bluuebunny/crossref_metadata_2025 huggingface dataset:
Trajectory Optimization in Terms of Energy and Performance of an Industrial Robot in the Manufacturing Industry
Authors: Garriz Carlos , Domingo Rosario
Published: October 2022

Currently, the high demand for new products in the automotive sector requires large investments in factories. The automotive industry is characterized by high automatization, largely achieved by manipulator robots capable of multitasking. This work presents a method for the optimization of trajectories in robots with six degrees of freedom and a spherical wrist. The optimization of trajectories is based on the maximization of manipulability and the minimization of electrical energy. For this purpose, it is necessary to take into account the kinematics and dynamics of the manipulator in order to integrate an algorithm for calculation. The algorithm is based on the Kalman method. This algorithm was implemented in a simulation of the trajectories of a serial industrial robot, in which the robot has a sealer gun located on its sixth axis and the quality of the sealer application depends directly on the orientation of the gun. During the optimization of the trajectory, the application of the sealer must be guaranteed. This method was also applied to three different trajectories in the automotive sector. The obtained results for manipulability and electrical energy consumption prove the efficiency of the algorithm. Therefore, this method searches for the optimal solution within the limits of the manipulator and maintains the orientation of the final effector. This can be used for a known trajectory.
The Duality of Ray-Based and Pinhole-Camera Modeling and 3D Measurement Improvements Using the Ray-Based Model
Authors: Bräuer-Burchardt Christian , Ramm Roland , Kühmstedt Peter , Notni Gunther
Published: October 2022

Geometrical camera modeling is the precondition for 3D-reconstruction tasks using photogrammetric sensor systems. The purpose of this study is to describe an approach for possible accuracy improvements by using the ray-based-camera model. The relations between the common pinhole and the generally valid ray-based-camera model are shown. A new approach to the implementation and calibration of the ray-based-camera model is introduced. Using a simple laboratory setup consisting of two cameras and a projector, experimental measurements were performed. The experiments and results showed the possibility of easily transforming the common pinhole model into a ray-based model and of performing calibration using the ray-based model. These initial results show the model’s potential for considerable accuracy improvements, especially for sensor systems using wide-angle lenses or with deep 3D measurements. This study presents several approaches for further improvements to and the practical usage of high-precision optical 3D measurements.
Anomalous Network Traffic Detection Method Based on an Elevated Harris Hawks Optimization Method and Gated Recurrent Unit Classifier
Authors: Xiao Yao , Kang Chunying , Yu Hongchen , Fan Tao , Zhang Haofang
Published: October 2022

In recent years, network traffic contains a lot of feature information. If there are too many redundant features, the computational cost of the algorithm will be greatly increased. This paper proposes an anomalous network traffic detection method based on Elevated Harris Hawks optimization. This method is easier to identify redundant features in anomalous network traffic, reduces computational overhead, and improves the performance of anomalous traffic detection methods. By enhancing the random jump distance function, escape energy function, and designing a unique fitness function, there is a unique anomalous traffic detection method built using the algorithm and the neural network for anomalous traffic detection. This method is tested on three public network traffic datasets, namely the UNSW-NB15, NSL-KDD, and CICIDS2018. The experimental results show that the proposed method does not only significantly reduce the number of features in the dataset and computational overhead, but also gives better indicators for every test.
Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar
Authors: Ishtiaq Saima , Wang Xiangrong , Hassan Shahid , Mohammad Alsharef , Alahmadi Ahmad Aziz , Ullah Nasim
Published: October 2022

Multi-target tracking (MTT) generally needs either a Doppler radar network with spatially separated receivers or a single radar equipped with costly phased array antennas. However, Doppler radar networks have high computational complexity, attributed to the multiple receivers in the network. Moreover, array signal processing techniques for phased array radar also increase the computational burden on the processing unit. To resolve this issue, this paper investigates the problem of the detection and tracking of multiple targets in a three-dimensional (3D) Cartesian space based on range and 3D velocity measurements extracted from dual-orthogonal baseline interferometric radar. The contribution of this paper is twofold. First, a nonlinear 3D velocity measurement function, defining the relationship between the state of the target and 3D velocity measurements, is derived. Based on this measurement function, the design of the proposed algorithm includes the global nearest neighbor (GNN) technique for data association, an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation, and a rule-based M/N logic for track management. Second, Monte Carlo simulation results for different multi-target scenarios are presented to demonstrate the performance of the algorithm in terms of track accuracy, computational complexity, and IMM mean model probabilities.
High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression
Authors: Kaczyński Maciej , Piotrowski Zbigniew , Pietrow Dymitr
Published: October 2022

This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 × 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.
Study on Doppler Spectra of Electromagnetic Scattering of Time-Varying Kelvin Wake on Sea Surface
Authors: Yang Tian-Ci , Zhao Ye , Wu Guo-Shan , Ren Xin-Cheng
Published: October 2022

In general, it is more practical to detect ship wake under the background of a complicated sea state than the ship directly. Thus, in this paper, the Doppler spectra of time-varying Kelvin wake on a time-varying sea surface are numerically investigated by considering the change of ship wake with time in ocean environments. For this purpose, the linear superposition model of a time-varying sea surface and a time-varying Kelvin wake is established. Combined with the facet scattering field model of sea surface and Kirchhoff approximation (KA), the Doppler of the radar scattering echo signal of the time-varying wake on the sea surface is simulated and analyzed under different polarizations, incident angles, ship speeds, and wind speeds, as well as wind directions. It can be observed that the Doppler spectrum changes as the conditions change. This study provides a reference for extracting motion features of ship wakes in a sea clutter environment.
Fast Design Optimization and Comparative Analysis for Linear Permanent Magnet Motor with Magnet Skew, Auxiliary Tooth and Overhang Structure
Authors: Kim Ki-Hoon , Woo Dong-Kyun
Published: October 2022

This paper presents a fast design optimization using an effective characteristic analysis for linear permanent magnet motors (LPMMs) with techniques for improving motor performance such as using an auxiliary tooth, permanent magnet (PM) skew, and overhang structures. These techniques have different effects on the characteristics of the LPMM depending on the combinations of each other, resulting in complexity in the design optimization process. In particular, the three-dimensional (3-D) effect of the PM skew and overhang structure takes a lot of time to be analyzed. To deal with this problem, an effective magnetic field analysis method and a novel optimization algorithm are proposed. Preferentially, the field reconstruction method is used for a fast and accurate evaluation of the magnetic field of the LPMM. In the proposed magnetic field analysis method, the change of magnetic field distribution due to the addition of an auxiliary tooth is predicted, and the 3-D magnetic field effect of PM skew and overhang structure is considered. By reducing the computational burden in the magnetic field analysis, the electromagnetic characteristics of LPMMs can be calculated quickly, such as detent force, end force, thrust force, and back-EMF. The effect of the auxiliary tooth and overhang structure on the optimal PM skew length is investigated with comparative study results. Subsequently, the proposed optimization algorithm has the advantage of reducing time cost by providing multimodal optimization and robustness evaluation of local peaks at the same time. The proposed method is verified via comparison with finite element analysis and experimental results.
Broadband Terahertz Detection by Laser Plasma with Balanced Optical Bias
Authors: Sun Xu , Lyu Zhi-Hui , Wu Hai-Zhong , Meng Cong-Sen , Zhang Dong-Wen , Lu Zhi-Zhong , Wang Xiao-Wei , Zhao Zeng-Xiu , Yuan Jian-Min
Published: October 2022

Using a controlled optical bias and balanced geometry, we propose a new scheme for broadband terahertz detection by laser-gas interaction without high-voltage manipulation. Compared to the conventional optical bias scheme, the common noise is reduced and the dynamic range as well as the signal-to-noise ratio are doubled. It provides a simple alternative for coherent broadband terahertz detection. The influence of optical bias on terahertz waveform is also investigated, and the evolution of the terahertz-induced second harmonic with probe delay is further revealed. This new detection scheme for broadband terahertz will boost the application of terahertz time-domain spectroscopy for its miniaturization and integrability.
Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm
Authors: Urgelles Helen , Picazo-Martinez Pablo , Garcia-Roger David , Monserrat Jose F.
Published: October 2022

Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.
Over-the-Air Firmware Updates for Constrained NB-IoT Devices
Authors: Mahfoudhi Farouk , Sultania Ashish Kumar , Famaey Jeroen
Published: October 2022

The Internet of Things (IoT) is being deployed to provide smart solutions for buildings, logistics, hospitals, and many more. It is growing with billions of connected devices. However, with such tremendous growth, maintenance and support are the hidden burdens. The devices deployed for IoT generally have a light microcontroller, low-power, low memory, and lightweight software. The software, which includes firmware and applications, can be managed remotely via a wireless connection. This improves flexibility, installation time, accessibility, effectiveness, and cost. The firmware can be updated constantly to remove known bugs and improve the functionality of the device. This work presents an approach to update firmware over-the-air (OTA) for constrained IoT devices. We used Narrowband IoT (NB-IoT) as the wireless communication standard to communicate between the managing server and devices. NB-IoT is one of the most promising low power wide area (LPWA) network protocols that supports more than 50k devices within a cell using a licensed spectrum. This work is a proof of concept demonstrating the usage of NB-IoT to update firmware for constrained devices. We also calculated the overall power consumption and latency for different sizes of the firmware.
Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam
Authors: Sun Ying , Hu Jun , Yun Juntong , Liu Ying , Bai Dongxu , Liu Xin , Zhao Guojun , Jiang Guozhang , Kong Jianyi , Chen Baojia
Published: October 2022

Simultaneous localization and mapping (SLAM) technology can be used to locate and build maps in unknown environments, but the constructed maps often suffer from poor readability and interactivity, and the primary and secondary information in the map cannot be accurately grasped. For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. Our proposed method can not only reduce the absolute positional errors (APE) and improve the positioning performance of the system but also construct the object-oriented dense semantic point cloud map and output point cloud model of each object to reconstruct each object in the indoor scene. In fact, eight categories of objects are used for detection and semantic mapping using coco weights in our experiments, and most objects in the actual scene can be reconstructed in theory. Experiments show that the number of points in the point cloud is significantly reduced. The average positioning error of the eight categories of objects in Technical University of Munich (TUM) datasets is very small. The absolute positional error of the camera is also reduced with the introduction of semantic constraints, and the positioning performance of the system is improved. At the same time, our algorithm can segment the point cloud model of objects in the environment with high accuracy.
First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method
Authors: Bargees Amen , Al-Shuhail Abdullatif A.
Published: October 2022

First arrival travel time picking is an important step in many seismic data-processing applications. Most first arrival picking methods search for a sudden jump in seismic energy at trace onsets, which is clearly appropriate for minimum-phase data. This paper proposes a method for the first arrival picking of non-minimum phase data based on complex trace analysis. The Hilbert integral transform generates a complex seismic trace, followed by extraction of the envelope. The first arrival identification introduces an outlier detection method that uses the widely used three-sigma rule of thumb, which is commonly used in most software algorithms to identify outliers. The proposed method ultimately generates logical windows of ones (at the locations of outliers) and zeros (elsewhere). The first arrival is selected in the middle of the first outlier window. Testing the proposed method on zero-phase synthetic data with added 10% and 20% random noise, the method detected the true first arrivals accurately. Furthermore, tests on real Vibroseis data showed that the method recognizes the first arrivals with 67% accuracy within 20 milliseconds of their corresponding arrival times manually picked by an experienced geophysicist.
A Systematic Literature Review and Meta-Analysis on Scalable Blockchain-Based Electronic Voting Systems
Authors: Jafar Uzma , Ab Aziz Mohd Juzaiddin , Shukur Zarina , Hussain Hafiz Adnan
Published: October 2022

Electronic voting systems must find solutions to various issues with authentication, data privacy and integrity, transparency, and verifiability. On the other hand, Blockchain technology offers an innovative solution to many of these problems. The scalability of Blockchain has arisen as a fundamental barrier to realizing the promise of this technology, especially in electronic voting. This study seeks to highlight the solutions regarding scalable Blockchain-based electronic voting systems and the issues linked with them while also attempting to foresee future developments. A systematic literature review (SLR) was used to complete the task, leading to the selection of 76 articles in the English language from 1 January 2017 to 31 March 2022 from the famous databases. This SLR was conducted to identify well-known proposals, their implementations, verification methods, various cryptographic solutions in previous research to evaluate cost and time. It also identifies performance parameters, the primary advantages and obstacles presented by different systems, and the most common approaches for Blockchain scalability. In addition, it outlines several possible research avenues for developing a scalable electronic voting system based on Blockchain technology. This research helps future research before proposing or developing any solutions to keep in mind all the voting requirements, merits, and demerits of the proposed solutions and provides further guidelines for scalable voting solutions.
Analysis of Security Issues in Wireless Body Area Networks in Heterogeneous Networks
Authors: Muthuvel Somasundaram , Rajagopal Sivakumar , Subramaniam Shamala K.
Published: October 2022

Body Area Network (BAN) is one of the most important techniques for observing patient health in real time and identifying and analyzing diseases. For effective implementation of this technology in practice and to benefit from it, there are some key issues which are to be addressed, and among those issues, security is highly critical. WBAN will have to operate in a cooperative networking model of multiple networks such as those of homogeneous networks, for the purpose of performance and reliability, or those of heterogeneous networks, for the purpose of data transfer and processing from application point of view, with the other networks such as the networks of hospitals, clinics, medical experts, etc. and the patient himself/herself, who may be moving from one network to another. This paper brings out the issues related to security in WBAN in separate networks as well as in multiple networks. For WBAN working in a separate network, the IEEE 802.15.6 standard is considered. For WBANs working in multiple networks, especially heterogeneous networks, the security issues are considered. Considering the advancements of artificial intelligence (AI), the paper describes how AI is addressing some challenges faced by WBAN. The paper describes possible approaches which can be taken to address these issues by modeling a security mechanism using various artificial intelligence techniques. The paper proposes game theory with Stackelberg security equilibrium (GTSSE) for modeling security in heterogeneous networks in WBAN and describes the experiments conducted by the authors and the results proving the suitability of the modeling using GTSSE.
A Height Nonlinear Velocity Field Algorithm for CORS Station Based on GARCH Model
Authors: Zhang Hengjing , Liu Huanling , Cui Dongdong , Zhang Fang
Published: October 2022

In this study, the basic concept of height nonlinear velocity field modeling in the CORS station is described. The noise results in a large deviation between the observation and predicted height. An ARCH testing method for heteroscedasticity of CORS height residual square series was proposed and the non-stationary characteristic of CORS height residual square time series was proved. A CORS height nonlinear velocity field reconstruction method based on the GARCH model was proposed. First, a nonlinear LS periodic fitting model was established for CORS height series data. Then, a GARCH model was established for the fitted non-stationary residual series. Finally, the signal term, linear trend term, and GARCH model noise term of nonlinear LS modeling were combined to reconstruct the nonlinear velocity field of the CORS height. The RMSE of nonlinear LS cycle modeling for 25 CORS stations worldwide ranged from 5 to 10 mm. The differences between the velocity, approximate annual and semi-annual amplitudes, and SOPAC results were 0.73 mm/a, 0.94 mm, and 0.51 mm, respectively. Compared with the centimeter amplitude of the CORS station height, the accuracy of the nonlinear model established in this study met the requirements. The results of height nonlinear velocity field reconstruction at 25 CORS stations worldwide showed that the mean square error of prediction of the one-year height movement reached 9 mm, and the average prediction accuracy of the semi-annual was 7 mm. Compared with the calculation accuracy of the current global CORS elevation component of 3–5 mm, the prediction error in this study was about 3 mm. The expected goal was achieved regarding the accuracy of the CORS station height nonlinear velocity field model.
Development of High-Temperature Wire-Grid Thin Film Strain Gauges
Authors: Cui Yunxian , Li Xin , Zhang Tenglun , Ding Wanyu , Yin Junwei
Published: October 2022

Aero-engine turbine stator blades are often used in harsh environments with high temperatures and high pressure and are prone to fatigue fractures. Real-time and accurate monitoring of blade surface stress and strain is critical to ensure safe operation. In this study, thin-film strain gauges (TFSGs) that can be used in high-temperature environments above 1000 °C were designed and fabricated using a PtRh6 thin film as the sensitive material. The hysteresis effect of the stress transfer upon establishing a thermo-mechanical coupling finite element model of the Inconel718 high-temperature nickel-based alloy equal-strength beam PtRh6 TFSGs was analyzed and the optimal combination of thin-film thickness and longitudinal grid length of wire-grid TFSGs was determined. In order to solve the problem of high-temperature insulation, the insulating properties of a single-layer Al2O3 insulating film, a single-layer ZrO2 insulating film, a double-layer Al2O3/ZrO2 composite insulating film, and a four-layer Al2O3/ZrO2/Al2O3/ZrO2 composite insulating film at high temperature were compared and studied using scanning electron microscopy to analyze the microscopic morphology and composition of the four insulating film structures. The results showed that the four-layer Al2O3/ZrO2/Al2O3/ZrO2 composite insulating film had the best insulating properties at high temperatures. On this basis, an Al2O3/ZrO2/Al2O3/ZrO2 composite insulating film, PtRh6 sensitive layer, and Al2O3 protective film were sequentially deposited on a high-temperature nickel-based alloy equal-strength beam using DC pulsed magnetron sputtering technology to obtain an Inconel718 high-temperature nickel-based alloy equal-strength beam PtRh6 TFSG. Its gauge factor (GF) and temperature coefficient of resistance (TCR) were calibrated, and the results showed that the sensor could be used in harsh environments of 1000 °C. The above results provide new ideas for measuring stress and strain in aerospace under high-temperature and high-pressure environments.
A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology
Authors: Chang Chin-Chun , Ubina Naomi A. , Cheng Shyi-Chyi , Lan Hsun-Yu , Chen Kuan-Chu , Huang Chin-Chao
Published: October 2022

Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics.
A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains
Authors: Wu Xuecheng , Zhou Houkui , Yu Huimin , Hu Roland , Zhang Guangqun , Hu Junguo , He Tao
Published: October 2022

An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.
An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems
Authors: Wei Qiushuo , Zha Feng , He Hongyang , Li Bao
Published: October 2022

The system-level calibration technology of rotational inertial navigation is one of the main methods to improve the accuracy of inertial navigation, and the design of the calibration scheme is the key to calibration technology. By the establishment of the error model of inertial navigation system, a 30-position calibration scheme is designed in this study. Based on the 30-dimensional Kalman filter, the constant errors, scale factor errors and installation error of gyroscope and accelerometer are identified. Comparing the traditional schemes and the 30-position scheme with the simulation experiment, the observability of the 30-position scheme is higher, the residual error of the estimated sensor is smaller and the navigation positioning accuracy after the estimated inertial sensor error parameter compensation is higher, which verifies the feasibility of the 30-position scheme. Finally, the measured experiment uses the 30-position scheme to estimate the error of a certain type of IMU sensor, and the calibration curve of the error parameter is well converged before the end of the calibration experiment, so it has certain practical value.
Dust Particle Counter for Powder Bed Fusion Process
Authors: Karcz Stanisław , Skrabalak Grzegorz , Brudnik Andrzej , Gajoch Grzegorz
Published: October 2022

The paper presents a novel dust detector based on an innovative laser system that can be successfully used in applications where continuous dust monitoring is necessary. The measurements obtained with FeNi18Co9Mo5 (maraging MS1 steel) particles are compared with the particle fall times calculated using the Navier–Stokes equation. The measurement powder was subjected to sieve analysis and laser system detection. Based on the results obtained, a formula was developed to determine the dust concentration depending on the number and size of particles. With filtration applied, the detector measurement range was from 16 to 100 µm. The developed solution can be the basis for the development of a dedicated sensor for powder bed fusion processes.
Technical Development of the CeCi Social Robot
Authors: Flores-Vázquez Carlos , Angulo Cecilio , Vallejo-Ramírez David , Icaza Daniel , Pulla Galindo Santiago
Published: October 2022

This research presents the technical considerations for implementing the CeCi (Computer Electronic Communication Interface) social robot. In this case, this robot responds to the need to achieve technological development in an emerging country with the aim of social impact and social interaction. There are two problems with the social robots currently on the market, which are the main focus of this research. First, their costs are not affordable for companies, universities, or individuals in emerging countries. The second is that their design is exclusively oriented to the functional part with a vision inherent to the engineers who create them without considering the vision, preferences, or requirements of the end users, especially for their social interaction. This last reason ends causing an aversion to the use of this type of robot. In response to the issues raised, a low-cost prototype is proposed, starting from a commercial platform for research development and using open source code. The robot design presented here is centered on the criteria and preferences of the end user, prioritizing acceptability for social interaction. This article details the selection process and hardware capabilities of the robot. Moreover, a programming section is provided to introduce the different software packages used and adapted for the social interaction, the main functions implemented, as well as the new and original part of the proposal. Finally, a list of applications currently developed with the robot and possible applications for future research are discussed.
Fundamentals of Fast Tsunami Wave Parameter Determination Technology for Hazard Mitigation
Authors: Lavrentiev Mikhail , Lysakov Konstantin , Marchuk Andrey , Oblaukhov Konstantin
Published: October 2022

This paper describes two basic elements of the smart technology, allowing us to bring to a new level the problem of early warning and mitigation of tsunami hazards for the so-called near zone events (when a destructive tsunami wave reaches the nearest coast in tens of minutes after the earthquake). The sensors system, installed in a reasonable way (to detect a wave as early as possible), is capable of transmitting the necessary raw data (measured wave profile) in a real time mode to a processing center. The smart (based on mathematical theory) algorithm can reconstruct an actual source shape within a few seconds using just a part of the measured wave record. Using modern computer architectures (Graphic Processing Units or Field Programmable Gates Array) allows computing tsunami wave propagation from the source to shoreline in 1–2 min, which is comparable to the performance of a supercomputer. As is observed, the inundation zone could be evaluated reasonably correctly as the coastal area below two thirds of the tsunami wave height at a particular location. In total, the achieved performance of the two above mentioned algorithms makes it possible to evaluate timely the tsunami wave heights along the coastline to approximate the expected inundation zone, and therefore, to suggest (in case of necessity) evacuation measures to save lives.
Few-Shot Fine-Grained Image Classification via GNN
Authors: Zhou Xiangyu , Zhang Yuhui , Wei Qianru
Published: October 2022

Traditional deep learning methods such as convolutional neural networks (CNN) have a high requirement for the number of labeled samples. In some cases, the cost of obtaining labeled samples is too high to obtain enough samples. To solve this problem, few-shot learning (FSL) is used. Currently, typical FSL methods work well on coarse-grained image data, but not as well on fine-grained image classification work, as they cannot properly assess the in-class similarity and inter-class difference of fine-grained images. In this work, an FSL framework based on graph neural network (GNN) is proposed for fine-grained image classification. Particularly, we use the information transmission of GNN to represent subtle differences between different images. Moreover, feature extraction is optimized by the method of meta-learning to improve the classification. The experiments on three datasets (CIFAR-100, CUB, and DOGS) have shown that the proposed method yields better performances. This indicates that the proposed method is a feasible solution for fine-grained image classification with FSL.
Noncontact Sleeping Heartrate Monitoring Method Using Continuous-Wave Doppler Radar Based on the Difference Quadratic Sum Demodulation and Search Algorithm
Authors: Chen Xiao , Ni Xuxiang
Published: October 2022

Continuous-wave doppler radar, which has the advantages of simple structure, low cost, and low power consumption, has attracted extensive attention in the detection of human vital signs. However, while respiration and heartbeat signals are mixed in the echo phase, the amplitude difference between the two signals is so large that it becomes difficult to measure the heartrate (HR) from the interference of respiration stably and accurately. In this paper, the difference quadratic sum demodulation method is proposed. According to the mixed characteristics of respiration and heartbeat after demodulation, the heartbeat features can be extracted with the help of the easy-to-detect breathing signal; combined with the constrained nearest neighbor search algorithm, it can realize sleeping HR monitoring overnight without body movements restraint. Considering the differences in vital-sign characteristics of different individuals and the irregularity of sleep movements, 54 h of sleep data for nine nights were collected from three subjects, and then compared with ECG-based HR reference equipment. After excluding the periods of body turning over, the HR error was within 10% for more than 70% of the time. Experiments confirmed that this method, as a tool for long-term HR monitoring, can play an important role in sleeping monitoring, smart elderly care, and smart homes.
A PID-Based kNN Query Processing Algorithm for Spatial Data
Authors: Qiao Baiyou , Ma Ling , Chen Linlin , Hu Bing
Published: October 2022

As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. The centralized solutions are not suitable for spatial big data due to their poor scalability, while the existing distributed solutions are not efficient enough to meet the high real-time requirements of some spatial applications. Therefore, we introduce the Proportional Integral Derivative (PID) control technology into kNN query processing and propose a PID-based kNN query processing algorithm (PIDKNN) for spatial big data based on Spark. In this algorithm, the whole data space is divided into grid cells of the same size using the grid partition method, and the grid-based index is constructed. On this basis, the grid-based density peak clustering algorithm is used to cluster spatial data, and the corresponding PID parameters are set for each cluster. When performing kNN queries, the PID algorithm is used to estimate the radius growth step size of kNN queries, thereby realizing kNN query processing with a variable query radius growth step based on a feedback mechanism, which greatly improves the efficiency of kNN query processing. A series of experimental results show that the PIDKNN algorithm has good performance and scalability and is superior to the existing parallel kNN query processing methods.
A Simple Method to Optimally Select Upper-Limb Joint Angle Trajectories from Two Kinect Sensors during the Twisting Task for Posture Analysis
Authors: Liu Pin-Ling , Chang Chien-Chi , Li Li , Xu Xu
Published: October 2022

A trunk-twisting posture is strongly associated with physical discomfort. Measurement of joint kinematics to assess physical exposure to injuries is important. However, using a single Kinect sensor to track the upper-limb joint angle trajectories during twisting tasks in the workplace is challenging due to sensor view occlusions. This study provides and validates a simple method to optimally select the upper-limb joint angle data from two Kinect sensors at different viewing angles during the twisting task, so the errors of trajectory estimation can be improved. Twelve healthy participants performed a rightward twisting task. The tracking errors of the upper-limb joint angle trajectories of two Kinect sensors during the twisting task were estimated based on concurrent data collected using a conventional motion tracking system. The error values were applied to generate the error trendlines of two Kinect sensors using third-order polynomial regressions. The intersections between two error trendlines were used to define the optimal data selection points for data integration. The finding indicates that integrating the outputs from two Kinect sensor datasets using the proposed method can be more robust than using a single sensor for upper-limb joint angle trajectory estimations during the twisting task.
Reconfigurable Intelligent Surface-Aided Cooperative NOMA with p-CSI Fading Channel toward 6G-Based IoT System
Authors: Chen Hsing-Chung , Widodo Agung Mulyo , Lin Jerry Chun-Wei , Weng Chien-Erh
Published: October 2022

Addressing the challenges of internet-based 5G technology, namely increasing density through micro-cell systems, frequency spectrum, and reducing resource costs, is needed to meet the use of IoT-based 6G technology with the goal of high-speed, high-capacity, and low-latency communication. In this research, we considered the coverage performance and ergodic capacity of the Reconfigurable Intelligent Surface (RIS)-aided cooperative nonorthogonal multiple-access network (NOMA) of an IoT system. This enables the upgrading of 5G- toward 6G-technology-based IoT systems. We developed a closest-form formula of near and far user coverage probabilities as a function of perfect channel statistical information (p-CSI) using only a single-input single-output (SISO) system with a finite number of RIS elements under the Nakagami-m fading channel. We also define ergodic capacity as a simple upper limit by simplifying the use of symbolic functions and it could be used for a sustained period. The simulation findings suggest that RIS-assisted NOMA has a reduced risk of outage than standard NOMA. All of the derived closed-form formulas agree with Monte Carlo simulations, indicating that the distant user’s coverage probability outperforms the nearby user. The bigger the number of RIS parts, however, the greater the chance of coverage. They also disclose the scaling law of the number of phase shifts at the RIS-aided NOMA based on the asymptotic analysis and the upper bound on channel capacity. In both arbitrary and optimum phase shifts, the distant user’s ergodic capacity outperforms the near user.
Development and Application of Dual-Polarization Antenna for Dielectric Logging Sensor
Authors: Li Chen , Deng Shaogui , Li Zhiqiang , Wu Jian , Lu Baoyin , Yang Jutao
Published: October 2022

Because the dielectric constant of water is greater than that of oil and gas, dielectric logging sensors can effectively distinguish oil and gas reservoirs from water layers by measuring the dielectric parameters of formations. Under the special working conditions during the logging of boreholes drilled for oil and gas exploration, such as high temperature and pressure and a narrow working space, the endurance and effectiveness of the antenna in the dielectric logging sensor are crucial. This paper presents a design method for a dual-polarization slot antenna for such working conditions. We theoretically analyzed the working principle of this antenna, established the antenna model, and evaluated its radiation characteristics through simulation. Subsequently, we developed and tested the proposed antenna. The antenna could withstand a high temperature and pressure of 175 °C and 140 MPa, respectively. A dielectric logging sensor using the proposed antenna was successfully applied in oilfield logging.
A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices
Authors: Ibrahim Nur Fatin Adini , Sabani Norhayati , Johari Shazlina , Manaf Asrulnizam Abd , Wahab Asnida Abdul , Zakaria Zulkarnay , Noor Anas Mohd
Published: October 2022

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte’s response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance.
Numerical Calculation and Analysis of Water Dump Distribution Out of the Belly Tanks of Firefighting Helicopters
Authors: Zhou Tejun , Lu Jiazheng , Wu Chuanping , Lan Shilong
Published: October 2022

Helicopters are more and more widely used for water dumping in fire extinguishing operations nowadays. Increasing attention is being paid to improving helicopter firefighting efficiency. Water distribution onto the ground from the helicopter tank is a key reference target to evaluate firefighting efficiency. Numerical simulations and calculations were carried out concerning water dumping out of the belly tank of a helicopter using the VOF (Volume of Fluent Model) model and mesh adaptation in ANSYS Fluent, and the effects of two parameters, the height of the tank above the ground and the wind speed, on the wake flow and water distribution were discussed. The results showed that for forward flight, the higher the forward flight speed, the less the average water depth on the ground. Similar results were obtained for flight height. The average water depth was one order of magnitude less than in the cases of the corresponding hovering helicopter for a given wind speed. As for hovering flight, the higher the wind speed, the less the average water depth on the ground. The simulation results were basically consistent with the conclusions of water dump tests of fire-fighting equipment carried by helicopters. For example, when the helicopter flew at a forward flight speed of 15 m/s and the tank bottom was 30 m above the ground, the area covered by the dumped water would be 337.5 m2, and the average water depth accumulated per square meter would be 0.3 cm. This result was close to the 0.34 cm obtained under Hayden Biggs’s test condition with a forward flight speed of 70 km/h and a height above the ground of 24 m.
Road Users’ Reports on Danger Spots: The Crowd as an Underestimated Expert?
Authors: Olma Joshua , Bode Tina , Ehlers Jörg , Sutter Christine
Published: October 2022

As part of the project EDDA+ (Early Detection of Dangerous Areas in road traffic using smart data), a web-based crowdsourcing platform has been launched on which road users can report danger spots they face in everyday traffic. Whereas official police collision data can only be used reactively, these user reports are intended to warn other road users and provide road safety stakeholders with detailed information for proactive measures. Since this approach is relatively novel, the present pilot study aimed to evaluate the validity of these subjective road user reports. A quasi-randomized sample of N = 77 danger spots distributed over four major German cities was audited using a 70-item objective road safety deficit inventory to identify infrastructural deficits. Based on these items, an overall rating of objective hazardousness for each danger spot was derived. In more than half of the audited danger spots, infrastructural deficits were identified in the audit (=confirmed hazard). In another quarter of audited dangers spots, the reported hazard could not be identified without any doubt due to a lack of infrastructural deficit or detailed information about the nature of the hazard (=uncertain, no certain match between audit and report). Our analysis further revealed that an increased number of road user interactions for the respective danger spot yielded a higher likelihood of confirmation of a danger spot’s hazardousness. Descriptively, pedestrians and bicyclists were most often mentioned as exposed to danger, with the most prevalent nature of danger being areas with poor visibility and misconduct by drivers. The results were blended with police collision data in the next step. We did not find a significant relationship between our danger spots’ rating and the number of collisions at the respective spot. Our results indicate that reports of danger spots and the increased user related activity can serve as an indicator for the early detection of road traffic hazards.
Integrated Electro-Ozonation and Fixed-Bed Column for the Simultaneous Removal of Emerging Contaminants and Heavy Metals from Aqueous Solutions
Authors: Mojiri Amin , Ozaki Noriatsu , Zhou John L. , Kazeroon Reza Andasht , Zahed Mohammad Ali , Rezania Shahabaldin , Vakili Mohammadtaghi , Gavanji Shahin , Farraji Hossein
Published: October 2022

In the current study, an integrated physiochemical method was utilized to remove tonalide (TND) and dimethyl phthalate (DMP) (as emerging contaminants, ECs), and nickel (Ni) and lead (Pb) (as heavy metals), from synthetic wastewater. In the first step of the study, pH, current (mA/cm2), and voltage (V) were set to 7.0, 30, and 9, respectively; then the removal of TND, DMP, Ni, and Pb with an electro-ozonation reactor was optimized using response surface methodology (RSM). At the optimum reaction time (58.1 min), ozone dosage (9.4 mg L−1), initial concentration of ECs (0.98 mg L−1), and initial concentration of heavy metals (28.9 mg L−1), the percentages of TND, DMP, Ni, and Pb removal were 77.0%, 84.5%, 59.2%, and 58.2%, respectively. For the electro-ozonation reactor, the ozone consumption (OC) ranged from 1.1 kg to 3.9 kg (kg O3/kg Ecs), and the specific energy consumption (SEC) was 6.95 (kWh kg−1). After treatment with the optimum electro-ozonation parameters, the synthetic wastewater was transferred to a fixed-bed column, which was filled with a new composite adsorbent (named BBCEC), as the second step of the study. BBCEC improved the efficacy of the removal of TND, DMP, Ni, and Pb to more than 92%.