bluuebunny/crossref_metadata_2025 huggingface dataset:
Multisensor Satellite Data and Field Studies for Unravelling the Structural Evolution and Gold Metallogeny of the Gerf Ophiolitic Nappe, Eastern Desert, Egypt
Authors: Abd El-Wahed Mohamed , Kamh Samir , Abu Anbar Mohamed , Zoheir Basem , Hamdy Mohamed , Abdeldayem Abdelaziz , Lebda El Metwally , Attia Mohamed
Published: April 2023

The gold mineralization located in the southern Eastern Desert of Egypt mostly occurs in characteristic geologic and structural settings. The gold-bearing quartz veins and the alteration zones are confined to the ductile shear zones between the highly deformed ophiolitic blocks, sheared metavolcanics, and gabbro-diorite rocks. The present study attempts to integrate multisensor remotely sensed data, structural analysis, and field investigation in unraveling the geologic and structural controls of gold mineralization in the Gabal Gerf area. Multispectral optical sensors of Landsat-8 OLI/TIRS (L8) and Sentinel-2B (S2B) were processed to map the lithologic rock units in the study area. Image processing algorithms including false color composite (FCC), band ratio (BR), principal component analysis (PCA), minimum noise fraction (MNF), and Maximum Likelihood Classifier (MLC) were effective in producing a comprehensive geologic map of the area. The mafic index (MI) = (B13-0.9147) × (B10-1.4366) of ASTER (A) thermal bands and a combined band ratio of S2B and ASTER of (S2B3+A9)/(S2B12+A8) were dramatically successful in discriminating the ophiolitic assemblage, that are considered the favorable lithology for the gold mineralization. Three alteration zones of argillic, phyllic and propylitic were spatially recognized using the mineral indices and constrained energy minimization (CEM) approach to ASTER data. The datasets of ALSO PALSAR and Sentinel-1B were subjected to PCA and filtering to extract the lineaments and their spatial densities in the area. Furthermore, the structural analysis revealed that the area has been subjected to three main phases of deformation; (i) NE-SW convergence and sinistral transpression (D2); (ii) ~E-W far field compressional regime (D3), and (iii) extensional tectonics and terrane exhumation (D4). The gold-bearing quartz veins in several occurrences are controlled by D2 and D3 shear zones that cut heterogeneously deformed serpentinites, sheared metavolcanic rocks and gabbro-diorite intrusions. The information extracted from remotely sensed data, structural interpretation and fieldwork were used to produce a gold mineralization potential zones map which was verified by reference and field observations. The present study demonstrates the remote sensing capabilities for the identification of alteration zones and structural controls of the gold mineralization in highly deformed ophiolitic regions.
Monitoring Impervious Surface Area Dynamics in Urban Areas Using Sentinel-2 Data and Improved Deeplabv3+ Model: A Case Study of Jinan City, China
Authors: Liu Jiantao , Zhang Yan , Liu Chunting , Liu Xiaoqian
Published: April 2023

Timely and rapidly mapping impervious surface area (ISA) and monitoring its spatial-temporal change pattern can deepen our understanding of the urban process. However, the complex spectral variability and spatial heterogeneity of ISA caused by the increased spatial resolution poses a great challenge to accurate ISA dynamics monitoring. This research selected Jinan City as a case study to boost ISA mapping performance through integrating the dual-attention CBAM module, SE module and focal loss function into the Deeplabv3+ model using Sentinel-2 data, and subsequently examining ISA spatial-temporal evolution using the generated annual time-series ISA data from 2017 to 2021. The experimental results demonstrated that (a) the improved Deeplabv3+ model achieved satisfactory accuracy in ISA mapping, with Precision, Recall, IoU and F1 values reaching 82.24%, 92.38%, 77.01% and 0.87, respectively. (b) In a comparison with traditional classification methods and other state-of-the-art deep learning semantic segmentation models, the proposed method performed well, qualitatively and quantitatively. (c) The time-series analysis on ISA distribution revealed that the ISA expansion in Jinan City had significant directionality from northeast to southwest from 2017 to 2021, with the number of patches as well as the degree of connectivity and aggregation increasing while the degree of fragmentation and the complexity of shape decreased. Overall, the proposed method shows great potential in generating reliable times-series ISA data and can be better served for fine urban research.
MSFANet: Multiscale Fusion Attention Network for Road Segmentation of Multispectral Remote Sensing Data
Authors: Tong Zhonggui , Li Yuxia , Zhang Jinglin , He Lei , Gong Yushu
Published: April 2023

With the development of deep learning and remote sensing technologies in recent years, many semantic segmentation methods based on convolutional neural networks (CNNs) have been applied to road extraction. However, previous deep learning-based road extraction methods primarily used RGB imagery as an input and did not take advantage of the spectral information contained in hyperspectral imagery. These methods can produce discontinuous outputs caused by objects with similar spectral signatures to roads. In addition, the images obtained from different Earth remote sensing sensors may have different spatial resolutions, enhancing the difficulty of the joint analysis. This work proposes the Multiscale Fusion Attention Network (MSFANet) to overcome these problems. Compared to traditional road extraction frameworks, the proposed MSFANet fuses information from different spectra at multiple scales. In MSFANet, multispectral remote sensing data is used as an additional input to the network, in addition to RGB remote sensing data, to obtain richer spectral information. The Cross-source Feature Fusion Module (CFFM) is used to calibrate and fuse spectral features at different scales, reducing the impact of noise and redundant features from different inputs. The Multiscale Semantic Aggregation Decoder (MSAD) fuses multiscale features and global context information from the upsampling process layer by layer, reducing information loss during the multiscale feature fusion. The proposed MSFANet network was applied to the SpaceNet dataset and self-annotated images from Chongzhou, a representative city in China. Our MSFANet performs better over the baseline HRNet by a large margin of +6.38 IoU and +5.11 F1-score on the SpaceNet dataset, +3.61 IoU and +2.32 F1-score on the self-annotated dataset (Chongzhou dataset). Moreover, the effectiveness of MSFANet was also proven by comparative experiments with other studies.
Extraction of Cotton Information with Optimized Phenology-Based Features from Sentinel-2 Images
Authors: Tian Yuhang , Shuai Yanmin , Shao Congying , Wu Hao , Fan Lianlian , Li Yaoming , Chen Xi , Narimanov Abdujalil , Usmanov Rustam , Baboeva Sevara
Published: April 2023

The spatial distribution of cotton fields is primary information for national farm management, the agricultural economy and the textile industry. Therefore, accurate cotton information at the regional scale is required with a rapid increase due to the chance provided by the huge amounts of satellite images accumulated in recent decades. Research has started to introduce the phenology characteristics shown at special growth phases of cotton but frequently focuses on limited vegetation indices with less consideration on the whole growth period. In this paper, we investigated a set of phenological and time-series features with optimization depending on each feature permutation’s importance and redundancy, followed by its performance evaluation through the cotton extraction using the Random Forest (RF) classifier. Three sets of 31 features are involved: (1) phenological features were determined by the biophysical and biochemical characteristics in the spectral space of cotton during each of its five distinctive phenological stages, which were identified from 2307 representative cotton samples using 21,237 Sentinel-2 images; (2) three typical vegetation indices were functionalized into time-series features by harmonic analysis; (3) three terrain factors were derived from the digital elevation model. Our analysis of feature determination revealed that the most valuable discriminators for cotton involve the boll opening stage and harmonic coefficients. Moreover, both qualitative and quantitative validation were performed to evaluate the retrieval of the optimized features-based cotton information. Visual examination of the map exhibited high spatial consistency and accurate delineation of the cotton field. Quantitative comparison indicates that classification of RF-coupled optimized features achieves improved overall accuracy 5.53% higher than that which works with either the limited vegetation indices. Compared with all 31 features, the optimized features realized greater identification accuracy while using only about half the number of features. Compared with test samples, the cotton map achieved an overall accuracy greater than 98% and a kappa more than 0.96. Further comparison of the cotton map area at the county-level showed a high level of consistency with the National Bureau of Statistics data from 2020, with R2 over 0.96, RMSE no more than 14.62 Kha and RRMSE less than 17.78%.
An Adaptive Feature Fusion Network with Superpixel Optimization for Crop Classification Using Sentinel-2 Imagery
Authors: Tian Xiangyu , Bai Yongqing , Li Guoqing , Yang Xuan , Huang Jianxi , Chen Zhengchao
Published: April 2023

Crop-type mapping is the foundation of grain security and digital agricultural management. Accuracy, efficiency and large-scale scene consistency are required to perform crop classification from remote sensing images. Many current remote-sensing crop extraction methods based on deep learning cannot account for adaptation effects in large-scale, complex scenes. Therefore, this study proposes a novel adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images. The selective patch module implemented in the network can adaptively integrate the features of different patch sizes to assess complex scenes better. TabNet was used simultaneously to extract spectral information from the center pixels of the patches. Multitask learning was used to supervise the extraction process to improve the weight of the spectral characteristics while mitigating the negative impact of a small sample size. In the network, superpixel optimization was applied to post-process the classification results to improve the crop edges. By conducting the crop classification of peanut, rice, and corn based on Sentinel-2 images in 2022 in Henan Province, China, the novel method proposed in this paper was more accurate, indicated by an F1 score of 96.53%, than other mainstream methods. This indicates our model’s potential for application in crop classification in large scenes.
Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer
Authors: Barmpoutis Panagiotis , Kastridis Aristeidis , Stathaki Tania , Yuan Jing , Shi Mengjie , Grammalidis Nikos
Published: April 2023

In the current context of climate change and demographic expansion, one of the phenomena that humanity faces are the suburban wildfires. To prevent the occurrence of suburban forest fires, fire risk assessment and early fire detection approaches need to be applied. Forest fire risk mapping depends on various factors and contributes to the identification and monitoring of vulnerable zones where risk factors are most severe. Therefore, watchtowers, sensors, and base stations of autonomous unmanned aerial vehicles need to be placed carefully in order to ensure adequate visibility or battery autonomy. In this study, fire risk assessment of an urban forest was performed and the recently introduced 360-degree data were used for early fire detection. Furthermore, a single-step approach that integrates a multiscale vision transformer was introduced for accurate fire detection. The study area includes the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which is prone to wildfires. For the evaluation of the performance of the proposed workflow, real and synthetic 360-degree images were used. Experimental results demonstrate the great potential of the proposed system, which achieved an F-score for real fire event detection rate equal to 91.6%. This indicates that the proposed method could significantly contribute to the monitoring, protection, and early fire detection of the suburban forest of Thessaloniki.
LiteST-Net: A Hybrid Model of Lite Swin Transformer and Convolution for Building Extraction from Remote Sensing Image
Authors: Yuan Wei , Zhang Xiaobo , Shi Jibao , Wang Jin
Published: April 2023

Extracting building data from remote sensing images is an efficient way to obtain geographic information data, especially following the emergence of deep learning technology, which results in the automatic extraction of building data from remote sensing images becoming increasingly accurate. A CNN (convolution neural network) is a successful structure after a fully connected network. It has the characteristics of saving computation and translation invariance with improved local features, but it has difficulty obtaining global features. Transformers can compensate for the shortcomings of CNNs and more effectively obtain global features. However, the calculation number of transformers is excessive. To solve this problem, a Lite Swin transformer is proposed. The three matrices Q, K, and V of the transformer are simplified to only a V matrix, and the v of the pixel is then replaced by the v with the largest projection value on the pixel feature vector. In order to better integrate global features and local features, we propose the LiteST-Net model, in which the features extracted by the Lite Swin transformer and the CNN are added together and then sampled up step by step to fully utilize the global feature acquisition ability of the transformer and the local feature acquisition ability of the CNN. The comparison experiments on two open datasets are carried out using our proposed LiteST-Net and some classical image segmentation models. The results show that compared with other networks, all metrics of LiteST-Net are the best, and the predicted image is closer to the label.
A Deep Learning-Based Unbalanced Force Identification of the Hypergravity Centrifuge
Authors: Lin Kuigeng , Li Yuke , Wu Yunhao , Fu Haoran , Jiang Jianqun , Chen Yunmin
Published: April 2023

Accurate and quantitative identification of unbalanced force during operation is of utmost importance to reduce the impact of unbalanced force on a hypergravity centrifuge, guarantee the safe operation of a unit, and improve the accuracy of a hypergravity model test. Therefore, this paper proposes a deep learning-based unbalanced force identification model, then establishes a feature fusion framework incorporating the Residual Network (ResNet) with meaningful handcrafted features in this model, followed by loss function optimization for the imbalanced dataset. Finally, after an artificially added, unbalanced mass was used to build a shaft oscillation dataset based on the ZJU-400 hypergravity centrifuge, we used this dataset to train the unbalanced force identification model. The analysis showed that the proposed identification model performed considerably better than other benchmark models based on accuracy and stability, reducing the mean absolute error (MAE) by 15% to 51% and the root mean square error (RMSE) by 22% to 55% in the test dataset. Simultaneously, the proposed method showed high accuracy and strong stability in continuous identification during the speed-up process, surpassing the current traditional method by 75% in the MAE and by 85% in the median error, which provided guidance for counterweight and guaranteed the unit’s stability.
Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer
Authors: Ji Yue , Ma Jiedong , Zhou Zhanqing , Li Jinyi , Song Limei
Published: April 2023

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton’s principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.
The Performance Investigation of Smart Diagnosis for Bearings Using Mixed Chaotic Features with Fractional Order
Authors: Li Shih-Yu , Tam Lap-Mou , Wu Shih-Ping , Tsai Wei-Lin , Hu Chia-Wen , Cheng Li-Yang , Xu Yu-Xuan , Cheng Shyi-Chyi
Published: April 2023

This article presents a performance investigation of a fault detection approach for bearings using different chaotic features with fractional order, where the five different chaotic features and three combinations are clearly described, and the detection achievement is organized. In the architecture of the method, a fractional order chaotic system is first applied to produce a chaotic map of the original vibration signal in the chaotic domain, where small changes in the signal with different bearing statuses might be present; then, a 3D feature map can be obtained. Second, five different features, combination methods, and corresponding extraction functions are introduced. In the third action, the correlation functions of extension theory used to construct the classical domain and joint fields are applied to further define the ranges belonging to different bearing statuses. Finally, testing data are fed into the detection system to verify the performance. The experimental results show that the proposed different chaotic features perform well in the detection of bearings with 7 and 21 mil diameters, and an average accuracy rate of 94.4% was achieved in all cases.
Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes
Authors: Francini Mauro , Salvo Carolina , Vitale Alessandro
Published: April 2023

Although many authors have observed a degradation in greening cover alongside an increase in the built-up areas, resulting in a deterioration of the essential environmental services for the well-being of ecosystems and society, few studies have measured how greening developed in its full spatiotemporal configuration with urban development using innovative remote sensing (RS) technologies. Focusing on this issue, the authors propose an innovative methodology for the analysis of the urban and greening changes over time by integrating deep learning (DL) technologies to classify and segment the built-up area and the vegetation cover from satellite and aerial images and geographic information system (GIS) techniques. The core of the methodology is a trained and validated U-Net model, which was tested on an urban area in the municipality of Matera (Italy), analyzing the urban and greening changes from 2000 to 2020. The results demonstrate a very good level of accuracy of the U-Net model, a remarkable increment in the built-up area density (8.28%) and a decline in the vegetation cover density (5.13%). The obtained results demonstrate how the proposed method can be used to rapidly and accurately identify useful information about urban and greening spatiotemporal development using innovative RS technologies supporting sustainable development processes.
A Portable Pull-Out Soil Profile Moisture Sensor Based on High-Frequency Capacitance
Authors: Sheng Zhentao , Liao Yaoyao , Zhang Shuo , Ni Jun , Zhu Yan , Cao Weixing , Jiang Xiaoping
Published: April 2023

Soil profile moisture is a crucial parameter of agricultural irrigation. To meet the demand of soil profile moisture, simple fast-sensing, and low-cost in situ detection, a portable pull-out soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of a moisture-sensing probe and a data processing unit. The probe converts soil moisture into a frequency signal using an electromagnetic field. The data processing unit was designed for signal detection and transmitting moisture content data to a smartphone app. The data processing unit and the probe are connected by a tie rod with adjustable length, which can be moved up and down to measure the moisture content of different soil layers. According to indoor tests, the maximum detection height for the sensor was 130 mm, the maximum detection radius was 96 mm, and the degree of fitting (R2) of the constructed moisture measurement model was 0.972. In the verification tests, the root mean square error (RMSE) of the measured value of the sensor was 0.02 m3/m3, the mean bias error (MBE) was ±0.009 m3/m3, and the maximum error was ±0.039 m3/m3. According to the results, the sensor, which features a wide detection range and good accuracy, is well suited for the portable measurement of soil profile moisture.
Detection and Mitigation of SYN Flooding Attacks through SYN/ACK Packets and Black/White Lists
Authors: Yang Chun-Hao , Wu Jhen-Ping , Lee Fang-Yi , Lin Ting-Yu , Tsai Meng-Hsun
Published: April 2023

Software-defined networking (SDN) is a new network architecture that provides programmable networks, more efficient network management, and centralized control than traditional networks. The TCP SYN flooding attack is one of the most aggressive network attacks that can seriously degrade network performance. This paper proposes detection and mitigation modules against SYN flooding attacks in SDN. We combine those modules, which have evolved from the cuckoo hashing method and innovative whitelist, to get better performance compared to current methods Our approach reduces the traffic through the switch and improves detection accuracy, also the required register size is reduced by half for the same accuracy.
Resource-Efficient Parallelized Random Access for Reliable Connection Establishment in Cellular IoT Networks
Authors: Kim Taehoon , Chae Seongho , Lim Jin-Taek , Bang Inkyu
Published: April 2023

The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT scenario. In IoT scenario, connection establishment is essential and primary for enabling IoT devices to communicate with centralized unit (e.g., base station (BS)). This connection establishment procedure in cellular networks, random access procedure, is generally operated in a contention-based manner. So, it is vulnerable to simultaneous connection requests from multiple IoT devices to the BS, which becomes worse as the contention participants increase. In this article, we newly propose a resource-efficient parallelized random access (RePRA) procedure for resource-efficiently ensuring reliable connection establishment in cellular-based massive IoT networks. Key features of our proposed technique are twofold: (1) Each IoT device simultaneously performs multiple RA procedures in parallel to improve connection establishment success probability, and (2) the BS handles excessive use of radio resources based on newly proposed two types of redundancy elimination mechanisms. Through extensive simulations, we evaluate the performance of our proposed technique in terms of connection establishment success probability and resource efficiency under various combinations of control parameters. Consequently, we verify the feasibility of our proposed technique for reliably and radio-efficiently supporting a large number of IoT devices.
Relay Selection for Over-the-Air Computation Achieving Both Long Lifetime and High Reliability
Authors: Zhou Jingyang , Tang Suhua
Published: April 2023

In a general wireless sensor network, a sink node collects data from each node successively and then post-processes the data to obtain useful information. However, conventional methods have a scalability problem: the data collection/processing time increases with the number of nodes, and frequent transmission collisions degrade spectrum efficiency. If only statistical values of the data are needed, using over-the-air computation (AirComp) can efficiently perform data collection and computation. However, AirComp also has its problems: when the channel gain of a node is too low, (i) the transmission power of that node will be high, decreasing the lifetime of that node and the entire network, and (ii) sometimes, the computation error still occurs even though the maximal transmission power is used. To jointly solve these two problems, in this paper we investigate the relay communication for AirComp and study a relay selection protocol. The basic method selects an ordinary node with a good channel condition as a relay node, considering both computation error and power consumption. This method is further enhanced to explicitly consider network lifetime in relay selection. Extensive simulation evaluations confirm that the proposed method helps to prolong the lifetime of the entire network and reduce computation errors as well.
Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision
Authors: Du Qiwei , Bai Heting , Zhu Zhongpan
Published: April 2023

With the changes in human work and lifestyle, the incidence of cervical spondylosis is increasing substantially, especially for adolescents. Cervical spine exercises are an important means to prevent and rehabilitate cervical spine diseases, but no mature unmanned evaluating and monitoring system for cervical spine rehabilitation training has been proposed. Patients often lack the guidance of a physician and are at risk of injury during the exercise process. In this paper, we first propose a cervical spine exercise assessment method based on a multi-task computer vision algorithm, which can replace physicians to guide patients to perform rehabilitation exercises and evaluations. The model based on the Mediapipe framework is set up to construct a face mesh and extract features to calculate the head pose angles in 3-DOF (three degrees of freedom). Then, the sequential angular velocity in 3-DOF is calculated based on the angle data acquired by the computer vision algorithm mentioned above. After that, the cervical vertebra rehabilitation evaluation system and index parameters are analyzed by data acquisition and experimental analysis of cervical vertebra exercises. A privacy encryption algorithm combining YOLOv5 and mosaic noise mixing with head posture information is proposed to protect the privacy of the patient’s face. The results show that our algorithm has good repeatability and can effectively reflect the health status of the patient’s cervical spine.
Implementation of a Hybrid Intelligence System Enabling the Effectiveness Assessment of Interaction Channels Use in HMI
Authors: Gardecki Arkadiusz , Rut Joanna , Klin Bartlomiej , Podpora Michal , Beniak Ryszard
Published: April 2023

The article presents a novel idea of Interaction Quality Sensor (IQS), introduced in the complete solution of Hybrid INTelligence (HINT) architecture for intelligent control systems. The proposed system is designed to use and prioritize multiple information channels (speech, images, videos) in order to optimize the information flow efficiency of interaction in HMI systems. The proposed architecture is implemented and validated in a real-world application of training unskilled workers—new employees (with lower competencies and/or a language barrier). With the help of the HINT system, the man–machine communication information channels are deliberately chosen based on IQS readouts to enable an untrained, inexperienced, foreign employee candidate to become a good worker, while not requiring the presence of either an interpreter or an expert during training. The proposed implementation is in line with the labor market trend, which displays significant fluctuations. The HINT system is designed to activate human resources and support organizations/enterprises in the effective assimilation of employees to the tasks performed on the production assembly line. The market need of solving this noticeable problem was caused by a large migration of employees within (and between) enterprises. The research results presented in the work show significant benefits of the methods used, while supporting multilingualism and optimizing the preselection of information channels.
Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection
Authors: Shah Milind , Borade Himanshu , Sanghavi Vedant , Purohit Anshuman , Wankhede Vishal , Vakharia Vinay
Published: April 2023

Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower productivity, and increased downtime. In recent years, there has been a rise in the popularity of implementing TCM systems using various signal processing methods and machine learning algorithms. In the present paper, the authors propose a TCM system that incorporates the Walsh–Hadamard transform for signal processing, DCGAN aims to circumvent the issue of the availability of limited experimental dataset, and the exploration of three machine learning models: support vector regression, gradient boosting regression, and recurrent neural network for tool wear prediction. The mean absolute error, mean square error and root mean square error are used to assess the prediction errors from three machine learning models. To identify these relevant features, three metaheuristic optimization feature selection algorithms, Dragonfly, Harris hawk, and Genetic algorithms, were explored, and prediction results were compared. The results show that the feature selected through Dragonfly algorithms exhibited the least MSE (0.03), RMSE (0.17), and MAE (0.14) with a recurrent neural network model. By identifying the tool wear patterns and predicting when maintenance is required, the proposed methodology could help manufacturing companies save money on repairs and replacements, as well as reduce overall production costs by minimizing downtime.
EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism
Authors: Khan Taimoor , Choi Gyuho , Lee Sokjoon
Published: April 2023

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver’s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver’s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.
Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
Authors: Oluwasanmi Ariyo , Aftab Muhammad , Qin Zhiguang , Sarfraz Muhammad , Yu Yang , Rauf Hafiz
Published: April 2023

Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.
Micro-Motion Extraction for Marine Targets by Multi-Pulse Delay Conjugate Multiplication and Layered Tracking
Authors: Mao Tong , Zhang Yi , Zhu Kaiqiang , Sun Houjun
Published: April 2023

The detection and recognition of marine targets can be improved by utilizing the micro-motion induced by ocean waves. However, distinguishing and tracking overlapping targets is challenging when multiple extended targets overlap in the range dimension of the radar echo. In this paper, we propose a multi-pulse delay conjugate multiplication and layered tracking (MDCM-LT) algorithm for micro-motion trajectory tracking. The MDCM method is first applied to obtain the conjugate phase from the radar echo, which enables high-precision micro-motion extraction and overlapping state identification of extended targets. Then, the LT algorithm is proposed to track the sparse scattering points belonging to different extended targets. In our simulation, the root mean square errors of the distance and velocity trajectories were better than 0.277 m and 0.016 m/s, respectively. Our results demonstrate that the proposed method has the potential to improve the precision and reliability of marine target detection through radar.
Comparison of Performance between Single- and Multiparameter Luminescence Thermometry Methods Based on the Mn5+ Near-Infrared Emission
Authors: Alrebdi Tahani A. , Alodhayb Abdullah N. , Ristić Zoran , Dramićanin Miroslav D.
Published: April 2023

Herein, we investigate the performance of single- and multiparametric luminescence thermometry founded on the temperature-dependent spectral features of Ca6BaP4O17:Mn5+ near-infrared emission. The material was prepared by a conventional steady-state synthesis, and its photoluminescence emission was measured from 7500 to 10,000 cm−1 over the 293–373 K temperature range in 5 K increments. The spectra are composed of the emissions from 1E → 3A2 and 3T2 → 3A2 electronic transitions and Stokes and anti-Stokes vibronic sidebands at 320 cm−1 and 800 cm−1 from the maximum of 1E → 3A2 emission. Upon temperature increase, the 3T2 and Stokes bands gained in intensity while the maximum of 1E emission band is redshifted. We introduced the procedure for the linearization and feature scaling of input variables for linear multiparametric regression. Then, we experimentally determined accuracies and precisions of the luminescence thermometry based on luminescence intensity ratios between emissions from the 1E and 3T2 states, between Stokes and anti-Stokes emission sidebands, and at the 1E energy maximum. The multiparametric luminescence thermometry involving the same spectral features showed similar performance, comparable to the best single-parameter thermometry.
A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach
Authors: Falcioni Renan , Antunes Werner Camargos , Demattê José Alexandre Melo , Nanni Marcos Rafael
Published: April 2023

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500–600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440–485 nm) and red (626–700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.
A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs
Authors: Jiang Xian , Cui Qing , Wang Chongguo , Wang Fan , Zhao Yingxiang , Hou Yongjie , Zhuang Rujun , Mei Yunfei , Shi Gang
Published: April 2023

Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall.
Analysis of the Hanging Actions and Operating Heights of Storage Furniture Suitable for the Elderly
Authors: Shi Xinao , Zhang Fan
Published: April 2023

The current functional scale design of storage furniture which the elderly use does not meet their needs, and unsuitable storage furniture may bring many physiological and psychological problems to their daily lives. The purpose of this study is to start with the hanging operation, to study the factors influencing the hanging operation heights of elderly people undergoing self-care in a standing posture and to determine the research methods to be used to study the appropriate hanging operation height of the elderly so as to provide data and theoretical support for the functional design scale of storage furniture suitable for the elderly. This study quantifies the situations of elderly people’s hanging operation through an sEMG test performed on 18 elderly people at different hanging heights combined with a subjective evaluation before and after the operation and a curve fitting between the integrated sEMG indexes and the test heights. The test results show that the height of the elderly subjects had a significant effect on the hanging operation, and the main power muscles of the suspension operation were the anterior deltoid, upper trapezius and brachioradialis. Elderly people in different height groups had their own performance of the most comfortable hanging operation ranges. The suitable range for the hanging operation was 1536–1728 mm for seniors aged 60 or above whose heights were within the range of 1500–1799 mm, which could obtain a better action view and ensure the comfort of the operation. External hanging products, such as wardrobe hangers and hanging hooks, could also be determined according to this result.
Self-Calibration Sensor for Contactless Voltage Measurement Based on Dynamic Capacitance
Authors: Suo Chunguang , Huang Rujin , Zhou Guoqiong , Zhang Wenbin , Wang Yanyun , He Mingxing
Published: April 2023

Noncontact voltage measurement has the advantages of simple handling, high construction safety, and not being affected by line insulation. However, in practical measurement of noncontact voltage, sensor gain is affected by wire diameter, wire insulation material, and relative position deviation. At the same time, it is also subject to interference from interphase or peripheral coupling electric fields. This paper proposes a noncontact voltage measurement self-calibration method based on dynamic capacitance, which realizes self-calibration of sensor gain through unknown line voltage to be measured. Firstly, the basic principle of the self-calibration method for noncontact voltage measurement based on dynamic capacitance is introduced. Subsequently, the sensor model and parameters were optimized through error analysis and simulation research. Based on this, a sensor prototype and remote dynamic capacitance control unit that can shield against interference are developed. Finally, the accuracy test, anti-interference ability test, and line adaptability test of the sensor prototype were conducted. The accuracy test showed that the maximum relative error of voltage amplitude was 0.89%, and the phase relative error was 1.57%. The anti-interference ability test showed that the error offset was 0.25% when there were interference sources. The line adaptability test shows that the maximum relative error in testing different types of lines is 1.01%.
Frequency Switchable Global RFID Tag Antennae with Metal Compatibility for Worldwide Vehicle Transportation
Authors: Mazumder Krishna , Ghosh Anumoy , Bhattacharya Anagha , Ahmad Sarosh , Ghaffar Adnan , Hussein Mousa
Published: April 2023

This paper presents an effective way to design an RFID tag antenna to operate at three different frequencies by incorporating a switching technique. PIN diode has been used to switch the RF frequency because of its good efficiency and simplicity. The conventional dipole-based RFID tag has been improvised with added co-planar ground and PIN diode. The layout of the antenna is designed with a size of 0.083 λ0 × 0.094 λ0 at UHF (80–960) MHz, where λ0 is the free-space wavelength corresponding to the mid-point of the targeted UHF range. The RFID microchip is connected to the modified ground and dipole structures. Bending and meandering techniques on the dipole length help to match the complex chip impedance with the dipole impedance. Additionally, it scales down the total structure of the antenna. Two PIN diodes are placed along the dipole length at appropriate distances with proper biasing. The ON-OFF switching states of the PIN diodes enable the RFID tag antenna to switch over the frequency ranges (840–845) MHz (India), 902–928 MHz (North America), and 950–955 MHz (Japan).
Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels
Authors: Lin Yi-Jia , Lee Chia-Chien , Huang Tzu-Wei , Hsu Wei-Chun , Wu Li-Wei , Lin Chen-Chun , Hsiu Hsin
Published: April 2023

Background and Aims: Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3. Methods: The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals. Results: Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups. Conclusions: The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.