bluuebunny/crossref_metadata_2025 huggingface dataset:
Convolutional Neural Network Chemometrics for Rock Identification Based on Laser-Induced Breakdown Spectroscopy Data in Tianwen-1 Pre-Flight Experiments
Authors: Yang Fan , Xu Weiming , Cui Zhicheng , Liu Xiangfeng , Xu Xuesen , Jia Liangchen , Chen Yuwei , Shu Rong , Li Luning
Published: October 2022

Laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics is an efficient method for rock identification and classification, which has considerable potential in planetary geology. A great challenge facing the LIBS community is the difficulty to accurately discriminate rocks with close chemical compositions. A convolutional neural network (CNN) model has been designed in this study to identify twelve types of rock, among which some rocks have similar compositions. Both the training set and the testing set are constructed based on the LIBS spectra acquired by Mars Surface Composition Detector (MarSCoDe) for China’s Tianwen-1 Mars exploration mission. All the spectra were collected from dedicated rock pellet samples, which were placed in a simulated Martian atmospheric environment. The classification performance of the CNN has been compared with that of three alternative machine learning algorithms, i.e., logistic regression (LR), support vector machine (SVM), and linear discriminant analysis (LDA). Among the four methods, it is on the CNN model that the highest classification correct rate has been obtained, as assessed by precision score, recall score, and the harmonic mean of precision and recall. Furthermore, the classification accuracy is inspected more quantitatively via Brier score, and the CNN is still the best performing model. The results demonstrate that the CNN-based chemometrics are an efficient tool for rock identification with LIBS spectra collected in a simulated Martian environment. Despite the relatively small sample set, this study implies that CNN-supported LIBS classification is a promising analytical technique for Tianwen-1 Mars mission and more planetary explorations in the future.
Evaluation of Air-Cavities behind Concrete Tunnel Linings Using GPR Measurements
Authors: Harseno Regidestyoko Wasistha , Lee Sung-Jin , Kee Seong-Hoon , Kim Sungmo
Published: October 2022

The primary objectives of this study are to investigate the interference of ground penetrating radar (GPR) signals with air-cavities behind concrete linings subjected to three experimental parameters (GPR antenna frequency, thickness of concrete lining, and thickness of air-cavities), and their effects on the evaluation of important engineering parameters (thickness of concrete, and length and thickness of air-cavities) for tunnel structure inspection using GPR. For these purposes, first, a concrete model specimen is fabricated in the laboratory, which simulates air-cavity defects with various thicknesses (0 mm, 30 mm, 100 mm, 200 mm, and 300 mm) behind concrete with four different thicknesses (100 mm, 200 mm, 300 mm, and 400 mm) in tunnel structures. Second, a series of GPR survey is performed on the fabricated concrete model specimen using three ground-coupled GPR antennas with nominal center frequencies of 400 MHz, 900 MHz, and 1600 MHz. Third, a practical signal interpretation method is proposed to evaluate air-cavities in concrete tunnel linings subjected to the three experimental parameters. It is demonstrated that the cross-correlation analysis is effective for identifying air-cavities behind concrete, and for characterizing the length of the air-cavities. In addition, the thicknesses of air-cavities are estimated by a new approach in this study, measuring the apparent time delay of partially overlapped electromagnetic wave reflections within air-cavities. The results in this study provide a fundamental basis to improve GPR signal interpretation techniques for the evaluation of air-cavities behind concrete tunnel linings.
Assessing Through-Water Structure-from-Motion Photogrammetry in Gravel-Bed Rivers under Controlled Conditions
Authors: Zhang Chendi , Sun Ao’ran , Hassan Marwan A. , Qin Chao
Published: October 2022

Structure-from-Motion (SfM) photogrammetry has become a popular solution for three-dimensional topographic data collection in geosciences and can be used for measuring submerged bed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for gravel-bed surfaces, which limits its application to the morphodynamics of gravel-bed rivers in both field investigations and flume experiments. In order to evaluate the influence of bed texture, flow rate, ground control point (GCP) layout, and refraction correction (RC) on the measurement quality of through-water SfM photogrammetry, we conducted a series of experiments in a 70 m-long and 7 m-wide flume with a straight artificial channel. Bed surfaces with strongly contrasting textures in two 4 m-long reaches were measured under five constant flow regimes with three GCP layouts, including both dry and underwater GCPs. All the submerged surface models with/without RC were compared with the corresponding dry bed surfaces to quantify their elevation errors. The results illustrated that the poorly sorted gravel-bed led to the better performance of through-water SfM photogrammetry than the bed covered by fine sand. Fine sediment transport caused significant elevation errors, while the static sand dunes and grain clusters did not lead to noticeable errors in the corrected models with dry GCPs. The elevation errors of the submerged models linearly increased with water depth for all the tested conditions of bed textures, GCP layouts, and discharges in the uncorrected models, but the slopes of the increasing relations varied with texture. The use of underwater GCPs made significant improvements to the performance of direct through-water SfM photogrammetry, but counteracted with RC. The corrected models with dry GCPs outperformed the uncorrected ones with underwater GCPs, which could still be used to correct the underestimation in surface elevation caused by RC. Based on the new findings, recommendations for through-water SfM photogrammetry in measuring submerged gravel-bed surfaces were provided.
Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China
Authors: Zhang Ying , Hou Jinliang , Huang Chunlin
Published: October 2022

Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm).
PU-WGCN: Point Cloud Upsampling Using Weighted Graph Convolutional Networks
Authors: Gu Fan , Zhang Changlun , Wang Hengyou , He Qiang , Huo Lianzhi
Published: October 2022

Point clouds are sparse and unevenly distributed, which makes upsampling a challenging task. The current upsampling algorithm encounters the problem that neighboring nodes are similar in terms of specific features, which tends to produce hole overfilling and boundary blurring. The local feature variability of the point cloud is small, and the aggregated neighborhood feature operation treats all neighboring nodes equally. These two reasons make the local node features too similar. We designed the graph feature enhancement module to reduce the similarity between different nodes as a solution to the problem. In addition, we calculate the feature similarity between neighboring nodes based on both spatial information and features of the point cloud, which is used as the boundary weight of the point cloud graph to solve the problem of boundary blurring. We fuse the graph feature enhancement module with the boundary information weighting module to form the weighted graph convolutional networks (WGCN). Finally, we combine the WGCN module with the upsampling module to form a point cloud upsampling network named PU-WGCN. Compared with other upsampling networks, the experimental results show that PU-WGCN can solve the problems of hole overfilling and boundary blurring and improve the upsampling accuracy.
Impact of Hurricane Harvey on the Upper Texas Coast: Using Airborne Lidar Data Sets with UAV-Derived Topographic Data to Monitor Change and Track Recovery
Authors: Rojas Sara S. , Khan Shuhab D. , Shahtakhtinskiy Aydin
Published: October 2022

The frequency of hurricanes and tropical storms is increasing; for example, there were a record-breaking 31 named storms during the 2020 Atlantic hurricane season. Texas has historically been susceptible to hurricanes and tropical storms; however, Hurricane Harvey in 2017 was the highest category storm event to cross Texas since 2000. Our regional change analysis used 2016 and 2018 lidar-derived elevation models with 1 m spatial resolution to determine above-sea level changes due to Hurricane Harvey. The upper Texas coast experienced shoreline erosion, with local depositional events occurring on the southeastern sides of jetties and groins. Incidents of dune washout and overwash fans were present along the barrier islands of the upper Texas coast, as well as erosion to foredune complexes and a decrease in dune heights. As of March 2018, recovery is visible through berm buildup and backbeach aggradation. Our multiyear analysis (above sea level) of four sites within Galveston and Follett’s Islands determined the immediate impact of Harvey (2016–2017) and followed recovery until March 2019. The multiyear analysis determined that all four sites experienced varying levels of recovery by 2018. UAV surveys conducted in 2022 showed potential in acquiring topographic data for comparison with 2019 beach-dune conditions.
Sea Surface Salinity Inversion Model for Changjiang Estuary and Adjoining Sea Area with SMAP and MODIS Data Based on Machine Learning and Preliminary Application
Authors: Zhang Xiaoyu , Wu Mingfei , Han Wencong , Bi Lei , Shang Yongheng , Yang Yingchun
Published: October 2022

Sea surface salinity (SSS) is one of the most important basic parameters for studying the oceanographic processes and is of great significance in identifying oceanic currents. However, for a long time, the salinity observation in the estuary and coastal waters has not been well resolved due to the technology limitation. In this study, the SSS inversion models for the Changjiang Estuary and the adjacent sea waters were established based on machine learning methods, using SMAP (Soil Moisture Active and Passive) salinity data combined with the specific bands and bands ratios of MODIS (Moderate Resolution Imaging Spectroradiometer). The performance of the three machine learning methods (Random Forest, Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Automatic Machine Learning (TPOT)) are compared with accuracy verification by the in-situ measured SSS. Random Forest is proven to be effective for the SSS inversion in flood season, whereas TPOP performs the best for the dry season. The machine learning-based models effectively solve the problem of insufficient time span of SSS observation from salinity satellites. At the same time, an empirical algorithm was established for the SSS inversion for the sea areas with low salinity (<30 psu) where the machine learning based model fails with great errors. The average deviation of the complex SSS inversion models is −0.86 psu, validated with Copernicus Global Ocean Reanalysis Data. The long term series SSS dataset of March and August from 2003 to 2020 was then constructed to observe the salinity distribution characteristics of the flood season and the dry season, respectively. It is indicated that the distribution pattern of CDW can be divided into three categories: northeast-oriented expansion pattern, multi direction isotropic expansion pattern, and a turn pattern of which CDW shows changing direction, namely the northeast-southeast expansion pattern. The pattern of CDW expansion is indicated to be the comprehensive effect of the interaction of different currents. In addition, it is noteworthy that CDW shows increasing expansion with decreasing SSS in the front plume, especially in the flood season. This study not only gives a feasible solution for effective SSS observation, but also provides a dataset of basic oceanographic parameters for studying the coastal biogeochemical processes, evolution of land-sea interaction, and changing trend of material and energy transport by the CDW in the west Pacific boundary.
Three-Dimensional Vulnerability Assessment of Peanut (Arachis hypogaea) Based on Comprehensive Drought Index and Vulnerability Surface: A Case Study of Shandong Province, China
Authors: Wei Sicheng , Yang Yueting , Li Kaiwei , Guo Ying , Zhang Jiquan
Published: October 2022

Agricultural drought is a major problem facing China’s agricultural production. In this study, the cash crop ‘peanut’ was used as an example to explore vulnerability. Through the atmosphere–plant–soil continuum system, a single index that could represent different types of droughts affecting peanuts was selected and weighted using the CRITIC weighting method to construct a multi-source data fusion drought index (MFDI). Then, Pearson correlation analysis between the comprehensive drought index and relative meteorological yield and the Mann–Kendall trend test for different growth periods of peanuts were used to verify MFDI and analyze the variation over time. A three-dimensional vulnerability assessment method of drought intensity–drought duration–yield reduction rate was established based on the run theory and trend surface analysis. The results show that the constructed multi-source data fusion drought index (MFDI) can more accurately characterize the actual drought conditions of peanuts in Shandong Province. The MFDI results showed that the drought severity in the coastal areas of the study area decreased with the growth and development of peanuts, while the drought became more severe in the western and northern parts during the late growth period of peanuts. The vulnerability surface of the drought intensity–drought duration–yield reduction rate showed that when the drought intensity was < 0.8 and the duration was < 3.5 months, the vulnerability of peanut crops was low, and then with the increase in drought intensity or duration, the vulnerability increased. The impact of drought duration cannot be ignored. In contrast to traditional vulnerability assessment methods, this study established a three-dimensional vulnerability surface, which provides a new approach for agricultural drought vulnerability assessment. The research results are helpful for a deeper understanding of the relationship between drought and crop vulnerability and provide scientific support for local governments in formulating disaster prevention and mitigation policies.
Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau
Authors: Feng Senyao , Li Wenlong , Xu Jing , Liang Tiangang , Ma Xuanlong , Wang Wenying , Yu Hongyan
Published: October 2022

The upper Yellow River basin over the Tibetan Plateau (TP) is an important ecological barrier in northwestern China. Effective LULC products that enable the monitoring of changes in regional ecosystem types are of great importance for their environmental protection and macro-control. Here, we combined an 18-class LULC classification scheme based on ecosystem types with Sentinel-2 imagery, the Google Earth Engine (GEE) platform, and the random forest method to present new LULC products with a spatial resolution of 10 m in 2018 and 2020 for the upper Yellow River Basin over the TP and conducted monitoring of changes in ecosystem types. The results indicated that: (1) In 2018 and 2020, the overall accuracy (OA) of LULC maps ranged between 87.45% and 93.02%. (2) Grassland was the main LULC first-degree class in the research area, followed by wetland and water bodies and barren land. For the LULC second-degree class, the main LULC was grassland, followed by broadleaf shrub and marsh. (3) In the first-degree class of changes in ecosystem types, the largest area of progressive succession (positive) was grassland–shrubland (451.13 km2), whereas the largest area of retrogressive succession (negative) was grassland–barren (395.91 km2). In the second-degree class, the largest areas of progressive succession (positive) were grassland–broadleaf shrub (344.68 km2) and desert land–grassland (302.02 km2), whereas the largest areas of retrogressive succession (negative) were broadleaf shrubland–grassland (309.08 km2) and grassland–bare rock (193.89 km2). The northern and southwestern parts of the study area showed a trend towards positive succession, whereas the south-central Huangnan, northeastern Gannan, and central Aba Prefectures showed signs of retrogressive succession in their changes in ecosystem types. The purpose of this study was to provide basis data for basin-scale ecosystem monitoring and analysis with more detailed categories and reliable accuracy.
Investigation of Precise Single-Frequency Time and Frequency Transfer with Galileo E1/E5a/E5b/E5/E6 Observations
Authors: Xu Wei , Yan Chao , Chen Jian
Published: October 2022

With the rapid upgrade of global navigation satellite system (GNSS) single-frequency (SF) receivers and the increasing market demand for low-cost hardware, SF precise point positioning (PPP) technology has been widely applied in the time and frequency field. The five-frequency signals provided by the whole constellation of Galileo bring more opportunities for the application of SF PPP in time and frequency transfer. In this contribution, using Galileo’s multi-frequency observations, three SF PPP time and frequency transfer models, i.e., the un-combined (UC) model, the ionosphere-free-half (IFH) model, and the ionosphere-weighted constraints (IWCs) model are established. SF PPP time and frequency transfer performance with Galileo E1, E5a, E5b, E5, and E6 multi-frequency observations is evaluated using four links (947.7 km to 1331.6 km) with five external high-precision atomic clocks stations. The results show that the time and frequency transfer performance of SF-UC and SF-IWC is better than that of SF-IFH, and the timing accuracy of SF-UC and SF-IWC is similar. SF PPP time and transfer performance with E5, E5a, E5b, and E6 signals is improved compared with traditional E1 signal. Among them, the frequency stability of E5 improves the most (about 58%), and that of E6 improves the least (about 14%). In addition, the difference in frequency stability between SF and double-frequency (DF) PPP decreases gradually with an increase in average time, and the frequency stability difference between SF and DF PPP can reach 2 × 10−16 in 120,000 s, indicating that SF PPP has the potential to achieve DF PPP frequency stability. Considering the possible frequency data loss during actual observation, the cost of the GNSS SF receiver, and the advantages of Galileo multi-frequency observations, SF PPP can also meet the long-time time and frequency transfer requirements, and the SF-IWC model based on Galileo E5 observations is more recommended.
K-Means Clustering and Bidirectional Long- and Short-Term Neural Networks for Predicting Performance Degradation Trends of Built-In Relays in Meters
Authors: Chen Jiayan , Zhong Chaochun , Chen Jing , Han Yuanxun , Zhou Juan , Wang Limin
Published: October 2022

The built-in relay in a meter is a key control component of a smart meter, and its reliability determines whether the user can use electricity safely and smoothly. In this paper, the degradation characteristics of the arc-burning energy are enhanced by the method of K-means clustering to replace degradation data, such as the overtravel time, release time, and other data. In existing methods, the meter needs to be disassembled to describe the degradation trend of the meter relay. The proposed method is combined with a bidirectional long short-term memory (Bi-LSTM) neural network to predict the degradation trend of the relay’s performance. In this paper, K-means clustering is used to enhance the extraction of arc energy data features, and then the arc energy data obtained from the reliability lifetime test is assessed to predict the degradation trend of the meter relay by means of a bidirectional LSTM.
The Effect of Rare Earths on the Response of Photo UV-Activate ZnO Gas Sensors
Authors: Sayago Isabel , Santos José Pedro , Sánchez-Vicente Carlos
Published: October 2022

In this work, ZnO nanoparticle resistive sensors decorated with rare earths (REs; including Er, Tb, Eu and Dy) were used at room temperature to detect atmospheric pollutant gases (NO2, CO and CH4). Sensitive films were prepared by drop casting from aqueous solutions of ZnO nanoparticles (NPs) and trivalent RE ions. The sensors were continuously illuminated by ultraviolet light during the detection processes. The effect of photoactivation of the sensitive films was studied, as well as the influence of humidity on the response of the sensors to polluting gases. Comparative studies on the detection properties of the sensors showed how the presence of REs increased the response to the gases detected. Low concentrations of pollutant gases (50 ppb NO2, 1 ppm CO and 3 ppm CH4) were detected at room temperature. The detection mechanisms were then discussed in terms of the possible oxidation-reduction (redox) reaction in both dry and humid air atmospheres.
Speech Emotion Recognition Based on Modified ReliefF
Authors: Li Guo-Min , Liu Na , Zhang Jun-Ao
Published: October 2022

As the key of human–computer natural interaction, the research of emotion recognition is of great significance to the development of computer intelligence. In view of the issue that the current emotional feature dimension is too high, which affects the classification performance, this paper proposes a modified ReliefF feature selection algorithm to screen out feature subsets with smaller dimensions and better performance from high-dimensional features to further improve the efficiency and accuracy of emotion recognition. In the modified algorithm, the selection range of random samples is adjusted; the correlation between features is measured by the maximum information coefficient, and the distance measurement method between samples is established based on the correlation. The experimental results on the eNTERFACE’05 and SAVEE speech emotional datasets show that the features filtered based on the modified algorithm significantly reduce the data dimensions and effectively improve the accuracy of emotion recognition.
Digital Twin for a Multifunctional Technology of Flexible Assembly on a Mechatronics Line with Integrated Robotic Systems and Mobile Visual Sensor—Challenges towards Industry 5.0
Authors: Mincă Eugenia , Filipescu Adrian , Cernega Daniela , Șolea Răzvan , Filipescu Adriana , Ionescu Dan , Simion Georgian
Published: October 2022

A digital twin for a multifunctional technology for flexible manufacturing on an assembly, disassembly, and repair mechatronics line (A/D/RML), assisted by a complex autonomous system (CAS), is presented in the paper. The hardware architecture consists of the A/D/RML and a six-workstation (WS) mechatronics line (ML) connected to a flexible cell (FC) and equipped with a six-degree of freedom (DOF) industrial robotic manipulator (IRM). The CAS has in its structure two driving wheels and one free wheel (2DW/1FW)-wheeled mobile robot (WMR) equipped with a 7-DOF robotic manipulator (RM). On the end effector of the RM, a mobile visual servoing system (eye-in-hand MVSS) is mounted. The multifunctionality is provided by the three actions, assembly, disassembly, and repair, while the flexibility is due to the assembly of different products. After disassembly or repair, CAS picks up the disassembled components and transports them to the appropriate storage depots for reuse. Disassembling or repairing starts after assembling, and the final assembled product fails the quality test. The virtual world that serves as the digital counterpart consists of tasks assignment, planning and synchronization of A/D/RML with integrated robotic systems, IRM, and CAS. Additionally, the virtual world includes hybrid modeling with synchronized hybrid Petri nets (SHPN), simulation of the SHPN models, modeling of the MVSS, and simulation of the trajectory-tracking sliding-mode control (TTSMC) of the CAS. The real world, as counterpart of the digital twin, consists of communication, synchronization, and control of A/D/RML and CAS. In addition, the real world includes control of the MVSS, the inverse kinematic control (IKC) of the RM and graphic user interface (GUI) for monitoring and real-time control of the whole system. The “Digital twin” approach has been designed to meet all the requirements and attributes of Industry 4.0 and beyond towards Industry 5.0, the target being a closer collaboration between the human operator and the production line.
Two-Dimensional (PEA)2PbBr4 Perovskites Sensors for Highly Sensitive Ethanol Vapor Detection
Authors: Tien Ching-Ho , Lee Kuan-Lin , Tao Chun-Cheng , Lin Zhan-Qi , Lin Zi-Hao , Chen Lung-Chien
Published: October 2022

Two-dimensional (2D) perovskite have been widely researched for solar cells, light-emitting diodes, photodetectors because of their excellent environmental stability and optoelectronic properties in comparison to three-dimensional (3D) perovskite. In this study, we demonstrate the high response of 2D-(PEA)2PbBr4 perovskite of the horizontal vapor sensor was outstandingly more superior than 3D-MAPbBr3 perovskite. 2D transverse perovskite layer have the large surface-to-volume ratio and reactive surface, with the charge transfer mechanism, which was suitable for vapor sensing and trapping. Thus, 2D perovskite vapor sensors demonstrate the champion current response ratio R of 107.32 under the ethanol vapors, which was much faster than 3D perovskite (R = 2.92).
Experimental Evaluation of Sensor Fusion of Low-Cost UWB and IMU for Localization under Indoor Dynamic Testing Conditions
Authors: Liu Chengkun , Kadja Tchamie , Chodavarapu Vamsy P.
Published: October 2022

Autonomous systems usually require accurate localization methods for them to navigate safely in indoor environments. Most localization methods are expensive and difficult to set up. In this work, we built a low-cost and portable indoor location tracking system by using Raspberry Pi 4 computer, ultra-wideband (UWB) sensors, and inertial measurement unit(s) (IMU). We also developed the data logging software and the Kalman filter (KF) sensor fusion algorithm to process the data from a low-power UWB transceiver (Decawave, model DWM1001) module and IMU device (Bosch, model BNO055). Autonomous systems move with different velocities and accelerations, which requires its localization performance to be evaluated under diverse motion conditions. We built a dynamic testing platform to generate not only the ground truth trajectory but also the ground truth acceleration and velocity. In this way, our tracking system’s localization performance can be evaluated under dynamic testing conditions. The novel contributions in this work are a low-cost, low-power, tracking system hardware–software design, and an experimental setup to observe the tracking system’s localization performance under different dynamic testing conditions. The testing platform has a 1 m translation length and 80 μm of bidirectional repeatability. The tracking system’s localization performance was evaluated under dynamic conditions with eight different combinations of acceleration and velocity. The ground truth accelerations varied from 0.6 to 1.6 m/s2 and the ground truth velocities varied from 0.6 to 0.8 m/s. Our experimental results show that the location error can reach up to 50 cm under dynamic testing conditions when only relying on the UWB sensor, with the KF sensor fusion of UWB and IMU, the location error decreases to 13.7 cm.
Speed Estimation of Six-Phase Induction Motors, Using the Rotor Slot Harmonics
Authors: Laadjal Khaled , Bento Fernando , Antunes Hugo R. P. , Sahraoui Mohamed , Cardoso Antonio J. Marques
Published: October 2022

Multiphase machines have recently been promoted as a viable alternative to traditional three-phase machines. Most experts are looking for strategies to estimate the rotation speed of such complex systems, since speed data are required for high-performance control purposes. Traditionally, electromechanical sensors were used to detect the rotor speed of electric motors. These devices are extremely accurate, but they are also delicate and costly to deploy. New speed estimating algorithms must be created for these situations. This paper looks at how to estimate rotor speed in symmetrical six-phase induction motors (IMs) using a novel strategy for rotor speed estimation based on the Short Time Fourier Transform (STFT) method. The technique is based on tracking the frequencies of the rotor slot harmonics (RSH) seen in most squirrel-cage IM stator currents, thus assuring a broad range of applications. To monitor the RSH, the STFT employs a sliding window to perform the discrete Fourier transform technique, making it more suitable for online use with noisy and nonstationary signals. Experimental tests demonstrate the effectiveness of the suggested approach.
A Low-Cost Foot-Placed UWB and IMU Fusion-Based Indoor Pedestrian Tracking System for IoT Applications
Authors: Naheem Khawar , Kim Mun Sang
Published: October 2022

Among existing wireless and wearable indoor pedestrian tracking solutions, the ultra-wideband (UWB) and inertial measurement unit (IMU) sensors are the popular options due to their accurate and globally referenced positioning, and low-cost and compact size, respectively. However, the UWB position accuracy is compromised by the indoor non-line of sight (NLOS) and the IMU estimation suffers from orientation drift as well as requiring position initialization. To overcome these limitations, this paper proposes a low-cost foot-placed UWB and IMU fusion-based indoor pedestrian tracking system. Our data fusion model is an improved loosely coupled Kalman filter with the inclusion of valid UWB observation detection. In this manner, the proposed system not only adjusts the consumer-grade IMU’s accumulated drift but also filters out any NLOS instances in the UWB observation. We validated the performance of the proposed system with two experimental scenarios in a complex indoor environment. The root mean square (RMS) positioning accuracy of our data fusion model is enhanced by 60%, 53%, and 27% compared to that of the IMU-based pedestrian dead reckoning, raw UWB position, and conventional fusion model, respectively, in the single-lap NLOS scenario, and by 70%, 34%, and 12%, respectively, in the multi-lap LOS+NLOS scenario.
Influence of the Size of Damage to the Steel Wire Rope on the Magnetic Signature
Authors: Mazurek Paweł , Roskosz Maciej , Kwaśniewski Jerzy
Published: October 2022

This article presents diagnostic tests of wire ropes using passive magnetic methods. The study used two types of wire ropes with different constructions and diameters. Defects of various depths were modeled in the ropes, which reflected the degree of loss of metallic cross-section. After a series of measurements, a correlation was observed between the amplitude of the module signal and the degree of damage to the rope. The signals were recorded with the advantage of the SpinMeter-3D magnetometer. The obtained results were subjected to the extraction of features, the analysis of which allowed the damage to be identified.
Magnetically Tunable Micro-Ring Resonators for Massive Magneto-Optical Modulation in Dense Wavelength Division Multiplexing Systems
Authors: Neto Josino Villela S. , Carvalho William O. F. , Mejía-Salazar Jorge Ricardo
Published: October 2022

We demonstrate, numerically, a new concept for on-chip magneto-optical (MO) modulation in dense wavelength division multiplexing (DWDM) applications. Our idea uses materials and mechanisms that are compatible with current silicon-on-insulator fabrication and CMOS technologies for monolithic integration. The physics behind our idea stems in the exploitation of the enhanced MO activity of a micro-ring, made of cerium substituted yttrium iron garnet (Ce:YIG) material, to actively manipulate the resonance wavelengths of an adjacent micro-ring resonator (MRR) of silicon (Si). This active manipulation of the latter MO-MRR structure is used to modulate the optical signal traveling through a side-coupled Si bus waveguide. Moreover, by proper tailoring multiple MO-MRRs (side-coupled to the single Si bus waveguide) to match wavelength channels in DWDM across the entire C-band optical communications spectrum, we extend our proposal to massive and dynamic MO modulation in DWDM applications. Significantly, we noticed that the active MO shifting of the resonant wavelength (used for MO modulation here) can be used for improvements in the spectrum utilization efficiency in future elastic optical networks (EONs).
Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud
Authors: Jung Tae-Won , Jeong Chi-Seo , Kim In-Seon , Yu Min-Su , Kwon Soon-Chul , Jung Kye-Dong
Published: October 2022

Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative method in GNNs, in the context of computer vision, utilize conventional Convolutional Neural Networks (CNNs) to process data supported by graphs. This paper proposes a one-stage GCN approach for 3D object detection and poses estimation by structuring non-linearly distributed points of a graph. Our network provides the required details to analyze, generate and estimate bounding boxes by spatially structuring the input data into graphs. Our method proposes a keypoint attention mechanism that aggregates the relative features between each point to estimate the category and pose of the object to which the vertices of the graph belong, and also designs nine degrees of freedom of multi-object pose estimation. In addition, to avoid gimbal lock in 3D space, we use quaternion rotation, instead of Euler angle. Experimental results showed that memory usage and efficiency could be improved by aggregating point features from the point cloud and their neighbors in a graph structure. Overall, the system achieved comparable performance against state-of-the-art systems.
High-Precision ADC Spectrum Testing under Non-Coherent Sampling Conditions
Authors: Peng Xiaofei , Li Jie , Zhang Debiao , Hu Chenjun , Sun Ning , Jiang Jie
Published: October 2022

Realizing coherent sampling is one of the major bottlenecks in high-precision ADC spectrum testing. In spectrum analysis, if coherent sampling is not implemented, spectral leakage will result, which in turn leads to inaccurate test results. In this paper, a combined four-parameter sine-curve-fitting algorithm is proposed incorporating non-coherent sampling, with the amplitude, initial phase, and frequency parameters of the sine wave being obtained by fitting. The corresponding coherent sine wave is then calculated and replaced according to the obtained sine wave to reconstruct the new test data, eliminating the requirement of coherent sampling. Numerous simulations demonstrated the functionality and robustness of the algorithm, which was then used to process and analyze the measured data of two commercial high-precision ADCs. The results show that our algorithm can achieve accurate testing of ADC parameters under relaxed test conditions, which verifies the effectiveness and superiority of the scheme.
Combining Photogrammetry and Photometric Stereo to Achieve Precise and Complete 3D Reconstruction
Authors: Karami Ali , Menna Fabio , Remondino Fabio
Published: October 2022

Image-based 3D reconstruction has been employed in industrial metrology for micro-measurements and quality control purposes. However, generating a highly-detailed and reliable 3D reconstruction of non-collaborative surfaces is still an open issue. In this paper, a method for generating an accurate 3D reconstruction of non-collaborative surfaces through a combination of photogrammetry and photometric stereo is presented. On one side, the geometric information derived with photogrammetry is used in areas where its 3D measurements are reliable. On the other hand, the high spatial resolution capability of photometric stereo is exploited to acquire a finely detailed topography of the surface. Finally, three different approaches are proposed to fuse both geometric information and high frequency details. The proposed method is tested on six different non-collaborative objects with different surface characteristics. To evaluate the accuracy of the proposed method, a comprehensive cloud-to-cloud comparison between reference data and 3D points derived from the proposed fusion methods is provided. The experiments demonstrated that, despite correcting global deformation up to an average RMSE of less than 0.1 mm, the proposed method recovers the surface topography at the same high resolution as the photometric stereo.
Radiation Effects on Fiber Bragg Gratings: Vulnerability and Hardening Studies
Authors: Morana Adriana , Marin Emmanuel , Lablonde Laurent , Blanchet Thomas , Robin Thierry , Cheymol Guy , Laffont Guillaume , Boukenter Aziz , Ouerdane Youcef , Girard Sylvain
Published: October 2022

Fiber Bragg gratings (FBGs) are point optical fiber sensors that allow the monitoring of a diversity of environmental parameters, e.g., temperature or strain. Several research groups have studied radiation effects on the grating response, as they are implemented in harsh environments: high energy physics, space, and nuclear facilities. We report here the advances made to date in studies regarding the vulnerability and hardening of this sensor under radiation. First, we introduce its principle of operation. Second, the different grating inscription techniques are briefly illustrated as well as the differences among the various types. Then, we focus on the radiation effects induced on different FBGs. Radiation induces a shift in their Bragg wavelengths, which is a property serving to measure environmental parameters. This radiation-induced Bragg wavelength shift (RI-BWS) leads to a measurement error, whose amplitude and kinetics depend on many parameters: inscription conditions, fiber type, pre- or post-treatments, and irradiation conditions (nature, dose, dose rate, and temperature). Indeed, the radiation hardness of an FBG is not directly related to that of the fiber where it has been photo-inscribed by a laser. We review the influence of all these parameters and discuss how it is possible to manufacture FBGs with limited RI-BWS, opening the way to their implementation in radiation-rich environments.
Ultraviolet-C Photoresponsivity Using Fabricated TiO2 Thin Films and Transimpedance-Amplifier-Based Test Setup
Authors: Cadatal-Raduban Marilou , Pope Jade , Olejníček Jiří , Kohout Michal , Harrison John A. , Hasan S. M. Rezaul
Published: October 2022

We report on fabricated titanium dioxide (TiO2) thin films along with a transimpedance amplifier (TIA) test setup as a photoconductivity detector (sensor) in the ultraviolet-C (UV-C) wavelength region, particularly at 260 nm. TiO2 thin films deposited on high-resistivity undoped silicon-substrate at thicknesses of 100, 500, and 1000 nm exhibited photoresponsivities of 81.6, 55.6, and 19.6 mA/W, respectively, at 30 V bias voltage. Despite improvements in the crystallinity of the thicker films, the decrease in photocurrent, photoconductivity, photoconductance, and photoresponsivity in thicker films is attributed to an increased number of defects. Varying the thickness of the film can, however, be leveraged to control the wavelength response of the detector. Future development of a chip-based portable UV-C detector using TiO2 thin films will open new opportunities for a wide range of applications.
A Generalized Laser Simulator Algorithm for Mobile Robot Path Planning with Obstacle Avoidance
Authors: Muhammad Aisha , Ali Mohammed A. H. , Turaev Sherzod , Abdulghafor Rawad , Shanono Ibrahim Haruna , Alzaid Zaid , Alruban Abdulrahman , Alabdan Rana , Dutta Ashit Kumar , Almotairi Sultan
Published: October 2022

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm.
The Influence of Climate Change on Droughts and Floods in the Yangtze River Basin from 2003 to 2020
Authors: Cui Lilu , He Mingrui , Zou Zhengbo , Yao Chaolong , Wang Shengping , An Jiachun , Wang Xiaolong
Published: October 2022

In recent decades, extreme floods and droughts have occurred frequently around the world, which seriously threatens the social and economic development and the safety of people’s lives and properties. Therefore, it is of great scientific significance to discuss the causes and characteristic quantization of extreme floods and droughts. Here, the terrestrial water storage change (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to characterize the floods and droughts in the Yangtze River basin (YRB) during 2003 and 2020. To reduce the uncertainty of TWSC results, the generalized three-cornered hat and least square methods were used to fuse TWSC results from six GRACE solutions. Then combining precipitation (PPT), evapotranspiration, soil moisture (SM), runoff, and extreme climate index data, the influence of climate change on floods and droughts in the YRB was discussed and analyzed. The results show that the fused method can effectively improve the uncertainty of TWSC results. And seven droughts and seven floods occurred in the upper of YRB (UY) and nine droughts and six floods appeared in the middle and lower of YRB (MLY) during the study period. The correlation between TWSC and PPT (0.33) is the strongest in the UY, and the response time between the two is 1 month, while TWSC and SM (0.67) are strongly correlated with no delay in the MLY. The reason for this difference is mainly due to the large-scale hydropower development in the UY. Floods and droughts in the UY and MLY are more influenced by the El Niño-Southern Oscillation (ENSO) (correlation coefficients are 0.39 and 0.50, respectively) than the Indian Ocean Dipole (IOD) (correlation coefficients are 0.19 and 0.09, respectively). The IOD event is usually accompanied by the ENSO event (the probability is 80%), and the hydrological hazards caused by independent ENSO events are less severe than those caused by these two extreme climate events in the YRB. Our results provide a reference for the study on the formation, development, and recovery mechanism of regional floods and droughts on a global scale.
Optical Measurement of Molar Absorption Coefficient of HbA1c: Comparison of Theoretical and Experimental Results
Authors: Hossain Shifat , Satter Shama , Kwon Tae-Ho , Kim Ki-Doo
Published: October 2022

Diabetes can cause dangerous complications if not diagnosed in a timely manner. The World Health Organization accepts glycated hemoglobin (HbA1c) as a measure of diagnosing diabetes as it provides significantly more information on the glycemic behavior from a single blood sample than the fasting blood sugar reading. The molar absorption coefficient of HbA1c is needed to quantify the amount of HbA1c present in a blood sample. In this study, we measured the molar absorption coefficient of HbA1c in the range of 450 nm to 700 nm using optical methods experimentally. We observed that the characteristic peaks of the molar absorption coefficient of HbA1c (at 545 nm and 579 nm for level 1, at 544 nm and 577 nm for level 2) are in close agreement with those reported in previous studies. The molar absorption coefficient values were also found to be close to those of earlier reports. The average molar absorption coefficient values of HbA1c were found to be 804,403.5 M−1cm−1 at 545 nm and 703,704.5 M−1cm−1 at 579 nm for level 1 as well as 503,352.4 M−1cm−1 at 544 nm and 476,344.6 M−1cm−1 at 577 nm for level 2. Our experiments focused on calculating the molar absorption coefficients of HbA1c in the visible wavelength region, and the proposed experimental method has an advantage of being able to easily obtain the molar absorption coefficient at any wavelength in the visible wavelength region. The results of this study are expected to help future investigations on noninvasive methods of estimating HbA1c levels.