bluuebunny/crossref_metadata_2025_split huggingface dataset:
CT reconstruction algorithm based on truncated TV
Authors: Shi Hongyan , Wu Aidi , Yang Shidi , Ji Dongjiang 
Published: May 2021

Abstract
This paper proposes a simultaneous algebraic reconstruction technique (SART) based on truncated total variation (truncated TV), called SART-truncated TV algorithm (SART-TTV). Truncated TV is particularly effective in removing unimportant details while preserving significant edges. Moreover, the truncated TV only penalizes part of the gradient of the image, so it can solve the problem of excessive smoothness caused by the classic TV penalizing larger gradient amplitude. In this paper, the classic Shepp-Logan model is simulated and the results of sparse angle reconstruction are compared and analyzed. The results show that the quality of the reconstructed image obtained by the SART-TTV algorithm is the highest.
Improved calculation method of dynamic current carrying capacity of transmission line
Authors: Zhang Ruopeng , Tian Mingxing , Sun Lijun , Su Zhaoxu 
Published: May 2021

Abstract
The calculation of the dynamic current carrying capacity of transmission lines in a domestic traction network is based on IEEE 738 standard. However, because IEEE 738 standard is derived under specific climate and geographical conditions, only when the climate and geographical conditions are stable can the accurate ampacity be calculated. However, in practical application, due to the rapid change of climate factors and large meteorological monitoring error, the results calculated by IEEE 738 standard have large errors. Therefore, this paper puts forward the method of using convective heat dissipation coefficient and equivalent radiation heat dissipation coefficient to deal with the problems caused by unstable meteorological factors. By this method, the dynamic current carrying capacity of the transmission line can be calculated only by measuring the conductor temperature and ambient temperature, which simplifies the calculation process of the dynamic current carrying capacity of the transmission line and reduces the dependence on meteorological parameters.
Research on seismic wave first arrival Picking Based on improved U-Net
Authors: Liu Ping , Yao Mengrou , Han Zhenhua , Li Yanrong , Li Xinrui , Shi Juncai 
Published: May 2021

AbstractFor the current problem of low accuracy of seismic wave phase pickup, this paper proposes a method based on the improved U-Net seismic wave P-wave and S-wave first-to-arrival pickup. Firstly, in order to reduce the impact of pre-processing operations such as filtering, the 20,000 three-component seismic waveform data will be normalized as input. Secondly, in order to reduce the loss of information during the seismic waveform extraction and increase the amount of information of effective features at the starting point, the Residual Unit is added to the encoder part of U-Net only, instead of adding it to the decoder part. In order to reduce the loss of high frequency information of seismic waveform, convolution operation is used instead of pooling operation. Finally, the probability distributions of P-wave, S-wave and noise are used as the output. The results show that the accuracy of the first-to-arrival pickup of P-wave is 93% and the accuracy of the first-to-arrival pickup of S-wave is 90% of the test set. The improved U-Net increased 30.9% and 6.2% in P-wave accuracy, 7.6% and 2.8% in S-wave accuracy, 39.6% and 4.0% in the P-wave recall, and 41.5% and 4.7% in the S-wave recall, respectively, compared with the conventional STA/LTA and U-Net. The improved U-Net outperforms the other two models in both evaluation metrics of recall and accuracy. The research results of this paper are of great practical significance in the localization of earthquakes, the interpretation of the earthquake gestation mechanism, and the prevention and mitigation of earthquakes.
Restoration control decision of distribution network based on dynamic evaluation of restoration value
Authors: Jiang Ke , Yu Chen , Chen Keyi , Yuan Guichuan , Yang Lintao , Hao Lili 
Published: May 2021

Abstract
The power outage scenario, recovery process, and load function in emergency situations will all affect the urgency of the load to restore power supply during distribution network restoration. Usually the importance level of the object to be restored is statically set according to its value of restoration, which restricts the refined management of limited restoration resources. This paper proposes a dynamic evaluation method of bus restoration value that integrates restoration benefits, costs and risks, which considers the influence of the cold load pick-up and the functional coupling relationship between the load on the recovery value of the load unit, the potential recovery value brought by the bus recovery to other surrounding objects to be recovered, and the system power supply shortage, recovery cost and uncertainty of recovery operation. Based on this, a multi-process optimization model for distribution network restoration and a partition parallel restoration decision-making method are established. Finally, simulation analysis verifies that the strategy proposed in this paper can effectively improve the utilization of recovery resources.
Research on typical failure modes of hybrid integrated quartz flexible accelerometer servo circuit caused by multi-layer ceramic capacitors
Authors: Zhang Ming , Wan Huanhuan , Xu Xin , Ruan Xiaoming , Gao Weidong , Xiao Zeping 
Published: May 2021

Abstract
The quartz flexible accelerometer servo circuit is one of the key components in the inertial measurement system, and the multi-layer ceramic capacitor is an important electronic component in the quartz flexible accelerometer servo circuit. This paper introduced the typical failure modes of hybrid integrated quartz flexible accelerometer servo circuit caused by multi-layer ceramic capacitors. These phenomena are analysed and studied using the circuit failure mode matching method, and improvements are proposed. The measures are of guiding significance for improving the failure analysis technology and enhancing the reliability of hybrid integrated circuit.
Transfer learning and its application research
Authors: Zhou Yancong , Zhang Xuemei , Wang Yan , Zhang Bo 
Published: May 2021

Abstract
Transfer learning relaxes the assumption’s limitation requirements of the independent and identical distribution of training data and test data in machine learning. It aims to help the target domain complete the learning task by learning one or more source domains similar to the target domain, solve the problem of scarcity of annotation data and enhance the model’s robustness and generalization performance. The article is a survey on the progress of transfer learning. According to “how to transfer”, transfer learning is divided into four categories: instance-based transfer learning, feature-based transfer learning, model-based transfer learning, and relation-based transfer learning. The paper introduces the basic assumptions, main research questions, common methods, related research of various transfer learning algorithms, and the application of the transfer learning. Finally, we try to point out future research trends.
Vehicle tracking algorithm based on deep learning
Authors: Feng Xiao , Piao Yan , Sun Sitong 
Published: May 2021

Abstract
Vehicle detection is one of the most important detection targets in target detection. overcoming this problem is of great significance in the field of traffic detection and automatic driving. In this paper, based on yolov4, firstly, the slice module is added to the input, and the silce module slices the image into the backbone to get the sampling feature map without information loss. Then, the SPP module is added to the neck of neck module, and then the Kitti dataset is trained, detected and tested. Experiments show that adding slice module in the input circuit and spp module in the neck can effectively improve the accuracy of vehicle detection Map@0.5 3.4%.
Noise Reduction Method for Low Voltage Power Line Carrier Communication
Authors: Li Fei , Shen Hongtao , Shi Lun , Wang Hongxi , Gao Bo 
Published: May 2021

Abstract
In order to improve the performance of electricity information collection system, this paper studies and proposes a new method of low-voltage power line carrier signal frequency domain noise reduction. It includes frequency domain decomposition of carrier signal, filtering noise reduction and signal frequency domain restoration. Then compared our method with the wavelet packet noise reduction method based on multi-resolution. The experimental results show that the proposed algorithm is better than the comparison algorithm under different modulation modes and carrier frequencies, especially suitable for carrier signal noise reduction under bad channel conditions. The algorithm proposed in this paper has good anti signal attenuation performance, and it can effectively enhance the SNR, and ensure the transmission performance and system reliability of the electricity information collection system.
Removal methods of EMG Artifacts from EEG Signals
Authors: Yu Mohan 
Published: May 2021

Abstract
Electromyography (EMG) is the superposition of motor unit action potential (MUAP) in many muscle fibers in time and space. In real measurement, EMG signals will contaminate Electromyography signals, therefore they bring great difficulties to the qualified analysis and interpretation of EEG signals, and it is a momentous step to remove EMG artifacts from EEG signals. In the recent years, new methods were developed for EEG artifacts removal such as Multivariate Empirical Mode Decomposition and Singular Spectrum Analysis. In particular, some researchers combined the two methods and used their respective advantages to remove artifacts more thoroughly without affecting the EEG signal, such as the combination of Independent Component Analysis and Wavelet Method. In this paper, new methods for muscular artifacts removal from EEG above are discussed. Moreover, traditional methods including signal transform, filtering methods and Blind source separation (BSS) are also reviewed.
Design of data encryption storage module for coal mine law enforcement record terminal
Authors: Li Qiwei 
Published: May 2021

Abstract
Coal mine law enforcement record terminal is mostly used in Android operating system. Aiming at the data security problem of Android system, this paper designs a data encryption storage module based on Threadx real-time operating system with i.MX RT1021 processor as the core. This paper introduces the hardware and software design of the encryption storage module in detail. Using DSA data encryption algorithm, combined with the characteristic information, through the actual test, this storage module realizes the reliable encryption storage of audio, video and text data of the law enforcement record terminal, which can effectively prevent the electronic evidence from being tampered with.
A technique of Data Acquisition and Processing in Fiber Grating Sensing System
Authors: Li Nan 
Published: May 2021

Abstract
The multi-channel intelligent optical sensing technology is used in the gyroscope, and the optical fiber sensing technology is optimized through the fiber grating sensing system for data acquisition and processing. The MAX120 chip is used to realize the A/D conversion of the initial spectral signal, the hardware system connects the optical fiber sensor to realize the organic integration between the circuit components. And the preprocessing process of the intelligent information signal is realized through the two steps of data transformation filtering and improving the spectral resolution of the signal. It can solve the problems of slow acquisition speed and low signal-to-noise ratio in the traditional spectral acquisition system. After system testing, it is concluded that compared with the traditional spectral acquisition system, the time of primary spectral acquisition in the intelligent information signal spectral acquisition system based on optical fiber sensing is saved by 1.47s, and the signal-to-noise ratio is improved by 14dB.
Intelligent Course Scheduling System Based on Case-based Reasoning
Authors: Zhang Suqi , Wang Xinxin 
Published: May 2021

Abstract
In view of the current situation of tight teaching resources, the intelligent scheduling system alleviates this problem to a certain extent, and the advantages and disadvantages of the scheduling method determine the performance of the scheduling system. a method based on Case-based reasoning is presented in this paper to solve the course scheduling problem. Using graph theory, a case representation model is built for the course scheduling problem, a measure is established for case similarity, and a case retrieval method based on maximum common subgraph selection is brought forward. Finally, the validity of the proposed method for solving course scheduling problem based on case-based reasoning is verified through an experiment.
Antenna Performanc Evaluation Method for 5G AGPS Wireless Devices
Authors: Wang Na , Chen Lei , Liu Xianhui , Liang Panpan , Zhang Qinjuan 
Published: May 2021

Abstract
The 5G AGPS positioning technology has been widely used in wireless devices. The principle, test method and test system of the 5G AGPS positioning technology are presented in this paper. This method can evaluate positioning antenna performance in simulated environment that is close to real use. The test results show that the system can reflect the positioning antenna performance of EUT very well and provide the test guarantee for user experience.
Design of an intelligent underground parking guided and charging system based on WiFi
Authors: Su Mingxia , zhou Fang 
Published: May 2021

Abstract
With the development of the society, the improvement of people’s living standards, countless underground parking lots seem to be difficult to park and find cars. Underground parking lot was built under the thick concrete. Because the underground parking lot is built under the thick steel and cement, the GPS satellite signal can not penetrate the thick bunker, which leads to the problem that the navigation accuracy in the building is too low to be able to locate accurately. In order to solve this problem which is put forward an intelligent underground parking guidance system based on the wifi, the system install wifi routers on the underground parking lot, and mobile phones own wifi module scan wifi hotspots around the information, and then match the cloud database tagged wifi hotspots information, and get wifi hot spot on the map coordinate information. According to the mobile phone access to the location of the WIFI hotspot intensity information, the system gets to the phone’s current location information, and the coordinates of the map is updated. Fianlly, according to the system for the whole test, the test results show that the system has achieve the expected goals, and the operation is convenient, has certain market practical value.
A Method for Pulse Signal Denoising Based on VMD Parameter Optimization and Grey Wolf Optimizer
Authors: Zhang Lin , Yu Chengbo , Tan Yong 
Published: May 2021

Abstract
Aiming at the difficulty of parameter determination when using variational modal decomposition algorithm (VMD) to decompose photoplethysmographic(PPG) pulse wave signals, α VMD parameter optimization method based on Grey Wolf Optimizer (GWO) is proposed. In this paper, the envelope entropy of the K eigenmode component (IMF) after VMD decomposition is used as the fitness function, and the grey wolf optimization algorithm (GWO) is used to find the K sum corresponding to the minimum envelope entropy of the eigenmode component α, to determine the optimal value of the VMD algorithm parameters, and then use the parameter optimized VMD algorithm to filter out the noise in the PPG signal. The experimental results show that after decomposing the PPG signal using the algorithm in this paper, the effective component and the noise component of the signal are well separated. After selecting the appropriate IMF component for reconstruction, the noise in the PPG signal is effectively filtered out.
Study on Shot peening Coverage of Metal Surface Based on Deep Learning
Authors: Qiu Jiyuan , Li Wenrui , wang Fushuai , Yao Qi 
Published: May 2021

Abstract
The surface shot peening coverage of industrial parts will affect the performance of the parts. Therefore, it is of great significance for the identification of the metal surface shot peening coverage area and the uncovered area for the metallurgical process. This paper uses the idea of image semantic segmentation, using CCD industrial camera to select and shoot the metal surface of 67%,89% and 98% shot peening coverage. Based on UNet and UNet++ model, the shot peening area is segmented. After training 200 epoch, the network converges and the training accuracy can reach 98.45%. Comparing the two kinds of network training results, using the UNet++ model with good results to predict the selected 18 metal surfaces, the proportion of shot peening coverage area and uncovered area is counted, and then the K-means clustering algorithm is used to classify. The classification accuracy can reach 100%.
Radar detection of multi-target vital signs based on blind source separation
Authors: Zhang Jilin , Li Xingguang , Ma Lingzhe 
Published: May 2021

Abstract
Radar non-contact monitoring of vital signs has a broad application prospect in clinical monitoring. Aiming at the problem of strong interference in non-contact vital signs detection (Such as multi-target, random body motion), a blind source separation (BSS) signal detection method based on Fast-ICA is proposed to reduce the interference of multi-target. In this algorithm, entropy is used to evaluate the non Gaussian property, and the appropriate transformation matrix is selected, according to the statistical independence of the signals, the source signals are separated from the observed mixed signals. On this basis, the traditional blind source separation process is improved, and the wavelet transform preprocessing algorithm based on translation invariant is added to suppress the interference of static clutter. The feasibility of this method is verified by simulation experiments.
Research on control algorithm of a automatic driving robot based on improved model predictive control
Authors: Wang Zengxi , Zhang Qingyu , Jia Tong , Zhang Sulin 
Published: May 2021

Abstract
The automatic driving robot can replace the driver in the traditional test and ADAS test; Model Predictive Control (MPC) is used in the horizontal and vertical control process of high-speed driving. According to the uncertainties and complex constraints of automatic driving horizontal and vertical control, the adaptive improvement of MPC is proposed, which can better adapt to the characteristics of self driving vehicles, sensors and tyres. This paper proposes a multi-layer and multi time automatic driving control strategy based on improved MPC. Visual studio is used to write the upper computer software of the automatic driving robot, which realizes good human-computer interaction. The automatic driving robot is verified in real vehicle test. The experimental results show that the transverse and longitudinal control strategy of the improved model predictive control is stable, and the automatic driving robot can realize the high precision and high dynamic control of vehicle steering, pedal and shift.
Mask Recognition Method Based on Graph Convolutional Network
Authors: Ye Qing , Li Rui 
Published: May 2021

Abstract
The rapid spread of COVID-19 worldwide has exacerbated the health crisis and affected our daily lives. Medical researchers have shown that wearing masks in public places is essential to reduce the spread of COVID-19 infection. The increase in the accuracy of the identification of masks in public places can effectively prevent the further spread of the epidemic. In this paper, we propose a mask recognition network based on graph convolutional network, D-GCN. The network adopts the method of combining convolutional neural network and graph convolutional neural network. First, Dense Net 101 is used to extract features of the real-time image to be tested, and then GCN processes the training label information to form a directed graph through the word embedding vector, it forms a new classifier based on the label connection between the training pictures, and processes the extracted feature vectors. Finally the classification result is output and completing the recognition. The experiment is carried out on the mask dataset and MAFA dataset, and the recognition accuracy is significantly improved compared with previous methods.
EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using ensemble models
Authors: Yu Xin , Kong De-Xin 
Published: May 2021

Abstract

Motivation
Based on the concept that contiguous cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand may be modified by a methyltransferase or demethylase together, current study found that the combination of multiple CpGs into a single block may promote cancer diagnosis. However, there is no R package available for building models based on methylation correlated blocks.

Results
Here, we present a package named stacked ensemble of machine learning models for methylation correlated blocks (EnMCB) to build signatures based on DNA methylation correlated blocks for survival prediction. The Cox regression, support vector regression, mboost and elastic-net model were combined in the ensemble model. Methylation profiles from The Cancer Genome Atlas were used as real datasets. The package automatically partitions the genome into blocks of tightly co-methylated CpG sites, termed methylation correlated blocks. After partitioning and modeling, the diagnostic capacities for predicting patients’ survivals are given.

Availability and implementation
EnMCB is freely available for download at GitHub (https://github.com/whirlsyu/EnMCB/) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/EnMCB.html).

Supplementary information
Supplementary data are available at Bioinformatics online.
A framework to decipher the genetic architecture of combinations of complex diseases: applications in cardiovascular medicine
Authors: Yin Liangying , Chau Carlos Kwan-Long , Lin Yu-Ping , Rao Shitao , Xiang Yong , Sham Pak-Chung , So Hon-Cheong 
Published: May 2021

AbstractMotivationCurrently, most genome-wide association studies (GWAS) are studies of a single disease against controls. However, an individual is often affected by more than one condition. For example, coronary artery disease (CAD) is often comorbid with type 2 diabetes mellitus (T2DM). Similarly, it is clinically meaningful to study patients with one disease but without a related comorbidity. For example, obese T2DM may have different pathophysiology from nonobese T2DM.ResultsWe developed a statistical framework (CombGWAS) to uncover susceptibility variants for comorbid disorders (or a disorder without comorbidity), using GWAS summary statistics only. In essence, we mimicked a case–control GWAS in which the cases are affected with comorbidities or a disease without comorbidity. We extended our methodology to analyze continuous traits with clinically meaningful categories (e.g. lipids), and combination of more than two traits. We verified the feasibility and validity of our method by applying it to simulated scenarios and four cardiometabolic (CM) traits. In total, we identified 384 and 587 genomic risk loci respectively for 6 comorbidities and 12 CM disease ‘subtypes’ without a relevant comorbidity. Genetic correlation analysis revealed that some subtypes may be biologically distinct from others. Further Mendelian randomization analysis showed differential causal effects of different subtypes to relevant complications. For example, we found that obese T2DM is causally related to increased risk of CAD (P = 2.62E−11).Availability and implementationR code is available at: https://github.com/LiangyingYin/CombGWAS.Supplementary informationSupplementary data are available at Bioinformatics online.