bluuebunny/crossref_metadata_2025 huggingface dataset:
Synchronously Intracavity-Pumped Picosecond Optical Parametric Oscillators for Sensors
Authors: Zavadilová Alena , Kubeček Václav , Vyhlídal David
Published: April 2022

The research and development of laser systems for intracavity phase interferometry is described. These systems are based on an intracavity synchronously pumped optical parametric oscillator (OPO), enabling the generation of two trains of picosecond pulses inside a single cavity. In such a configuration, it is possible to measure the beat note frequency between two pulses and to very precisely determine the phase difference between them. The pump source is a diode-pumped passively mode-locked Nd:YVO4 laser. A periodically poled magnesium-doped lithium niobate crystal is used as the optical parametric oscillator crystal coupling the pump and the signal cavities. We designed a synchronously pumped OPO in a linear and ring cavity configuration allowing generation in a dual-pulse regime. By a mutual detuning of both cavity lengths, the quasi-synchronous regime of pumping was achieved and high harmonics of repetition rate frequencies were generated. Such a system can be useful for applications such as pump-probe spectroscopy or for testing telecommunication systems. We also realized the subharmonic OPO cavity as a source of two independent trains of picosecond pulses suitable for intracavity phase interferometry; we also measured the beat note signal.
Test-Retest Reliability of the MotionMetrix Software for the Analysis of Walking and Running Gait Parameters
Authors: Jaén-Carrillo Diego , Ruiz-Alias Santiago A. , Chicano-Gutiérrez Jose M. , Ruiz-Malagón Emilio J. , Roche-Seruendo Luis E. , García-Pinillos Felipe
Published: April 2022

The use of markerless motion capture systems is becoming more popular for walking and running analysis given their user-friendliness and their time efficiency but in some cases their validity is uncertain. Here, the test-retest reliability of the MotionMetrix software combined with the use of Kinect sensors is tested with 24 healthy volunteers for walking (at 5 km·h−1) and running (at 10 and 15 km·h−1) gait analysis in two different trials. All the parameters given by the MotionMetrix software for both walking and running gait analysis are tested in terms of reliability. No significant differences (p > 0.05) were found for walking gait parameters between both trials except for the phases of loading response and double support, and the spatiotemporal parameters of step length and step frequency. Additionally, all the parameters exhibit acceptable reliability (CV < 10%) but step width (CV > 10%). When analyzing running gait, although the parameters here tested exhibited different reliability values at 10 km·h−1, the system provided reliable measurements for most of the kinematic and kinetic parameters (CV < 10%) when running at 15 km·h−1. Overall, the results obtained show that, although some variables must be interpreted with caution, the Kinect + MotionMetrix system may be useful for walking and running gait analysis. Nevertheless, the validity still needs to be determined against a gold standard system to fully trust this technology and software combination.
CTT: CNN Meets Transformer for Tracking
Authors: Yang Chen , Zhang Ximing , Song Zongxi
Published: April 2022

Siamese networks are one of the most popular directions in the visual object tracking based on deep learning. In Siamese networks, the feature pyramid network (FPN) and the cross-correlation complete feature fusion and the matching of features extracted from the template and search branch, respectively. However, object tracking should focus on the global and contextual dependencies. Hence, we introduce a delicate residual transformer structure which contains a self-attention mechanism called encoder-decoder into our tracker as the part of neck. Under the encoder-decoder structure, the encoder promotes the interaction between the low-level features extracted from the target and search branch by the CNN to obtain global attention information, while the decoder replaces cross-correlation to send global attention information into the head module. We add a spatial and channel attention component in the target branch, which can further improve the accuracy and robustness of our proposed model for a low price. Finally, we detailly evaluate our tracker CTT on GOT-10k, VOT2019, OTB-100, LaSOT, NfS, UAV123 and TrackingNet benchmarks, and our proposed method obtains competitive results with the state-of-the-art algorithms.
Multi-Object Positioning and Imaging Based on Single-Pixel Imaging Using Binary Patterns
Authors: Meng Wenwen , Shi Dongfeng , Yang Wei , Zha Linbin , Zhao Yuefeng , Wang Yingjian
Published: April 2022

Single-pixel imaging (SPI) is a new type of imaging technology that uses a non-scanning single-pixel detector to image objects and has important application prospects and value in many fields. Most of the modulators currently used in SPI systems are digital micromirror device (DMD) modulators, which use a higher frequency for binary modulation than other alternatives. When modulating grayscale information, the modulation frequency is significantly reduced. This paper conducts research on multiple discrete objects in a scene and proposes using binary patterns to locate and image these objects. Compared with the existing methods of using gray patterns to locate and image multiple objects, the method proposed in this paper is more suitable for DMD-type SPI systems and has wider applicability and greater prospects. The principle of the proposed method is introduced, and the effectiveness of the method is experimentally verified. The experimental results show that, compared to traditional SPI methods, the number of patterns required by the proposed method is reduced by more than 85%.
Research on an LEO Constellation Multi-Aircraft Collaborative Navigation Algorithm Based on a Dual-Way Asynchronous Precision Communication-Time Service Measurement System (DWAPC-TSM)
Authors: Ye Lvyang , Yang Yikang , Ma Jiangang , Deng Lingyu , Li Hengnian
Published: April 2022

In order to solve the collaborative navigation problems in challenging environments such as insufficient visible satellites, obstacle reflections and multipath errors, and in order to improve the accuracy, usability, and stability of collaborative navigation and positioning, we propose a dual-way asynchronous precision communication–timing–measurement system (DWAPC-TSM) LEO constellation multi-aircraft cooperative navigation and positioning algorithm which gives the principle, algorithm structure, and error analysis of the DWAPC-TSM system. In addition, we also analyze the effect of vehicle separation range on satellite observability. The DWAPC-TSM system can achieve high-precision ranging and time synchronization accuracy. With the help of this system, by adding relative ranging and speed measurement observations in an unscented Kalman filter (UKF), the multi-aircraft coordinated navigation and positioning of aircraft is finally realized. The simulation results show that, even without the aid of an altimeter, the multi-aircraft cooperative navigation and positioning algorithm based on the DWAPC-TSM system can achieve good navigation and positioning results, and with the aid of the altimeter, the cooperative navigation and positioning accuracy can be effectively improved. For the formation flight configurations of horizontal collinear and vertical collinear, the algorithm is universal, and in the case of vertical collinear, the navigation performance of the formation members tends to be consistent. Under different relative measurement accuracy, the algorithm can maintain good robustness; compared with some existing classical algorithms, it can significantly improve the navigation and positioning accuracy. A reference scheme for exploring the feasibility of a new cooperative navigation and positioning mode for LEO communication satellites is presented.
Multihydrophone Fusion Network for Modulation Recognition
Authors: Wang Haiwang , Wang Bin , Wu Lulu , Tang Qiang
Published: April 2022

Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) for multisensory reception scenarios. MHFNet consists of a feature extraction module and a fusion module. The feature extraction module extracts the features of the signals received by the multiple hydrophones. Then, through the neural network, the fusion module fuses and classifies the features of the multiple signals. MHFNet takes full advantage of neural networks and multihydrophone reception to effectively fuse signal features for realizing improved modulation recognition performance. Experimental results on simulation and practical data show that MHFNet is superior to other fusion methods. The classification accuracy is improved by about 16%.
A Digital Twin-Based Operation Status Monitoring System for Port Cranes
Authors: Zhou Yong , Fu Zhengkang , Zhang Jie , Li Wenfeng , Gao Chaoyang
Published: April 2022

To address the problems of the lack of an online data simulation test environment, the poor openness of data collection, and the low degree of data visualization in the online control process of port cranes, an operation state monitoring system framework for port cranes based on digital twins is proposed. In this framework, the digital twin port crane is used as the core, and the multi-sensor data acquisition method, OPC UA information model, and plug-in programming method are combined to realize multi-source heterogeneous virtual and real data fusion. The digital twin crane monitoring system based on this framework can fulfil the following functions: crane historical operation process reproduction, control program simulation testing, synchronous mapping simulation, and remote control. In order to verify the proposed method, a digital twin-based physical platform for monitoring a rail-mounted gantry crane (RMGC) was built, in which a virtual test of anti-swing control and a digital twin monitoring experiment were carried out. The results show that the virtual RMGC can test the control algorithm and map the movement process of the physical RMGC, and the crane operation monitoring system has high real-time performance and good visualization effect. In addition, the remote control of the software platform is accurate and effective.
Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment
Authors: Nebuloni Roberto , Cazzaniga Greta , D’Amico Michele , Deidda Cristina , De Michele Carlo
Published: April 2022

Despite the several sources of inaccuracy, commercial microwave links (CML) have been recently exploited to estimate the average rainfall intensity along the radio path from signal attenuation. Validating these measurements against “ground truth” from conventional rainfall sensors, as rain gauges, is a challenging issue due to the different spatial sampling involved. Here, we assess the performance of a network of CML as opportunistic rainfall sensors in a challenging mountainous environment located in Northern Italy. The benchmark dataset was provided by an operational network of rain gauges and by three disdrometers. Moreover, disdrometer data were used to establish an accurate relationship between path attenuation and rainfall intensity. A new method was developed for assessing CML: time series of rainfall occurrence and rainfall depth, representative of CML radio path, were derived from the nearby rain gauges and disdrometers and compared with the same quantities gathered from the CML. It turns out that, over the very short integration times considered (10 min), CML perform well in detecting rainfall, whereas quantitative rainfall estimates may have large discrepancies.
Field Campaign Evaluation of Sensors Lufft GMX500 and MaxiMet WS100 in Peruvian Central Andes
Authors: Valdivia Jairo M. , Guizado David A. , Flores-Rojas José L. , Gamarra Delia P. , Silva-Vidal Yamina F. , Huamán Edith R.
Published: April 2022

The research presents the inter-comparison of atmospheric variables measured by 9 automatic weather stations. This set of data was compared with the measurements of other weather stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with an optical disdrometer Parsivel2. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution were evaluated. A pair of GMX500 sensors presented high data dispersion; it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. It was found that the WS100 sensors overestimate the precipitation with a percentage bias close to 100% and the differences increase with the greater intensity of rain. The drop size distribution retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve.
High Sensitivity Surface Plasmon Resonance Sensor Based on a Ge-Doped Defect and D-Shaped Microstructured Optical Fiber
Authors: Cunha Nilson H. O. , Da Silva José P.
Published: April 2022

In this work a plasmonic sensor with a D-Shaped microstructured optical fiber (MOF) is proposed to detect a wide range of analyte refractive index (RI ;na) by doping the pure silica (SiO2) core with distinct concentrations of Germanium Dioxide (GeO2), causing the presentation of high spectral sensitivity. In this case, the fiber is shaped by polishing a coating of SiO2, on the region that will be doped with GeO2, in the polished area, a thin gold (Au) layer, which constitutes the plasmonic material, is introduced, followed by the analyte, in a way which the gold layer is deposited between the SiO2. and the analyte. The numerical results obtained in the study shows that the sensor can determine efficiently a range of 0.13 refractive index units (RIU), with a limit operation where na varies from 1.32 to 1.45. Within this application, the sensor has reached an average wavelength sensitivity (WS) of up to 11,650.63 nm/RIU. With this level of sensitivity, the D-Shaped format and wide range of na detection, the proposed fiber has great potential for sensing applications in several areas.
Colorimetric Paper-Based Sensors against Cancer Biomarkers
Authors: Carneiro Mariana C. C. G. , Rodrigues Ligia R. , Moreira Felismina T. C. , Sales Maria Goreti F.
Published: April 2022

Cancer is a major cause of mortality and morbidity worldwide. Detection and quantification of cancer biomarkers plays a critical role in cancer early diagnosis, screening, and treatment. Clinicians, particularly in developing countries, deal with high costs and limited resources for diagnostic systems. Using low-cost substrates to develop sensor devices could be very helpful. The interest in paper-based sensors with colorimetric detection increased exponentially in the last decade as they meet the criteria for point-of-care (PoC) devices. Cellulose and different nanomaterials have been used as substrate and colorimetric probes, respectively, for these types of devices in their different designs as spot tests, lateral-flow assays, dipsticks, and microfluidic paper-based devices (μPADs), offering low-cost and disposable devices. However, the main challenge with these devices is their low sensitivity and lack of efficiency in performing quantitative measurements. This review includes an overview of the use of paper for the development of sensing devices focusing on colorimetric detection and their application to cancer biomarkers. We highlight recent works reporting the use of paper in the development of colorimetric sensors for cancer biomarkers, such as proteins, nucleic acids, and others. Finally, we discuss the main advantages of these types of devices and highlight their major pitfalls.
Voltammetric Sensor Based on SeO2 Nanoparticles and Surfactants for Indigo Carmine Determination
Authors: Kavieva Liya , Ziyatdinova Guzel
Published: April 2022

Indigo carmine is a widely used colorant in the food and pharmaceutical industry a high concentration of which can lead to a wide range of negative effects on human health. Therefore, colorant contents have to be strictly controlled. SeO2-nanoparticle-modified glassy carbon electrodes (GCE) have been developed as a voltammetric sensor for indigo carmine. Various types and concentrations of surfactants have been used as reagents for the stabilization of SeO2 nanoparticle dispersions and as electrode surface co-modifiers. An amount of 1.0 mM cationic cetylpyridinium bromide (CPB) provides the best response of the indigo carmine on the modified electrode. The electrodes were characterized by cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy (EIS). SeO2 nanoparticle–CPB-modified electrodes show 4.2-fold higher electroactive area vs. GCE as well as a dramatic 5043-fold decrease in the electron transfer resistance indicating effectivity of the modifier developed. The surface-controlled electrooxidation of indigo carmine proceeds irreversibly (αa = 0.46) with the participation of two electrons and two protons. A linear dynamic range of 0.025–1.0 and 1.0–10 µM of indigo carmine were obtained with the detection and quantification limits of 4.3 and 14.3 nM, respectively. The practical applicability of the sensor was successfully shown on the pharmaceutical dosage forms.
The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review
Authors: Preatoni Ezio , Bergamini Elena , Fantozzi Silvia , Giraud Lucie I. , Orejel Bustos Amaranta S. , Vannozzi Giuseppe , Camomilla Valentina
Published: April 2022

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features—such as research design, scope, experimental settings, and applied context—were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.
A High-Performance Synthetic Jet Piezoelectric Air Pump with Petal-Shaped Channel
Authors: Li Xingqi , Liu Xiaopeng , Dong Luntao , Sun Xiaodong , Tang Huajie , Liu Guojun
Published: April 2022

The synthetic jet piezoelectric air pump is a potential miniature device for electronic cooling. In order to improve the performance of the device, a small-sized synthetic jet piezoelectric air pump is proposed in this work, which is mainly characterized by petal-shaped inlet channels. First, the structure and working principle of the piezoelectric vibrator and the proposed pump are analyzed. Then, three synthetic jet piezoelectric air pumps with different inlet channels are compared. These inlets are the direct channels, the diffuser/nozzle channels, and the petal-shaped channels, respectively. Furthermore, the performance of the synthetic jet piezoelectric air pump with the petal-shaped inlet channels is optimized by orthogonal tests. Finally, the simulation was used to investigate the heat dissipation capability of the synthetic jet piezoelectric pump. The experimental results show that among the three inlet channels, the petal-shaped channel can greatly improve the performance of the pump. The unoptimized pump with petal-shaped channels has a maximum flow rate of 1.8929 L/min at 100 V, 3.9 kHz. Additionally, the optimized pump with petal-shaped channels reaches a maximum flow rate of 3.0088 L/min at 100 V, 3.7 kHz, which is 58.95% higher than the unoptimized one. The proposed synthetic jet piezoelectric air pump greatly improves the output performance and has the advantages of simple structure, low cost, and easy integration. The convective heat transfer coefficient of the synthetic jet piezoelectric pump is 28.8 W/(m2·°C), which can prove that the device has a better heat dissipation capability.
Ultra-Wideband Antennas for Biomedical Imaging Applications: A Survey
Authors: Rafique Umair , Pisa Stefano , Cicchetti Renato , Testa Orlandino , Cavagnaro Marta
Published: April 2022

Microwave imaging is an active area of research that has garnered interest over the past few years. The main desired improvements to microwave imaging are related to the performances of radiating systems and identification algorithms. To achieve these improvements, antennas suitable to guarantee demanding requirements are needed. In particular, they must operate in close proximity to the objects under examination, ensure an adequate bandwidth, as well as reduced dimensions and low production costs. In addition, in near-field microwave imaging systems, the antenna should provide an ultra-wideband (UWB) response. Given the relevance of the foreseen applications, many UWB antenna designs for microwave imaging applications have been proposed in the literature. In this paper, a comprehensive review of different UWB antenna designs for near-field microwave imaging is presented. The antennas are classified according to the manufacturing technology and radiative performances. Particular attention is also paid to the radiation mechanisms as well as the techniques used to reduce the size and improve the bandwidth.
Effect of Phase Fluctuation on the Proper Operation of Smart Gear Health Monitoring System
Authors: Mac Thanh-Tung , Iba Daisuke , Matsushita Yusuke , Mukai Seiya , Miura Nanako , Masuda Arata , Moriwaki Ichiro
Published: April 2022

A smart gear sensor system has been developed for the condition monitoring of gear. This system includes a smart gear—the operation gear and a monitoring antenna. The analysis of the return loss of the monitoring antenna magnetically coupled with the smart gear gives the health condition of the gear. This research considers the effects of the distance and phase fluctuations between two components on the magnetic resonant return loss. The impacts of phase fluctuations include both static and high-speed conditions. Two experimental rigs have been built for the two cases. The coupling distance and static phase fluctuation are conducted via the first experimental rig. The second experimental rig performs both the coupling distance and phase fluctuation effect simultaneously while the smart gear rotates at high speed. During each test, the monitoring antenna return loss is captured thanks to a network analyzer. Analysis of the return loss data demonstrates that both the coupling distance and the change of the phase angle at the static condition and high speed have influenced the resonant return loss of the monitoring antenna. These findings are meaningful to the authors for evaluating and improving the accuracy of this gear health monitoring technique.
Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting
Authors: Huang Liangjun , Shen Shihui , Zhu Luning , Shi Qingxuan , Zhang Jianwei
Published: April 2022

In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.
Automatic Implementation Algorithm of Pressure Relief Drilling Depth Based on an Innovative Monitoring-While-Drilling Method
Authors: Wu Zheng , Zhang Wen-Long , Li Chen
Published: April 2022

An innovative monitoring-while-drilling method of pressure relief drilling was proposed in a previous study, and the periodic appearance of amplitude concentrated enlargement zone in vibration signals can represent the drilling depth. However, there is a lack of a high accuracy model to automatically identify the amplitude concentrated enlargement zone. So, in this study, a neural network model is put forward based on single-sensor and multi-sensor prediction results. The neural network model consists of one Deep Neural Network (DNN) and four Long Short-Term Memory (LSTM) networks. The accuracy is only 92.72% when only using single-sensor data for identification, while the proposed multiple neural network model could improve the accuracy to being greater than 97.00%. In addition, an optimization method was supplemented to eliminate some misjudgment due to data anomalies, which improved the final accuracy to the level of manual recognition. Finally, the research results solved the difficult problem of identifying the amplitude concentrated enlargement zone and provided the foundation for automatically identifying the drilling depth.
A Magnetic Crawler System for Autonomous Long-Range Inspection and Maintenance on Large Structures
Authors: Chahine Georges , Schroepfer Pete , Ouabi Othmane-Latif , Pradalier Cédric
Published: April 2022

The inspection and maintenance of large-scale industrial structures are major challenges that require time-efficient and reliable solutions to ensure the healthy condition of structures during operation. Autonomous robots may provide a promising solution for this purpose. In particular, they could lead to faster and more reliable inspection and maintenance without direct intervention from human operators. In this paper, we present a custom magnetic crawler system, and sensor suit and sensing modalities to enable such robotic operation. We first describe a localization framework based on a mesh created from a point cloud coupled with Inertial Measurement Unit (IMU) and Ultra-Wide Band (UWB) readings. Next, we introduce a mapping framework that relies on a 3D laser, and explicitly state how autonomous navigation and obstacle avoidance can be developed. Lastly, we present how ultrasonic guided waves (UGWs) are integrated into the system to provide accurate robot localization and structural feature mapping by relying on acoustic reflections in combination with the other systems. It is envisioned that long-range inspection capabilities that are not yet available in current industrial mobile platforms could emerge from the designed robotic system.
Event-Based Motion Capture System for Online Multi-Quadrotor Localization and Tracking
Authors: Iaboni Craig , Lobo Deepan , Choi Ji-Won , Abichandani Pramod
Published: April 2022

Motion capture systems are crucial in developing multi-quadrotor systems due to their ability to provide fast and accurate ground truth measurements for tracking and control. This paper presents the implementation details and experimental validation of a relatively low-cost motion-capture system for multi-quadrotor motion planning using an event camera. The real-time, multi-quadrotor detection and tracking tasks are performed using a deep learning network You-Only-Look-Once (YOLOv5) and a k-dimensional (k-d) tree, respectively. An optimization-based decentralized motion planning algorithm is implemented to demonstrate the effectiveness of this motion capture system. Extensive experimental evaluations were performed to (1) compare the performance of four deep-learning algorithms for high-speed multi-quadrotor detection on event-based data, (2) study precision, recall, and F1 scores as functions of lighting conditions and camera motion, and (3) investigate the scalability of this system as a function of the number of quadrotors flying in the arena. Comparative analysis of the deep learning algorithms on a consumer-grade GPU demonstrates a 4.8× to 12× sampling/inference rate advantage that YOLOv5 provides over representative one- and two-stage detectors and a 1.14× advantage over YOLOv4. In terms of precision and recall, YOLOv5 performed 15% to 18% and 27% to 41% better than representative state-of-the-art deep learning networks. Graceful detection and tracking performance degradation was observed in the face of progressively darker ambient light conditions. Despite severe camera motion, YOLOv5 precision and recall values of 94% and 98% were achieved, respectively. Finally, experiments involving up to six indoor quadrotors demonstrated the scalability of this approach. This paper also presents the first open-source event camera dataset in the literature, featuring over 10,000 fully annotated images of multiple quadrotors operating in indoor and outdoor environments.
A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff
Authors: Aieb Amir , Liotta Antonio , Kadri Ismahen , Madani Khodir
Published: April 2022

Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) and CART (classification and regression tree) machine-learning methods, applied to an IARR predicted data series obtained from a set of non-parametric and empirical water balance models in five climatic floors of northern Algeria between 1960 and 2020. A comparative analysis showed that the Yang, Sharif, and Zhang’s models were reliable for estimating input data of the hybrid model in all climatic classes. In addition, Schreiber’s model was more efficient in very humid, humid, and semi-humid areas. A set of performance and distribution statistical tests were applied to the estimated IARR data series to show the reliability and dynamicity of each model in all study areas. The results showed that our hybrid model provided the best performance and data distribution, where the R2Adj and p-values obtained in each case were between (0.793, 0.989), and (0.773, 0.939), respectively. The MR model showed good data distribution compared to the CART method, where p-values obtained by signtest and WSR test were (0.773, 0.705), and (0.326, 0.335), respectively.
Fine-Grained Ship Recognition from the Horizontal View Based on Domain Adaptation
Authors: Sun Shicheng , Gu Yu , Ren Mengjun
Published: April 2022

Ship recognition is a fundamental and essential step in maritime activities, and it can be widely used in maritime rescue, vessel management, and other applications. However, most studies conducted in this area use synthetic aperture radar (SAR) images and space-borne optical images, and those studies utilizing visible images are limited to the coarse-grained level. In this study, we constructed a fine-grained ship dataset with real images and simulation images that consisted of five categories of ships. To solve the problem of low accuracy in fine-grained ship classification with different angles in visible images, a network based on domain adaptation and a transformer was proposed. Concretely, style transfer was first used to reduce the gap between the simulation images and real images. Then, with the goal of utilizing the simulation images to execute classification tasks on the real images, a domain adaptation network based on local maximum mean discrepancy (LMMD) was used to align the different domain distributions. Furthermore, considering the innate attention mechanism of the transformer, a vision transformer (ViT) was chosen as the feature extraction module to extract the fine-grained features, and a fully connected layer was used as the classifier. Finally, the experimental results showed that our network had good performance on the fine-grained ship dataset with an overall accuracy rate of 96.0%, and the mean average precision (mAP) of detecting first and then classifying with our network was 87.5%, which also verified the feasibility of using images generated by computer simulation technology for auxiliary training.
Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm
Authors: Moon Kyoung-Sook , Lee Hee Won , Kim Hee Jean , Kim Hongjoong , Kang Jeehoon , Paik Won Chul
Published: April 2022

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method.
Representation of In-Service Performance for Cable-Stayed Railway–Highway Combined Bridges Based on Train-Induced Response’s Sensing Data and Knowledge
Authors: Zhao Han-Wei , Ding You-Liang , Li Ai-Qun
Published: April 2022

Real-time representation of the current performance of structures is an important task for perceiving potential danger in in-service bridges. Methods driven by the multisource sensing data of structural health monitoring systems are an effective way to achieve this goal. Due to the explicit zero-point of signals, the live load-induced response has an inherent advantage for quantitatively representing the performance of bridges. Taking a long-span cable-stayed railway–highway combined bridge as the case study, this paper presents a representation method of in-service performance. First, the non-stationary sections of train-induced response are automatically extracted by wavelet transform and window with threshold. Then, the data of the feature parameter of each non-stationary section are automatically divided into four cases of train load according to the calculational theory of bridge vibration under train effect and clustering analysis. Finally, the performance indexes for structural deformation and dynamics are determined separately, based on hierarchical clustering and statistical modeling. Fusing the real variability of massive data from monitoring and the knowledge of mechanics of theoretical calculations, accurate and robust indexes of bridge deflection distribution and forced vibration frequency are obtained in real time. The whole process verifies the feasibility of the representation of bridge in-service performance from massive multisource sensing data. The presented method, framework, and analysis results can be used as a reference for the design, operation, and maintenance works of long-span railway bridges.
Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning
Authors: Kasimati Aikaterini , Espejo-García Borja , Darra Nicoleta , Fountas Spyros
Published: April 2022

Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive. In recent years, several machine learning-based methods have been developed to predict crop quality. Although these techniques require the extensive involvement of experts, automated machine learning (AutoML) offers the possibility to improve this task, saving time and resources. In this paper, we propose an innovative approach for robust prediction of grape quality attributes by combining open-source AutoML techniques and Normalized Difference Vegetation Index (NDVI) data for vineyards obtained from four different platforms-two proximal vehicle-mounted canopy reflectance sensors, orthomosaics from UAV images and Sentinel-2 remote sensing imagery-during the 2019 and 2020 growing seasons. We investigated AutoML, extending our earlier work on manually fine-tuned machine learning methods. Results of the two approaches using Ordinary Least Square (OLS), Theil-Sen and Huber regression models and tree-based methods were compared. Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD) were included in the analysis and different combinations of sensors and data collected over two growing seasons were investigated. Results showed promising performance of Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data in predicting grape sugars, especially in mid to late season with full canopy growth. Regression models with both manually fine-tuned ML (R² = 0.61) and AutoML (R² = 0.65) provided similar results, with the latter slightly improved for both 2019 and 2020. When combining multiple sensors and growth stages per year, the coefficient of determination R² improved even more averaging 0.66 for the best-fitting regressions. Also, when considering combinations of sensors and growth stages across both cropping seasons, UAV and Spectrosense+ GPS, as well as Véraison and Flowering, each had the highest average R² values. These performances are consistent with previous work on machine learning algorithms that were manually fine-tuned. These results suggest that AutoML has greater long-term performance potential. To increase the efficiency of crop quality prediction, a balance must be struck between manual expert work and AutoML.
Analysis of Multiclass Pesticide Residues in Tobacco by Gas Chromatography Quadrupole Time-of-Flight Mass Spectrometry Combined with Mini Solid-Phase Extraction
Authors: Bie Rui , Zhang Jiguang , Wang Yunbai , Jin Dongmei , Yin Rui , Jiang Bin , Cao Jianmin
Published: April 2022

A screening method using gas chromatography quadrupole time-of-flight mass spectrometry (GC-QTOF/MS) combined with mini solid-phase extraction (mini-SPE) was established for the quantification and validation of multiclass pesticide residues in tobacco. The method was quicker and easier, with sample purity higher than that obtained by traditional SPE and dispersed-SPE. Box-Behnken design, an experimental design for response-surface methodology, was used to optimize the variables affecting the target pesticide recovery. Under the optimized conditions, 92% of the pesticides showed satisfactory recoveries of 70%–120% with precision <20% at spiking levels of 50, 250, and 500 ng/g. The limits of detection and quantification for all the analyses were 0.05–29.9 ng/g and 0.20–98.8 ng/g, respectively. In addition, a screening method based on the retention time and a homebuilt high-resolution mass spectrometry database were established. Under the proposed screening parameters and at spiking levels of 50, 100, and 500 ng/g, 76.6%, 94.7%, and 99.0% multiclass pesticide residues were detected, respectively, using the workflow software. The validated method was successfully applied to the analysis of real tobacco samples. Thus, the combination of mini-SPE and GC-QTOF/MS serves as a suitable method for the quantitative analysis and rapid screening of multiclass pesticide residues in tobacco.
Green and Simple Extraction of Arsenic Species from Rice Flour Using a Novel Ultrasound-Assisted Enzymatic Hydrolysis Method
Authors: Li Xiao , Ma Qian , Wei Chao , Cai Wei , Chen Huanhuan , Xing Rui , Song Panshu
Published: April 2022

It is well established that arsenic (As) has many toxic compounds, and in particular, inorganic As (iAs) has been classified as a type-1 carcinogen. The measuring of As species in rice flour is of great importance since rice is a staple of the diet in many countries and a major contributor to As intake in the Asian diet. In this study, several solvents and techniques for the extraction of As species from rice flour samples prior to their analysis by HPLC-ICP-MS were investigated. The extraction methods were examined for their efficiency in extracting various arsenicals from a rice flour certified reference material, NMIJ-7532a, produced by the National Metrology Institute of Japan. Results show that ultrasound-assisted extraction at 60 °C for 1 h and then heating at 100 °C for 2.5 h in the oven using a thermostable α-amylase aqueous solution was highly effective in liberating the arsenic species. The recoveries of iAs and dimethylarsinic acid (DMA) in NMIJ-7532a were 99.7% ± 1.6% (n = 3) and 98.1% ± 2.3% (n = 3), respectively, in comparison with the certificated values. Thus, the proposed extraction method is a green procedure that does not use any acidic, basic, or organic solvents. Moreover, this extraction method could effectively maintain the integrity of the native arsenic species of As(III), As(V), monomethylarsonate (MMA), DMA, arsenobetaine (AsB) and arsenocholine (AsC). Under the optimum extraction, chromatography and ICP-MS conditions, the limits of detection (LOD) obtained were 0.47 ng g−1, 1.67 ng g−1 and 0.80 ng g−1 for As(III), As(V) and DMA, respectively, while the limits of quantification (LOQ) achieved were 1.51 ng g−1, 5.34 ng g−1 and 2.57 ng g−1 for As(III), As(V) and DMA, respectively. Subsequently, the proposed method was successfully applied to As speciation analysis for several rice flour samples collected from contaminated areas in China.
Racial and Ethnic Discrimination in Portugal in Times of Pandemic Crisis
Authors: Casquilho-Martins Inês , Belchior-Rocha Helena , Alves David Ramalho
Published: April 2022

During the last two years, the pandemic has dominated the public attention and debate around the world, centering on socio-economic aspects and having camouflaged other social, cultural and even environmental issues. This study sought to analyze ethno-racial discrimination processes, identifying significant events in Portugal during the period of the current global crisis. We utilized document analysis of national and international reports produced in the last five years, complementing with secondary statistical data and the analysis of online news and users’ comments published in national media during the last two years. The results show that, although there has been a development in Portuguese legislation and in anti-discrimination measures, there has been an increase in racist and xenophobic phenomena in Portugal. Citizens’ perceptions, indicators and reports from official bodies show evidence of discriminatory behavior. Additionally, within this framework, we see a greater political presence of extreme right-wing movements, as well as an increase in hate speech in online news comments. Thus, although there is an awareness of the seriousness of these phenomena, there is an urgent need for actions against racial and ethnic intolerance and greater effective measures for ideological crimes.
On the Road Together: Issues Observed in the Process of a Research Duo Working Together in a Long-Term and Intense Collaboration in an Inclusive Research Project
Authors: Sergeant Sofie , Sandvoort Henriëtte , Van Hove Geert , Embregts Petri , van den Bogaard Kim , Taminiau Elsbeth , Schippers Alice
Published: April 2022

Inclusive research practices can lead to progress towards an inclusive society. With this study, we aimed to gain insight into dilemmas and catalysing processes within the long-term collaboration of an inclusive research duo: one non-academic researcher who lives with the label of intellectual disabilities and visual impairment, and one academic researcher. Both researchers kept personal diaries about their collaboration process. Inductive thematic analysis, individually and as a group of authors, was employed. Our findings reveal six necessary conditions for diversity-sensitive work in inclusive research: (a) experiencing belonging within the research group, (b) empowering people in a team through growing self-awareness and competence-building, (c) having room for reflection and searching for various ways of communication, (d) sharing power and ownership of research processes, (e) having enough time to foster the above conditions, and (f) joining in a mutual engagement in accommodating vulnerability in dialogue and collaborative work. Awareness of stigma-related issues and the risk of tokenism is also required.
Regional Bioelectrical Phase Angle Is More Informative than Whole-Body Phase Angle for Monitoring Neuromuscular Performance: A Pilot Study in Elite Young Soccer Players
Authors: Bongiovanni Tindaro , Rossi Alessio , Trecroci Athos , Martera Giulia , Iaia F. Marcello , Alberti Giampietro , Pasta Giulio , Lacome Mathieu
Published: April 2022

Background: The objective of this study was to investigate the association between regional and total phase angle (PhA) with lower-body neuromuscular performance in young elite soccer players. Methods: Sixteen elite male soccer players (14.3 ± 1.0 years) participated in this study. Lower (LPhA)- and upper (UPhA)-hemisome PhA together with whole-body PhA (WBPhA) were measured by a bioelectrical-impedance analysis (BIA), while appendicular arm and leg lean soft tissue (ALST and LLST, respectively) were estimated. Urine osmolarity (UOsm) and urine-specific gravity (USG) were also considered. Sprints over 10 m and 20 m and countermovement jump (CMJ) tests were employed to evaluate neuromuscular performance. Results: LPhA (p = 0.003) and UOsm (p = 0.012) explained 62% of the variance in the 10 m sprint. UOsm (p = 0.001) and both LPhA (p < 0.001) and WBPhA (p = 0.024) explained 81% of the total variance in the 20 m sprint. The CMJ height was affected by LPhA (p < 0.001) and UOsm (p = 0.024), which overall explained 68% of its variance (p < 0.05), while 93% of the CMJ power variance was explained by LPhA (p < 0.001), ALST (p < 0.001), and WBPhA (p = 0.011). Conclusions: Regional PhA is a relevant and non-invasive tool to monitor lower-body neuromuscular performance in elite youth soccer. Specifically, LPhA may be favored over WBPhA as more informative.
Photovoltaic Qualification and Approval Tests
Authors: Firman Andrés , Cáceres Manuel , González Mayans Alexis Raúl , Vera Luis Horacio
Published: April 2022

This paper presents the main aspects of implementing a laboratory for testing qualification and approval related to crystalline silicon terrestrial photovoltaic devices. In this aspect, a simplified review-based IEC 61215 standard methodology for mechanical and electrical tests is presented from a practical-experimental view. The instrumental requirements and uncertainties are discussed. Specially focused on the nowadays high-size PV modules.