bluuebunny/crossref_metadata_2025 huggingface dataset:
UAV-Based Characterization of Tree-Attributes and Multispectral Indices in an Uneven-Aged Mixed Conifer-Broadleaf Forest
Authors: Vivar-Vivar Eduardo D. , Pompa-García Marín , Martínez-Rivas José A. , Mora-Tembre Luis A.
Published: June 2022

Unmanned aerial vehicles (UAVs) have contributed considerably to forest monitoring. However, gaps in the knowledge still remain, particularly for natural forests. Species diversity, stand heterogeneity, and the irregular spatial arrangement of trees provide unique opportunities to improve our perspective of forest stands and the ecological processes that occur therein. In this study, we calculated individual tree metrics, including several multispectral indices, in order to discern the spectral reflectance of a natural stand as a pioneer area in Mexican forests. Using data obtained by UAV DJI 4, and in the free software environments OpenDroneMap and QGIS, we calculated tree height, crown area, number of trees and multispectral indices. Digital photogrammetric procedures, such as the ForestTools, Structure from Motion and Multi-View Stereo algorithms, yielded results that improved stand mapping and the estimation of stand attributes. Automated tree detection and quantification were limited by the presence of overlapping crowns but compensated by the novel stand density mapping and estimates of crown attributes. Height estimation was in line with expectations (R2 = 0.91, RMSE = 0.36) and is therefore a useful parameter with which to complement forest inventories. The diverse spectral indices applied yielded differential results regarding the potential vegetation activity present and were found to be complementary to each other. However, seasonal monitoring and careful estimation of photosynthetic activity are recommended in order to determine the seasonality of plant response. This research contributes to the monitoring of natural forest stands and, coupled with accurate in situ measurements, could refine forest productivity parameters as a strategy for the validity of results. The metrics are reliable and rapid and could serve as model inputs in modern inventories. Nevertheless, increased efforts in the configuration of new technologies and algorithms are required, including full consideration of the costs implied by their adoption.
Assessment of Polar Ionospheric Observations by VIPIR/Dynasonde at Jang Bogo Station, Antarctica: Part 1—Ionospheric Densities
Authors: Kim Eunsol , Jee Geonhwa , Ham Young-Bae , Zabotin Nikolay , Lee Changsup , Kwon Hyuck-Jin , Hong Junseok , Kim Jeong-Han , Bullett Terence
Published: June 2022

Vertical incidence pulsed ionospheric radar (VIPIR) has been operated to observe the polar ionosphere with Dynasonde analysis software at Jang Bogo Station (JBS), Antarctica, since 2017. The JBS-VIPIR-Dynasonde (JVD) provides ionospheric parameters such as the height profile of electron density with NmF2 and hmF2, the ion drift, and the ionospheric tilt in the bottomside ionosphere. The JBS (74.6°S, 164.2°E) is located in the polar cap, cusp, or auroral region depending on the geomagnetic activity and local time. In the present study, an initial assessment of JVD ionospheric densities is attempted by the comparison with GPS TEC measurements which are simultaneously obtained from the GPS receiver at JBS during the solar minimum period from 2017 to 2019. It is found that the JVD NmF2 and bottomside TEC (bTEC) show a generally good correlation with GPS TEC for geomagnetically quiet conditions. However, the bTEC seems to be less correlated with the GPS TEC with slightly larger spreads especially during the daytime and in summer, which seems to be associated with the characteristics of the polar ionosphere such as energetic particle precipitations and large density irregularities. It is also found that the Dynasonde analysis seems to show some limitations to handle these characteristics of the polar ionosphere and needs to be improved to produce more accurate ionospheric density profiles especially during disturbed conditions.
Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests
Authors: Chen Qingda , Gao Tian , Zhu Jiaojun , Wu Fayun , Li Xiufen , Lu Deliang , Yu Fengyuan
Published: June 2022

Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synergetic benefits for ITS and tree height estimation by comparing different types of segmentation algorithms (i.e., watershed segmentation, point cloud segmentation and layer stacking segmentation) in the dense deciduous forests of Northeast China. Field validation was conducted in the four typical stands, including mixed broadleaved forest (MBF), Mongolian oak forest (MOF), mixed broadleaf-conifer forest (MBCF) and larch plantation forest (LPF). The results showed that the combination of FULD and the layer stacking segmentation (LSS) algorithm produced the highest accuracies across all forest types (F-score: 0.70 to 0.85). The FULD also showed a better performance on tree height estimation, with a root mean square error (RMSE) of 1.54 m at individual level. Compared with using the leaf-on dataset solely, the RMSE of tree height estimation was reduced by 0.22 to 0.27 m, and 12.3% more trees were correctly segmented by the FULD, which are mainly contributed by improved detection rate at nearly all DBH levels and by improved detection accuracy at low DBH levels. The improvements are attributed to abundant points from the bole to the treetop of FULD, as well as each layer point being included for segmentation by LSS algorithm. These findings provide useful insights to guide the application of FULD when more multi-temporal LiDAR data are available in future.
Pseudo-Spectral Time-Domain Method for Subsurface Imaging with the Lunar Regolith Penetrating Radar
Authors: Zhang Yuxian , Feng Naixing , Xie Guoda , Yang Lixia , Huang Zhixiang
Published: June 2022

Recently and successfully, the Chang’E-5 (CE-5) lander was launched on a mission to bring 1.731 kg of lunar soil back to Earth. To investigate various compositions of lunar regolith, we apply the Lunar Regolith Penetrating Radar (LRPR) as the same scientific payload installed on the CE-5 lander. Based on the high-accuracy imaging technique, we achieve subsurface imaging to process LRPR-measured data collected from the lunar-like exploration tests in our laboratory. In this paper, we propose the pseudo-spectral time-domain (PSTD) method as the underlying code to implement the reverse-time migration (RTM) method and restore the uncertain subsurface area. With the significant advantage of lower spatial sampling density, the PSTD-RTM method not only saves major computational resources, but also rapidly confirms the object prediction in the effective imaging area. To further analyze the LRPR measured data, we employ the spectrum window to remove high- and low-frequency noise, and thus improve imaging visibility to some extent. The imaging results in this paper can prove the reliability and efficiency of the PSTD-RTM method for subsurface discoveries in planetary exploration.
Do Seabirds Control Wind Drift during Their Migration across the Strait of Gibraltar? A Study Using Remote Tracking by Radar
Authors: Muñoz Arroyo Gonzalo , Mateos-Rodríguez María
Published: June 2022

This study presents data on the directional flying behaviour of the five most abundant seabird species migrating across the Strait of Gibraltar in relation to the wind, as observed from the north coast, based on radar tracking, and identified to species level by visual observations. A total of 318 seabird trajectories were analysed, illustrating the expected east–west or west–east movements in spring and autumn. We hypothesised that the seabirds that cross the Strait channel during their migrations would behave differently with respect to compensation for wind direction, depending on their flight styles, the migratory period, and the prevailing winds. In this regard, our results showed that flapping birds (Razorbill, Puffin, Northern Gannet, and Balearic shearwater) compensated for wind drift independently of the season and the predominant wind direction. This agrees with the theory that suggests that under moderate winds and whenever visual contact with the coastline is present (as in the case of our study), migrants should compensate for wind drift to avoid being drifted towards the coast, off their main direction of flight. However, Cory’s shearwater, an active gliding seabird with long, slender wings, showed an adaptive directional response to wind, allowing it to be drifted in spring when westerly tailwinds were prevalent, but compensated for wind in autumn, when both easterly and westerly winds were similarly frequent. This adaptive flight behaviour allows it to take advantage of the prevailing tailwinds in spring, gaining ground speed and saving energy during its passage through the Strait, while in autumn, more frequent headwind conditions and a more directional migration to the south may favour compensating for wind drift. Our results support the usefulness of bird radar as a remote tool for describing the pattern of animal movements in the marine environment, as well as their behavioural response to atmospheric conditions. These studies are particularly relevant in the current framework of climate change.
Theoretical Calculations of Directional Scattering Intensities of Small Nonspherical Ice Crystals: Implications for Forward Scattering Probes
Authors: Jang Seonghyeon , Kim Jeonggyu , McFarquhar Greg M. , Park Sungmin , Lee Seoung Soo , Jung Chang Hoon , Park Sang Seo , Cha Joo Wan , Lee Kyoungmi , Um Junshik
Published: June 2022

In situ aircraft measurements of the sizes and concentrations of liquid cloud droplets and ice crystals with maximum dimensions (Dmax) less than ~50 μm have been measured mainly using forward scattering probes over the past half century. The operating principle of forward scattering probes is that the measured intensity of light scattered by a cloud particle at specific forward scattering angles can be related to the size of that particle assuming the shape and thermodynamic phase of the target are known. Current forward-scattering probes assume spherical liquid cloud droplets and use the Lorenz–Mie theory to convert the scattered light to particle size. Uncertainties in sizing ice crystals using forward scattering probes are unavoidable since the single-scattering properties of ice crystals differ from those of spherical liquid cloud droplets and because their shapes can vary. In this study, directional scattering intensities of four different aspect ratios (ARs = 0.25, 0.50, 1.00, and 2.00) of hexagonal ice crystals with random orientations and of spherical liquid cloud droplets were calculated using the discrete dipole approximation (i.e., ADDA) and Lorenz–Mie code, respectively, to quantify the errors in sizing small ice crystals and cloud droplets using current forward scattering probes and to determine the ranges of optimal scattering angles that would be used in future forward scattering probes. The calculations showed that current forward scattering probes have average 5.0% and 17.4% errors in sizing liquid cloud droplets in the forward (4–12°) and backward (168–176°) direction, respectively. For measurements of hexagonal ice crystals, average sizing errors were 42.1% (23.9%) in the forward (backward) direction and depended on the ARs of hexagonal ice crystals, which are larger than those for liquid cloud droplets. A newly developed size conversion table based on the calculated single-scattering properties of hexagonal ice crystals using the ADDA reduced the sizing errors for the hexagonal ice crystals down to 14.2% (21.9%) in the forward (backward) direction. This study is a purely theoretical examination of the operating principle of forward scattering probes and there are several limitations, such as assumed hexagonal ice crystals with smooth surfaces and random orientations.
Urban Sprawl in Poland (2016–2021): Drivers, Wildcards, and Spatial Externalities
Authors: Werner Piotr A. , Kaleyeva Veranika , Porczek Mariusz
Published: June 2022

The COVID-19 lockdown in 2020–2021 and the refugee crisis in 2021–2022 were two new and unexpected social and political events in Poland in recent years. These “wildcards” will certainly have major effects on individuals and cities, both directly and indirectly, through the influence of “externalities.” The paper examines trends in the spatial development of Polish cities during the last five years (2016–2021), focusing on residential suburbanization and urban sprawl. The study aims to reveal the elements that determine the spatial scale of suburbanization, as well as “wildcards” that may have an indirect impact on the process but are difficult to quantify and include in spatial analysis. The use of location quotient (LQ) metrics, as well as a subset of the Global Human Settlement Layer in the spatial analysis allow for comparisons of locations with intensified urbanization throughout different periods, serving a task that is comparable to feature standardization from a time and space viewpoint. The analysis provides evidence of growing suburbanization surrounding major Polish cities from 2016 to 2021, while also exposing distinct elements of spatial development during a period that was marked by social and political stress (2021).
Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
Authors: Gonçalves Vinicius , Ribeiro Eduardo , Imai Nilton
Published: June 2022

Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the case for the restingas on Santa Catarina Island. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by Pinus sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in an RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for Pinus sp. Processing was conducted with open-source software to reduce operational costs.
UAV Remote Sensing for Detecting within-Field Spatial Variation of Winter Wheat Growth and Links to Soil Properties and Historical Management Practices. A Case Study on Belgian Loamy Soil
Authors: Goffart Dimitri , Dvorakova Klara , Crucil Giacomo , Curnel Yannick , Limbourg Quentin , Van Oost Kristof , Castaldi Fabio , Planchon Viviane , Goffart Jean-Pierre , van Wesemael Bas
Published: June 2022

Intra-field heterogeneity of soil properties, such as soil organic carbon (SOC), nitrogen (N), phosphorous (P), exchangeable cations, pH, or soil texture, is a function of complex interactions between biological factors, physical factors, and historic agricultural management. Mapping the crop growth and final yield heterogeneity and quantifying their link with soil properties can contribute to an optimization of amendment/fertilizer application and crop yield in a management variable zones (MVZ) approach. To this end, we studied a field of 17 ha consisting of four former fields that were merged in early 2017 and cropped with winter wheat in 2018. Historical management practices data were collected. The topsoil characteristics were analyzed by grid-based sampling and kriged to create maps. We tested the capacity of a multispectral MicaSense® RedEdge-MTM camera sensor embedded on an unmanned aerial vehicle (UAV) to map in-season growth of winter wheat. Relating several vegetation indices (VIs) to the plant area index (PAI) measured in the field highlighted the red-edge NDVI (RENDVI) as the most suitable to follow the crop growth throughout the growing season. The georeferenced final grain yield of the winter wheat was measured by a combine harvester. The spatial patterns in RENDVI at three phenological stages were mapped and analyzed together with the yield map. For each of these images a conditional inference forest (CI-forest) algorithm was used to identify the soil properties significantly influencing these spatial patterns. Historical management practices of the four former fields have induced significant heterogeneity in soil properties and crop growth. The spatial patterns of RENDVI are rather constant over time and their Spearman rank correlation with yield is similar along the growing season (r ≃ 0.7). Soil properties explain between 87% (mid-March) to 78% (mid-May) of the variance in RENDVI throughout the growing season, as well as 66% of the variance in yield. The pH and exchangeable K are the most significant factors explaining from 15 to 26% of the variance in crop growth. The methodology proposed in this paper to quantify the importance of soil parameters based on the CI-forest algorithm can contribute to a better management of amendment/fertilizer inputs by stressing the most important parameters to take into consideration for site-specific management. We also showed that heterogeneity induced by the soil properties can be described by a crop map early in the season and that this crop map can be used to optimize soil sampling and thus amendment/fertilizer management.
An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China
Authors: He Sicong , Dong Heng , Zhang Zili , Yuan Yanbin
Published: June 2022

NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.
Deriving 3-D Surface Deformation Time Series with Strain Model and Kalman Filter from GNSS and InSAR Data
Authors: Ji Panfeng , Lv Xiaolei , Wang Rui
Published: June 2022

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.
Spatiotemporal Variation of Snow Cover Frequency in the Qilian Mountains (Northwestern China) during 2000–2020 and Associated Circulation Mechanisms
Authors: Du Wentao , Kang Shichang , Qian Libing , Jiang Youyan , Sun Wenxuan , Chen Jizu , Xu Zhilong , Sun Weijun , Qin Xiang , Chai Xian
Published: June 2022

Linking snow cover frequency (SCF) and atmospheric circulation is vital for comprehension of hemispheric-scale change mechanisms and for accurate forecasting. This study combined MODIS imagery with meteorological observations to investigate the variation of annual SCFs in the Qilian Mountains. Results indicated that more than 80% of annual SCF is distributed at high elevations and mostly on northern slopes, and that SCF is greater in the west than in the east. Abrupt change in the increase in annual SCF was not detected; however, significant (0.05 confidence level) variation with quasi-3-year and quasi-5-year periods indicated potential connection with monsoons. Topographically, SCF increased at high elevations and decreased in valleys. Moreover, SCF increased significantly with a rise in slope below 23° and then decreased between 23° and 45°, and it decreased with a change in aspect from 70° to 200° and then increased from 200° to 310°. Annual SCF variation in the Qilian Mountains is dominated by precipitation rather than by temperature. In the years with high SCFs, southeasterly winds associated with an anticyclone over southeastern China and southwesterly winds associated with the cyclone over the Iranian Plateau brought warm moisture across northwestern China, favoring snowfall in the Qilian Mountains. Meanwhile, cold moisture outbreaks from the Arctic into the mid-latitudes are conducive to maintaining snow cover. However, in the years with low SCFs, the cold air might be difficultly transporting out of the Arctic region due to the strengthening polar vortex. Moreover, the water vapor was less than that of the mean state and divergence over the Qilian Mountains, which difficultly conduced snowfall over the Qilian Mountains.
Efficient Detection of Earthquake−Triggered Landslides Based on U−Net++: An Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw = 6.6 Earthquake
Authors: Yang Zhiqiang , Xu Chong
Published: June 2022

Efficient detection of earthquake−triggered landslides is crucial for emergency response and risk assessment. With the development of multi−source remote sensing images, artificial intelligence has gradually become a powerful landslide detection method for similar tasks, aiming to mitigate time−consuming problems and meet emergency requirements. In this study, a relatively new deep learning (DL) network, called U−Net++, was designed to detect landslides for regions affected by the Iburi, Japan Mw = 6.6 earthquake, with only small training samples. For feature extraction, ResNet50 was selected as the feature extraction layer, and transfer learning was adopted to introduce the pre−trained weights for accelerating the model convergence. To prove the feasibility and validity of the proposed model, the random forest algorithm (RF) was selected as the benchmark, and the F1−score, Kappa coefficient, and IoU (Intersection of Union) were chosen to quantitatively evaluate the model’s performance. In addition, the proposed model was trained with different sample sizes (256,512) and network depths (3,4,5), respectively, to analyze their impacts on performance. The results showed that both models detected the majority of landslides, while the proposed model obtained the highest metric value (F1−score = 0.7580, Kappa = 0.7441, and IoU = 0.6104) and was capable of resisting the noise. In addition, the proposed model trained with sample size 256 possessed optimal performance, proving that the size is a non−negligible parameter in U−Net++, and it was found that the U−Net++ trained with shallower layer 3 yielded better results than that with the standard layer 5. Finally, the outstanding performance of the proposed model on a public landslide dataset demonstrated the generalization of U−Net++.
The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning
Authors: Yang Jiachen , Ni Jingfei , Li Yang , Wen Jiabao , Chen Desheng
Published: June 2022

Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.
Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine
Authors: Montoya-Santiyanes L. A. , Rodríguez-Abreo Omar , Rodríguez Eloy E. , Rodríguez-Reséndiz Juvenal
Published: June 2022

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 ∘C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.
Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space
Authors: Dudczyk Janusz , Czyba Roman , Skrzypczyk Krzysztof
Published: June 2022

The paper focuses on the problem of detecting unmanned aerial vehicles that violate restricted airspace. The main purpose of the research is to develop an algorithm that enables the detection, identification and recognition in 3D space of a UAV violating restricted airspace. The proposed method consists of multi-sensory data fusion and is based on conditional complementary filtration and multi-stage clustering. On the basis of the review of the available UAV detection technologies, three sensory systems classified into the groups of passive and active methods are selected. The UAV detection algorithm is developed on the basis of data collected during field tests under real conditions, from three sensors: a radio system, an ADS-B transponder and a radar equipped with four antenna arrays. The efficiency of the proposed solution was tested on the basis of rapid prototyping in the MATLAB simulation environment with the use of data from the real sensory system obtained during controlled UAV flights. The obtained results of UAV detections confirmed the effectiveness of the proposed method and theoretical expectations.
Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey
Authors: Zhang Tianhao , Aftab Waqas , Mihaylova Lyudmila , Langran-Wheeler Christian , Rigby Samuel , Fletcher David , Maddock Steve , Bosworth Garry
Published: June 2022

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.
Automatic Rate and Rate-Integrating Mode-Switchable Axisymmetric Gyroscope
Authors: Xuan Lin , Lu Mingyang , Liu Jing , Guo Shuwen , Xu Dacheng
Published: June 2022

This paper describes a novel rate and rate-integrating mode-switchable axisymmetric gyroscope. A precession angle tracking algorithm is developed to enable the gyro to switch automatically between rate and rate-integrating modes at preset rate points through a digital control system within the gyro. We also propose a vibrating amplitude control method for the rate-integrating mode that directly extracts the angular rate output to ensure switching stability. In rate mode, the bias instability and angle random walk of the gyro reach 0.106°/h and 0.011°/√h, respectively. Additionally, an input range of over ±5000°/s is measured in rate-integrating mode. The scale factor nonlinearity reaches approximately 116 ppm over the full-scale range. The control system implements effective steering control of the gyroscope, with a switching delay of 10 ms from rate mode to rate-integrating mode and 100 ms from rate-integrating to rate mode. The proposed system actualizes a new type of gyroscope with high accuracy and a wide input range, which combines the benefits of rate and rate-integrating modes.
Few-Shot Emergency Siren Detection
Authors: Cantarini Michela , Gabrielli Leonardo , Squartini Stefano
Published: June 2022

It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This paper proposes a workflow based on few-shot metric learning for emergency siren detection performed in steps: prototypical networks are trained on publicly available sources or synthetic data in multiple combinations, and at inference time, the best knowledge learned in associating a sound with its class representation is transferred to identify ambulance sirens, given only a few instances for the prototype computation. Performance is evaluated on siren recordings acquired by sensors inside and outside the cabin of an equipped car, investigating the contribution of filtering techniques for background noise reduction. The results show the effectiveness of the proposed approach, achieving AUPRC scores equal to 0.86 and 0.91 in unfiltered and filtered conditions, respectively, outperforming a convolutional baseline model with and without fine-tuning for domain adaptation. Extensive experiments conducted on several recording sensor placements prove that few-shot learning is a reliable technique even in real-world scenarios and gives valuable insights for developing an in-car emergency vehicle detection system.
Treatment Strategy Research on a Squirrel-Cage Induction Motor with Broken Rotor Bar Faults
Authors: Wu Yucai , Sun Shuqiong , An Qingfei , Lie Xu
Published: June 2022

Squirrel-cage induction motors are increasingly displaying a broken rotor bar fault, which represents both a technical problem and an economic problem. After confirming that the broken rotor bars do not affect the normal start-up and basic working performance of the squirrel-cage induction motor, this paper focuses on the loss and efficiency changes of the motor brought about by the broken rotor bar fault. Using finite element simulation and experimentation, various losses like stator copper loss, iron loss, rotor copper loss, mechanical loss and additional losses, total loss and efficiency are obtained. By combining price and cost factors, the cost-effective measures that can be taken after the occurrence of different degrees of broken bars are evaluated here to provide guidance for correctly handling this problem.
Model-Free High-Order Sliding Mode Controller for Station-Keeping of an Autonomous Underwater Vehicle in Manipulation Task: Simulations and Experimental Validation
Authors: González-García Josué , Gómez-Espinosa Alfonso , García-Valdovinos Luis Govinda , Salgado-Jiménez Tomás , Cuan-Urquizo Enrique , Cabello Jesús Arturo Escobedo
Published: June 2022

The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in the literature that exhibits robust performance against external disturbances. However, they are either model-based or require an observer to deal with the disturbances. Moreover, most of them have been evaluated only by numerical simulations. In this paper, the feasibility of a model-free high-order sliding mode controller for the station-keeping problem is validated. The proposed controller was evaluated through numerical simulations and experiments in a semi-Olympic swimming pool, introducing external disturbances that remained unknown to the controller. Results have shown robust performance in terms of the root mean square error (RMSE) of the vehicle position. The simulation resulted in the outstanding station-keeping of the BlueROV2 vehicle, as the tracking errors were kept to zero throughout the simulation, even in the presence of strong ocean currents. The experimental results demonstrated the robustness of the controller, which was able to maintain the RMSE in the range of 1–4 cm for the depth of the vehicle, outperforming related work, even when the disturbance was large enough to produce thruster saturation.
Upgrading the Power Grid Functionalities with Broadband Power Line Communications: Basis, Applications, Current Trends and Challenges
Authors: González-Ramos Jon , Uribe-Pérez Noelia , Sendin Alberto , Gil David , de la Vega David , Fernández Igor , Núñez Ignacio Javier
Published: June 2022

This article reviews the basis and the main aspects of the recent evolution of Broadband Power Line Communications (BB-PLC or, more commonly, BPL) technologies. The article starts describing the organizations and alliances involved in the development and evolution of BPL systems, as well as the standardization institutions working on PLC technologies. Then, a short description of the technical foundation of the recent proposed technologies and a comparison of the main specifications are presented; the regulatory activities related to the limits of emissions and immunity are also addressed. Finally, some representative applications of BPL and some selected use cases enabled by these technologies are summarized, together with the main challenges to be faced.
Simulation and Experiment Analysis of Temperature Field of Magnetic Suspension Support Based on FBG
Authors: Wu Huachun , Huang Cong , Cui Ruifang , Zhou Jian
Published: June 2022

Temperature rise is an important factor limiting the development of magnetic suspension support technology. Traditional temperature sensors such as thermocouples are complicated and vulnerable to electromagnetic interference due to their point contact temperature measurement methods. In this paper, the equivalent model of magnetic suspension support is established, and the temperature field is simulated and analyzed by magnetic thermal coupling calculation in ANSYS software. Then, a quasi-distributed temperature measurement system is designed, and the FBG temperature sensor is introduced to measure the temperature of the magnetic suspension support system by “one-line and multi-point”. By comparing the analysis experiments and simulations, the equivalent accuracy of the simulation model and the FBG temperature sensor can accurately measure the temperature of the magnetic suspension support.
Strategies to Enhance the Data Density in Synchronous Electromagnetic Encoders
Authors: Paredes Ferran , Karami-Horestani Amirhossein , Martín Ferran
Published: June 2022

In this paper, we report two different strategies to enhance the data density in electromagnetic encoders with synchronous reading. One approach uses a periodic chain of rectangular metallic patches (clock chain) that determines the encoder velocity, and dictates the instants of time for retrieving the bits of the identification (ID) code. However, contrary to previous electromagnetic encoders, the ID is inferred at both the rising and the falling edges of the clock signal generated by the clock chain. Moreover, the bits of information are not given by the presence or absence of metallic patches at their predefined positions in the so-called ID code chain. With this novel encoding system, a bit state corresponding to a certain instant of time is identical to the previous bit state, unless there is a change in the envelope function of the ID code signal, determined by the additional non-periodic ID code chain. The other encoding strategy utilizes a single chain of C-shaped resonators, and encoding is achieved by considering four different resonator dimensions, corresponding to four states and, hence, to two bits per resonator of the chain. Thus, with these two strategies, the data density is twice the one achievable in previously reported synchronous electromagnetic encoders.
Deep Learning Approach to Impact Classification in Sensorized Panels Using Self-Attention
Authors: Karmakov Stefan , Aliabadi M. H. Ferri
Published: June 2022

This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network architecture, the Transformer has the potential to greatly improve the speed and robustness of impact detection. This paper investigates the suitability of this new network, confronting the advantages and disadvantages offered by the Transformer and a well-known and established neural network for impact detection, the Convolutional Neural Network (CNN). The comparison is undertaken on performance, scalability, and computational time. The inputs to the networks were created using a data transformation technique, which transforms the raw time series data collected from the network of piezoelectric sensors, installed on a composite panel, through the use of Fourier Transform. It is demonstrated that the Transformer method reduces the computational complexity of the impact detection significantly, while achieving excellent prediction results.
A Novel Microwave Resonant Sensor for Measuring Cancer Cell Line Aggressiveness
Authors: D’Alvia Livio , Carraro Serena , Peruzzi Barbara , Urciuoli Enrica , Palla Luigi , Del Prete Zaccaria , Rizzuto Emanuele
Published: June 2022

The measurement of biological tissues’ dielectric properties plays a crucial role in determining the state of health, and recent studies have reported microwave biosensing to be an innovative method with great potential in this field. Research has been conducted from the tissue level to the cellular level but, to date, cellular adhesion has never been considered. In addition, conventional systems for diagnosing tumor aggressiveness, such as a biopsy, are rather expensive and invasive. Here, we propose a novel microwave approach for biosensing adherent cancer cells with different malignancy degrees. A circular patch resonator was designed adjusting its structure to a standard Petri dish and a network analyzer was employed. Then, the resonator was realized and used to test two groups of different cancer cell lines, based on various tumor types and aggressiveness: low- and high-aggressive osteosarcoma cell lines (SaOS-2 and 143B, respectively), and low- and high-aggressive breast cancer cell lines (MCF-7 and MDA-MB-231, respectively). The experimental results showed that the sensitivity of the sensor was high, in particular when measuring the resonant frequency. Finally, the sensor showed a good ability to distinguish low-metastatic and high-metastatic cells, paving the way to the development of more complex measurement systems for noninvasive tissue diagnosis.
Hierarchical Novelty Detection for Traffic Sign Recognition
Authors: Ruiz Idoia , Serrat Joan
Published: June 2022

Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy.
Inspection of Underwater Hull Surface Condition Using the Soft Voting Ensemble of the Transfer-Learned Models
Authors: Kim Byung Chul , Kim Hoe Chang , Han Sungho , Park Dong Kyou
Published: June 2022

In this study, we propose a method for inspecting the condition of hull surfaces using underwater images acquired from the camera of a remotely controlled underwater vehicle (ROUV). To this end, a soft voting ensemble classifier comprising six well-known convolutional neural network models was used. Using the transfer learning technique, the images of the hull surfaces were used to retrain the six models. The proposed method exhibited an accuracy of 98.13%, a precision of 98.73%, a recall of 97.50%, and an F1-score of 98.11% for the classification of the test set. Furthermore, the time taken for the classification of one image was verified to be approximately 56.25 ms, which is applicable to ROUVs that require real-time inspection.
Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks
Authors: Popescu Dan , El-khatib Mohamed , Ichim Loretta
Published: June 2022

Skin lesion detection and analysis are very important because skin cancer must be found in its early stages and treated immediately. Once installed in the body, skin cancer can easily spread to other body parts. Early detection would represent a very important aspect since, by ensuring correct treatment, it could be curable. Thus, by taking all these issues into consideration, there is a need for highly accurate computer-aided systems to assist medical staff in the early detection of malignant skin lesions. In this paper, we propose a skin lesion classification system based on deep learning techniques and collective intelligence, which involves multiple convolutional neural networks, trained on the HAM10000 dataset, which is able to predict seven skin lesions including melanoma. The convolutional neural networks experimentally chosen, considering their performances, to implement the collective intelligence-based system for this purpose are: AlexNet, GoogLeNet, GoogLeNet-Places365, MobileNet-V2, Xception, ResNet-50, ResNet-101, InceptionResNet-V2 and DenseNet201. We then analyzed the performances of each of the above-mentioned convolutional neural networks to obtain a weight matrix whose elements are weights associated with neural networks and classes of lesions. Based on this matrix, a new decision matrix was used to build the multi-network ensemble system (Collective Intelligence-based System), combining each of individual neural network decision into a decision fusion module (Collective Decision Block). This module would then have the responsibility to take a final and more accurate decision related to the prediction based on the associated weights of each network output. The validation accuracy of the proposed system is about 3 percent better than that of the best performing individual network.