bluuebunny/crossref_metadata_2025 huggingface dataset:
Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network
Authors: Shen Jianming , Liu Po , Xia Jun , Zhao Yanjun , Dong Yi
Published: August 2022

To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of 0.05°, correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of 0.25° or 0.1°, correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations.
LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
Authors: Melman Floor Thomas , Zoccarato Paolo , Orgel Csilla , Swinden Richard , Giordano Pietro , Ventura-Traveset Javier
Published: August 2022

With the renewed interest in lunar surface exploration, the European Space Agency envisions to stimulate the creation of lunar communications and navigation services (LCNS) to enable, among others, autonomous navigation capabilities for lunar rovers. As the number of satellites foreseen in such a service is much smaller compared to Earth-based global navigation satellite systems, different complementary technologies are pursued to improve the attainable navigation accuracy for lunar rovers. One way to improve the position accuracy provided by the LCNS satellites is to constrain their vertical position using a high resolution digital elevation model (DEM). This article presents the results of a variance covariance analysis of an extended Kalman filter implementation in which the LCNS ranging measurements are used together with the altitude provided by a DEM from the Lunar Orbiter Laser Altimeter instrument of the Lunar Reconnaissance Orbiter. Assuming a realistic orbit determination and time synchronization accuracy of the LCNS satellites, the usage of a navigation-grade inertial measurement unit and an oven-controlled crystal oscillator, a 3-sigma position accuracy of less than 10 m can be obtained. Furthermore, the availability is substantially improved as the DEM-aided solution enables a position solution in case of only 3 visible satellites.
Multi-Circular SAR Three-Dimensional Image Formation via Group Sparsity in Adjacent Sub-Apertures
Authors: Yang Weixing , Zhu Daiyin
Published: August 2022

Multi-circular SAR(MCSAR) can obtain holographic three-dimensional (3D) images of interesting observation targets, which is a significant research field at present. For anisotropic scatterers, the multi-circular SAR incoherent 3D imaging strategy combines the principle of tomography SAR inversion to obtain better reconstruction results. However, in incoherent 3D imaging, the traditional L1-norm regularization method only considers sparse representation and reconstruction in the sub-aperture pixel unit along the elevation direction, and the target structural sparsity in the same pixel unit between adjacent sub-apertures is not considered. The hierarchical sparsity constraint method in multi-circular SAR 3D target reconstruction is proposed in this paper, the L1-norm regularization is used in sub-aperture elevation, and the L2,1-regularization-based group sparsity constraint is adopted in elevation of the adjacent sub-aperture. In this paper, the sparse group thresholding iterative method is proposed to reconstruct the 3D observed target image with MCSAR data. Compared with the traditional L1-norm regularization method and IAA-GLRT method for MCSAR target reconstruction in elevation, the proposed method in this paper could obtain clearer 3D observation target images, with fewer desultory points along the elevation direction. Detailed simulation data analysis and 3D imaging processing of real MCSAR data demonstrate the advantages of the proposed method.
Efficient DOA Estimation for Wideband Sources in Multipath Environment
Authors: Zhang Xiaoyu , Tao Haihong , Fang Ziye , Xie Jian
Published: August 2022

A novel direction-of-arrival (DOA) estimator is proposed for the mixed uncorrelated and correlated wideband sources in the multipath environment. Firstly, the novel signal model is established using the aligned envelope technique. Next, the estimator can be divided into two parts. Theoretical analyses show that the correlated components cannot form peaks and only the DOA of uncorrelated signals can be acquired in the first part. Then, the correlated components are extracted from the corresponding covariance matrix and handled in the second part. Using the concept of linear search, the computational complexity of the algorithm is significantly reduced. Simulation results show the direction of uncorrelated and correlated wideband signals can be processed and distinguished effectively in the proposed method. Compared with previous DOA estimators for correlated wideband sources, the estimation accuracy has been improved.
Object-Oriented Unsupervised Classification of PolSAR Images Based on Image Block
Authors: Han Binbin , Han Ping , Cheng Zheng
Published: August 2022

Land Use and Land Cover (LULC) classification is one of the tasks of Polarimetric Synthetic Aperture Radar (PolSAR) images’ interpretation, and the classification performance of existing algorithms is highly sensitive to the class number, which is inconsistent with the reality that LULC classification should have multiple levels of detail in the same image. Therefore, an object-oriented unsupervised classification algorithm for PolSAR images based on the image block is proposed. Firstly, the image is divided into multiple non-overlapping image blocks, and h/q/gray-Wishart classification is performed in each block. Secondly, each cluster obtained is regarded as an object, and the affinity matrix of objects is calculated in the global image. Finally, the objects are merged into the specified class number by density peak clustering (DPC), and the adjacent objects at the block boundary are checked and forced to merge. Experiments are carried out with the measured data of the airborne AIRSAR and E-SAR and the spaceborne GF-3. The experimental results show that the proposed algorithm achieves good classification results under a variety of class numbers.
Contributing Factors and Trend Prediction of Urban-Settled Population Distribution Based on Human Perception Measurement: A Study on Beijing, China
Authors: Qi Junnan , Meng Qingyan , Zhang Linlin , Wang Xuemiao , Gao Jianfeng , Jing Linhai , Jancsó Tamás
Published: August 2022

Population migration, accompanied by urbanization, has led to an increase in the urban-settled population. However, quantitative studies on the distribution of urban-settled population, especially at fine scale, are limited. This study explored the relationship between characteristics of human perceived environment and the distribution of settled population, and proposed a quantitative method to predict the distribution trend of settled population. Through the semantic segmentation of street view images and accessibility calculation based on traffic isochrone and points-of-interest, we determined human perception factors. The influence of human perception factors was quantified using the geographic detector method, and the settlement intention index (SII) was constructed combining the analytic hierarchy process to predict the distribution trend of settled population. The results indicated the following. (1) Human perception was one of the important factors influencing the distribution of urban-settled population, and the cycling accessibility to traffic facilities was closely related to the distribution of settled population. (2) The accessibility and visibility of green space with low independent influence portrayed a strong enhancement on the interactive effect of other perception factors. (3) The SII mapping of Beijing showed that the SII was reliable. This study analyzes the role of human perception in shaping the environment, and provides reference for population-related urban planning problems.
Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021
Authors: Xu Yang , Yang Yaping , Chen Xiaona , Liu Yangxiaoyue
Published: August 2022

As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research.
Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
Authors: Ma Xinrong , Mo Jiaqing , Zhang Jiangwei , Huang Jincheng
Published: August 2022

Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and the characteristics of automatic feature extraction by a convolutional neural network (CNN) to construct a new endpoint detection algorithm and a method of fusing multiple–scale features CNN to recognize fiber vibration signals. Firstly, a new endpoint detection algorithm combining spectral centroid and energy spectral entropy product is used to detect the vibration part of the original signal, which is used to improve the detection effect of endpoint detection. Then, CNNs of different scales are used to extract the multi–level and multi–scale features of the signal. Aiming at the problem of information loss in the pooling process, a new method of combining differential pooling features is used. Finally, a multi–layer perceptron (MLP) is used to recognize the extracted features. Experiments show that the method has an average recognition accuracy rate of 98.75% for the four types of vibration signals. Compared with traditional EMD and VMD pattern recognition and 1D–CNN methods, the accuracy of the optical fiber vibration signal recognition is higher.
A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions
Authors: Mohsenzadegan Kabeh , Tavakkoli Vahid , Kyamakya Kyandoghere
Published: August 2022

In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur (focus blur, motion blur, etc.), shadow, bad contrast, etc. Document-image distortions significantly decrease the performance of OCR systems, to the extent that they reach a performance close to zero. Therefore, a robust OCR model that performs robustly even under hard (distortion) conditions is still sorely needed. However, our comprehensive study in this paper shows that various related works can somewhat improve their respective OCR recognition performance of degraded document images (e.g., captured by smartphone cameras under different conditions and, thus, distorted by shadows, contrast, blur, etc.), but it is worth underscoring, that improved recognition is neither sufficient nor always satisfactory—especially in very harsh conditions. Therefore, in this paper, we suggest and develop a much better and fully different approach and model architecture, which significantly outperforms the aforementioned previous related works. Furthermore, a new dataset was gathered to show a series of different and well-representative real-world scenarios of hard distortion conditions. The new OCR model suggested performs in such a way that even document images (even from the hardest conditions) that were previously not recognizable by other OCR systems can be fully recognized with up to 97.5% accuracy/precision by our new deep-learning-based OCR model.
A Robust and Low Computational Cost Pitch Estimation Method
Authors: Wang Desheng , Wei Yangjie , Wang Yi , Wang Jing
Published: August 2022

Pitch estimation is widely used in speech and audio signal processing. However, the current methods of modeling harmonic structure used for pitch estimation cannot always match the harmonic distribution of actual signals. Due to the structure of vocal tract, the acoustic nature of musical equipment, and the spectrum leakage issue, speech and audio signals’ harmonic frequencies often slightly deviate from the integer multiple of the pitch. This paper starts with the summation of residual harmonics (SRH) method and makes two main modifications. First, the spectral peak position constraint of strict integer multiple is modified to allow slight deviation, which benefits capturing harmonics. Second, a main pitch segment extension scheme with low computational cost feature is proposed to utilize the smooth prior of pitch more efficiently. Besides, the pitch segment extension scheme is also integrated into the SRH method’s voiced/unvoiced decision to reduce short-term errors. Accuracy comparison experiments with ten pitch estimation methods show that the proposed method has better overall accuracy and robustness. Time cost experiments show that the time cost of the proposed method reduces to around 1/8 of the state-of-the-art fast NLS method on the experimental computer.
Low-Temperature Plasma Diagnostics to Investigate the Process Window Shift in Plasma Etching of SiO2
Authors: Lee Youngseok , Kim Sijun , Lee Jangjae , Cho Chulhee , Seong Inho , You Shinjae
Published: August 2022

As low-temperature plasma plays an important role in semiconductor manufacturing, plasma diagnostics have been widely employed to understand changes in plasma according to external control parameters, which has led to the achievement of appropriate plasma conditions normally termed the process window. During plasma etching, shifts in the plasma conditions both within and outside the process window can be observed; in this work, we utilized various plasma diagnostic tools to investigate the causes of these shifts. Cutoff and emissive probes were used to measure the electron density and plasma potential as indicators of the ion density and energy, respectively, that represent the ion energy flux. Quadrupole mass spectrometry was also used to show real-time changes in plasma chemistry during the etching process, which were in good agreement with the etching trend monitored via in situ ellipsometry. The results show that an increase in the ion energy flux and a decrease in the fluorocarbon radical flux alongside an increase in the input power result in the breaking of the process window, findings that are supported by the reported SiO2 etch model. By extending the SiO2 etch model with rigorous diagnostic measurements (or numerous diagnostic methods), more intricate plasma processing conditions can be characterized, which will be beneficial in applications and industries where different input powers and gas flows can make notable differences to the results.
CFRV: A Decentralized Control-Flow Attestation Schema Using Mutual Secret Sharing
Authors: Li Yuanpei , Zhou Qinglei , Li Bin , Zhuang Yan
Published: August 2022

Control-flow attestation (CFA) is a mechanism that securely logs software execution paths running on remote devices. It can detect whether a device is being control-flow hijacked by launching a challenge–response process. In the growing landscape of the Internet of Things, more and more peer devices need to communicate to share sensed data and conduct inter-operations without the involvement of a trusted center. Toward the scalability of CFA mechanisms and mitigating the single-point failure, it is important to design a decentralized CFA schema. This paper proposed a decentralized schema (CFRV) to verify the control flow on remote devices. Moreover, it introduces a token (asymmetric secret slices) into peer devices to make the attestation process mutual. In this case, CFRV can mitigate a particular kind of man-in-the-middle attack called response defraud. We built our prototype toolbox on Raspberry-Pi to formulate our proof of concept. In our evaluation, CFRV protects the verification process from malicious verifiers and the man-in-the-middle attack. The proposed mechanism can also limit the PKI (Public Key Infrastructure) usage to a single stage to save the peer devices’ computational cost. Compared to related decentralized schemes, the cryptographic operation’s duration is reduced by 40%.
Electroactive Biofilms of Activated Sludge Microorganisms on a Nanostructured Surface as the Basis for a Highly Sensitive Biochemical Oxygen Demand Biosensor
Authors: Kurbanalieva Saniyat , Arlyapov Vyacheslav , Kharkova Anna , Perchikov Roman , Kamanina Olga , Melnikov Pavel , Popova Nadezhda , Machulin Andrey , Tarasov Sergey , Saverina Evgeniya , Vereshchagin Anatoly , Reshetilov Anatoly
Published: August 2022

The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with phase contrast, scanning electron microscopy, and laser confocal microscopy. The features of charge transfer in the obtained electroactive biofilms were studied using the methods of cyclic voltammetry and electrochemical impedance spectroscopy. The rate constant of the interaction of microorganisms with the extracellular electron carrier (0.79 ± 0.03 dm3(g s)−1) and the heterogeneous rate constant of electron transfer (0.34 ± 0.02 cm s−1) were determined using the cyclic voltammetry method. These results revealed that the modification of the carbon nanotubes’ (CNT) electrode surface makes it possible to create electroactive biofilms. An analysis of the metrological and analytical characteristics of the created biosensors showed that the lower limit of the biosensor based on an electroactive biofilm of activated sludge is 0.41 mgO2/dm3, which makes it possible to analyze almost any water sample. Analysis of 12 surface water samples showed a high correlation (R2 = 0.99) with the results of the standard method for determining biochemical oxygen demand.
Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms
Authors: Hu Yating , Wang Zhi , Li Xiaofeng , Li Lei , Wang Xigang , Wei Yanlin
Published: August 2022

Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed’s spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm’s optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF.
Propagation Characteristics of Magnetic Tomography Method Detection Signals of Oil and Gas Pipelines Based on Boundary Conditions
Authors: Liu Linlin , Yang Lijian , Gao Songwei
Published: August 2022

The magnetic tomography method (MTM) is a non-contact external inspection method for detecting metal magnetic memory signals. It has great potential for application in long-distance oil pipeline and subsea pipeline inspection. However, the spatial distribution characteristics and propagation laws of magnetic signals are not yet clear, which makes the MTM passive detection. In this study, a three-dimensional mathematical model of the magnetic field distribution of the stress concentration zone outside the pipe was established based on the boundary conditions. For the two cases in which the stress concentration zone was located at the top and bottom of the inner wall of the pipe, the model was solved by finite element analysis. The variation law of the magnetic signal outside the pipe was analyzed, and experiments were designed to verify the model. The results show that the shape of the magnetic memory signal remained unchanged after passing through the pipe wall. As the magnetic permeability of the pipe medium is much larger than that of air, the magnetic memory signal is significantly attenuated after penetrating the pipe wall. As the detection height increases, the magnetic induction outside the tube decays exponentially. The results also prove that the magnetic tomography method can detect the stress concentration zone at any position of the pipeline, and the detection accuracy is higher when it is located at the top of the pipeline.
Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic
Authors: Sola Yoann , Le Chenadec Gilles , Clement Benoit
Published: August 2022

The marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking task, using a machine learning-based controller. There has been great progress in machine learning (in many different domains) in recent years; in the subfield of deep reinforcement learning, several algorithms suitable for the continuous control of dynamical systems have been designed. We implemented the soft actor–critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm that allows fulfilling a learning task and encourages the exploration of the environment simultaneously. We compared a SAC-based controller with a proportional integral derivative (PID) controller on a waypoint tracking task using specific performance metrics. All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub.
An Improved Trilateration Positioning Algorithm with Anchor Node Combination and K-Means Clustering
Authors: Luo Qinghua , Yang Kexin , Yan Xiaozhen , Li Jianfeng , Wang Chenxu , Zhou Zhiquan
Published: August 2022

As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes’ coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood.
Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture
Authors: Muzammel Muhammad , Yusoff Mohd Zuki , Saad Mohamad Naufal Mohamad , Sheikh Faryal , Awais Muhammad Ahsan
Published: August 2022

Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.
Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load
Authors: Borges Fábio Galvão , Guerreiro Márcio , Sampaio Monteiro Paulo Eduardo Sampaio , Janzen Frederic Conrad , Corrêa Fernanda Cristina , Stevan Sergio Luiz , Siqueira Hugo Valadares , Kaster Mauricio dos Santos
Published: August 2022

This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains. It is a smooth function with smooth derivatives. As a result, it helps avoid problems related to abrupt increases transition, commonly found in other adaptive methods. Because there is no mathematical methodology to set these parameters, this work used bio-inspired optimization algorithms. The test plant is a DC motor with a beam with a variable load. Results obtained by load and gain sweep tests prove the GAPID presents fast responses with very low overshoot and good robustness to load changes, with minimal variations, which is impossible to achieve when using the linear PID.
Multi-Geometric Reasoning Network for Insulator Defect Detection of Electric Transmission Lines
Authors: Zhai Yongjie , Hu Zhedong , Wang Qianming , Yang Qiang , Yang Ke
Published: August 2022

To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.
Facial Emotion Recognition in Verbal Communication Based on Deep Learning
Authors: Alsharekh Mohammed F.
Published: August 2022

Facial emotion recognition from facial images is considered a challenging task due to the unpredictable nature of human facial expressions. The current literature on emotion classification has achieved high performance over deep learning (DL)-based models. However, the issue of performance degradation occurs in these models due to the poor selection of layers in the convolutional neural network (CNN) model. To address this issue, we propose an efficient DL technique using a CNN model to classify emotions from facial images. The proposed algorithm is an improved network architecture of its kind developed to process aggregated expressions produced by the Viola–Jones (VJ) face detector. The internal architecture of the proposed model was finalised after performing a set of experiments to determine the optimal model. The results of this work were generated through subjective and objective performance. An analysis of the results presented herein establishes the reliability of each type of emotion, along with its intensity and classification. The proposed model is benchmarked against state-of-the-art techniques and evaluated on the FER-2013, CK+, and KDEF datasets. The utility of these findings lies in their application by law-enforcing bodies in smart cities.
A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal
Authors: Tong Qingbin , Liu Ziyu , Lu Feiyu , Feng Ziwei , Wan Qingzhu
Published: August 2022

The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao’s method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal.
Comparison of Satellite-Based and Ångström–Prescott Estimated Global Horizontal Irradiance under Different Cloud Cover Conditions in South African Locations
Authors: Mabasa Brighton , Lysko Meena D. , Moloi Sabata J.
Published: August 2022

The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms.
Aspects of Developmental Pathways toward World-Class Parasport
Authors: Storli Lise , Aune Morten , Lorås Håvard
Published: August 2022

The developmental pathways of athletes with a physical disability into world-class parasport are much less researched and understood compared to able-bodied athletes’ participation histories. The purpose of this study was to investigate the developmental pathways of para-athletes toward elite performance. Data from eight athletes with physical disabilities ranked among the top performers in Paralympics, World Championships, and/or European Championships were gathered. Thematic analysis of retrospective semi-structured interviews led to the identification of four themes. The findings showed the importance of early childhood sport-related encounters in a family environment followed by sampling of various organized and coach-led sports throughout the childhood period. The youth sport period was highly heterogenous, albeit with important transitions towards elite-level practice environments, competition, coaching, equipment adaptability and the first intentions of becoming an elite-level athlete. Throughout, significant contributions are attributed towards parents, friends, coaches, athletes, and others, towards fostering a sustained motivational climate focused on improvement and further progress. These findings could provide useful information to tailor developmental models towards elite-level performance in parasport.
Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology
Authors: Aslam Muhammad
Published: August 2022

This paper addresses the modification of the F-test for count data following the Poisson distribution. The F-test when the count data are expressed in intervals is considered in this paper. The proposed F-test is evaluated using real data from climatology. The comparative study showed the efficiency of the F-test for count data under neutrosophic statistics over the F-test for count data under classical statistics.
Technical Note on Unilateral Biportal Lumbar Endoscopic Interbody Fusion
Authors: Lau Eugene Tze-Chun , Wu Pang Hung
Published: August 2022

Unilateral biportal lumbar endoscopic interbody fusion is a relatively new technique in the field of minimally invasive spine surgery. It combines the benefits of preservation of the normal anatomy of the spine with direct visualization of the decompression of neural elements and endplate preparation for fusion. This results in high union rates and excellent outcomes for patients with back pain and lumbar spinal stenosis from spondylolisthesis while reducing the risk of injuries to the neural elements, endplate fractures and the theoretical rate of adjacent segment disease from disruption of the musculature. In this paper, we describe the steps and technical pearls pertaining to this technique and methods to avoid common pitfalls and complications. In conclusion, this technique would be a good tool in the armamentarium of a spinal surgeon specializing in minimally invasive spinal surgery.
Determining Strategic Priorities for Smart City Development: Case Studies of South Korean and International Smart Cities
Authors: Kim Seung-Chul , Hong Paul , Lee Taewon , Lee Ayeon , Park So-Hyun
Published: August 2022

Increasingly, nations are engaging in smart city development projects. Smart city development aims to build urban areas with a wholesome quality of life with modern infrastructure capabilities (e.g., economic opportunities, cultural richness, and demographic-geographical balance), technological benefits (e.g., healthcare, education, government) and sustainability goals (e.g., clean, green, safe, and secure environments). This requires huge investments of resources and participation of diverse stakeholders, whose goals are often ambiguous and conflicting. It is not unusual that many of these projects are delayed because of a lack of consensus, which further adds unnecessary social costs and a waste of time and efforts. In this sense, determining strategic priorities for smart city development is crucial for achieving a set of integrative purposes in terms of resource allocation and social benefits goals. This paper aims to determine strategic priorities by examining multiple cases of smart city projects in Korea, Europe, and the U.S. A hierarchical strategic framework, which shows the vision, core values and strategic goals, is developed and proposed from the case studies. Lessons and practical implications are discussed for future study.
Breaking the Negative Feedback Loop of Disaster, Conflict, and Fragility: Analyzing Development Aid by Japan and South Korea
Authors: Lee Suyeon , Kwon Huck-ju
Published: August 2022

Disaster risk reduction (DRR) has become an important element of donor policy, because numerous governments have expressed their commitment to helping countries vulnerable to natural hazards by mainstreaming DRR into their development programs. Meanwhile, countries that are considered fragile, as well as conflict-affected states, have faced a high risk of disasters brought on by natural hazards. However, there has been little research that addresses the complex relationship between disasters, conflict, and fragility in the context of development cooperation. Against this backdrop, this study analyzed the determinants of DRR aid allocation from Japan and South Korea—two East Asian countries that have shown a strong commitment to disaster resilience and peacebuilding—to investigate whether they are responsive to countries experiencing the combined risks of disasters and conflicts and/or fragility. Despite the vulnerable countries being in the most need, the study found that both Japan and Korea’s aid allocation has not been influenced much by the concurrence of disasters and conflict. Rather, it has been more driven by the level of a country’s climate vulnerability than the level of a country’s fragility. This suggests that developing countries facing multiple risks and challenges are at a major disadvantage in terms of the responsiveness of donors toward their needs and vulnerability.
Concentration-Temporal Multilevel Calibration of Low-Cost PM2.5 Sensors
Authors: Day Rong-Fuh , Yin Peng-Yeng , Huang Yuh-Chin T. , Wang Cheng-Yi , Tsai Chih-Chun , Yu Cheng-Hsien
Published: August 2022

Ambient aerosols have a significant impact on plant species mortality, air pollution, and climate change. It is critical to monitor the concentrations of aerosols, especially particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5), which has a direct relationship with human respiratory diseases. Recently, low-cost PM2.5 sensors have been deployed to provide a denser monitoring coverage than that of government-built monitoring supersites, which only give a macro perspective of air quality. To increase the measurement accuracy, low-cost sensors need to be calibrated. In current practice, regression techniques are used to calibrate sensors. This paper proposes a concentration-temporal multilevel calibration method to cope with the varying regression relation in different concentration and temporal domains. The performance of our method is evaluated with real field data from a supersite sensor and a low-cost sensor deployed in Puli, Taiwan. The experimental results show that our calibration method significantly outperforms linear regression in terms of R2, Root Mean Square Error, and Normalized Mean Error. Moreover, our method compares favorably with a machine learning calibration method based on gradient regression tree boosting.
The Effects of COVID-19 on the Socio-Economic Conditions of Marginal People: A Case Study in the Selected Districts of Bangladesh
Authors: Rahman Mohammad Mafizur , Alam Khosrul
Published: August 2022

In this paper, we have examined the effects of COVID-19 on the socio-economic condition of the three-wheeled electric vehicle drivers in some selected areas of Bangladesh from the cross-sectional data (September–November 2020). The results of linear regression indicate that under COVID-19 conditions, age (p = 0.022) and hardship (p = 0.000) positively, and education (p = 0.036), driving duration (p = 0.023), COVID consciousness (p = 0.086) and easy bike vehicle (p = 0.000) negatively affects income of the respondents. The deaths of COVID in the district (p = 0.003), income (p = 0.000), age (p = 0.037), easy bike vehicle (p = 0.018), debt (p = 0.059) and sufferings of diseases (p = 0.044) positively, and property holdings (p = 0.028), residence in urban areas (p = 0.004) and COVID consciousness (p = 0.082) negatively affect the family expenditure. The results from binary logistics regressions show that diseases sufferings (adjusted p = <0.001; unadjusted p = <0.001), corona fear (unadjusted p = 0.005; adjusted p = <0.001) have positive, and income (unadjusted p = <0.001; adjusted p = <0.001), cooking fuel (unadjusted p = 0.003; adjusted p = 0.091) and easy bike vehicle (unadjusted p = <0.001; adjusted p = 0.288) have negative association with hardship or misery due to COVID-19; death of COVID-19 in the district (unadjusted p = 0.008; adjusted p = 0.037), hardship or misery (adjusted p = 0.005; adjusted p = 0.001), and urban dwelling area (unadjusted p = 0.002; adjusted p = 0.004) have positive, and access to pure drinking water (unadjusted p = 0.005; adjusted p = 0.011) has negative link with corona fear; and, family savings (unadjusted p = 0.001; adjusted p = 0.013), satisfaction in the current job (unadjusted p = <0.001; adjusted p = <0.001), and government medical service (unadjusted p = 0.065; adjusted p = 0.012) have positive affiliation, and household size (unadjusted p = 0.007; adjusted p = 0.020) has negative affiliation with the continuation desire of the current job of respondents. All the obtained results are consistent and have significant policy implications.