bluuebunny/crossref_metadata_2025 huggingface dataset:
An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems
Authors: Gouran Payam , Nadimi-Shahraki Mohammad H. , Rahmani Amir Masoud , Mirjalili Seyedali
Published: July 2023

In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy.
High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy
Authors: Xia Shuangzhi , Wang Yuanyuan , Zhang Juan , Dai Fengzhou
Published: July 2023

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.
Spatial–Temporal Patterns of Interannual Variability in Planted Forests: NPP Time-Series Analysis on the Loess Plateau
Authors: Amantai Nigenare , Meng Yuanyuan , Song Shanshan , Li Zihui , Hou Bowen , Tang Zhiyao
Published: July 2023

Investigating how the productivity dynamics of planted forests vary over time is important for understanding the resilience of forests against disturbance and for maximizing ecological restoration and replanting efforts. In this study, the patterns of interannual variability in net primary production (NPP) were analyzed for planted forests as indicated by the inverse of the coefficient of variation (ICV) time series at a ten-year moving window on the Loess Plateau, China, from 2000 to 2021. The spatial–temporal patterns were defined based on the increase or decrease trend obtained using the ordinary least squares method between abrupt change points performed by a Mann–Kendall test in an ICV time series, as follows: only one linear trend, increase (LI), and decrease (LD); at least two trends, increase firstly and decrease lastly (ID) and decrease firstly and increase lastly (DI); and other trends. The results showed that 82.74% of the ICV on the Loess Plateau displayed LD and ID patterns, indicating an increasing variability of forest productivity in this region. Overall, 73.83% of the ICV had a lower degree of rate decrease in the last phase than during the initial increase. Thus, the variability was in an early stage of increasing degree. The ICV time series showed an LI pattern in the eastern Gansu and the southern Shanxi, indicating a decreased variability, due partly to the improved forest restoration. When the plantation age was considered, the newly planted forests (less than 19 a) exhibited a decreasing variability, indicating the proactive role of forest management and restoration in averting environmental disruptions in dry environments.
PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation
Authors: Dong Hao , Yuan Mu , Wang Shu , Zhang Long , Bao Wenxia , Liu Yong , Hu Qingyuan
Published: July 2023

Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.
A Human Digital-Twin-Based Framework Driving Human Centricity towards Industry 5.0
Authors: Modoni Gianfranco E. , Sacco Marco
Published: July 2023

This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory’s digital twin, which instead does not adequately consider the human component. The overall goal of the authors is to provide a reference architecture to manufacturing companies for a digital-twin-based platform that promotes harmonization and orchestration between humans and (physical and virtual) machines through the monitoring, simulation, and optimization of their interactions. In addition, the platform enhances the interactions of the stakeholders with the digital twin, considering that the latter cannot always be fully autonomous, and it can require human intervention. The paper also presents an implemented scenario adhering to the proposed framework’s specifications, which is also validated with a real case study set in a factory plant that produces wooden furniture, thus demonstrating the validity of the overall proposed approach.
ZnO-Loaded Graphene for NO2 Gas Sensing
Authors: Alouani Mohamed Ayoub , Casanova-Cháfer Juan , Güell Frank , Peña-Martín Elisa , Ruiz-Martínez-Alcocer Sara , de Bernardi-Martín Santiago , García-Gómez Alejandra , Vilanova Xavier , Llobet Eduard
Published: July 2023

This paper investigates the effect of decorating graphene with zinc oxide (ZnO) nanoparticles (NPs) for the detection of NO2. In this regard, two graphene sensors with different ZnO loadings of 5 wt.% and 20 wt.% were prepared, and their responses towards NO2 at room temperature and different conditions were compared. The experimental results demonstrate that the graphene loaded with 5 wt.% ZnO NPs (G95/5) shows better performance at detecting low concentrations of the target gas than the one loaded with 20 wt.% ZnO NPs (G80/20). Moreover, measurements under dry and humid conditions of the G95/5 sensor revealed that the material is very sensitive to ambient moisture, showing an almost eight-fold increase in NO2 sensitivity when the background changes from dry to 70% relative humidity. Regarding sensor selectivity, it presents a significant selectivity towards NO2 compared to other gas compounds.
Fabrication and Performance of a Ta2O5 Thin Film pH Sensor Manufactured Using MEMS Processes
Authors: Guo Yuzhen , Zhang Zengxing , Yao Bin , Chai Jin , Zhang Shiqiang , Liu Jianwei , Zhao Zhou , Xue Chenyang
Published: July 2023

In this work, a capacitive pH sensor consisting of Ta2O5 functional film is designed and fabricated by employing MEMS-based procedures. The Ta2O5 thin film has an amorphous microstructure, and its surface roughness is less than 3.17 nm. A signal processing circuit and a software filtering algorithm are also designed to measure the pH value, thus improving the detection accuracy and anti-interference ability. Good linearity (R2 = 0.99904) and sensitivity (63.12 mV/pH) are recorded for the proposed sensing element in the range of pH 2~12. In addition, the sensor’s drift and hysteresis are equal to 5.1 mV and 5.8 mV, respectively. The enhanced sensing performance in combination with the facile miniaturization process, low fabrication cost, and suitability for mass production render the fabricated sensor attractive for applications where pH change measurements in a water environment are required.
Technology Trends for Massive MIMO towards 6G
Authors: Huo Yiming , Lin Xingqin , Di Boya , Zhang Hongliang , Hernando Francisco Javier Lorca , Tan Ahmet Serdar , Mumtaz Shahid , Demir Özlem Tuğfe , Chen-Hu Kun
Published: July 2023

At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) in combination with leading-edge technologies, methodologies, and architectures are poised to be a cornerstone technology. Capitalizing on its successful integration and scalability within 5G and beyond, massive MIMO has proven its merits and adaptability. Notably, a series of evolutionary advancements and revolutionary trends have begun to materialize in recent years, envisioned to redefine the landscape of future 6G wireless systems and networks. In particular, the capabilities and performance of future massive MIMO systems will be amplified through the incorporation of cutting-edge technologies, structures, and strategies. These include intelligent omni-surfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), Terahertz (THz) communications, and cell-free architectures. In addition, an array of diverse applications built on the foundation of massive MIMO will continue to proliferate and thrive. These encompass wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, and inter-planetary communications, among others.
LiDAR Point Cloud Data Combined Structural Analysis Based on Strong Form Meshless Method Using Essential Boundary Condition Capturing
Authors: Seo Kyung-Wan , Yoon Young-Cheol , Lee Sang-Ho
Published: July 2023

This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques.
Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning
Authors: Venketeswaran Abhishek , Lalam Nageswara , Lu Ping , Bukka Sandeep R. , Buric Michael P. , Wright Ruishu
Published: July 2023

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.
Precise and Efficient Pointing Control of a 2.5-m-Wide Field Survey Telescope Using ADRC and Nonlinear Disturbance Observer
Authors: Liu Yang , Deng Yongting , Li Hongwen , Wang Jianli , Wang Dejun
Published: July 2023

Linear active disturbance rejection control (LADRC) has been widely used to improve the tracking accuracy and anti-disturbance performance of telescope servo control under disturbances. However, the linear extended state observer (LESO) is sensitive to noise, and its bandwidth is limited by the resonant frequency of the telescope. To enhance the LARDC’s ability to attenuate disturbances, a novel cascade anti-disturbance structure (NCADS) with LADRC on the outer speed loop and a nonlinear disturbance observer (NDOB) on the inner current loop is proposed. The NDOB compensates for the dominant disturbance through feedforwarding the q-axis current reference, and the LESO compensates for the residual disturbance on the outer speed loop. First, the NCADS is introduced in a three-closed-loop control framework of PMSM. Then, the design method of the controller for each loop and the NDOB are presented, the parameter-tuning method based on bandwidth is demonstrated, and the convergence of the NDOB is proved. Furthermore, to improve the searching and tracking efficiency of wide-field survey telescopes, the nonlinear tracking differentiator (NTD) was modified to plan the transition process of the position loop, which only needs to set the maximum speed and acceleration of the telescope. Finally, simulations and experiments were performed on a 2.5-m-wide field survey telescope. The experimental results verify that the proposed NCADS method has a better anti-disturbance performance and higher tracking precision than the conventional method, and the improved NTD method does not need to tune parameters and achieved a fast and smooth transition process of the position loop.
Comparative Evaluations on Real-Time Monitoring of Temperature Sensors during Endoscopic Laser Application
Authors: Ta Minh Duc , Truong Van Gia , Lim Seonghee , Lee Byeong-Il , Kang Hyun Wook
Published: July 2023

Temperature sensors, such as Fiber Bragg Grating (FBG) and thermocouple (TC), have been widely used for monitoring the interstitial tissue temperature during laser irradiation. The aim of the current study was to compare the performance of both FBG and TC in real-time temperature monitoring during endoscopic and circumferential laser treatment on tubular tissue structure. A 600-µm core-diameter diffusing applicator was employed to deliver 980-nm laser light (30 W for 90 s) circumferentially for quantitative evaluation. The tip of the TC was covered with a white tube (W-TC) in order to prevent direct light absorption and to minimize temperature overestimation. The temperature measurements in air demonstrated that the measurement difference in the temperature elevations was around 3.5 °C between FBG and W-TC. Ex vivo porcine liver tests confirmed that the measurement difference became lower (less than 1 °C). Ex vivo porcine esophageal tissue using a balloon-integrated catheter exhibited that both FBG and W-TC consistently showed a comparable trend of temperature measurements during laser irradiation (~2 °C). The current study demonstrated that the white tube-covered TC could be a feasible sensor to monitor interstitial tissue temperature with minimal overestimation during endoscopic laser irradiation. Further in vivo studies on gastroesophageal reflux disease will investigate the performance of the W-TC to monitor the temperature of the esophageal mucosa surface in real-time mode to warrant the safety of endoscopic laser treatment.
Image-Processing-Based Subway Tunnel Crack Detection System
Authors: Liu Xiaofeng , Hong Zenglin , Shi Wei , Guo Xiaodan
Published: July 2023

With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to not only prolong the service life of the tunnel but also reduce the incidence of accidents. In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology. This method fully considers the characteristics of tunnel images and the combination of these characteristics with deep learning, while a deep convolutional network (Single-Shot MultiBox Detector (SSD)) is proposed based on deep learning for object detection in complex images. The experimental results show that the test set accuracy and training set accuracy of the support vector machine (SVM) in the classification comparison test are up to 88% and 87.8%, respectively; while the test accuracy of Alexnet’s deep convolutional neural network-based classification and identification is up to 96.7%, and the training set accuracy is up to 97.5%. It can be seen that this deep convolutional network recognition algorithm based on deep learning and image processing is better and more suitable for the detection of cracks in subway tunnels.
k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation
Authors: White Jacob , Power Sarah D.
Published: July 2023

In passive BCI studies, a common approach is to collect data from mental states of interest during relatively long trials and divide these trials into shorter “epochs” to serve as individual samples in classification. While it is known that using k-fold cross-validation (CV) in this scenario can result in unreliable estimates of mental state separability (due to autocorrelation in the samples derived from the same trial), k-fold CV is still commonly used and reported in passive BCI studies. What is not known is the extent to which k-fold CV misrepresents true mental state separability. This makes it difficult to interpret the results of studies that use it. Furthermore, if the seriousness of the problem were clearly known, perhaps more researchers would be aware that they should avoid it. In this work, a novel experiment explored how the degree of correlation among samples within a class affects EEG-based mental state classification accuracy estimated by k-fold CV. Results were compared to a ground-truth (GT) accuracy and to “block-wise” CV, an alternative to k-fold which is purported to alleviate the autocorrelation issues. Factors such as the degree of true class separability and the feature set and classifier used were also explored. The results show that, under some conditions, k-fold CV inflated the GT classification accuracy by up to 25%, but block-wise CV underestimated the GT accuracy by as much as 11%. It is our recommendation that the number of samples derived from the same trial should be reduced whenever possible in single-subject analysis, and that both the k-fold and block-wise CV results are reported.
CBIR-SAR System Using Stochastic Distance
Authors: Sousa Alcilene Dalília , Silva Pedro Henrique dos Santos , Silva Romuere Rodrigues Veloso , Rodrigues Francisco Alixandre Àvila , Medeiros Fatima Nelsizeuma Sombra
Published: July 2023

This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness (α^) and scale (γ^) parameters of the GI0 distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval.
Efficient Fine Tuning for Fashion Object Detection
Authors: Ma Benjiang , Xu Wenjin
Published: July 2023

Pre-trained models have achieved success in object detection. However, challenges remain due to dataset noise and lack of domain-specific data, resulting in weaker zero-shot capabilities in specialized fields such as fashion imaging. We addressed this by constructing a novel clothing object detection benchmark, Garment40K, which includes more than 140,000 human images with bounding boxes and over 40,000 clothing images. Each clothing item within this dataset is accompanied by its corresponding category and textual description. The dataset covers 2 major categories, pants and tops, which are further divided into 15 fine-grained subclasses, providing a rich and high-quality clothing resource. Leveraging this dataset, we propose an efficient fine-tuning method based on the Grounding DINO framework to tackle the issue of missed and false detections of clothing targets. This method incorporates additional similarity loss constraints and adapter modules, leading to a significantly enhanced model named Improved Grounding DINO. By fine-tuning only a small number of additional adapter module parameters, we considerably reduced computational costs while achieving performance comparable to full parameter fine tuning. This allows our model to be conveniently deployed on a variety of low-cost visual sensors. Our Improved Grounding DINO demonstrates considerable performance improvements in computer vision applications in the clothing domain.
LPDNet: A Lightweight Network for SAR Ship Detection Based on Multi-Level Laplacian Denoising
Authors: Zhao Congxia , Fu Xiongjun , Dong Jian , Feng Cheng , Chang Hao
Published: July 2023

Intelligent ship detection based on synthetic aperture radar (SAR) is vital in maritime situational awareness. Deep learning methods have great advantages in SAR ship detection. However, the methods do not strike a balance between lightweight and accuracy. In this article, we propose an end-to-end lightweight SAR target detection algorithm, multi-level Laplacian pyramid denoising network (LPDNet). Firstly, an intelligent denoising method based on the multi-level Laplacian transform is proposed. Through Convolutional Neural Network (CNN)-based threshold suppression, the denoising becomes adaptive to every SAR image via back-propagation and makes the denoising processing supervised. Secondly, channel modeling is proposed to combine the spatial domain and frequency domain information. Multi-dimensional information enhances the detection effect. Thirdly, the Convolutional Block Attention Module (CBAM) is introduced into the feature fusion module of the basic framework (Yolox-tiny) so that different weights are given to each pixel of the feature map to highlight the effective features. Experiments on SSDD and AIR SARShip-1.0 demonstrate that the proposed method achieves 97.14% AP with a speed of 24.68FPS and 92.19% AP with a speed of 23.42FPS, respectively, with only 5.1 M parameters, which verifies the accuracy, efficiency, and lightweight of the proposed method.
A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN
Authors: Fu Yanming , Shen Yuming , Tang Liang
Published: July 2023

With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform’s development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.
FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas
Authors: Benifa J. V. Bibal , Chola Channabasava , Muaad Abdullah Y. , Hayat Mohd Ammar Bin , Bin Heyat Md Belal , Mehrotra Rajat , Akhtar Faijan , Hussein Hany S. , Vargas Debora Libertad Ramírez , Castilla Ángel Kuc , Díez Isabel de la Torre , Khan Salabat
Published: July 2023

A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.
Research on Heterogeneous Traveler Travel Mode Choices with Differences under a Mixed Traffic Environment
Authors: Shen Yutong , Wu Yuelong , Yao Baozhen
Published: July 2023

Autonomous vehicles (AVs) have been made possible by advances in sensing and computing technologies. However, the high cost of AVs makes privatization take longer. Therefore, companies with autonomous vehicles can develop shared autonomous vehicle (SAV) projects. AVs with a high level of automation require high upgrade and use costs. In order to meet the needs of more customers and reduce the investment cost of the company, SAVs with different levels of automation may coexist for a long time. Faced with multiple travel modes (autonomous cars with different levels of automation, private cars, and buses), travelers’ travel mode choices are worth studying. To further differentiate the types of travelers, this paper defines high-income travelers and low-income travelers. The difference between these two types of travelers is whether they have a private car. The differences in time value and willingness to pay of the two types of travelers are considered. Based on the above considerations, this paper establishes a multi-modal selection model with the goal of maximizing the total utility of all travelers and uses the imperial competition algorithm to solve it. The results show that low-income travelers are more likely to choose buses and autonomous vehicles with lower levels of automation, while high-income travelers tend to choose higher levels of automation due to their high value of travel time.
Influence of Ion Exchange Process Parameters on Broadband Differential Interference
Authors: Gut Kazimierz , Błahut Marek
Published: July 2023

The paper presents theoretical analyses and experimental investigations of broadband differential interference in planar gradient waveguides made via K+-Na+ ion exchange in BK-7 glass. This technology, due to its large polarimetric dispersion, is especially useful for applications in differential interferometry. We discuss the influence of technological parameters on the operation characteristics of the structure in terms of sensor applications. The refractive index variation in the measured external surroundings affects the modal properties of TE and TM modes and the spectral distribution at the output of the differential interferometer. The optical system described in this work has been designed specifically for use in biological systems where variations in the index of refraction need to be measured.
Advanced Stiffness Sensing through the Pincer Grasping of Soft Pneumatic Grippers
Authors: Sithiwichankit Chaiwuth , Chancharoen Ratchatin
Published: July 2023

In this study, a comprehensive approach for sensing object stiffness through the pincer grasping of soft pneumatic grippers (SPGs) is presented. This study was inspired by the haptic sensing of human hands that allows us to perceive object properties through grasping. Many researchers have tried to imitate this capability in robotic grippers. The association between gripper performance and object reaction must be determined for this purpose. However, soft pneumatic actuators (SPA), the main components of SPGs, are extremely compliant. SPA compliance makes the determination of the association challenging. Methodologically, the connection between the behaviors of grasped objects and those of SPAs was clarified. A new concept of SPA modeling was then introduced. A method for stiffness sensing through SPG pincer grasping was developed based on this connection, and demonstrated on four samples. This method was validated through compression testing on the same samples. The results indicate that the proposed method yielded similar stiffness trends with slight deviations in compression testing. A main limitation in this study was the occlusion effect, which leads to dramatic deviations when grasped objects greatly deform. This is the first study to enable stiffness sensing and SPG grasping to be carried out in the same attempt. This study makes a major contribution to research on soft robotics by progressing the role of sensing for SPG grasping and object classification by offering an efficient method for acquiring another effective class of classification input. Ultimately, the proposed framework shows promise for future applications in inspecting and classifying visually indistinguishable objects.
Dynamic Modeling of Carbon Dioxide Transport through the Skin Using a Capnometry Wristband
Authors: Grangeat Pierre , Duval Comsa Maria-Paula , Koenig Anne , Phlypo Ronald
Published: July 2023

The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle with a continuous circulation of air flow in the collection cell. We propose a model for the temporal dynamics of the carbon dioxide exchange between the blood and the gas channel inside the device. The transport of carbon dioxide is modeled by convection–diffusion equations. We consider four compartments: blood, skin, the measurement cell and the collection cell. We introduce the state-space equations and the associated transition matrix associated with a Markovian model. We define an augmented system by combining a first-order autoregressive model describing the supply of carbon dioxide concentration in the blood compartment and its inertial resistance to change. We propose to use a Kalman filter to estimate the carbon dioxide concentration in the blood vessels recursively over time and thus monitor arterial carbon dioxide blood pressure in real time. Four performance factors with respect to the dynamic quantification of the CO2 blood concentration are considered, and a simulation is carried out based on data from a previous clinical study. These demonstrate the feasibility of such a technological concept.
Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration
Authors: Ibrahim Ahmed , Abosekeen Ashraf , Azouz Ahmed , Noureldin Aboelmagd
Published: July 2023

High-precision navigation solutions are a main requirement for autonomous vehicle (AV) applications. Global navigation satellite systems (GNSSs) are the prime source of navigation information for such applications. However, some places such as tunnels, underpasses, inside parking garages, and urban high-rise buildings suffer from GNSS signal degradation or unavailability. Therefore, another system is required to provide a continuous navigation solution, such as the inertial navigation system (INS). The vehicle’s onboard inertial measuring unit (IMU) is the main INS input measurement source. However, the INS solution drifts over time due to IMU-associated errors and the mechanization process itself. Therefore, INS/GNSS integration is the proper solution for both systems’ drawbacks. Traditionally, a linearized Kalman filter (LKF) such as the extended Kalman filter (EKF) is utilized as a navigation filter. The EKF deals only with the linearized errors and suppresses the higher orders using the Taylor expansion up to the first order. This paper introduces a loosely coupled INS/GNSS integration scheme using the invariant extended Kalman filter (IEKF). The IEKF state estimate is independent of the Jacobians that are derived in the EKF; instead, it uses the matrix Lie group. The proposed INS/GNSS integration using IEKF is applied to a real road trajectory for performance validation. The results show a significant enhancement when using the proposed system compared to the traditional INS/GNSS integrated system that uses EKF in both GNSS signal presence and blockage cases. The overall trajectory 2D-position RMS error reduced from 19.4 m to 3.3 m with 82.98% improvement and the 2D-position max error reduced from 73.9 m to 14.2 m with 80.78% improvement.
Fast Determination of Optimal Transmission Rate for Wireless Blockchain Networks: A Graph Convolutional Neural Network Approach
Authors: Ju Yucong , Song Fei , Jiao Yutao , Wang Weiyi , Dai Wenting , Xu Yuhua
Published: July 2023

One of the primary challenges in wireless blockchain networks is to ensure security and high throughput with constrained communication and energy resources. In this paper, with curve fitting on the collected blockchain performance dataset, we explore the impact of the data transmission rate configuration on the wireless blockchain system under different network topologies, and give the blockchain a utility function which balances the throughput, energy efficiency, and stale rate. For efficient blockchain network deployment, we propose a novel Graph Convolutional Neural Network (GCN)-based approach to quickly and accurately determine the optimal data transmission rate. The experimental results demonstrate that the average relative deviation between the blockchain utility obtained by our GCN-based method and the optimal utility is less than 0.21%.
Modified U Slot Patch Antenna with Large Frequency Ratio for Vehicle-to-Vehicle Communication
Authors: Govindarajulu Sandhiya Reddy , Tarek Md Nurul Anwar , Guerra Marisol Roman , Hassan Asif , Alwan Elias
Published: July 2023

This paper presents a single-fed, single-layer, dual-band antenna with a large frequency ratio of 4.74:1 for vehicle-to-vehicle communication. The antenna consists of a 28 GHz inset-fed rectangular patch embedded into a 5.9 GHz patch antenna for dual-band operation. The designed dual-band antenna operates from 5.81 to 5.99 GHz (Dedicated Short Range Communications, DSRC) and 27.9 to 30.1 GHz (5G millimeter-wave (mm-wave) band). Furthermore, the upper band patch was modified by inserting slots near the inset feed line to achieve an instantaneous bandwidth of 4.5 GHz. The antenna was fabricated and measured. The manufactured prototype operates simultaneously from 5.8 to 6.05 GHz and from 26.8 to 31.3 GHz. Notably, the designed dual-band antenna offers a high peak gain of 7.7 dBi in the DSRC band and 6.38 dBi in the 5G mm-wave band.
Fractions of Methanol Extracts from the Resurrection Plant Haberlea rhodopensis Have Anti-Breast Cancer Effects in Model Cell Systems
Authors: Zasheva Diana , Mladenov Petko , Rusanov Krasimir , Simova Svetlana , Zapryanova Silvina , Simova-Stoilova Lyudmila , Moyankova Daniela , Djilianov Dimitar
Published: July 2023

Breast cancer is among the most problematic diseases and a leading cause of death in women. The methods of therapy widely used, so far, are often with many side effects, seriously hampering patients’ quality of life. To overcome these constraints, new cancer treatment alternatives are constantly tested, including bioactive compounds of plant origin. Our aim was to study the effects of Haberlea rhodopensis methanol extract fractions on cell viability and proliferation of two model breast cancer cell lines with different characteristics. In addition to the strong reduction in cell viability, two of the fractions showed significant influence on the proliferation rate of the hormone receptor expressing MCF7 and the triple negative MDA-MB231 breast cancer cell lines. No significant effects on the benign MCF10A cell line were observed. We applied a large scale non-targeted approach to purify and identify highly abundant compounds from the active fractions of H. rhodopensis extracts. By the combined NMR/MS approach, myconoside was identified in the fractions and hispidulin 8-C-(6-O-acetyl-2″-O-syringoyl-β-glucopyranoside) was found in one of them. We further performed molecular docking analysis of possible myconoside interactions with several proteins, important for breast cancer proliferation. High probability of binding was established for GLUT1 transporter, estrogen receptor and MYST acetyltransferase. Our results are a good background for future studies on the use of myconoside for targeted breast cancer therapy.
Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study
Authors: Mimi Safaa , Ben Maissa Yann , Tamtaoui Ahmed
Published: July 2023

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.
E-Governance in Smart Cities: Global Trends and Key Enablers
Authors: Kuzior Aleksandra , Pakhnenko Olena , Tiutiunyk Inna , Lyeonov Serhiy
Published: July 2023

A smart city is a complex concept that can be analyzed from different aspects and points of view. E-governance plays a key role in facilitating the integration of all elements of a smart city. The purpose of the article is to investigate key enablers of e-governance in terms of economic, social, political, information and technological indicators. The research base includes 68 smart cities selected on the basis of different regional affiliations and different economic, social and political developments. The authors apply the methods of cluster analysis (to divide smart cities into clusters according to e-governance indicators); construction of an integral indicator using the linear mathematical model and the Fishburn formula; VAR/VEC modeling (to stud the key factors influencing the development of e-government in smart cities). It was found that the Human Development Index has the greatest impact on e-governance, while the GNI per capita indicator demonstrated the absence of influence for all clusters. The factor of information technologies was defined as the main factor of direct influence on the Smart City Governance Index for smart cities of the first cluster with the highest indicators of e-governance.
“I’m Not Back to Where I Was”: COVID-19 and Gendered Mental Health Outcomes among Working Parents in the U.S.
Authors: Hoppen Charlotte
Published: July 2023

This study examines how dual-earner parents in the U.S. experienced mental health in relation to their caregiving and work obligations during the COVID-19 pandemic. Current research shows that parents experienced heightened negative mental health outcomes during the pandemic yet does not analyze how parents understood their mental health while balancing multiple ongoing priorities. Research on parental mental health during COVID-19 largely remains quantitative. I analyze 48 semi-structured interviews with dual-earner parents in the U.S. between January 2021 and August 2022 to understand how parents interpreted their mental health during the pandemic, and how their work and caregiving obligations directly affected their mental health outcomes. Findings indicate that during the COVID-19 pandemic: (1) parents experienced an increase in negative mental health outcomes; (2) mothers attributed negative mental health outcomes to both work and childcare obligations, while fathers attributed negative mental health outcomes only to work obligations; and (3) parents, but more frequently mothers, stressed the need for a more flexible work environment in a post-COVID-19 world.