bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical simulation of fatigue crack growth life of single side notched specimen
Authors: Wang Shuaiqiang , Zhirong WU 
Published: July 2021

Abstract
Under the action of cyclic load, fatigue fracture failure often occurs in many engineering structures during their services. Fatigue cracks in engineering structures begin to propagate under cyclic loading, and eventually lead to the failure of engineering structures. The study of fatigue crack is of great significance for evaluating the service life of engineering structures under cyclic loading. The prediction of fatigue crack growth life and path is the key problem of fatigue crack research. In this paper, the numerical simulation of fatigue crack growth of single side notched specimen is carried out by using the extended finite element method. According to the calculated stress intensity factor and the generalized Paris formula, the fatigue crack growth rate of single side notched specimen is obtained and the fatigue crack growth life is predicted. The results show that this method can effectively predict the fatigue crack growth life.
The Experimental Study on the Influence of Hydrogen Corrosion on the Crack Growth Rate of 45 Steel
Authors: Jing Wang , Kuichao Huang , Lei Dou 
Published: July 2021

Abstract
In order to comprehensively study the effect of hydrogen corrosion on the crack growth rate, 45 steel was used as a typical material in this paper and fatigue crack growth rate (FCGR) of under different corrosion conditions and different stress ratios at room temperature were tested. 9 groups of da/dN-Δढ curves were obtained and Paris expressions were fitted. The results show that under the experimental conditions of air and different concentrations of H2S corrosion environment, the value of parameter n of Pairs expression is basically stable at 3.5. Under the same stress ratio conditions, the FCGR of metals after H2S corrosion is greater than that of metals in the air environment, and it increases with the increase of corrosion concentration, Stress ratio affects FCGR greatly, and the effect of corrosion time is limited.
Application of Computer Vision and Image Processing for Micro Deformation
Authors: Xi Yujie 
Published: July 2021

Abstract
In this study, a micro deformation monitoring system was designed for metal plate based on computer vision. A rhombic mark on the aluminum plate was made to measure the micro deformation inside the aluminum sheet. For achieving high accuracy, the sub-pixel-level corner detection was applied to achieve the corner coordinates information of the mark. The micro deformation would be calculated in real deformation situation by using the proposed method. This monitoring system could be used in industrial production to detect whether samples slightly deformed.
Intelligent fault diagnosis of rolling bearing based on singular value decomposition
Authors: Shen Nanyan , Su Kun , Li Jing , Guo Jianrong , Li Xiaoying , He Tianqiang , Pu Junhao 
Published: July 2021

Abstract
In view of the scarcity of labeled fault samples in industrial scenarios and the difficulty of obtaining the size parameters of rolling bearings, this paper firstly combines the obtained averaged singular value (ASV) and ratio of neighboring singular values (NSVR) as a novel type of fault feature. Then K-means clustering method is used to classify feature vector ASV-NSVR automatically. The experimental results show that the proposed fault diagnosis method has a high recognition accuracy in the case of bad data.
Research on Risk Prediction of Train Derailment based on Relative Wheel-rail Displacement
Authors: Feifei Qin , Yu Dong 
Published: July 2021

Abstract
Derailment safety is the basis for safe train operation, and the derailment coefficient is an important indicator for judging whether a train is derailed. Traditional derailment coefficient prediction methods have problems such as high cost, low prediction accuracy, and the inability to monitor the contact status between wheels and rails of running trains in real time. This paper proposes a train derailment coefficient prediction method based on image processing. Firstly, use the generative adversarial network to extract the edge curve of the wheel-rail contact area. Secondly, determine the wheel-rail lateral relative displacement based on the positional relationship between the rail and the centerline of the wheelset. Use the speed and acceleration sensors to obtain the train speed, lateral acceleration, and longitudinal acceleration, and finally use improvements. The BP neural network integrates wheel-rail lateral displacement, speed, lateral acceleration, and longitudinal acceleration data to obtain the train derailment coefficient at the next moment. The field test results show that the derailment coefficient prediction model in this paper can achieve accurate prediction of the derailment coefficient and has good robustness.
Stress-strain Test of Exoskeleton Legs Based on Different Postures
Authors: Peng Xuefeng , Wang Kaiyuan , Dai Zongmiao , Liu Jiasheng 
Published: July 2021

Abstract
Aiming at the design requirements of high-strength and lightweight of exoskeleton leg materials, this paper designs a kind of exoskeleton leg mechanism made of T700 carbon fiber and TC4 titanium alloy. The mechanical properties of exoskeleton leg mechanism under three different postures of standing, stranding and squatting were tested by DH5921 dynamic strain gauge. The results showed that the maximum stress and strain were located at the ankle auricular part under three typical postures, and the maximum stress was 150.7MPa. The maximum strain value of the T700 composite material in the middle of the thigh is 848.05με, which meets the requirements of use. In the three different postures, the stress value of each place in the squatting posture increases obviously, but it is still far less than the required strength of the design material, and meets the requirements of use.
A research of action recognition based on attention mechanism
Authors: Zhang Ruoxi , She Jianan , Yu Li 
Published: July 2021

Abstract
To solve the problem of inadequate expression of action behavior features, this paper proposes an action recognition method based on attention mechanism. Firstly, in the feature extraction part, a CSE module is designed to model action features spatio-temporally, and then this module is incorporated into the residual network to improve the feature extraction ability of the model; after that, the LSTM network is used to solve the problem of temporal association of features; finally, the actions are classified by Softmax. The experimental results show that the improved recognition rates of this method on UCF101, HMDB51 and Kinetics400 datasets are 96.23%, 92.03% and 75.65%, respectively.
Research of semantic role labeling based on long short-term memory neural networks
Authors: Liang Xuheng 
Published: July 2021

Abstract
Semantic Role Labeling (SRL) is a shallow semantic analysis in the field of NLP, and a relatively basic and important step. Traditionally, SRL has been performed based on the results of syntactic analysis and has problems such as over-reliance on feature engineering. With the development of deep learning, many neural network models for NLP have been proposed and SRL tasks can be performed well by neural networks. Among these, long short-term memory networks form a very good fit with the SRL task by virtue of their sequence-based features. In this paper, in order, we first analyze the SRL task based on grammatical analysis and neural networks, then the SRL task based on LSTM and its improved models, then the dataset and model evaluation of the SRL task, and finally conclude and look forward.
Research on visual object tracking algorithm based on improved twin network
Authors: Dong-mei Shi , Xun Chen 
Published: July 2021

Abstract
The tracking accuracy of traditional algorithm decreases when the target is in a complex environment or the target itself changes, and an improved correlation filtering twin network algorithm combining the attention mechanism is proposed. Fast wavelet is used for filtering and multi-scale feature signal extraction, and loss function is used to minimize the squared error between sample and label. Then correlation filtering algorithm is used to calculate the similarity of image between target template and candidate region. Attention mechanism is introduced to improve correlation filtering twin network structure, and the GloU’s online learning is advanced as an aid to determine template updating. The experimental results show that the algorithm have certain improvement effect for target shade, quick movement and deformation problems, so performance and accuracy of target tracking, the algorithm has better competitiveness.
Selective Partial Update of NLMS Adaptive Filter Algorithm
Authors: Wang Weizhi , Li Jingyan , Li Mingdong 
Published: July 2021

Abstract
In view of the low convergence speed of the LMS algorithm and the high computational complexity of the NLMS algorithm in wireless communication system, a selective partial update NLMS algorithm is proposed. This algorithm only updates part of the filter coefficients instead of all filter coefficients during each adaptation. It effectively reduce the complexity of classic adaptive filtering algorithms. In this paper, a selective partial update of the NLMS algorithm is deduced, and the simulation verification is carried out to compare with the LMS algorithm and the NLMS algorithm, and the superiority of the algorithm is verified.
An Efficient Arrhythmia Classifier Using Convolutional Neural Network with Incremental Quantification
Authors: Huang Junguang , Liu Zijing , Liu Hao 
Published: July 2021

Abstract
Cardiovascular disease (CVD) is a dangerous disease, which can be effectively prevented by detecting arrhythmia early. In order to detect arrhythmia accurately, more and more researches use artificial intelligence methods to realize the classification and detection of electrocardiogram (ECG) signals. However, most of these designs are unfriendly to the hardware design due to too many parameters which lead to large calculation power and data accessing power. In this paper, we present an efficient arrhythmia classifier based on the convolutional neural network with the incremental quantification. This more efficient design can classify ECG signals accurately with lower capacity of parameters. The simulation results show that the recognition rate of the network with the incremental quantification has reached 92.76% with 39.34KB memory footprint, which is beneficial to hardware design and has better accuracy than other advanced quantification methods.
Abnormal heart rate detection based on double slope QRS beat location
Authors: Zhang Yiheng , Guo Bin , Zhang Hongyuan , Sun Jianan 
Published: July 2021

Abstract
In order to improve the accuracy and reliability of abnormal heart rate detection, a method of abnormal heart rate detection based on double slope QRS beat location is proposed. Firstly, according to the singularity of QRS wave, the slope difference between the left and right sides is used for localization, and double thresholds are set to track the real-time change of the signal to ensure the robustness of localization. Secondly, wavelet coefficients are extracted as beat frequency features by wavelet transform. Finally, XGBoost algorithm is used for classification. In the case of the same data set, compared with the existing arrhythmia detection algorithm, the performance is further improved. The sensitivity of QRS detection is 99.65%, the positive prediction rate is 99.41%, and the accuracy of heart rate anomaly classification is 99.12%. This method can locate the beat more effectively, which provides a new method for the research of abnormal heart rate detection.
Millimeter-Wave Radar and Video Fusion Vehicle Detection based on Adaptive Kalman Filtering
Authors: Wang Qian , Song Yansong , Xu Dongpo 
Published: July 2021

Abstract
Road traffic safety has always been the focus of social concern, and the actual road traffic scenes, where only a single sensor is applied cannot cope with the interference brought by complex external factors, which makes vehicle detection extremely challenging. This paper focuses on a vehicle detection algorithm for the fusion of millimeter-wave radar sensor and monocular camera sensor, including the calibration of millimeter-wave radar and camera, the establishment of a temporal fusion model of the two sensors. Finally, the target information obtained from the two sensors is fused with the data using the adaptive Kalman filter fusion algorithm, which can reduce data ambiguity and increase the reliability and validity of the data. Experiments show that the method can overcome the shortcomings of single sensor in target detection, and the obtained target information is more comprehensive compared with the monocular camera detection results.
EEG Emotion Recognition Based on Optimal Feature Selection
Authors: Ma Xin , Liu Peng , Wang Xuemeng , Bai Xuemei 
Published: July 2021

Abstract
Aiming at the problems of dimensional disasters and low classification accuracy caused by too many features extracted in the emotion recognition process, an EEG emotion recognition method with optimized feature selection is proposed. The individual rhythmic signals of the EEG are obtained by wavelet packet decomposition and the sample entropy, energy and power spectral density are extracted as EEG features. A discrete binarization of the feature matrix using the Beetle Antennae Search (BAS) algorithm, while introducing a subset of features into the objective function and searching for the optimal subset of features. Finally, the SVM classifier is used for classification. The experimental results show that it achieves 89.72% accuracy on the DEAP dataset and significantly reduces the original feature dimension compared with the traditional feature selection method, which has good application prospect.
Research on 5G Security Technology for Industrial Internet
Authors: Zhao Zichen 
Published: July 2021

Abstract
5G network has great application potential in the field of industrial Internet. However, the current lack of development of 5G network in the field of security technology restricts its application on the Industrial Internet. In this article, the author combines the industry’s needs for Industrial Internet security, and summarizes the research status of 5G network security technology, gives the development direction of three 5G network security technologies with development potential. This paper found that due to the three end-to-end characteristics of the Industrial Internet: physical security, information security, and system self-healing, there are security requirements; the other three 5G network security technologies, namely lightweight encryption mechanism, physical layer security technology and network slicing security technology will help 5G network be more widely used in the Industrial Internet field.
LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation
Authors: Hong Liangyi , Duan Shukai , Wang Lidan , Pan Yongbin 
Published: July 2021

Abstract
Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing.
Design and characterization of Kerberos: a 48-channel analog pulse processing and data acquisition platform
Authors: King P. , Gugiatti M. , Carminati M. , Buonanno L. , Borghi G. , Pepponi G. , Lechner P. , Siegmann D. , Urban K. , Houdy T. , Mertens S. , Fiorini C. 
Published: July 2021

Abstract
A multi-channel data processing and acquisition system based
on an analog ASIC (SFERA) has been designed and realized. The
platform, called Kerberos, is suitable for the readout of large
arrays of Silicon Drift Detector (SDD) for X-ray, γ-ray and
electron spectroscopy applications. Each one of its 48 inputs is
equipped with a 9th order semi-Gaussian shaping
amplifier with programmable peaking time (0.5, 1, 2, 3, 4 and 6
). The pulse amplitudes are multiplexed into three 16-bit
high linearity SAR ADCs and digitized into an Artix-7 FPGA
module. Kerberos will be used for the characterization of monolithic
SDD matrices for the TRISTAN project. Many different readout
strategies can be selected on Kerberos Graphic User Interface: for
TRISTAN it has been decided to use a full detector readout strategy,
with maximum input throughput of about 166 kcount/s. This work
presents a full characterization of this scalable platform and its
use with several detectors types (SDD, micro-strips) in X-ray, gamma
and beta spectroscopy.
The effects on public health of climate change adaptation responses: a systematic review of evidence from low- and middle-income countries
Authors: Scheelbeek Pauline F D , Dangour Alan D , Jarmul Stephanie , Turner Grace , Sietsma Anne J , Minx Jan C , Callaghan Max , Ajibade Idowu , Austin Stephanie E , Biesbroek Robbert , Bowen Kathryn J , Chen Tara , Davis Katy , Ensor Tim , Ford James D , Galappaththi Eranga K , Joe Elphin T , Musah-Surugu Issah J , Alverio Gabriela Nagle , Schwerdtle Patricia Nayna , Pokharel Pratik , Salubi Eunice A , Scarpa Giulia , Segnon Alcade C , Siña Mariella , Templeman Sienna , Xu Jiren , Zavaleta-Cortijo Carol , Berrang-Ford Lea 
Published: July 2021

Abstract
Climate change adaptation responses are being developed and delivered in many parts of the world in the absence of detailed knowledge of their effects on public health. Here we present the results of a systematic review of peer-reviewed literature reporting the effects on health of climate change adaptation responses in low- and middle-income countries (LMICs). The review used the ‘Global Adaptation Mapping Initiative’ database (comprising 1682 publications related to climate change adaptation responses) that was constructed through systematic literature searches in Scopus, Web of Science and Google Scholar (2013–2020). For this study, further screening was performed to identify studies from LMICs reporting the effects on human health of climate change adaptation responses. Studies were categorised by study design and data were extracted on geographic region, population under investigation, type of adaptation response and reported health effects. The review identified 99 studies (1117 reported outcomes), reporting evidence from 66 LMICs. Only two studies were ex ante formal evaluations of climate change adaptation responses. Papers reported adaptation responses related to flooding, rainfall, drought and extreme heat, predominantly through behaviour change, and infrastructural and technological improvements. Reported (direct and intermediate) health outcomes included reduction in infectious disease incidence, improved access to water/sanitation and improved food security. All-cause mortality was rarely reported, and no papers were identified reporting on maternal and child health. Reported maladaptations were predominantly related to widening of inequalities and unforeseen co-harms. Reporting and publication-bias seems likely with only 3.5% of all 1117 health outcomes reported to be negative. Our review identified some evidence that climate change adaptation responses may have benefits for human health but the overall paucity of evidence is concerning and represents a major missed opportunity for learning. There is an urgent need for greater focus on the funding, design, evaluation and standardised reporting of the effects on health of climate change adaptation responses to enable evidence-based policy action.
Theology of disaster: a study on west bandung people’s responses to the potency of earthquake
Authors: Kosasih A , Surahman C , Yuniartin T , Firmansyah M I 
Published: July 2021

AbstractThis article deals with the beliefs living among societies in Indonesia. These beliefs are studied through the responses of West Bandung people to the potency of earthquake. Geographically, West Bandung area is located in the ring of fire where natural calamities potentially could occur. These responses shed light varied beliefs and theological perspectives of people. This study belongs to qualitative research. However, the reseachers also use questionnares, Through data collection and analysis, the researchers conclude that West Bandung people are religious (they see the earthquake by religious meaning and explanation where they regard natural disaster as God’s decree and will, God’s testing, therefore they have to be patient and repent to God). However, they also look it as natural phenomenon which can be observed by scientific approach. In term of Islamic theology, The people are close to tradisional theology, especially Asy’ariyah school of Islamic theology. However, this reseach shows the religious pattern of contemporary Indonesia where the grouping to certain theological school seems irrelevant. Due to the diversity of religious authorities and sources of knowledge, the pattern of religiosity of society is now no longer leading to a particular school of thought, but rather shows a hybrid pattern (hodgepodge).
Impact of the Electromagnetic Environment on UAV’s Datalink
Authors: Bennageh I. , Mahmoudi H. , Labbadi M. 
Published: July 2021

Abstract
The deployment of drones for any mission relies heavily on a ground control system. The UAV must therefore maintain a stable datalink with the ground control station to allow it to be controlled in real time by the ground operator. The evolution of drones in specific electromagnetic environments can seriously compromise data linkage, giving rise to electromagnetic susceptibility (EMS) issues. This article presents a review of the exploratory literature of electromagnetic interferences that the datalinks between UAVs and the ground station can face from the point of view of electromagnetic compatibility (EMC). The goal is to understand the effects of a given EM environment on the datalinks of an UAV system in order to predict its behaviour and perform the necessary precautions and improvements.
Power Sharing of Grid-connected AC Microgrid by A Simplified Distributed Control
Authors: Guo Shuping , Cai He 
Published: July 2021

Abstract
The power sharing problem is one of the core problems for microgrid control. By designing an elegant command generator, the power sharing problem for a grid-connected microgrid has been converted into a tracking problem for an interconnected leader-follower multiagent system, and was solved by a distributed control law. In this paper, we further consider the same tracking problem. By utilizing Lyapunov analysis, we have shown that a simplified version of the existing control law can still solve the same problem. Numerical simulations are provided to validate this result.