SMU particularly attractive for the power system domain, where synchronized measurements are increasingly required for the geographically distributed monitoring of grid operating conditions and power quality phenomena. We present the characterization of the SMU in terms of measurement and time synchronization accuracy, proving that this device, while low-cost, guarantees performance compliant with the requirements for synchrophasor-based applications in power systems.
Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm
Authors: Zacharakis Ilias , Giagopoulos Dimitrios
Published: July 2022

The continuous development of new materials and larger and/or more complex structures drives the need for the development of more robust, accurate, and sensitive Structural Health Monitoring (SHM) techniques. In the present work, a novel vibration-based damage-detection method that contributes into the SHM field is presented using Metaheuristic algorithms coupled with optimal Finite Element Models that can effectively localize damage. The proposed damage-detection framework can be applied in any kind of detailed structural FE model, while requiring only the output information of the dynamic response of the structure. It can effectively localize damage in a structure by highlighting not only the affected part of the structure but also the specific damaged area inside the part. First, the optimal FE model of the healthy structure is developed using appropriate FE model updating techniques and experimental vibration measurements, simulating the undamaged condition. Next, the main goal of the proposed method is to create a damaged FE model that approximates the dynamic response of the damaged structure. To achieve this, a parametric area is inserted into the FE model, changing stiffness and mass to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed damage-detection framework. On this specific implementation of the framework, the Particle Swarm Optimization (PSO) algorithm is selected which has been used for a wide variety of optimization problems in the past. On the PSO’s search space, two parameters control the stiffness and mass of the damaged area while additional location parameters control the exact position of the damaged area through the FE model. For effective damage localization, the Transmittance Functions from acceleration measurements are used which have been shown to be sensitive to structural damage while requiring output-only information. Finally, with proper selection of the objective function, the error that arises from modeling a physical damage with a linear damaged FE model can be minimized, thus creating a more accurate prediction for the damaged location. The effectiveness of the proposed SHM method is demonstrated via two illustrative examples: a simulated small-scale model of a laboratory-tested vehicle-like structure and a real experimental CFRP composite beam structure. In order to check the robustness of the proposed method, two small damage scenarios are examined for each validation model and combined with random excitations.
A Heartbeat Classifier for Continuous Prediction Using a Wearable Device
Authors: Pramukantoro Eko Sakti , Gofuku Akio
Published: July 2022

Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.
Quadcopter UAVs Extended States/Disturbance Observer-Based Nonlinear Robust Backstepping Control
Authors: Thanh Ha Le Nhu Ngoc , Huynh Tuan Tu , Vu Mai The , Mung Nguyen Xuan , Phi Nguyen Ngoc , Hong Sung Kyung , Vu Truong Nguyen Luan
Published: July 2022

A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method.
A Controller Design for Approaching Disabled Satellites Based on Discrete Sample Points
Authors: Li Peiyun , Dong Yunfeng , Liew Yingjia
Published: July 2022

When approaching and removing a disabled satellite, the accuracy of the controller is imperative to the success of the mission because if the mission fails, more space debris can be produced due to satellite collision. To address this issue, a controller directly driven by discrete sample data points is proposed in this paper. First, the input vector for the controller is placed into a state space as a point. The state space also contains points constructed by the input vectors of pre-generated samples, which are created by the GPOPS planning algorithm along with control commands as sample output vectors. Then, an adjacent range is selected and the sample points within are collected. To accelerate the process, a series of data processing methods are implemented, including the dichotomy method, table look-up method, and random selection method. Finally, the control commands are computed using the iteratively reweighted least-squares algorithm with the assumption that similar inputs have similar outputs. According to the simulation results, the discrete point controller is more precise than the neural network controller.
Sidescan Only Neural Bathymetry from Large-Scale Survey
Authors: Xie Yiping , Bore Nils , Folkesson John
Published: July 2022

Sidescan sonar is a small and low-cost sensor that can be mounted on most unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs). It has the advantages of high resolution and wide coverage, which could be valuable in providing an efficient and cost-effective solution for obtaining the bathymetry when bathymetric data are unavailable. This work proposes a method of reconstructing bathymetry using only sidescan data from large-scale surveys by formulating the problem as a global optimization, where a Sinusoidal Representation Network (SIREN) is used to represent the bathymetry and the albedo and the beam profile are jointly estimated based on a Lambertian scattering model. The assessment of the proposed method is conducted by comparing the reconstructed bathymetry with the bathymetric data collected with a high-resolution multi-beam echo sounder (MBES). An error of 20 cm on the bathymetry is achieved from a large-scale survey. The proposed method proved to be an effective way to reconstruct bathymetry from sidescan sonar data when high-accuracy positioning is available. This could be of great use for applications such as surface vehicles with Global Navigation Satellite System (GNSS) to obtain high-quality bathymetry in shallow water or small autonomous underwater vehicles (AUVs) if simultaneous localization and mapping (SLAM) can be applied to correct the navigation estimate.
Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification
Authors: Yang Dong-Hyuk , Kang Yong-Shin
Published: July 2022

Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; however, it only focuses on the mean value of each segment in the time-series. Here, we propose a novel time-series representation method—distance- and momentum-based symbolic aggregate approximation (DM-SAX)—that can secure time-series distributions by calculating the perpendicular distance from the time-axis to each data point and consider the time-series trend by adding a momentum factor reflecting the direction of previous data points. Experimental results for 29 highly imbalanced classification problems on the UCR datasets revealed that DM-SAX affords the optimal area under the curve (AUC) among competing time-series representation methods (SAX, extreme-SAX, overlap-SAX, and distance-based SAX). We statistically verified that performance improvements resulted in significant differences in the rankings. In addition, DM-SAX yielded the optimal AUC for real-world wire cutting and crimping process dataset. Meaningful data points such as outliers could be identified in a time-series outlier detection framework via the proposed method.
Design of a Fiber Bragg Grating Pressure Sensor Based on a Metal Diaphragm and Lever Structure
Authors: Liu Zhaoyue , Zeng Lina , Xu Ke , Li Zaijin , Chen Hao , Qiao Zhongliang , Qu Yi , Liu Guojun , Li Lin
Published: July 2022

In this paper, a pressure sensor based on a metal diaphragm and lever structure is designed, the sensing principle and mechanical structure of this sensor are analyzed and simulated, and its sensitization effectiveness and temperature compensation are verified. The maximum deflections of metal diaphragms of different sizes and materials were compared, and it was found that the square beryllium bronze diaphragm with a thickness of 1 mm and a side length of 20 mm had good elastic properties. The influence of the FBG in different positions of the lever on the center wavelength is analyzed. The sensitivity of the bare FBG is markedly improved under the influence of the two structures of the square elastic diaphragm and the lever, with a typical pressure sensitivity of 3.35 nm/MPa at 3 mm to the left of the lever center. The purpose of temperature compensation is achieved by adding another FBG that measures the temperature, and the sensing sensitivity can be tuned by adjusting the position of the FBG. It can meet the detection needs of a small range and high sensitivity.
Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data
Authors: Nieto-del-Amor Félix , Prats-Boluda Gema , Garcia-Casado Javier , Diaz-Martinez Alba , Diago-Almela Vicente Jose , Monfort-Ortiz Rogelio , Hao Dongmei , Ye-Lin Yiyao
Published: July 2022

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.
Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
Authors: Hussein Mahmoud , Mohammed Yehia Sayed , Galal Ahmed I. , Abd-Elrahman Emad , Zorkany Mohamed
Published: July 2022

The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.
Fog Droplet Collection by Corona Discharge in a Needle–Cylinder Electrostatic Precipitator with a Water Cooling System
Authors: Fu Hui , Xu Wenyi , Liu Zhen , Yan Keping
Published: July 2022

In this study, a needle–cylinder electrostatic precipitator with a water cooling system was designed to enhance the harvest of atmospheric water in wet flue gas. The effects of flow rate, temperature and particles on the collection of fog droplets were investigated. Meanwhile, the energy efficiency of water collection was analyzed at different voltages. The results show that the current decreases with the increase of air relative humidity under the same voltage, and the breakdown voltage increases obviously. Concurrently, by appropriately reducing the wet flue gas flow velocity, the residence time of fog droplets in the electric field can be increased, fully charging the droplets and improving the water collection efficiency. Moreover, experiments revealed that through decreasing the flue gas temperature, both the water collection rate and energy efficiency can be improved. In addition, the presence of particles in wet gas can improve the water collection rate by 5~8% at different discharge voltages. Finally, based on energy efficiency analysis, with the increase of voltage, although the water collection rate increased, the energy efficiency decreased.
Ionic Liquid-Assisted DLLME and SPME for the Determination of Contaminants in Food Samples
Authors: Nie Lirong , Cai Chaochao , Guo Runpeng , Yao Shun , Zhu Zhi , Hong Yanchen , Guo Dong
Published: July 2022

Developing effective and green methods for food analysis and separation has become an urgent issue regarding the ever-increasing concern of food quality and safety. Ionic liquids (ILs) are a new chemical medium and soft functional material developed under the framework of green chemistry and possess many unique properties, such as low melting points, low-to-negligible vapor pressures, excellent solubility, structural designability and high thermal stability. Combining ILs with extraction techniques not only takes advantage of ILs but also overcomes the disadvantages of traditional extraction methods. This subject has attracted intensive research efforts recently. Here, we present a brief review of the current research status and latest developments regarding the application of IL-assisted microextraction, including dispersive liquid–liquid microextraction (DLLME) and solid-phase microextraction (SPME), in food analysis and separation. The practical applications of ILs in determining toxic and harmful substances in food specimens with quite different natures are summarized and discussed. The critical function of ILs and the advantages of IL-based microextraction techniques over conventional extraction techniques are discussed in detail. Additionally, the recovery of ILs using different approaches is also presented to comply with green analytical chemistry requirements.
The Health and Healthcare Outcomes of Trans and/or Non-Binary Adults in England: Protocol for an Analysis of Responses to the 2021 GP Patient Survey
Authors: Saunders Catherine L. , Lund Jenny , Mason Amy M. , Roberts Meg , Smith Jack , Duschinsky Robbie
Published: July 2022

Background: The large-scale quantitative evidence base to understand and improve health and healthcare outcomes for people who are trans and/or non-binary is still developing, although what research there is suggests that risk of poor health is high, and experiences of healthcare services are often poor. In 2021 the GP Patient Survey, which is carried out annually to measure patient experience in primary care in England, added inclusive questions about gender identity and trans status for the first time. Methods: This protocol paper pre-registers the methods that we will use for this work for a secondary analysis of these data, including both the statistical analysis protocol and early patient and public involvement work, to answer the following three research questions: (1) What are the (a) demographic characteristics, (b) health conditions, and (c) healthcare experiences of trans and/or non-binary adults in England? (2) Was there any difference in whether people who are trans and/or non-binary had been asked to shield during the COVID-19 pandemic or not compared with all other survey responders? (3) Does the relationship between being trans and/or non-binary, and self-reported long-term mental health problems, autism and autistic spectrum disorder and learning disability vary by age, gender, ethnicity, deprivation, sexual orientation or region?