bluuebunny/crossref_metadata_2025 huggingface dataset:
Handover Management for Drones in Future Mobile Networks—A Survey
Authors: Shayea Ibraheem , Dushi Pabiola , Banafaa Mohammed , Rashid Rozeha A. , Ali Sawsan , Sarijari Mohd Adib , Daradkeh Yousef Ibrahim , Mohamad Hafizal
Published: August 2022

Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone’s free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones’ characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques.
Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application
Authors: Hartung Julia , Dold Patricia M. , Jahn Andreas , Heizmann Michael
Published: August 2022

Machine learning (ML) is a key technology in smart manufacturing as it provides insights into complex processes without requiring deep domain expertise. This work deals with deep learning algorithms to determine a 3D reconstruction from a single 2D grayscale image. The potential of 3D reconstruction can be used for quality control because the height values contain relevant information that is not visible in 2D data. Instead of 3D scans, estimated depth maps based on a 2D input image can be used with the advantage of a simple setup and a short recording time. Determining a 3D reconstruction from a single input image is a difficult task for which many algorithms and methods have been proposed in the past decades. In this work, three deep learning methods, namely stacked autoencoder (SAE), generative adversarial networks (GANs) and U-Nets are investigated, evaluated and compared for 3D reconstruction from a 2D grayscale image of laser-welded components. In this work, different variants of GANs are tested, with the conclusion that Wasserstein GANs (WGANs) are the most robust approach among them. To the best of our knowledge, the present paper considers for the first time the U-Net, which achieves outstanding results in semantic segmentation, in the context of 3D reconstruction tasks. Unlike the U-Net, which uses standard convolutions, the stacked dilated U-Net (SDU-Net) applies stacked dilated convolutions. Of all the 3D reconstruction approaches considered in this work, the SDU-Net shows the best performance, not only in terms of evaluation metrics but also in terms of computation time. Due to the comparably small number of trainable parameters and the suitability of the architecture for strong data augmentation, a robust model can be generated with only a few training data.
Convolutional Neural Network-Based Rapid Post-Earthquake Structural Damage Detection: Case Study
Authors: Moscoso Alcantara Edisson Alberto , Saito Taiki
Published: August 2022

It is necessary to detect the structural damage condition of essential buildings immediately after an earthquake to identify safe structures, evacuate, or resume crucial activities. For this reason, a CNN methodology proposed to detect the structural damage condition of a building is here improved and validated for two currently instrumented essential buildings (Tahara City Hall and Toyohashi Fire Station). Three-dimensional frames instead of lumped mass models are used for the buildings. Besides this, a methodology to select records is introduced to reduce the variability of the structural responses. The maximum inter-storey drift and absolute acceleration of each storey are used as damage indicators. The accuracy is evaluated by the usability of the building, total damage condition, storey damage condition, and total comparison of the damage indicators. Finally, the maximum accuracy and R2 of the responses are obtained as follows: for the Tahara City Hall building, 90.0% and 0.825, respectively; for the Toyohashi Fire Station building, 100% and 0.909, respectively.
Emerging Sensors Techniques and Technologies for Intelligent Environments
Authors: Anghel Ionut , Cioara Tudor
Published: August 2022

The trending techniques for managing indoor and outdoor intelligent environments rely heavily on data acquisition through a diversity of heterogeneous Internet of Things (IoT) devices and sensors [...]
Generative Adversarial Networks and Data Clustering for Likable Drone Design
Authors: Yamin Lee J. , Cauchard Jessica R.
Published: August 2022

Novel applications for human-drone interaction demand new design approaches, such as social drones that need to be perceived as likable by users. However, given the complexity of the likability perception process, gathering such design information from the interaction context is intricate. This work leverages deep learning-based techniques to generate novel likable drone images. We collected a drone image database (N=360) applicable for design research and assessed the drone’s likability ratings in a user study (N=379). We employed two clustering methodologies: 1. likability-based, which resulted in non-likable, neutral, and likable drone clusters; and 2. feature-based (VGG, PCA), which resulted in drone clusters characterized by visual similarity; both clustered using the K-means algorithm. A characterization process identified three drone features: colorfulness, animal-like representation, and emotional expressions through facial features, which affect drone likability, going beyond prior research. We used the likable drone cluster (N=122) for generating new images using StyleGAN2-ADA and addressed the dataset size limitation using specific configurations and transfer learning. Our results were mitigated due to the dataset size; thus, we illustrate the feasibility of our approach by generating new images using the original database. Our findings demonstrate the effectiveness of Generative Adversarial Networks (GANs) exploitation for drone design, and to the best of our knowledge, this work is the first to suggest GANs for such application.
Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems
Authors: Amaya Luis , Inga Esteban
Published: August 2022

The present work proposes to locate harmonic frequencies that distort the fundamental voltage and current waves in electrical systems using the compressed sensing (CS) technique. With the compressed sensing algorithm, data compression is revolutionized, a few samples are taken randomly, a measurement matrix is formed, and according to a linear transformation, the signal is taken from the time domain to the frequency domain in a compressed form. Then, the inverse linear transformation is used to reconstruct the signal with a few sensed samples of an electrical signal. Therefore, to demonstrate the benefits of CS in the detection of harmonics in the electrical network of this work, power quality analyzer equipment (commercial) is used. It measures the current of a nonlinear load and issues its results of harmonic current distortion (THD-I) on its screen and the number of harmonics detected in the network; this equipment acquires the data based on the Shannon–Nyquist theorem taken as a standard of measurement. At the same time, an electronic prototype senses the current signal of the nonlinear load. The prototype takes data from the current signal of the nonlinear load randomly and incoherently, so it takes fewer samples than the power quality analyzer equipment used as a measurement standard. The data taken by the prototype are entered into the Matlab software via USB, and the CS algorithm run and delivers, as a result, the harmonic distortions of the current signal THD-I and the number of harmonics. The results obtained with the compressed sensing algorithm versus the standard measurement equipment are analyzed, the error is calculated, and the number of samples taken by the standard equipment and the prototype, the machine time, and the maximum sampling frequency are analyzed.
Design and Implementation of SEMAR IoT Server Platform with Applications
Authors: Panduman Yohanes Yohanie Fridelin , Funabiki Nobuo , Puspitaningayu Pradini , Kuribayashi Minoru , Sukaridhoto Sritrusta , Kao Wen-Chung
Published: August 2022

Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal.
An Accurate and Convenient Method of Vehicle Spatiotemporal Distribution Recognition Based on Computer Vision
Authors: Chen Zhiwei , Feng Yuliang , Zhang Yao , Liu Jiantao , Zhu Cixiang , Chen Awen
Published: August 2022

The Convenient and accurate identification of the traffic load of passing vehicles is of great significance to bridge health monitoring. The existing identification approaches often require prior environment knowledge to determine the location of the vehicle load, i.e., prior information of the road, which is inconvenient in practice and therefore limits its application. Moreover, camera disturbance usually reduces the measurement accuracy in case of long-term monitoring. In this study, a novel approach to identify the spatiotemporal information of passing vehicles is proposed based on computer vision. The position relationship between the camera and the passing vehicle is established, and then the location of the passing vehicle can be calculated by setting the camera shooting point as the origin. Since the angle information of the camera is pre-determined, the identification result is robust to camera disturbance. Lab-scale test and field measurement have been conducted to validate the reliability and accuracy of the proposed method.
End-to-End Point Cloud Completion Network with Attention Mechanism
Authors: Li Yaqin , Han Binbin , Zeng Shan , Xu Shengyong , Yuan Cao
Published: August 2022

We propose a conceptually simple, general framework and end-to-end approach to point cloud completion, entitled PCA-Net. This approach differs from the existing methods in that it does not require a “simple” network, such as multilayer perceptrons (MLPs), to generate a coarse point cloud and then a “complex” network, such as auto-encoders or transformers, to enhance local details. It can directly learn the mapping between missing and complete points, ensuring that the structure of the input missing point cloud remains unchanged while accurately predicting the complete points. This approach follows the minimalist design of U-Net. In the encoder, we encode the point clouds into point cloud blocks by iterative farthest point sampling (IFPS) and k-nearest neighbors and then extract the depth interaction features between the missing point cloud blocks by the attention mechanism. In the decoder, we introduce a new trilinear interpolation method to recover point cloud details, with the help of the coordinate space and feature space of low-resolution point clouds, and missing point cloud information. This paper also proposes a method to generate multi-view missing point cloud data using a 3D point cloud hidden point removal algorithm, so that each 3D point cloud model generates a missing point cloud through eight uniformly distributed camera poses. Experiments validate the effectiveness and superiority of PCA-Net in several challenging point cloud completion tasks, and PCA-Net also shows great versatility and robustness in real-world missing point cloud completion.
Online Self-Calibration of 3D Measurement Sensors Using a Voxel-Based Network
Authors: Song Jingyu , Lee Joonwoong
Published: August 2022

Multi-sensor fusion is important in the field of autonomous driving. A basic prerequisite for multi-sensor fusion is calibration between sensors. Such calibrations must be accurate and need to be performed online. Traditional calibration methods have strict rules. In contrast, the latest online calibration methods based on convolutional neural networks (CNNs) have gone beyond the limits of the conventional methods. We propose a novel algorithm for online self-calibration between sensors using voxels and three-dimensional (3D) convolution kernels. The proposed approach has the following features: (1) it is intended for calibration between sensors that measure 3D space; (2) the proposed network is capable of end-to-end learning; (3) the input 3D point cloud is converted to voxel information; (4) it uses five networks that process voxel information, and it improves calibration accuracy through iterative refinement of the output of the five networks and temporal filtering. We use the KITTI and Oxford datasets to evaluate the calibration performance of the proposed method. The proposed method achieves a rotation error of less than 0.1° and a translation error of less than 1 cm on both the KITTI and Oxford datasets.
A Centrifugal Pump Fault Diagnosis Framework Based on Supervised Contrastive Learning
Authors: Ahmad Sajjad , Ahmad Zahoor , Kim Jong-Myon
Published: August 2022

A novel intelligent centrifugal pump (CP) fault diagnosis method is proposed in this paper. The method is based on the contrast in vibration data obtained from a centrifugal pump (CP) under several operating conditions. The vibration signals data obtained from a CP are non-stationary because of the impulses caused by different faults; thus, traditional time domain and frequency domain analyses such as fast Fourier transform and Walsh transform are not the best option to pre-process the non-stationary signals. First, to visualize the fault-related impulses in vibration data, we computed the kurtogram images of time series vibration sequences. To extract the discriminant features related to faults from the kurtogram images, we used a deep learning tool convolutional encoder (CE) with a supervised contrastive loss. The supervised contrastive loss pulls together samples belonging to the same class, while pushing apart samples belonging to a different class. The convolutional encoder was pretrained on the kurtograms with the supervised contrastive loss to infer the contrasting features belonging to different CP data classes. After pretraining with the supervised contrastive loss, the learned representations of the convolutional encoder were kept as obtained, and a linear classifier was trained above the frozen convolutional encoder, which completed the fault identification. The proposed model was validated with data collected from a real industrial testbed, yielding a high classification accuracy of 99.1% and an error of less than 1%. Furthermore, to prove the proposed model robust, it was validated on CP data with 3.0 and 3.5 bar inlet pressure.
Automatic Landing of Unmanned Aerial Vehicles via Wireless Positioning System with Pseudo-Conical Scanning
Authors: Iliev Ilia , Nachev Ivaylo
Published: August 2022

In this work, a wireless UAV unmanned landing system is considered, using the principles of pseudo conical scanning with a phased antenna array (PAA). The basic requirements for the characteristics and parameters of the system as a whole and of its components are defined. Special attention is paid to the primary sensor of the system—PAA with electronic scanning. A variant with a minimum number of four states on the radiation pattern of a low-budget patch PAA was studied. A linear regression of the difference characteristics of the measured radiation beams is proposed, which allows the practical application of the recommended landing algorithm with low computational complexity. Systematic and random positioning errors, both by measurement and by Monte Carlo simulation, were studied. Obtained statistical results prove the algorithm convergence and acceptable accuracy for the system implementation. They are applied if necessary to adjust the Kalman filter parameters. The proposed wireless system can be used for unmanned landing, tracking, and navigating the UAV in flight, or wireless navigation of other mobile objects.
Transportation Mode Detection Combining CNN and Vision Transformer with Sensors Recalibration Using Smartphone Built-In Sensors
Authors: Tian Ye , Hettiarachchi Dulmini , Kamijo Shunsuke
Published: August 2022

Transportation Mode Detection (TMD) is an important task for the Intelligent Transportation System (ITS) and Lifelog. TMD, using smartphone built-in sensors, can be a low-cost and effective solution. In recent years, many studies have focused on TMD, yet they support a limited number of modes and do not consider similar transportation modes and holding places, limiting further applications. In this paper, we propose a new network framework to realize TMD, which combines structural and spatial interaction features, and considers the weights of multiple sensors’ contributions, enabling the recognition of eight transportation modes with four similar transportation modes and four holding places. First, raw data is segmented and transformed into a spectrum image and then ResNet and Vision Transformers (Vit) are used to extract structural and spatial interaction features, respectively. To consider the contribution of different sensors, the weights of each sensor are recalibrated using an ECA module. Finally, Multi-Layer Perceptron (MLP) is introduced to fuse these two different kinds of features. The performance of the proposed method is evaluated on the public Sussex-Huawei Locomotion-Transportation (SHL) dataset, and is found to outperform the baselines by at least 10%.
Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling
Authors: Muller Antoine , Mecheri Hakim , Corbeil Philippe , Plamondon André , Robert-Lachaine Xavier
Published: August 2022

Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper aims to develop and evaluate an IMC-based approach to estimate back loading during manual material handling (MMH) tasks. During various representative workplace MMH tasks performed by nine participants, this approach was evaluated by comparing the results with the ones computed from optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found between both computations on indicators based on L5/S1 peak and cumulative flexion moments, while lower correlations were found on indicators based on asymmetric moments. Since no force measurement or load kinematics measurement is needed, this study shows the potential of using only the handler’s kinematics measured by IMC to estimate kinetics variables. The assessment of workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics evaluation and will improve the ecological validity compared to laboratory studies, where the situations are often simplified and standardized.
Anisotropic SpiralNet for 3D Shape Completion and Denoising
Authors: Kim Seong Uk , Roh Jihyun , Im Hyeonseung , Kim Jongmin
Published: August 2022

Three-dimensional mesh post-processing is an important task because low-precision hardware and a poor capture environment will inevitably lead to unordered point clouds with unwanted noise and holes that should be suitably corrected while preserving the original shapes and details. Although many 3D mesh data-processing approaches have been proposed over several decades, the resulting 3D mesh often has artifacts that must be removed and loses important original details that should otherwise be maintained. To address these issues, we propose a novel 3D mesh completion and denoising system with a deep learning framework that reconstructs a high-quality mesh structure from input mesh data with several holes and various types of noise. We build upon SpiralNet by using a variational deep autoencoder with anisotropic filters that apply different convolutional filters to each vertex of the 3D mesh. Experimental results show that the proposed method enhances the reconstruction quality and achieves better accuracy compared to previous neural network systems.
Applied Assessment Method for Varus Thrust during Walking in Patients with Knee Osteoarthritis Using Acceleration Data Measured by an Inertial Measurement Unit
Authors: Misu Shogo , Tanaka So , Ishihara Kohei , Asai Tsuyoshi , Nishigami Tomohiko
Published: August 2022

We developed a novel quantitative method to assess varus thrust during walking using acceleration data obtained from an inertial measurement unit (IMU). This study aimed to examine the reliability of the developed index and to evaluate its ability to distinguish patients with knee osteoarthritis (OA) with varus thrust from healthy adults. Overall, 16 patients with knee OA and 16 healthy adults walked on a treadmill with IMUs attached to the tibial tuberosity and lateral femoral condyle. As an index of varus thrust, we used the root mean square (RMS) of acceleration in the mediolateral direction. This value was adjusted by dividing it by swing speed while walking (adjusted RMS, A-RMS) because the RMS of the acceleration was strongly coupled with the speed of motion. The intraclass correlation coefficients of A-RMS of the tibia and femur were 0.85 and 0.73, respectively. Significant differences were observed in the A-RMSs of the tibia and femur, with large effect sizes between the patients with knee OA and healthy adults (Cohen’s d: 1.23 and 0.97, respectively). Our results indicate that A-RMS has good test–retest reproducibility and can differentiate patients with varus thrust from healthy adults.
Human Activity Recognition: Review, Taxonomy and Open Challenges
Authors: Arshad Muhammad Haseeb , Bilal Muhammad , Gani Abdullah
Published: August 2022

Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.
Machine Learning and Lexicon Approach to Texts Processing in the Detection of Degrees of Toxicity in Online Discussions
Authors: Machová Kristína , Mach Marián , Adamišín Kamil
Published: August 2022

This article focuses on the problem of detecting toxicity in online discussions. Toxicity is currently a serious problem when people are largely influenced by opinions on social networks. We offer a solution based on classification models using machine learning methods to classify short texts on social networks into multiple degrees of toxicity. The classification models used both classic methods of machine learning, such as naïve Bayes and SVM (support vector machine) as well ensemble methods, such as bagging and RF (random forest). The models were created using text data, which we extracted from social networks in the Slovak language. The labelling of our dataset of short texts into multiple classes—the degrees of toxicity—was provided automatically by our method based on the lexicon approach to texts processing. This lexicon method required creating a dictionary of toxic words in the Slovak language, which is another contribution of the work. Finally, an application was created based on the learned machine learning models, which can be used to detect the degree of toxicity of new social network comments as well as for experimentation with various machine learning methods. We achieved the best results using an SVM—average value of accuracy = 0.89 and F1 = 0.79. This model also outperformed the ensemble learning by the RF and Bagging methods; however, the ensemble learning methods achieved better results than the naïve Bayes method.
Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection
Authors: Wang Jinglei , Li Yixuan , Ji Yifan , Qian Jiaming , Che Yuxuan , Zuo Chao , Chen Qian , Feng Shijie
Published: August 2022

Fringe projection profilometry (FPP) is widely applied to 3D measurements, owing to its advantages of high accuracy, non-contact, and full-field scanning. Compared with most FPP systems that project visible patterns, invisible fringe patterns in the spectra of near-infrared demonstrate fewer impacts on human eyes or on scenes where bright illumination may be avoided. However, the invisible patterns, which are generated by a near-infrared laser, are usually captured with severe speckle noise, resulting in 3D reconstructions of limited quality. To cope with this issue, we propose a deep learning-based framework that can remove the effect of the speckle noise and improve the precision of the 3D reconstruction. The framework consists of two deep neural networks where one learns to produce a clean fringe pattern and the other to obtain an accurate phase from the pattern. Compared with traditional denoising methods that depend on complex physical models, the proposed learning-based method is much faster. The experimental results show that the measurement accuracy can be increased effectively by the presented method.
A Fusion Model Based on Dynamic Web Browsing Behavior Analysis for IoT Insider Threat Detection
Authors: Wang Jiarong , Liu Junyi , Yan Tian , Xia Mingshan , Hong Jianshu , Zhou Caiqiu
Published: August 2022

With the wide application of Internet of things (IoT) devices in enterprises, the traditional boundary defense mechanisms are difficult to satisfy the demands of the insider threats detection. IoT insider threat detection can be more challenging, since internal employees are born with the ability to escape the deployed information security mechanism, such as firewalls and endpoint protection. In order to detect internal attacks more accurately, we can analyze users’ web browsing behaviors to identify abnormal users. The existing web browsing behavior anomaly detection methods ignore the dynamic change of the web browsing behavior of the target user and the behavior consistency of the target user in its peer group, which results in a complex modeling process, low system efficiency and low detection accuracy. Therefore, the paper respectively proposes the individual user behavior model and the peer-group behavior model to characterize the abnormal dynamic change of user browsing behavior and compare the mutual behavioral inconsistency among one peer-group. Furthermore, the fusion model is presented for insider threat detection which simultaneously considers individual behavioral abnormal dynamic changes and mutual behavioral dynamic inconsistency from peers. The experimental results show that the proposed fusion model can accurately detect insider threat based on the abnormal user web browsing behaviors in the enterprise networks.
DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
Authors: Zhu Longtao , Wang Jinlin , Wang Yi , Ji Yulong , Ren Jinchang
Published: August 2022

The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of the global navigation infrastructures to improve the utilization efficiency of airspace and reduce flight delays and the dependence on ground navigation facilities. The approach stage is one of the most important and difficult stages in the whole flying. In this study, we proposed deep reinforcement learning (DRL)-based RNP procedure execution, DRL-RNP. By conducting an RNP approach procedure, the DRL algorithm was implemented, using a fixed-wing aircraft to explore a path of minimum fuel consumption with reward under windy conditions in compliance with the RNP safety specifications. The experimental results have demonstrated that the six degrees of freedom aircraft controlled by the DRL algorithm can successfully complete the RNP procedure whilst meeting the safety specifications for protection areas and obstruction clearance altitude in the whole procedure. In addition, the potential path with minimum fuel consumption can be explored effectively. Hence, the DRL method can be used not only to implement the RNP procedure with a simulated aircraft but also to help the verification and evaluation of the RNP procedure.
An Outlier Cleaning Based Adaptive Recognition Method for Degradation Stage of Bearings
Authors: Xie Jingsong , Xie Yujie , Wang Tiantian , Xiao Yougang
Published: August 2022

Accurate identification of the degradation stage is key to the prediction of the remaining useful life (RUL) of bearings. The 3σ method is commonly used to identify the degradation point. However, the recognition accuracy is seriously disturbed by the random outliers in the normal stage. Therefore, this paper proposes an adaptive recognition method for the degradation stage based on outlier cleaning. Firstly, an improved multi-scale kernel regression outlier detection method is adopted to roughly search the abnormal signal segments. Then, a method for the accurate locating of the start and end points of abnormal impulses is established. After that, indexes are constructed for screening abnormal segments and an iterative strategy is proposed to achieve an accurate and efficient removal of abnormal impulses. After outlier cleaning, the 3σ approach is used to set the degradation warning threshold adaptively to realize the degradation stage recognition of the bearings. The PHM 2012 rotating machinery dataset is used to verify the effectiveness of the proposed method. Experimental results show that the proposed method can accurately locate and remove the outliers adaptively. After the cleaning of the outliers, the identification of the degradation stage is no longer disturbed by the selection of the reference signal of the normal stage and the robustness and the accuracy of the degradation stage identification have been improved significantly.
Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography
Authors: Calazans Maria Alice Andrade , Ferreira Felipe Alberto B. S. , Alcoforado Maria de Lourdes Melo Guedes , Santos Andrezza dos , Pontual Andréa dos Anjos , Madeiro Francisco
Published: August 2022

Imaging examinations are of remarkable importance for diagnostic support in Dentistry. Imaging techniques allow analysis of dental and maxillofacial tissues (e.g., bone, dentine, and enamel) that are inaccessible through clinical examination, which aids in the diagnosis of diseases as well as treatment planning. The analysis of imaging exams is not trivial; so, it is usually performed by oral and maxillofacial radiologists. The increasing demand for imaging examinations motivates the development of an automatic classification system for diagnostic support, as proposed in this paper, in which we aim to classify teeth as healthy or with endodontic lesion. The classification system was developed based on a Siamese Network combined with the use of convolutional neural networks with transfer learning for VGG-16 and DenseNet-121 networks. For this purpose, a database with 1000 sagittal and coronal sections of cone-beam CT scans was used. The results in terms of accuracy, recall, precision, specificity, and F1-score show that the proposed system has a satisfactory classification performance. The innovative automatic classification system led to an accuracy of about 70%. The work is pioneer since, to the authors knowledge, no other previous work has used a Siamese Network for the purpose of classifying teeth as healthy or with endodontic lesion, based on cone-beam computed tomography images.
Design of Ultra-Narrow Band Graphene Refractive Index Sensor
Authors: Shangguan Qianyi , Chen Zihao , Yang Hua , Cheng Shubo , Yang Wenxing , Yi Zao , Wu Xianwen , Wang Shifa , Yi Yougen , Wu Pinghui
Published: August 2022

The paper proposes an ultra-narrow band graphene refractive index sensor, consisting of a patterned graphene layer on the top, a dielectric layer of SiO2 in the middle, and a bottom Au layer. The absorption sensor achieves the absorption efficiency of 99.41% and 99.22% at 5.664 THz and 8.062 THz, with the absorption bandwidths 0.0171 THz and 0.0152 THz, respectively. Compared with noble metal absorbers, our graphene absorber can achieve tunability by adjusting the Fermi level and relaxation time of the graphene layer with the geometry of the absorber unchanged, which greatly saves the manufacturing cost. The results show that the sensor has the properties of polarization-independence and large-angle insensitivity due to the symmetric structure. In addition, the practical application of testing the content of hemoglobin biomolecules was conducted, the frequency of first resonance mode shows a shift of 0.017 THz, and the second resonance mode has a shift of 0.016 THz, demonstrating the good frequency sensitivity of our sensor. The S (sensitivities) of the sensor were calculated at 875 GHz/RIU and 775 GHz/RIU, and quality factors FOM (Figure of Merit) are 26.51 and 18.90, respectively; and the minimum limit of detection is 0.04. By comparing with previous similar sensors, our sensor has better sensing performance, which can be applied to photon detection in the terahertz band, biochemical sensing, and other fields.
Advantages in Using Colour Calibration for Orthophoto Reconstruction
Authors: Tocci Francesco , Figorilli Simone , Vasta Simone , Violino Simona , Pallottino Federico , Ortenzi Luciano , Costa Corrado
Published: August 2022

UAVs are sensor platforms increasingly used in precision agriculture, especially for crop and environmental monitoring using photogrammetry. In this work, light drone flights were performed on three consecutive days (with different weather conditions) on an experimental agricultural field to evaluate the photogrammetric performances due to colour calibration. Thirty random reconstructions from the three days and six different areas of the field were performed. The results showed that calibrated orthophotos appeared greener and brighter than the uncalibrated ones, better representing the actual colours of the scene. Parameter reporting errors were always lower in the calibrated reconstructions and the other quantitative parameters were always lower in the non-calibrated ones, in particular, significant differences were observed in the percentage of camera stations on the total number of images and the reprojection error. The results obtained showed that it is possible to obtain better orthophotos, by means of a calibration algorithm, to rectify the atmospheric conditions that affect the image obtained. This proposed colour calibration protocol could be useful when integrated into robotic platforms and sensors for the exploration and monitoring of different environments.
Damage Identification of Semi-Rigid Joints in Frame Structures Based on Additional Virtual Mass Method
Authors: An Xinhao , Zhang Qingxia , Li Chao , Hou Jilin , Shi Yongkang
Published: August 2022

In civil engineering, the joints of structures are complex, and their damage is generally hard to be detected. Due to the insensitivity of structural modal information to local joint damage, this paper presents a method based on additional virtual mass for damage identification of a semi-rigid joint in a frame structure. Firstly, the modeling of a semi-rigid is described. Secondly, the frequency response of the virtual structure is constructed, and the natural frequency of the constructed virtual structure is extracted by the ERA method. By adding multiple values of virtual masses at different positions, the natural frequency information sensitive to joint damage for damage identification is effectively increased. Based on the above theory, qualitative identification of joint damage is proposed to detect the potential damage, and identification of both damage location and its extent is presented, using natural frequency. Improved Orthogonal Matching Pursuit (IOMP) algorithm is employed to improve the accuracy of the natural frequency-based method for damage identification. At last, numerical simulation of a three-story frame is performed to discuss and to verify the effectiveness of the proposed method.
A Novel Steganography Method for Character-Level Text Image Based on Adversarial Attacks
Authors: Ding Kangyi , Hu Teng , Niu Weina , Liu Xiaolei , He Junpeng , Yin Mingyong , Zhang Xiaosong
Published: August 2022

The Internet has become the main channel of information communication, which contains a large amount of secret information. Although network communication provides a convenient channel for human communication, there is also a risk of information leakage. Traditional image steganography algorithms use manually crafted steganographic algorithms or custom models for steganography, while our approach uses ordinary OCR models for information embedding and extraction. Even if our OCR models for steganography are intercepted, it is difficult to find their relevance to steganography. We propose a novel steganography method for character-level text images based on adversarial attacks. We exploit the complexity and uniqueness of neural network boundaries and use neural networks as a tool for information embedding and extraction. We use an adversarial attack to embed the steganographic information into the character region of the image. To avoid detection by other OCR models, we optimize the generation of the adversarial samples and use a verification model to filter the generated steganographic images, which, in turn, ensures that the embedded information can only be recognized by our local model. The decoupling experiments show that the strategies we adopt to weaken the transferability can reduce the possibility of other OCR models recognizing the embedded information while ensuring the success rate of information embedding. Meanwhile, the perturbations we add to embed the information are acceptable. Finally, we explored the impact of different parameters on the algorithm with the potential of our steganography algorithm through parameter selection experiments. We also verify the effectiveness of our validation model to select the best steganographic images. The experiments show that our algorithm can achieve a 100% information embedding rate and more than 95% steganography success rate under the set condition of 3 samples per group. In addition, our embedded information can be hardly detected by other OCR models.
Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks
Authors: Ramu Saru Meena , Chatzistergos Panagiotis , Chockalingam Nachiappan , Arampatzis Adamantios , Maganaris Constantinos
Published: August 2022

Existing approaches for automated tracking of fascicle length (FL) and pennation angle (PA) rely on the presence of a single, user-defined fascicle (feature tracking) or on the presence of a specific intensity pattern (feature detection) across all the recorded ultrasound images. These prerequisites are seldom met during large dynamic muscle movements or for deeper muscles that are difficult to image. Deep-learning approaches are not affected by these issues, but their applicability is restricted by their need for large, manually analyzed training data sets. To address these limitations, the present study proposes a novel approach that tracks changes in FL and PA based on the distortion pattern within the fascicle band. The results indicated a satisfactory level of agreement between manual and automated measurements made with the proposed method. When compared against feature tracking and feature detection methods, the proposed method achieved the lowest average root mean squared error for FL and the second lowest for PA. The strength of the proposed approach is that the quantification process does not require a training data set and it can take place even when it is not possible to track a single fascicle or observe a specific intensity pattern on the ultrasound recording.
A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication
Authors: Wang Mengtao , Fan Youchen , Fang Shengliang , Cui Tianshu , Cheng Donghang
Published: August 2022

Automatic modulation discrimination (AMC) is one of the critical technologies in spatial cognitive communication systems. Building a high-performance AMC model in intelligent receivers can help to realize adaptive signal synchronization and demodulation. However, tackling the intra-class diversity problem is challenging to AMC based on deep learning (DL), as 16QAM and 64QAM are not easily distinguished by DL networks. In order to overcome the problem, this paper proposes a joint AMC model that combines DL and expert features. In this model, the former builds a neural network that can extract the time series and phase features of in-phase and quadrature component (IQ) samples, which improves the feature extraction capability of the network in similar models; the latter achieves accurate classification of QAM signals by constructing effective feature parameters. Experimental results demonstrate that our proposed joint AMC model performs better than the benchmark networks. The classification accuracy is increased by 11.5% at a 10 dB signal-to-noise ratio (SNR). At the same time, it also improves the discrimination of QAM signals.
Three-Dimensional Quantitative Evaluation of the Scapular Skin Marker Movements in the Upright Posture
Authors: Yoshida Yuki , Matsumura Noboru , Yamada Yoshitake , Yamada Minoru , Yokoyama Yoichi , Miyamoto Azusa , Nakamura Masaya , Nagura Takeo , Jinzaki Masahiro
Published: August 2022

Motion capture systems using skin markers are widely used to evaluate scapular kinematics. However, soft-tissue artifact (STA) is a major limitation, and there is insufficient knowledge of the marker movements from the original locations. This study explores a scapular STA, including marker movements with shoulder elevation using upright computed tomography (CT). Ten healthy males (twenty shoulders in total) had markers attached to scapular bony landmarks and underwent upright CT in the reference and elevated positions. Marker movements were calculated and compared between markers. The bone-based and marker-based scapulothoracic rotation angles were also compared in both positions. The median marker movement distances were 30.4 mm for the acromial angle, 53.1 mm for the root of the scapular spine, and 70.0 mm for the inferior angle. Marker movements were significantly smaller on the superolateral aspect of the scapula, and superior movement was largest in the directional movement. Scapulothoracic rotation angles were significantly smaller in the marker-based rotation angles than in the bone-based rotation angles of the elevated position. We noted that the markers especially did not track the inferior movement of the scapular motion with shoulder elevation, resulting in an underestimation of the marker-based rotation angles.
Visual Pretraining via Contrastive Predictive Model for Pixel-Based Reinforcement Learning
Authors: Luu Tung M. , Vu Thang , Nguyen Thanh , Yoo Chang D.
Published: August 2022

In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our method enables the convolutional encoder to perceive the underlying dynamics through a pair of forward and inverse models under the supervision of the contrastive loss, thus resulting in better representations. In experiments with a diverse set of vision control tasks, by initializing the encoders with VPCPM, the performance of state-of-the-art vision-based RL algorithms is significantly boosted, with 44% and 10% improvement for RAD and DrQ at 100 steps, respectively. In comparison to the prior unsupervised methods, the performance of VPCPM matches or outperforms all the baselines. We further demonstrate that the learned representations successfully generalize to the new tasks that share a similar observation and action space.