bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning Application for Classification of Ionospheric Height Profiles Measured by Radio Occultation Technique
Authors: Hsieh Mon-Chai , Huang Guan-Han , Dmitriev Alexei V. , Lin Chia-Hsien
Published: September 2022

Modern space missions provide a great number of height profiles of ionospheric electron density, measured by the remote sensing technique of radio occultation (RO). The deducing of the profiles from the RO measurements suffers from bias, resulting in negative values of the electron density. We developed a machine learning technique that allows automatic identification of ionospheric layers and avoids the bias problem. An algorithm of convolutional neural networks was applied for the classification of the height profiles. Six classes of the profiles were distinguished on the base of prominent ionospheric layers F2, Es, E, F1 and F3, as well as distorted profiles (Sc). For the models, we selected the ground truth of more than 712 height profiles measured by the COSMIC/Formosat-3 mission above Taiwan from 2011 to 2013. Two different models, a 1D convolutional neural network (CNN) and fully convolutional network (FCN), were applied for classification. It was found that both models demonstrate the best classification performance, with the average accuracy around 0.8 for prediction of the F2 layer-related class and the E layer-related class. The F1 layer is classified by the models with good performance (>0.7). The CNN model can effectively classify the Es layer with an accuracy of 0.75. The FCN model has good classification performance (0.72) for the Sc-related profiles. The lowest performance (<0.4) was found for the F3 layer-related class. It was shown that the more complex FCN model has better classification performance for both large-scale and small-scale variations in the height profiles of the ionospheric electron density.
Mobile Laser Scanning for Estimating Tree Structural Attributes in a Temperate Hardwood Forest
Authors: Vandendaele Bastien , Martin-Ducup Olivier , Fournier Richard A. , Pelletier Gaetan , Lejeune Philippe
Published: September 2022

The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS also shows great potential for estimating inventory attributes that are difficult to measure in the field, such as wood volume or crown dimensions. Hardwood species represent a significant challenge for wood volume estimation compared to softwoods because a substantial portion of the volume is included in the crown, making them more prone to allometric bias and more complex to model. This study assessed the potential of MLS data to estimate tree structural attributes in a temperate hardwood stand: height, crown dimensions, diameter at breast height (DBH), and merchantable wood volume. Merchantable wood volume estimates were evaluated to the third branching order using the quantitative structural modeling (QSM) approach. Destructive field measurements and terrestrial laser scanning (TLS) data of 26 hardwood trees were used as reference to quantify errors on wood volume and inventory attribute estimations from MLS data. Results reveal that SLAM-based MLS systems provided accurate estimates of tree height (RMSE = 0.42 m (1.78%), R2 = 0.93), crown projected area (RMSE = 3.23 m2 (5.75%), R2 = 0.99), crown volume (RMSE = 71.4 m3 (23.38%), R2 = 0.99), DBH (RMSE = 1.21 cm (3.07%), R2 = 0.99), and merchantable wood volume (RMSE = 0.39 m3 (18.57%), R2 = 0.95), when compared to TLS. They also estimated operational merchantable volume with good accuracy (RMSE = 0.42 m3 (21.82%), R2 = 0.94) compared to destructive measurements. Finally, the merchantable stem volume derived from MLS data was estimated with high accuracy compared to TLS (RMSE = 0.11 m3 (8.32%), R2 = 0.96) and regional stem taper models (RMSE = 0.16 m3 (14.7%), R2 = 0.93). We expect our results would provide a better understanding of the potential of SLAM-based MLS systems to support in-situ forest inventory.
A Novel Knowledge Distillation Method for Self-Supervised Hyperspectral Image Classification
Authors: Chi Qiang , Lv Guohua , Zhao Guixin , Dong Xiangjun
Published: September 2022

Using deep learning to classify hyperspectral image(HSI) with only a few labeled samples available is a challenge. Recently, the knowledge distillation method based on soft label generation has been used to solve classification problems with a limited number of samples. Unlike normal labels, soft labels are considered the probability of a sample belonging to a certain category, and are therefore more informative for the sake of classification. The existing soft label generation methods for HSI classification cannot fully exploit the information of existing unlabeled samples. To solve this problem, we propose a novel self-supervised learning method with knowledge distillation for HSI classification, termed SSKD. The main motivation is to exploit more valuable information for classification by adaptively generating soft labels for unlabeled samples. First, similarity discrimination is performed using all unlabeled and labeled samples by considering both spatial distance and spectral distance. Then, an adaptive nearest neighbor matching strategy is performed for the generated data. Finally, probabilistic judgment for the category is performed to generate soft labels. Compared to the state-of-the-art method, our method improves the classification accuracy by 4.88%, 7.09% and 4.96% on three publicly available datasets, respectively.
Radiant Power Patterns Inferred from Remote Sensing Using a Cloud Computing Platform, during the 2021 Fagradalsfjall Eruption, Iceland
Authors: Aufaristama Muhammad , Hoskuldsson Armann , van der Meijde Mark , van der Werff Harald , Moreland William Michael , Jonsdottir Ingibjorg
Published: September 2022

The effusive eruption at Mt. Fagradalsfjall began on 19 March 2021 and it ended a period of about 800 years of volcano dormancy on the Reykjanes Peninsula. To monitor and evaluate power output of the eruption, we compiled in total 254 freely available satellite images from Terra MODIS and Landsat 8 OLI-TIRS via the Google Earth Engine platform over a six-month period. This cloud computing platform offers unique opportunities for remote sensing data collection, processing, analysis, and visualizations at a regional scale with direct access to a multi-petabyte analysis-ready data catalogue. The average radiant power from the lava during this time was 437 MW, with a maximum flux of 3253 MW. The intensity thermal power output of the 2021 Fagradalsfjall (3253 MW) is in marked contrast to radiant power observed at the 2014–2015 Holuhraun Iceland (11956 MW) where, while both eruptions also hosted active lava pools and channel, Holuhraun exhibited a much greater variability in radiant power over the same period of time. We performed Spearman correlation coefficient (SCC). Our results show a positive correlation (0.64) with radiative power from the MODVOLC system, which suggests that both results follow the same general trend. The results provide a unique temporal data set of heat flux, hosted, and processed by a cloud computing platform. This enabled the rapid assessment of eruption evolution via a cloud computing platform which can collect and process time series data within minutes.
A New Multiple Phenological Spectral Feature for Mapping Winter Wheat
Authors: Cai Wenxin , Tian Jinyan , Li Xiaojuan , Zhu Lin , Chen Beibei
Published: September 2022

Rapid and accurate mapping of winter wheat using remote sensing technology is essential for ensuring food security. Most of the existing studies have failed to fully characterize the phenological features of winter wheat in mapping, resulting in low classification accuracy. To this end, this study developed a new multiple phenological spectral feature (Mpsf) and then used the generated new features as input data for a one-class classifier (One-Class Support Vector Machine, OCSVM) to map winter wheat. The main steps in this work are as follows: (1) Identifying key phenological periods. The spectral indices temporal profiles of winter wheat (after cloud masking) were drawn separately using different spectral indices, and the key phenological periods of winter wheat were identified with a priori knowledge of phenology. (2) Composition for a new feature. Composited the spectral features of winter wheat for each key phenological period to generate a new feature. (3) Training using a one-class classifier. The new feature was put into OCSVM for training, and the final winter wheat mapping result in the Beijing region was obtained. The cost of this new winter wheat mapping method is low and the accuracy is high. To verify the accuracy of this study, we compared the Mpsf map with three kinds of reference data, and all of them got good results. In comparison, with ground truth samples from Sentinel-2, the total accuracy was overall higher than 97.9%. The relative error of the 2019 winter wheat mapping result was only 0.51%, compared with the data from the Beijing Bureau of Statistics. In comparison, with an up-to-date available winter wheat-mapping product for Beijing (spatial resolution: 30 m), the Mpsf map has significantly fewer misclassifications. To our knowledge, this study produced one of the highest accuracy winter wheat-mapping products in Beijing for 2018 and 2019 to date. In general, we hope that this work can promote the development of winter wheat mapping and provide a reference for sustainable agricultural development and governmental decision-making.
Spatial Cross-Correlation of GOSAT CO2 Concentration with Repeated Heat Wave-Induced Photosynthetic Inhibition in Europe from 2009 to 2017
Authors: Hwang Young-Seok , Schlüter Stephan , Um Jung-Sup
Published: September 2022

In recent decades, European countries have faced repeated heat waves. Traditionally, atmospheric CO2 concentration linked to repeated heat wave-induced photosynthetic inhibition has been explored based on local-specific in-situ observations. However, previous research based on field surveys has limitations in exploring area-wide atmospheric CO2 concentrations linked to repeated heat wave-induced photosynthetic inhibition. The present study aimed to evaluate the spatial cross-correlation of Greenhouse gases Observing SATellite (GOSAT) CO2 concentrations with repeated heat wave-induced photosynthetic inhibition in Europe from 2009 to 2017 by applying geographically weighted regression (GWR). The local standardized coefficient of a fraction of photosynthetically active radiation (FPAR: −0.24) and the normalized difference vegetation index (NDVI: −0.22) indicate that photosynthetic inhibition increases atmospheric CO2 in Europe. Furthermore, from 2009 to 2017, the heat waves in Europe contributed to CO2 emissions (27.2–32.1%) induced by photosynthetic inhibition. This study provides realistic evidence to justify repeated heat wave-induced photosynthetic inhibition as a fundamental factor in mitigating carbon emissions in Europe.
Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea
Authors: Yang Yue , Gao Shanhong , Wang Yongming , Shi Hao
Published: September 2022

Assimilation of satellite-derived humidity with a homogenous static background error covariance (B) matrix computed over the entire computational domain (Full-B) tends to overpredict sea fog coverage. A feature-dependent B (Fog-B) is proposed to address this issue. In Fog-B, the static error statistics for clear air and foggy areas are calculated separately using a feature-dependent binning method. The resultant error statistics are used simultaneously at appropriate locations guided by the satellite-derived sea fog. Diagnostics show that Full-B generally has broader horizontal and vertical length scales and larger error variances than Fog-B below ~300 m except for the vertical length scale near the surface. Experiments on three sea fog cases over the Yellow Sea are conducted to understand and examine the impact of Fog-B on sea fog analyses and forecasts. Results show that using Full-B produces greater and broader water vapor mixing ratio increments and thus predicts larger sea fog coverage than using Fog-B. Further evaluations suggest that using Fog-B has greater forecast skills in sea fog coverage and more accurate moisture conditions than using Full-B.
Airborne Elevation DBF-TOPS SAR/InSAR Method Based on LOS Motion Compensation and Channel Error Equalization
Authors: Suo Zhiyong , Ti Jingjing , Xiang Hongli , Zhang Leru , Xing Chao , Wang Tingting
Published: September 2022

Digital beamforming (DBF) TOPS SAR in elevation is a new synthetic aperture radar (SAR) system, which has the advantage of wide swath coverage and a high signal-to-noise ratio (SNR). In this paper, considering the phase preservation demand for interferometric SAR (InSAR) processing, the complete processing chain for DBF-TOPS SAR/InSAR in elevation is proposed with a wide beam angle and channels’ amplitude and phase errors. Firstly, we analyze the airborne motion compensation method along the line-of-sight direction for TOPS SAR with squint angle. Furthermore, for the large-range beam angle of DBF, the sub-swaths division process is presented for the range-dependent radar look angle, and the sub-swaths division criterion is also given in the analytic expression. Then, the relative amplitude and phase errors’ estimation and compensation method between channels is provided in the range frequency domain based on the pivoting filter with coherence weighting, which is convenient for DBF processing and SNR improvement. Finally, the DEMs are generated under different conditions to compare the phase preservation performance. The effectiveness of the proposed processing chain is verified with both simulated data and airborne real DBF-TOPS SAR/InSAR data.
A Numerical Analysis of the Changes in O3 Concentration in a Wildfire Plume
Authors: Kim Dongjin , Jeon Wonbae , Park Jaehyeong , Mun Jeonghyeok , Choi Hyunsik , Kim Cheol-Hee , Lee Hyo-Jung , Jo Hyun-Young
Published: September 2022

This study analyzed the characteristics of changes in O3 concentration in a plume induced by a wildfire in Andong, South Korea, from 24 to 26 April 2020, using the Community Multi-scale Air Quality (CMAQ) model. Fire INventory from National Center for Atmospheric Research (FINN) emissions data were used for the wildfire emissions. The increases in the concentrations of primary pollutants (CO, NOx, and volatile organic compounds (VOCs)) due to the wildfire peaked near the source at 09 LST and, as the plume was transported, the reduction in the supply of pollutants from wildfire, as well as chemical reactions, advection and diffusion, and deposition, caused the concentrations to continuously decrease. In contrast, O3 concentration showed a sustained increase during transport due to photochemical reactions caused by precursors (e.g., NOx, VOCs) emitted during the wildfire, peaking (1.40 ppb) at approximately 1 km at 13 LST over 60 km from the source. To analyze these results, a process analysis was conducted. Integrated process rate (IPR) analysis results showed that the production rate of O3 and loss rates of NOx and VOCs peaked at 09 LST due to the photochemical reactions of NOx and VOCs emitted due to wildfire. Then, as the plume was transported, the loss rates of NOx and VOCs that contributed to O3 production continued to decrease at 11 LST. The O3 production rate also decreased at 11 LST but increased at 13 LST due to increasing solar radiation. This indicates that the O3 concentration is complexly determined by O3 precursors and solar radiation. Additionally, IRR analysis revealed that NO and NO2 emitted during wildfire and solar radiation contributed to the production and loss processes of O3; the production reactions of O3 were predominant, and O3 was accumulated and transported in the plume, leading to the peak O3 concentration at 13 LST.
Towards Robust Semantic Segmentation of Land Covers in Foggy Conditions
Authors: Shi Weipeng , Qin Wenhu , Chen Allshine
Published: September 2022

When conducting land cover classification, it is inevitable to encounter foggy conditions, which degrades the performance by a large margin. Robustness may be reduced by a number of factors, such as aerial images of low quality and ineffective fusion of multimodal representations. Hence, it is crucial to establish a reliable framework that can robustly understand remote sensing image scenes. Based on multimodal fusion and attention mechanisms, we leverage HRNet to extract underlying features, followed by the Spectral and Spatial Representation Learning Module to extract spectral-spatial representations. A Multimodal Representation Fusion Module is proposed to bridge the gap between heterogeneous modalities which can be fused in a complementary manner. A comprehensive evaluation study of the fog-corrupted Potsdam and Vaihingen test sets demonstrates that the proposed method achieves a mean F1score exceeding 73%, indicating a promising performance compared to State-Of-The-Art methods in terms of robustness.
A Case Study on Ground Subsidence and Backfill Deformation Induced by Multi-Stage Filling Mining in a Steeply Inclined Ore Body
Authors: Li Guang , Wan Yang , Guo Jie , Ma Fengshan , Zhao Haijun , Li Zhiqing
Published: September 2022

The backfill mining method transports treated tailings to the mined-out area, which not only improves the surrounding environment of the mine but also enables the mined-out area to continue mining and production under the support of the filling body. However, with the growth in the depth and scale of mining, ground subsidence, and backfill deformation are becoming increasingly serious problems. As an example, in the Jinchuan mine, a typical multi-stage filling mining mine in China, the deformation law of surface rock mass and backfill are studied through a method combining field monitoring and numerical simulation. The major findings are as follows: (a) A settlement funnel is formed on the ground, and its radius gradually expands with continuous mining and filling. The location of the settlement center moves toward the surface above the footwall of the ore body, and the maximum subsidence reaches 739 mm in 14.5 years. (b) Three-section mining significantly affects the surface deformation, and the single subsidence center on the upper wall develops into the double subsidence center with the mining and filling. When the three-section mining is finished, the maximum value of the surface subsidence reaches about 1.35 m and the mining area is still in a relatively stable state. (c) The whole filling body presents obvious subsidence, with the development of the multi-stage mining and filling. Bed separation phenomena are found between the filling layers, and the closer to the interior, the more obvious it becomes. The backfill’s subsidence characteristics are similar to the surface’s; that is, both the subsidence amount and speed are higher on the hanging wall than on the footwall. (d) The backfill mainly shrinks inward in the horizontal direction, and the deformation is mainly manifested as an internal uplift and an external subsidence in the vertical direction. The mass instability of the backfill is difficult because of the insufficient deformation space, and the influence of large-scale deformation on the mining and overlying strata needs to be considered, as well as the local deformation near the rock contact zone surrounding the backfill. The results provide technical support for filling mining in the Jinchuan mine and provide a reference for other projects with similar engineering conditions.
New Insights into Surface Deposits in the Balmer-Kapteyn Cryptomare Region Provided by Chang’E-2 Microwave Radiometer Data
Authors: Tang Tianqi , Meng Zhiguo , Lian Yi , Xiao Zhiyong , Ping Jingsong , Cai Zhanchuan , Zhang Xiaoping , Dong Xuegang , Zhang Yuanzhi
Published: September 2022

Lunar cryptomare records both early-stage mare volcanisms and large-scale impact cratering, which can provide important information about the thermal evolution of the Moon. We built a mixing dielectric constant model to represent the cryptomare deposits mixed by highland debris and mare deposits, and the proper radiative transfer simulation was constructed to evaluate the thermal emission features of surface deposits in the cryptomare region. The microwave radiometer (MRM) data in the Balmer-Kapteyn region were extracted, and the linear interpolation method was used to generate brightness temperature (TB) maps at noon and at night. To enhance the correlation between cryptomare deposits and TB performances, normalized TB (nTB) and TB difference (dTB) maps were also generated. Combined with the datasets, including Lunar Reconnaissance Orbiter Wide Angle Camera, Lunar Orbiter Laser Altimeter, and Diviner and Clementine UV–VIS, the main findings are as follows: (1) The mare-like cryptomare deposits were discovered and identified according to the nTB and dTB performances. Combined with the surface compositions, at least two kinds of buried mare deposits were identified in the B-K region, which erupted during different episodes. (2) A construct-like volcanic feature was suggested by the nTB and dTB performances. (3) The results of our analysis indicated the presence of materials with low dTB anomalies in the northern and southwestern parts of the cryptomare region and in the mare unit within the Vendelinus crater, which illustrates the heterogeneity of the lunar crust in the vertical direction.
Precipitation over the U.S. Coastal Land/Water Using Gauge-Corrected Multi-Radar/Multi-Sensor System and Three Satellite Products
Authors: Xu Yike , Arevalo Jorge , Ouyed Amir , Zeng Xubin
Published: September 2022

The weather and climate over the coastal regions have received increasing attention because of substantial population growth, the rising sea level, and extreme weather. Satellite remote sensing provides global precipitation estimates (including coastal land/ocean). While these datasets have been extensively evaluated over land, they have rarely been assessed over coastal ocean. As precipitation radars cover both coastal land and ocean, we used the Multi-Radar/Multi-Sensor System (MRMS) gauge-corrected precipitation product from 2018 to 2020 to evaluate three widely used satellite-based precipitation products over the U.S. coastal land versus the ocean (and the water over the Great Lakes). These products included the Integrated Multi-satellite Retrievals for GPM (IMERG), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center Morphing technique (CMORPH). The MRMS data showed a precipitation climatology difference between the coastal land and the ocean that was higher in the winter and lower in the summer and autumn. IMERG and CMORPH performed best over land and water, respectively, while PERSIANN was the most consistent in its performance over land versus water. Heavy precipitation was overestimated by the three products, with larger overestimates over water than over land. These results were not affected by the MRMS uncertainties due to the gauge correction or by the use of different versions.
Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF
Authors: Tang Mingzhu , Cao Chenhuan , Wu Huawei , Zhu Hongqiu , Tang Jun , Peng Zhonghui , Wang Yifan
Published: September 2022

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.
Extending Non-Ambiguity Range of Dual-Comb Ranging for a Mobile Target Based on FPGA
Authors: Liu Ruoyu , Yu Haoyang , Wang Yue , Li Yu , Liu Xinda , Zhang Pengpeng , Zhou Qian , Ni Kai
Published: September 2022

Dual-comb ranging (DCR) is an important method in absolute distance ranging because of its high precision, fast acquisition rate, and large measuring range. DCR needs to obtain precise results during distance measurements for a mobile target. However, the non-ambiguity range (NAR) is a challenge when pushing the dual-comb ranging to the industry field. This paper presents a solution for extending NAR by designing an algorithm and realizing it on a field-programmable gate array (FPGA). The algorithm is robust when facing the timing jitter in the optical frequency comb. Without averaging, the Allan deviation of the results in 1 ms is ∼3.89 μm and the Allan deviation of the results is ∼0.37 μm at an averaging time of 100 ms when the target object is standstill near the NAR. In addition, several ranging experiments were conducted on a mobile target whose speed was from ∼5 mm/s to ∼10 mm/s. The experimental results verify the effectiveness and robustness of our design. The implemented design is an online and real-time data processing unit that shows great industrial potential for using the DCR system.
Machine Vision-Based Fatigue Crack Propagation System
Authors: Gebauer Jan , Šofer Pavel , Jurek Martin , Wagnerová Renata , Czebe Jiří
Published: September 2022

This paper introduces a machine vision-based system promising low-cost solution for detecting a fatigue crack propagation caused by alternating mechanical stresses. The fatigue crack in technical components usually starts on surfaces at stress concentration points. The presented system was designed to substitute a strain gauge sensor-based measurement using an industrial camera in cooperation with branding software. This paper presents implementation of a machine vision system and algorithm outputs taking on fatigue crack propagation samples.
Cost-Efficient Coverage of Wastewater Networks by IoT Monitoring Devices
Authors: Sikorski Arkadiusz , Solano Donado Fernando , Kozdrowski Stanisław
Published: September 2022

Wireless sensor networks are fundamental for technologies related to the Internet of Things. This technology has been constantly evolving in recent times. In this paper, we consider the problem of minimising the cost function of covering a sewer network. The cost function includes the acquisition and installation of electronic components such as sensors, batteries, and the devices on which these components are installed. The problem of sensor coverage in the sewer network or a part of it is presented in the form of a mixed-integer programming model. This method guarantees that we obtain an optimal solution to this problem. A model was proposed that can take into account either only partial or complete coverage of the considered sewer network. The CPLEX solver was used to solve this problem. The study was carried out for a practically relevant network under selected scenarios determined by artificial and realistic datasets.
Transmission Line Object Detection Method Based on Contextual Information Enhancement and Joint Heterogeneous Representation
Authors: Zhao Lijuan , Liu Chang’an , Qu Hongquan
Published: September 2022

Transmission line inspection plays an important role in maintaining power security. In the object detection of the transmission line, the large-scale gap of the fittings is still a main and negative factor in affecting the detection accuracy. In this study, an optimized method is proposed based on the contextual information enhancement (CIE) and joint heterogeneous representation (JHR). In the high-resolution feature extraction layer of the Swin transformer, the convolution is added in the part of the self-attention calculation, which can enhance the contextual information features and improve the feature extraction ability for small objects. Moreover, in the detection head, the joint heterogeneous representations of different detection methods are combined to enhance the features of classification and localization tasks, which can improve the detection accuracy of small objects. The experimental results show that this optimized method has a good detection performance on the small-sized and obscured objects in the transmission line. The total mAP (mean average precision) of the detected objects by this optimized method is increased by 5.8%, and in particular, the AP of the normal pin is increased by 18.6%. The improvement of the accuracy of the transmission line object detection method lays a foundation for further real-time inspection.
A PI Control Method with HGSO Parameter Regulator for Trajectory Planning of 9-DOF Redundant Manipulator
Authors: Liu Meijiao , Liu Tianyu , Zhu Mingchao , Chen Liheng
Published: September 2022

In order to solve the tracking accuracy problem of the redundant manipulator, a PI control method with Henry gas solubility optimization parameter regulator (PI-HGSO) is proposed in this paper. This method consists of the controller and the parameter regulator. The characteristic is that the position deviation of a manipulator is equivalent to a specific function; namely, the proportional-integral (PI) controller is used to adjust the deviation input. The error can be better corrected by the processing of the PI controller so that the inverse kinematics solution of the minimum error can be realized. At the same time, the parameter selection of PI controllers has always been a difficulty in controller design. To address the problem, Henry gas solubility optimization (HGSO) is selected as a parameter regulator to optimize the parameters and obtain the optimal controller, thereby achieving high-precision trajectory tracking. Experiments on 9-DOF redundant manipulator show that our method achieves competitive tracking accuracy in contrast with others. Meanwhile, the efficiency and accuracy of the PI controller are greatly guaranteed by using HGSO to automatically optimize controller parameters instead of making approximate adjustments through infinite manual trial and error. Therefore, the feasibility and competitive superiority of PI-HGSO is fully proved in trajectory planning of redundant manipulators.
EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone
Authors: Shu Mingcong , Chen Guoliang , Zhang Zhenghua
Published: September 2022

The pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophisticated gaits or heterogeneous devices, their inaccuracy varies dramatically. This paper proposes an efficient learning-based stride-length estimation model using a smartphone to obtain the correct stride length. The model uses adaptive learning to extract different elements for changing and recognition tasks, including Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) modules. The direct fusion method maps the eigenvectors to the appropriate stride length after combining the features from the learning modules. We presented an online learning module to update the model to increase the SLE model’s generalization. Extensive experiments are conducted with heterogeneous devices or users, various gaits, and switched scenarios. The results confirm that the proposed method outperforms other state-of-the-art methods and achieves an average 4.26% estimation error rate in various environments.
Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool
Authors: Asadi Samira , Moallem Mehrdad , Wang G.
Published: September 2022

This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An H∞ performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods.
Fixed Point Iteration Based Algorithm for Asynchronous TOA-Based Source Localization
Authors: Zou Yanbin , Fan Jingna , Wu Liehu , Liu Huaping
Published: September 2022

This paper investigates the problem of source localization using signal time-of-arrival (TOA) measurements in the presence of unknown start transmission time. Most state-of-art methods are based on convex relaxation technologies, which possess global solution for the relaxed optimization problem. However, computational complexity of the convex optimization–based algorithm is usually large, and need CVX toolbox to solve it. Although the two stage weighted least squares (2SWLS) algorithm has very low computational complexity, its estimate performance is susceptible to sensor geometry and threshold phenomenon. A new algorithm that is directly derived from maximum likelihood estimator (MLE) is developed. The newly proposed algorithm is named as fixed point iteration (FPI); it only involves simple calculations, such as addition, multiplication, division, and square-root. Unlike state-of-the-art methods, there is no matrix inversion operation and can avoid the unstable performance incurred by singular matrix. The FPI algorithm can be easily extended to the scenario with sensor position errors. Finally, simulation results demonstrate that the proposed algorithm reaches a good balance between computational complexity and localization accuracy.
A Lightweight and Accurate UAV Detection Method Based on YOLOv4
Authors: Cai Hao , Xie Yuanquan , Xu Jianlong , Xiong Zhi
Published: September 2022

At present, the UAV (Unmanned Aerial Vehicle) has been widely used both in civilian and military fields. Most of the current object detection algorithms used to detect UAVs require more parameters, and it is difficult to achieve real-time performance. In order to solve this problem while ensuring a high accuracy rate, we further lighten the model and reduce the number of parameters of the model. This paper proposes an accurate and lightweight UAV detection model based on YOLOv4. To verify the effectiveness of this model, we made a UAV dataset, which contains four types of UAVs and 20,365 images. Through comparative experiments and optimization of existing deep learning and object detection algorithms, we found a lightweight model to achieve an efficient and accurate rapid detection of UAVs. First, from the comparison of the one-stage method and the two-stage method, it is concluded that the one-stage method has better real-time performance and considerable accuracy in detecting UAVs. Then, we further compared the one-stage methods. In particular, for YOLOv4, we replaced MobileNet with its backbone network, modified the feature extraction network, and replaced standard convolution with depth-wise separable convolution, which greatly reduced the parameters and realized 82 FPS and 93.52% mAP while ensuring high accuracy and taking into account the real-time performance.
Effects of Receiver Beamforming for Vital Sign Measurements Using FMCW Radar at Various Distances and Angles
Authors: Ahmed Shahzad , Park Junbyung , Cho Sung Ho
Published: September 2022

Short-range millimeter wave radar sensors provide a reliable, continuous and non-contact solution for vital sign extraction. Off-The-Shelf (OTS) radars often have a directional antenna (beam) pattern. The transmitted wave has a conical main lobe, and power of the received target echoes deteriorate as we move away from the center point of the lobe. While measuring vital signs, the human subject is often located at the center of the antenna lobe. Since beamforming can increase signal quality at the side (azimuth) angles, this paper aims to provide an experimental comparison of vital sign extraction with and without beamforming. The experimental confirmation that beamforming can decrease the error in the vital sign extraction through radar has so far not been performed by researchers. A simple, yet effective receiver beamformer was designed and a concurrent measurement with and without beamforming was made for the comparative analysis. Measurements were made at three different distances and five different arrival angles, and the preliminary results suggest that as the observation angle increases, the effectiveness of beamforming increases. At an extreme angle of 40 degrees, the beamforming showed above 20% improvement in heart rate estimation. Heart rate measurement error was reduced significantly in comparison with the breathing rate.
Lifelong Adaptive Machine Learning for Sensor-Based Human Activity Recognition Using Prototypical Networks
Authors: Adaimi Rebecca , Thomaz Edison
Published: September 2022

Continual learning (CL), also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such systems is to extend the activity model to dynamically adapt to changes in people’s everyday behavior. Current research in CL applied to the HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems. To push this field forward, we build on recent advances in the area of continual learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on five publicly available activity datasets in terms of its ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free CL and uncover useful insights for future challenges.
SkiMon: A Wireless Body Area Network for Monitoring Ski Flex and Motion during Skiing Sports
Authors: Crandall Aaron S. , Mamolo Steven , Morgan Mathew
Published: September 2022

Monitoring and gathering data on sporting activities holds significant promise for athletes, equipment developers, and physical fitness clinicians. Wireless Body Area Networks are being used in sporting environments as a means of gathering data, providing feedback, and helping to gain understanding of athletic activities. Applying WBANs to skiing situations, which have higher vibration, velocities, and damp environments than many other sports, can open up opportunities to understand the dynamics of skiing equipment behaviors, skiing routes on mountains, and how individuals react when skiing. To support these outcomes, a prototype WBAN-style off the shelf component system called SkiMon was proposed, implemented, and tested. The SkiMon system uses inexpensive ESP8266, Raspberry Pi, and sensor devices to gather high quality motion and location tracking data on skiers in real-world skiing conditions. By using IEEE 802.11b/g/n wireless networks, SkiMon is able to sample data at a minimum of 50 Hz, which is enough to model most ski vibration behaviors. These data results are shown to reflect ground truth 3D maps and the acceleration data comports with earlier works on ski vibration testing. Overall, a WBAN-based commodity components solution shows promise as a high quality sensor platform for tracking and modeling skiing activities.
Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships
Authors: Stübinger Johannes , Walter Dominik
Published: September 2022

This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.
Crack Detection of Threaded Steel Rods Based on Ultrasonic Guided Waves
Authors: Peng Kunhong , Zhang Yi , Xu Xian , Han Jinsong , Luo Yaozhi
Published: September 2022

Fatigue cracks are typical damage of threaded steel rods under dynamic loads. This paper presents a study on ultrasonic guided waves-based, fatigue-crack detection of threaded rods. A threaded rod with given sizes is theoretically simplified as a cylindrical rod. The propagation characteristics of ultrasonic guided waves in the cylindrical rod are investigated by semi-analytical finite element method and the longitudinal L(0, 1) modal ultrasonic guided waves in low frequency band is proposed for damage detection of the rod. Numerical simulation on the propagation of the proposed ultrasonic guided waves in the threaded rod without damage shows that the thread causes echoes of the ultrasonic guided waves. A numerical study on the propagation of the proposed ultrasonic guided waves in the threaded rod with a crack on the intersection of the smooth segment and the threaded segment shows that both linear indexes (Rf and ARS) and nonlinear indexes (βre′ and β′) are able to detect the crack. A constant-amplitude tensile fatigue experiment was conducted on a specimen of the threaded rod to generate fatigue cracks in the specimen. After every 20,000 loading cycles, the specimen was tested by the proposed ultrasonic guided waves and evaluated by the linear indexes and nonlinear indexes. Experimental results show that both the linear and nonlinear indexes of the ultrasonic guided waves are able to identify the crack before it enters the rapid growth stage and the nonlinear indexes detect the crack easier than the linear indexes.
Characterization of Biocomposites and Glass Fiber Epoxy Composites Based on Acoustic Emission Signals, Deep Feature Extraction, and Machine Learning
Authors: Kek Tomaž , Potočnik Primož , Misson Martin , Bergant Zoran , Sorgente Mario , Govekar Edvard , Šturm Roman
Published: September 2022

This study presents the results of acoustic emission (AE) measurements and characterization in the loading of biocomposites at room and low temperatures that can be observed in the aviation industry. The fiber optic sensors (FOS) that can outperform electrical sensors in challenging operational environments were used. Standard features were extracted from AE measurements, and a convolutional autoencoder (CAE) was applied to extract deep features from AE signals. Different machine learning methods including discriminant analysis (DA), neural networks (NN), and extreme learning machines (ELM) were used for the construction of classifiers. The analysis is focused on the classification of extracted AE features to classify the source material, to evaluate the predictive importance of extracted features, and to evaluate the ability of used FOS for the evaluation of material behavior under challenging low-temperature environments. The results show the robustness of different CAE configurations for deep feature extraction. The combination of classic and deep features always significantly improves classification accuracy. The best classification accuracy (80.9%) was achieved with a neural network model and generally, more complex nonlinear models (NN, ELM) outperform simple models (DA). In all the considered models, the selected combined features always contain both classic and deep features.
How Non-Governmental-Organization-Built Small-Scale Irrigation Systems Are a Failure in Africa
Authors: Keita Amadou , Niang Dial , Sandwidi Sibri Alphonse
Published: September 2022

Every year, millions of dollars are invested in irrigation development in Sahelian African countries. After shifting from governmental organizations to non-governmental organizations (NGOs), the vulnerability of local populations has not changed much over the last 60 years in Africa. In this study, ten 1 ha small-scale irrigation systems—spread over the two driest climatic zones—were investigated in Burkina Faso. The soils and subsoils were characterized using double-ring infiltration measurements and two soil databases. The irrigation systems’ operability was assessed by sampling 10–12 farmers per system. A total of eight pumping tests were performed on a sample of wells. To assess the yield of cultivated onion, 5 to 7 squares were followed up in each of the 10 systems. Results indicated that water availability was ensured nowhere. The 32 wells were dug in clayey subsoils. Six of them yielded available water Ve flows ranging from 0.0 to 6.1 m3/day, far below the 80 m3/(ha·day) required by onion. To solve this issue, the NGOs shifted to a low-pressure drip irrigation solution, but the too-low pressure of 0.1 bar led to clogging. Ultimately, all 10 systems (except Louda) broke down a few months after the project’s end.