bluuebunny/crossref_metadata_2025 huggingface dataset:
A Wisdom (Not) of This Age: Paul’s Education from His Letters to the Early Acts
Authors: Lookadoo Jonathon
Published: May 2023

This article takes up the matter of Paul’s education and explores it within the Roman world of education. In order to do this, the article draws upon and contributes to reception historical studies of Paul. More specifically, the article illustrates the flexibility of Paul’s education as it is described in his letters, the Acts of the Apostles, and the Acts of Paul. While Paul downplays his education in several autobiographical statements within his letters, his letters nevertheless suggest that Paul received a high level of education. The Acts of the Apostles further contributes to an early Christian portrayal of Paul as an educated figure by giving readers a Paul who speaks eloquently and presents a controversial message that can be narrated with reference to both Jewish scripture and to Graeco-Roman philosophy. The Acts of Paul presents Paul as a persuasive speaker, but the speeches contained within this narrative are generally of a more concise nature. On the other hand, the Acts of Paul presents Paul as a writer who can read and respond eloquently to highly disputed queries from other groups of believers. These texts coalesce in depicting a Paul who is well educated, but they differ in their depictions of how his education was evident in his life.
Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019
Authors: Chen Yun , Guo Dongbao , Cao Wenjie , Li Yuqiang
Published: May 2023

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.
Multi-Pooling Context Network for Image Semantic Segmentation
Authors: Liu Qing , Dong Yongsheng , Jiang Zhiqiang , Pei Yuanhua , Zheng Boshi , Zheng Lintao , Fu Zhumu
Published: May 2023

With the development of image segmentation technology, image context information plays an increasingly important role in semantic segmentation. However, due to the complexity of context information in different feature maps, simple context capture operations can easily cause context information omission. Rich context information can better classify categories and improve the quality of image segmentation. On the contrary, poor context information will lead to blurred image category segmentation and an incomplete target edge. In order to capture rich context information as completely as possible, we constructed a Multi-Pooling Context Network (MPCNet), which is a multi-pool contextual network for the semantic segmentation of images. Specifically, we first proposed the Pooling Context Aggregation Module to capture the deep context information of the image by processing the information between the space, channel, and pixel of the image. At the same time, the Spatial Context Module was constructed to capture the detailed spatial context of images at different stages of the network. The whole network structure adopted the form of codec to better extract image context. Finally, we performed extensive experiments on three semantic segmentation datasets (Cityscapes, ADE20K, and PASCAL VOC2012 datasets), which fully proved that our proposed network effectively alleviated the lack of context extraction and verified the effectiveness of the network.
DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks
Authors: Dong Feihu , Song Jiaxin , Zhang Yasheng , Wang Yuqi , Huang Tao
Published: May 2023

Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.
MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)
Authors: Marchamalo-Sacristán Miguel , Ruiz-Armenteros Antonio Miguel , Lamas-Fernández Francisco , González-Rodrigo Beatriz , Martínez-Marín Rubén , Delgado-Blasco José Manuel , Bakon Matus , Lazecky Milan , Perissin Daniele , Papco Juraj , Sousa Joaquim J.
Published: May 2023

The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of −6 mm/year (1992–2000), −4 mm/year (2002–2010), and −2 mm/year (2015–2021) with median values of −2.6 and −3.0 mm/year in the first periods (ERS and Envisat) and −1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring.
Validation and Evaluation of GRACE-FO Estimates with In Situ Bottom Pressure Array Measurements in the South China Sea
Authors: Wang Xuecheng , Zheng Hua , Zhu Xiao-Hua , Zhao Ruixiang , Wang Min , Chen Juntian , Ma Yunlong , Nan Feng , Yu Fei
Published: May 2023

The Gravity Recovery and Climate Experiment (GRACE), and its follow-on mission (GRACE-FO), provides a novel measurement of the variations in ocean bottom pressure (OBP) at global and basin scales, including those in marginal seas. However, these measurements have not yet been validated rigorously for the South China Sea (SCS). In this study, the accuracy in the monthly GRACE-FO mascon solutions in the SCS from the Jet Propulsion Laboratory (JPL), Center for Space Research (CSR), and Goddard Space Flight Center (GSFC) was validated with the results of the comparison with the in situ OBP records from an array of 25 pressure-recording inverted echo sounders (PIESs) that are located west of the Luzon Strait (LS). The correlation coefficient (Cor) and root mean square difference (RMSD) between the 10-month period of GSFC and PIES, spanning from July 2018 to June 2019 (with missing satellite data for August and September 2018), were 0.77 (p-value = 0.005) and 0.41 mbar (1 mbar = 100 Pa), respectively. These values suggest that the accuracy of GSFC in the SCS in this period was substantially better than that of JPL (Cor = 0.35, p-value = 0.16; RMSD = 0.74 mbar) and CSR (Cor = 0.25, p-value = 0.24; RMSD = 0.89 mbar). Moreover, the volume transport anomaly of the SCS abyssal circulation was estimated and compared based on the OBP records from GSFC and PIES observations, indicating that the GRACE-FO OBP (GSFC) can be used to monitor seasonal or longer-period variations in the SCS abyssal volume transport. Additionally, the variations in OBP from GRACE-FO were significantly overestimated on the continental shelf of the SCS, which may be attributed to signal leakage. Our findings provide reliable evidence for the application of long-term, fully covered OBP records from GRACE-FO in the SCS, and also offer a valuable reference for the application of GRACE-FO in other regions.
Semi-Automated BIM Reconstruction of Full-Scale Space Frames with Spherical and Cylindrical Components Based on Terrestrial Laser Scanning
Authors: Cheng Guozhong , Liu Jiepeng , Li Dongsheng , Chen Y. Frank
Published: May 2023

As-built building information modeling (BIM) model has gained more attention due to its increasing applications in construction, operation, and maintenance. Although methods for generating the as-built BIM model from laser scanning data have been proposed, few studies were focused on full-scale structures. To address this issue, this study proposes a semi-automated and effective approach to generate the as-built BIM model for a full-scale space frame structure with terrestrial laser scanning data, including the large-scale point cloud data (PCD) registration, large-scale PCD segmentation, and geometric parameters estimation. In particular, an effective coarse-to-fine data registration method was developed based on sphere targets and the oriented bounding box. Then, a novel method for extracting the sphere targets from full-scale structures was proposed based on the voxelization algorithm and random sample consensus (RANSAC) algorithm. Next, an efficient method for extracting cylindrical components was presented based on the detected sphere targets. The proposed approach is shown to be effective and reliable through the application of actual space frame structures.
Generalized Persistent Polar Format Algorithm for Fast Imaging of Airborne Video SAR
Authors: Jiang Jiawei , Li Yinwei , Yuan Yinghao , Zhu Yiming
Published: May 2023

As a cutting-edge research direction in the field of radar imaging, video SAR has the capability of high-resolution and persistent imaging at any time and under any weather. Video SAR requires high computational efficiency of the imaging algorithm, and PFA has become the preferred imaging algorithm because of its applicability to the spotlight mode and relatively high computational efficiency. However, traditional PFA also has problems, such as low efficiency and limited scene size. To address the above problems, a generalized persistent polar format algorithm, called GPPFA, is proposed for airborne video SAR imaging that is applicable to the persistent imaging requirements of airborne video SAR under multitasking conditions. Firstly, the wavenumber domain resampling characteristics of video SAR PFA are analyzed, and a generalized resampling method is proposed to obtain higher efficiency. Secondly, for the problem of scene size limitation caused by wavefront curvature error, an efficient compensation method applicable to different scene sizes is proposed. GPPFA is capable of video SAR imaging at different wavebands, different slant ranges, and arbitrary scene sizes. Point target and extended target experiments verify the effectiveness and efficiency of the proposed method.
STMP-Net: A Spatiotemporal Prediction Network Integrating Motion Perception
Authors: Chen Suting , Yang Ning
Published: May 2023

This article proposes a video prediction network called STMP-Net that addresses the problem of the inability of Recurrent Neural Networks (RNNs) to fully extract spatiotemporal information and motion change features during video prediction. STMP-Net combines spatiotemporal memory and motion perception to make more accurate predictions. Firstly, a spatiotemporal attention fusion unit (STAFU) is proposed as the basic module of the prediction network, which learns and transfers spatiotemporal features in both horizontal and vertical directions based on spatiotemporal feature information and contextual attention mechanism. Additionally, a contextual attention mechanism is introduced in the hidden state to focus attention on more important details and improve the capture of detailed features, thus greatly reducing the computational load of the network. Secondly, a motion gradient highway unit (MGHU) is proposed by combining motion perception modules and adding them between adjacent layers, which can adaptively learn the important information of input features and fuse motion change features to significantly improve the predictive performance of the model. Finally, a high-speed channel is provided between layers to quickly transmit important features and alleviate the gradient vanishing problem caused by back-propagation. The experimental results show that compared with mainstream video prediction networks, the proposed method can achieve better prediction results in long-term video prediction, especially in motion scenes.
Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning
Authors: Qu Xiaofei , Zhang Yongkang
Published: May 2023

The roadheader is a core piece of equipment for underground mining. The roadheader bearing, as its key component, often works under complex working conditions and bears large radial and axial forces. Its health is critical to efficient and safe underground operation. The early failure of a roadheader bearing has weak impact characteristics and is often submerged in complex and strong background noise. Therefore, a fault diagnosis strategy that combines variational mode decomposition and a domain adaptive convolutional neural network is proposed in this paper. To start with, VMD is utilized to decompose the collected vibration signals to obtain the sub-component IMF. Then, the kurtosis index of IMF is calculated, with the maximum index value chosen as the input of the neural network. A deep transfer learning strategy is introduced to solve the problem of the different distributions of vibration data for roadheader bearings under variable working conditions. This method was implemented in the actual bearing fault diagnosis of a roadheader. The experimental results indicate that the method is superior in terms of diagnostic accuracy and has practical engineering application value.
Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones
Authors: Zhang Shufeng , Chen Yuxi , Liu Weizhen , Liu Bang , Zhou Xiang
Published: May 2023

Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method (R2 = 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry.
Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework
Authors: Ismail Shereen , Reza Hassan , Salameh Khouloud , Kashani Zadeh Hossein , Vasefi Fartash
Published: May 2023

The fish industry experiences substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) are expected to transform the fish supply chain (SC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promote secure data sharing and employ IUU prevention and detection methods. We have reviewed current research efforts directed toward incorporating Blockchain in fish SC systems. We have discussed traceability in both traditional and smart SC systems that make use of Blockchain and IoT technologies. We demonstrated the key design considerations in terms of traceability in addition to a quality model to consider when designing smart Blockchain-based SC systems. In addition, we proposed an Intelligent Blockchain IoT-enabled fish SC framework that uses DLT for the trackability and traceability of fish products throughout harvesting, processing, packaging, shipping, and distribution to final delivery. More precisely, the proposed framework should be able to provide valuable and timely information that can be used to track and trace the fish product and verify its authenticity throughout the chain. Unlike other work, we have investigated the benefits of integrating machine learning (ML) into Blockchain IoT-enabled SC systems, focusing the discussion on the role of ML in fish quality, freshness assessment and fraud detection.
Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis
Authors: Song Xinmin , Wei Weihua , Zhou Junbo , Ji Guojun , Hussain Ghulam , Xiao Maohua , Geng Guosheng
Published: May 2023

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.
Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning
Authors: Ni Youhao , Mao Jianxiao , Fu Yuguang , Wang Hao , Zong Hai , Luo Kun
Published: May 2023

Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement.
Improved YOLOv7 Network Model for Gangue Selection Robot for Gangue and Foreign Matter Detection in Coal
Authors: Yang Dengjie , Miao Changyun , Li Xianguo , Liu Yi , Wang Yimin , Zheng Yao
Published: May 2023

Coal production often involves a substantial presence of gangue and foreign matter, which not only impacts the thermal properties of coal and but also leads to damage to transportation equipment. Selection robots for gangue removal have garnered attention in research. However, existing methods suffer from limitations, including slow selection speed and low recognition accuracy. To address these issues, this study proposes an improved method for detecting gangue and foreign matter in coal, utilizing a gangue selection robot with an enhanced YOLOv7 network model. The proposed approach entails the collection of coal, gangue, and foreign matter images using an industrial camera, which are then utilized to create an image dataset. The method involves reducing the number of convolution layers of the backbone, adding a small size detection layer to the head to enhance the small target detection, introducing a contextual transformer networks (COTN) module, employing a distance intersection over union (DIoU) loss border regression loss function to calculate the overlap between predicted and real frames, and incorporating a dual path attention mechanism. These enhancements culminate in the development of a novel YOLOv71 + COTN network model. Subsequently, the YOLOv71 + COTN network model was trained and evaluated using the prepared dataset. Experimental results demonstrated the superior performance of the proposed method compared to the original YOLOv7 network model. Specifically, the method exhibits a 3.97% increase in precision, a 4.4% increase in recall, and a 4.5% increase in mAP0.5. Additionally, the method reduced GPU memory consumption during runtime, enabling fast and accurate detection of gangue and foreign matter.
SALSA-Net: Explainable Deep Unrolling Networks for Compressed Sensing
Authors: Song Heping , Ding Qifeng , Gong Jingyao , Meng Hongying , Lai Yuping
Published: May 2023

Deep unrolling networks (DUNs) have emerged as a promising approach for solving compressed sensing (CS) problems due to their superior explainability, speed, and performance compared to classical deep network models. However, the CS performance in terms of efficiency and accuracy remains a principal challenge for approaching further improvements. In this paper, we propose a novel deep unrolling model, SALSA-Net, to solve the image CS problem. The network architecture of SALSA-Net is inspired by unrolling and truncating the split augmented Lagrangian shrinkage algorithm (SALSA) which is used to solve sparsity-induced CS reconstruction problems. SALSA-Net inherits the interpretability of the SALSA algorithm while incorporating the learning ability and fast reconstruction speed of deep neural networks. By converting the SALSA algorithm into a deep network structure, SALSA-Net consists of a gradient update module, a threshold denoising module, and an auxiliary update module. All parameters, including the shrinkage thresholds and gradient steps, are optimized through end-to-end learning and are subject to forward constraints to ensure faster convergence. Furthermore, we introduce learned sampling to replace traditional sampling methods so that the sampling matrix can better preserve the feature information of the original signal and improve sampling efficiency. Experimental results demonstrate that SALSA-Net achieves significant reconstruction performance compared to state-of-the-art methods while inheriting the advantages of explainable recovery and high speed from the DUNs paradigm.
Development and Validation of a Low-Cost Device for Real-Time Detection of Fatigue Damage of Structures Subjected to Vibrations
Authors: Staffa Agnese , Palmieri Massimiliano , Morettini Giulia , Zucca Guido , Crocetti Francesco , Cianetti Filippo
Published: May 2023

This paper presents the development and validation of a low-cost device for real-time detection of fatigue damage of structures subjected to vibrations. The device consists of an hardware and signal processing algorithm to detect and monitor variations in the structural response due to damage accumulation. The effectiveness of the device is demonstrated through experimental validation on a simple Y-shaped specimen subjected to fatigue loading. The results show that the device can accurately detect structural damage and provide real-time feedback on the health status of the structure. The low-cost and easy-to-implement nature of the device makes it promising for use in structural health monitoring applications in various industrial sectors.
Decision Support System Proposal for Medical Evacuations in Military Operations
Authors: Lubkowski Piotr , Krygier Jaroslaw , Sondej Tadeusz , Dobrowolski Andrzej P. , Apiecionek Lukasz , Znaniecki Wojciech , Oskwarek Pawel
Published: May 2023

The area of military operations is a big challenge for medical support. A particularly important factor that allows medical services to react quickly in the case of mass casualties is the ability to rapidly evacuation of wounded soldiers from a battlefield. To meet this requirement, an effective medical evacuation system is essential. The paper presented the architecture of the electronically supported decision support system for medical evacuation during military operations. The system can also be used by other services such as police or fire service. The system meets the requirements for tactical combat casualty care procedures and is composed of following elements: measurement subsystem, data transmission subsystem and analysis and inference subsystem. The system, based on the continuous monitoring of selected soldiers’ vital signs and biomedical signals, automatically proposes a medical segregation of wounded soldiers (medical triage). The information on the triage was visualized using the Headquarters Management System for medical personnel (first responders, medical officers, medical evacuation groups) and for commanders, if required. All elements of the architecture were described in the paper.
A Novel Force-Sensing Smart Textile: Inserting Silicone-Embedded FBG Sensors into a Knitted Undergarment
Authors: Lee Ka-Po , Yip Joanne , Yick Kit-Lun , Lu Chao , Lu Linyue , Lei Qi-Wen Emma
Published: May 2023

A number of textile-based fiber optic sensors have recently been proposed for the continuous monitoring of vital signs. However, some of these sensors are likely unsuitable for conducting direct measurements on the torso as they lack elasticity and are inconvenient. This project provides a novel method for creating a force-sensing smart textile by inlaying four silicone-embedded fiber Bragg grating sensors into a knitted undergarment. The applied force was determined within 3 N after transferring the Bragg wavelength. The results show that the sensors embedded in the silicone membranes achieved enhanced sensitivity to force, as well as flexibility and softness. Additionally, by assessing the degree of FBG response to a range of standardized forces, the linearity (R2) between the shift in the Bragg wavelength and force was found to be above 0.95, with an ICC of 0.97, when tested on a soft surface. Furthermore, the real-time data acquisition could facilitate the adjustment and monitoring of force during the fitting processes, such as in bracing treatment for adolescent idiopathic scoliosis patients. Nevertheless, the optimal bracing pressure has not yet been standardized. This proposed method could help orthotists to adjust the tightness of brace straps and the location of padding in a more scientific and straightforward way. The output of this project could be further extended to determine ideal bracing pressure levels.
Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning
Authors: Gannouni Sofien , Belwafi Kais , Aledaily Arwa , Aboalsamh Hatim , Belghith Abdelfettah
Published: May 2023

It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usability testing based on emotion detection using EEG signals, which can significantly affect software production and user satisfaction. This approach can provide an in-depth understanding of user satisfaction accurately and precisely, making it a valuable tool in software development. The proposed framework includes a recurrent neural network algorithm as a classifier, a feature extraction algorithm based on event-related desynchronization and event-related synchronization analysis, and a new method for selecting EEG sources adaptively for emotion recognition. The framework results are promising, achieving 92.13%, 92.67%, and 92.24% for the valence–arousal–dominance dimensions, respectively.
Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones
Authors: Alsumayt Albandari , El-Haggar Nahla , Amouri Lobna , Alfawaer Zeyad M. , Aljameel Sumayh S.
Published: May 2023

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.
Relaxation-Based Radiometric Normalization for Multitemporal Cross-Sensor Satellite Images
Authors: Ryadi Gabriel Yedaya Immanuel , Syariz Muhammad Aldila , Lin Chao-Hung
Published: May 2023

Multitemporal cross-sensor imagery is fundamental for the monitoring of the Earth’s surface over time. However, these data often lack visual consistency because of variations in the atmospheric and surface conditions, making it challenging to compare and analyze images. Various image-normalization methods have been proposed to address this issue, such as histogram matching and linear regression using iteratively reweighted multivariate alteration detection (IR-MAD). However, these methods have limitations in their ability to maintain important features and their requirement of reference images, which may not be available or may not adequately represent the target images. To overcome these limitations, a relaxation-based algorithm for satellite-image normalization is proposed. The algorithm iteratively adjusts the radiometric values of images by updating the normalization parameters (slope (α) and intercept (β)) until a desired level of consistency is reached. This method was tested on multitemporal cross-sensor-image datasets and showed significant improvements in radiometric consistency compared to other methods. The proposed relaxation algorithm outperformed IR-MAD and the original images in reducing radiometric inconsistencies, maintaining important features, and improving the accuracy (MAE = 2.3; RMSE = 2.8) and consistency of the surface-reflectance values (R2 = 87.56%; Euclidean distance = 2.11; spectral angle mapper = 12.60).
The Smart Predicting of Algal Concentration for Safer Drinking Water Production with Sensor Data
Authors: Yu Han , Li Jing , Holmer Linda , Köhler Stephan J.
Published: May 2023

To better predict the timely variation of algal blooms and other vital factors for safer drinking water production, a new AI scanning–focusing process was investigated for improving the simulation and prediction of algae counts. With a feedforward neural network (FNN) as a base, nerve cell numbers in the hidden layer and the permutation and combination of factors, etc., were fully scanned to select the best models and highly correlated factors. All the factors involved in the modeling and selection included the date (year/month/day), sensor data (temperature, pH, conductivity, turbidity, UV254-dissolved organic matter, etc.), lab measurements (algae concentration) and calculated CO2 concentration. The new AI scanning–focusing process resulted in the best models with the most suitable key factors, which are named closed systems. In this case study, models with highest prediction performance are the (1) date–algae–temperature–pH (DATH) and (2) date–algae–temperature–CO2 (DATC) systems. After the model selection process, the best models from both DATH and DATC were used to compare the other two methods in the modeling simulation process: the simple traditional neural network method (SP), where only date and target factor as inputs, and a blind AI training process (BP), which considers all available factors as inputs. Validation results show that all methods except BP had comparable results for algae prediction and other water quality factors, such as temperature, pH and CO2, among which DATC displayed an obviously poorer performance through curve fitting with original CO2 data compared to that of SP. Therefore, DATH and SP were selected for the application test, where DATH outperformed SP due to the uncompromised performance after a long training period. Our AI scanning–focusing process and model selection showed the potential for improving water quality prediction by identifying the most suitable factors. This provides a new method to be considered in the enhancing of numerical prediction for the factors in water quality prediction and broader environment-related areas.
Identity-Guided Spatial Attention for Vehicle Re-Identification
Authors: Lv Kai , Han Sheng , Lin Youfang
Published: May 2023

In vehicle re-identification, identifying a specific vehicle from a large image dataset is challenging due to occlusion and complex backgrounds. Deep models struggle to identify vehicles accurately when critical details are occluded or the background is distracting. To mitigate the impact of these noisy factors, we propose Identity-guided Spatial Attention (ISA) to extract more beneficial details for vehicle re-identification. Our approach begins by visualizing the high activation regions of a strong baseline method and identifying noisy objects involved during training. ISA generates an attention map to mask most discriminative areas, without the need for manual annotation. Finally, the ISA map refines the embedding feature in an end-to-end manner to improve vehicle re-identification accuracy. Visualization experiments demonstrate ISA’s ability to capture nearly all vehicle details, while results on three vehicle re-identification datasets show that our method outperforms state-of-the-art approaches.
A Novel Equipment-Free Paper-Based Fluorometric Method for the Analytical Determination of Quinine in Soft Drink Samples
Authors: Tsaftari Vasiliki C. , Tarara Maria , Tzanavaras Paraskevas D. , Tsogas George Z.
Published: May 2023

A simple, equipment-free, direct fluorometric method, employing paper-based analytical devices (PADs) as sensors, for the selective determination of quinine (QN) is described herein. The suggested analytical method exploits the fluorescence emission of QN without any chemical reaction after the appropriate pH adjustment with nitric acid, at room temperature, on the surface of a paper device with the application of a UV lamp at 365 nm. The devices crafted had a low cost and were manufactured with chromatographic paper and wax barriers, and the analytical protocol followed was extremely easy for the analyst and required no laboratory instrumentation. According to the methodology, the user must place the sample on the detection area of the paper and read with a smartphone the fluorescence emitted by the QN molecules. Many chemical parameters were optimized, and a study of interfering ions present in soft drink samples was carried out. Additionally, the chemical stability of these paper devices was considered in various maintenance conditions with good results. The detection limit calculated as 3.3 S/N was 3.6 mg L−1, and the precision of the method was satisfactory, being from 3.1% (intra-day) to 8.8% (inter-day). Soft drink samples were successfully analyzed and compared with a fluorescence method.
A Mobile Sensing Framework for Bridge Modal Identification through an Inverse Problem Solution Procedure and Moving-Window Time Series Models
Authors: Talebi-Kalaleh Mohammad , Mei Qipei
Published: May 2023

With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing over the bridge can reduce the costs of the monitoring systems, compared with the traditional systems where fixed sensors are mounted on the bridge. This paper presents an innovative framework for determining the response and for identifying modal characteristics of the bridge, utilizing only the accelerometer sensors on the moving vehicle passing over it. In the proposed approach, the acceleration and displacement response of some virtual fixed nodes on the bridge is first determined using the acceleration response of the vehicle axles as the input. An inverse problem solution approach based on a linear and a novel cubic spline shape function provides the preliminary estimations of the bridge’s displacement and acceleration responses, respectively. Since the inverse solution approach is only capable of determining the response signal of the nodes with high accuracy in the vicinity of the vehicle axles, a new moving-window signal prediction method based on auto-regressive with exogenous time series models (ARX) is proposed to complete the responses in the regions with large errors (invalid regions). The mode shapes and natural frequencies of the bridge are identified using a novel approach that integrates the results of singular value decomposition (SVD) on the predicted displacement responses and frequency domain decomposition (FDD) on the predicted acceleration responses. To evaluate the proposed framework, various numerical but realistic models for a single-span bridge under the effect of a moving mass are considered; the effects of different levels of ambient noise, the number of axles of the passing vehicle, and the effect of its speed on the accuracy of the method are investigated. The results show that the proposed method can identify the characteristics of the three main modes of the bridge with high accuracy.
Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features
Authors: Upadhyay Shrikant , Kumar Mohit , Upadhyay Aditi , Verma Sahil , Kavita  , Kaur Maninder , Khurma Ruba Abu , Castillo Pedro A.
Published: May 2023

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.
Individual Pig Identification Using Back Surface Point Clouds in 3D Vision
Authors: Zhou Hong , Li Qingda , Xie Qiuju
Published: May 2023

The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig’s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig’s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.
Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm
Authors: Cheng Ruixiang , Qiao Zhihao , Li Jiarui , Huang Jiejun
Published: May 2023

With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.