bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electroforming-free flexible organic resistive random access memory based on a nanocomposite of poly(3-hexylthiophene-2,5-diyl) and orange dye with a low threshold voltage
Authors: Sherazi Syed Sibtul Hassan , Rehman Muhammad Muqeet , Ur Rehman Hafiz Mohammad Mutee , Kim Woo Young , Siddiqui Ghayas Uddin , Karimov Khasan S 
Published: September 2020

Abstract
The charge trapping characteristics of an organic nanocomposite based on two polymers, poly(3-hexylthiophene-2,5-diyl) (P3HT) and orange dye (OD), were investigated by fabricating a sandwiched structure on a flexible polyethylene terephthalate substrate. The fabricated flexible organic device with a configuration of silver (Ag)/P3HT–OD/Ag displayed highly stable results consistent with a bipolar non-volatile resistive random access memory (RRAM) device using simple and controllable fabrication technology. It was observed that by the addition of OD, low threshold voltages (V
th) of V
set = 1.5 V and V
reset = −1.5 V were achieved owing to the values of work function and energy bandgap of the materials used. Electrical rewritable effects of the fabricated memory device were tested for 102 voltage sweeps and 104s without any considerable degradation of its bistable resistive states. The switching mechanism was considered to be space charge limited current and this was verified by plotting the double logarithmic I–V curve. This is the first RRAM device based on P3HT polymer whose mechanical robustness has been tested for its mechanical robustness by bending it for 25 bending cycles at a bending diameter ranging from 15 cm to 5 cm without any considerable change in its properties. This finding offers important guidelines for reproducing next-generation flexible organic nanocomposite-based memory devices that are simple to fabricate and have low operating voltages and highly stable memory behavior.
Ultrafast dynamics and evolution of ion-induced opacity in transparent dielectrics
Authors: Coughlan M , Donnelly H , Breslin N , Arthur C , Nersisyan G , Yeung M , Villagomez-Bernabe B , Afshari M , Currell F , Zepf M , Dromey B 
Published: September 2020

Abstract
Recently, measurements of few-picosecond (ps, 10−12 s) pulses of laser-driven protons were realised by the observation of transient opacity in SiO2. This ultrafast response could be understood by the formation of self-trapped excitonic states in the material, creating a rapid de-excitation channel for conduction band electrons. Here we extend this work to examine the onset and evolution of an ion-induced opacity in transparent dielectrics, namely multicomponent variants of SiO2. The fast recovery observed in SiO2 is in sharp contrast to borosilicate (BK7) and soda-lime glasses. We find that the opacity decay timescales for BK7 and soda-lime glass are orders of magnitude greater than the 3.5 ps proton pump pulse duration and discuss the underlying processes which may be affecting the extended recovery of the material. Simultaneous probing with 2nd harmonic radiation allows estimates of ultrafast electron dynamics due to proton interactions in matter to be investigated, this indicates that a rapid evolution of an initially unstructured ion-induced dose distribution seeds the longer term recovery pathways in the irradiated dielectrics. When combined, these results demonstrate the efficacy of utilising ultrafast laser-driven ionising radiation along with highly synchronised probe pulses to enable the study of ion-induced damage in matter on ultrafast timescales in real time.
Investigation of viscous dissipation and entropy generation in third grade nanofluid flow over a stretched riga plate with Cattaneo-Christov Double Diffusion (CCDD) model
Authors: Chu Yu-Ming , Shah Faisal , Ijaz Khan M , Farooq Shabnam , Kadry Seifedine , Abdelmalek Zahra 
Published: September 2020

Abstract
Fluid flow and heat transport by a stretched surface is most important and significant area of research in mechanical and industrial engineering due to numerous applications. The influence of heat transport is seen in the field of polymer processing, metallurgy and chemical engineering, manufacturing of artificial films, food stuff processing, aerodynamics extrusion of plastic sheets, hot rolling, glass fiber production, metal spinning, metal extrusion, drawing of plastic films and wires and paper production. In view of the above applications, we have modeled two-dimensional, steady and incompressible flow of non-Newtonian fluid (third grade) over a stretched Riga surface with Cattaneo-Christov Double Diffusion (CCDD) model. Stagnation point flow is considered and the flow is generated due to stretched Riga surface. Furthermore, Cattaneo-Christov Double Diffusion concept is used instead of Fourier’s and Fick’s laws to model the energy and concentration equations. Important slip mechanisms of Buongiorno nanofluid model i.e., Brownian motion and thermophoresis diffusion are considered for the transportation of heat and mass transfer. Nield condition is imposed at the stretched boundary surface. Total entropy rate is calculated and discussed through second law of thermodynamics and important pertinent flow parameters. Appropriate similarity variable leads to system of ordinary ones and total residual error and convergence rate are obtained via Optimal Homotopy Analysis Method (OHAM). The influence of parameters on the velocity, temperature, concentration and skin friction coefficient are discussed graphically.
A RISC-V Based Coprocessor Accelerator Technology Research for Convolution Neural Networks
Authors: Zhang Shuhua , Tong Jie , Zhang Jun , Lei Yuqing , Zhang Minghao , Li Dang , Wang Lanruo 
Published: September 2020

Abstract
The advancements of neural networks have significantly improved the accuracy of a variety of intelligent applications, such as image processing, voice recognition and so on. However, one of the challenges is to accelerate the speed of inference with the networks designed to be deeper. In this paper, we first realize the algorithm involved in software. Then, combined with the expanded characteristic of RISC-V architecture, the acceleration of convolution operation taking up the largest proportion of computation in CNN is realized by coprocessor expansion mode on open-source Hummingbird E203 processor. Through tests, convolution coprocessor improves the performance of CNN while ensuring the accuracy in function. Finally, the Cifar-10 image classification, a common benchmark in machine learning, is used to verify the feasibility in functions and comparatively analysis the pure software-based and coprocessor-based implementations.
Research on Virtual Coupling Train Operations Based on Moving-Block and Vehicle-to-Vehicle Communication
Authors: Duan Haixiao , Yang Yunan , Duan Yi , Zhang Jinchuan 
Published: September 2020

Abstract
This paper introduces the current research status of train operation mode especially the one based on the train moving-block system. Then it improves on the basis of the Vehicle-to-Ground(V2G) communication technology and the traditional fixed-block system constructs a virtual combination model based on the train moving-block system and Vehicle-to-Vehicle(V2V) communication technology. Through the construction of state flow-diagram of the entering and leaving the Virtual Coupling train-following model and calculation of the train capacity, discussion and analysis are carried out in different scenarios, including train tracking at the station, arriving at the station, sending out from the station, etc. Finally, the scene of two trains tracking through the station is simulated and compared with the traditional fixed-block operation mode in ideal circumstances. The results show that the new operation mode proposed in this paper can improve the railway capacity and train operation efficiency. In the future railway development, this kind of operation mode has certain efficiency.
A Novel Superheat Identification of Aluminum Electrolysis with Kernel Semi-supervised Extreme Learning Machine
Authors: Jiang Ye , Li Peiqiang , Lei Yongxiang 
Published: September 2020

Abstract
In the aluminium reduction production industry, the superheat temperature (ST) is a vital index, which gives the distribution of the current efficiency. Keeping ST in an approximate range improves the lifespan of the electrolysis bath. However, in the practical production process, ST identification result is commonly evaluated by the experimental workers, the real-time measurement of ST is still a challenge that has not been solved. A novel ST measurement method based on kernel extreme learning machine (K-ELM) is studied in this paper. First, a few of input variables are selected according to expert experience. Then, a new activation function (IG kernel function) and regularization term are constructed in ELM to construct IG-SSELM. Finally, the ST model is built with all the dataset samples and further applied to ST real-time detection. The proposed method is applied to ST identification for the first time in the aluminium industrial process which is superior to the existing ST methods in accuracy and robustness.
Research on the Method of Individual Identification of Chickens Based on Depth Image
Authors: Zhang Baoquan , Qiu Yunlin , Wang Xinyu , Lu Huishan , Wang Fujie 
Published: September 2020

Abstract
In order to accurately extract the individual images of chickens from the complex background, the experiment took green shell laying hens which scatter-feed as the segmentation target, and proposed a method of individual identification of chickens based on range image, including image background segmentation and chicken overlapping segmentation. First of all, the depth image after background segmentation is obtained by using fixed threshold segmentation method and morphological processing, and then the image with adhesion is separated by using the method of concave point analysis. Through the recognition of 200 depth images, the results showed that the recognition algorithm can effectively separate the adhesive individuals, thereinto, the correct recognition rate for the image without adhesion is 100%, while the recognition accuracy was 93% for images with adhesion. The comprehensive recognition accuracy was 97%.
Research on Target Detection Technology Based on Convolutional Neural Network
Authors: Yang Liu , Zhang Huaguang 
Published: September 2020

Abstract
The research and application of neural network has been a research topic in recent years, and the efficient recognition of objects is undoubtedly an important part of the technology application. Convolution of the neural network is in the process of object recognition classification, has carried on the improvement of cognitive machine, thus avoiding the error back propagation in time loss problem, based on the biological neural network structure, convolution weights of neural network are more inclined to study sharing network structure, which shows good performance in the process of a number of image recognition, for translation, proportion to gather scaling and deformation and so on the many kinds of complex operation has certain robustness.
A Gait Recognition Method Based on the Combination of Human Body Posture and Human Body Contour
Authors: Ma Yue , Wei Chenghao , Long Hao 
Published: September 2020

Abstract
Aiming at the issues that affect the gait recognition, such as outfit changes and carry-on-objects during gait recognition, this paper proposes a gait recognition method based on the improved GaitSet network, which relies on the fusion of human posture and human contour. This method introduces key points of human posture, and improves the precision of human contour extraction by introducing the key points of human posture. At the same time, the human posture map composed of the key points of human posture is used as the synchronous attribute of the contour map to extract gait features. Because key points focus on the inherent walking characteristics of human body, they are not affected by the external information such as clothing and carrying objects. Combined with the rich gait change attributes of human contour, it can effectively improve the accuracy and robustness of the gait recognition model. In scenes of human body wearing coats, our experimental results show that the accuracy of the proposed method in CASIA-B gait data set and self-built database can reach over 73.5% accuracy.
A Practical Application for Text-Based Sentiment Analysis Based on Bayes-LSTM Model
Authors: Li Jiawen , Zhu Huaping 
Published: September 2020

Abstract
Text-based sentiment analysis algorithms have now become one of the active research areas in emotional analysis which has gained much attention nowadays. Text emotion classification can be widely used in social public opinion analysis, product use feedback, harmful information filtering, etc. In this paper, we first developed a robotic crawler to gather data about comment on Huawei cellphone from Sina weibo microblog sites (Chinese twitter). Then we generate the data text to be trained according to the input requirements of the Keras module, and perform formal training and learning on the model after data preprocessing. Subsequently, the classifier was constructed based on the Bayes-LSTM model in which TF-IDF model was used for feature selection. The LSTM model can be characterized by the ability to self-evaluate the usefulness of the information obtained, which makes up for the shortcoming of naive Bayes formula that only applies to two independent events. We finally have a practical application that generates a word cloud from text, showing frequently used words in larger font sizes, effectiveness of the algorithm was also verified by experiment.
A Dynamic Model of Otolith for Determinants of a BPPV Reposition Maneuver with Its Simulation
Authors: Peng Yunjian , Qi Jiaxin 
Published: September 2020

Abstract
Benign paroxysmal positional vertigo (BPPV) is a common cause of vertigo disease, and its treatment consists of several body movements to reposition the otolith. Due to the small scale of the otolith, it is impossible to observe it clinically and therefore the treatment process is empirical. The paper proposes an improved dynamic model of the movement of a single otolith in a single semicircular canal (SCC), which aims at simulating the movement behavior of the otolith. With simulation results of the otolith’s movements, we take two parameterized indices of the BPPV treatment maneuvers to assess their determinants, i.e., the rotational angular velocity and the pause time between operations. The former formulates the behavior of the otolith’s moving in the SCC, the latter is confirmed by simulation as an important index for a successful maneuver, which could provide guidance for clinical treatment and improve treatment efficiency.
Evaluation of Intelligent Speech Technology in Epidemic Prevention: Take Iflytek Input Software in Chinese and Japanese Recognition as an Example
Authors: Cao Chen , Wang Gaowu 
Published: September 2020

Abstract
This study aims to investigate public’s understanding of intelligent speech technology and to evaluate the word accuracy rate of speech recognition technology in Japanese and Chinese recognition and translation, using the simulating corpus of doctor-patient communication. It is concluded from the questionnaire that currently the public has a high awareness and demand of intelligent speech products. The results of speech recognition experiments show that the word accuracy rates are 99.4% in Mandarin, 96.1% in Japanese, 94.2% in Japanese translation of Chinese, and 82.7% in Chinese translation of Japanese, respectively. In the recognition of Mandarin and Japanese, the errors mainly include missing words and word errors caused by the same or similar pronunciation. In the translation between Japanese and Chinese recognition, the whole sentence omission, sentence patterns, tenses and so on are more common.
An Improved AdaBoost Algorithm for Hyperparameter Optimization
Authors: Gao Rongfang , Liu Zhanyu 
Published: September 2020

Abstract
AdaBoost algorithm is a typical Boosting algorithm, which belongs to a successful representative in the Boosting family. This algorithm can upgrade a weak classifier with a better classification effect than random classification to a strong classifier with high classification accuracy, where n_estimators represents the number of iterations of the base classifier. If the value is too large, it will easily cause the model to overfit, if it is too small, it is easy. The model is under-fitting, and the parameter setting is not set randomly, but according to the current status of the data set. Aiming at the problem that the number of iterations in the AdaBoost algorithm is uncertain, this paper introduces a Bayesian optimization algorithm for hyperparameter tuning, which makes the value of hyper parameter in AdaBoost algorithm suitable for the current data set, and finally obtains a hyperparameter optimization AdaBoost algorithm. The experiment result shows the method that adopt Bayesian optimization algorithm for hyperparameter optimization and apply the optimized hyperparameter value to the AdaBoost algorithm does not only improves the classification accuracy of the AdaBoost algorithm, but also avoids overfitting and underfitting of the model.
Research on Ultrasonic TOFD Testing Method of Electron Beam Weld
Authors: Chen Fanghao , Ding Fenglin , Song Fei , Wang Yuan 
Published: September 2020

Abstract
With the development of aerospace technology, electron beam welding technology has become an important part of the welding field. More and more aircraft and spacecraft parts adopt electron beam welding technology. However, in the process of electron beam welding, due to the influence of the incident angle of the electron beam, the deviation of the weld bead and the electromagnetic field, it is easy to produce defects such as unfused and blowholes. Therefore, it is necessary to conduct a non-destructive testing study on the electron beam weld. Aiming at the characteristics and defects of electron beam welds, the TOFD inspection method of electron beam welds was developed, the basic principles of TOFD inspection were introduced in detail, the TOFD acoustic beam coverage was calculated, and the TOFD inspection of electron beam welds was carried out based on self-developed systems Experiment and analyze the experimental results. Experiments show that the ultrasonic TOFD detection method can quickly and accurately detect electron beam welds, which is an effective method for electron beam weld detection.
The Semi-dense ICP Algorithm Based on the SIFT Feature Points Neighborhood
Authors: Han Baichuan , Wu Wei , Wang Yunfeng , Liu Jinfeng 
Published: September 2020

Abstract
The ICP algorithm based on RGB-D images is one of the most widely used and the most effective point cloud registration algorithms. How to improve the registration accuracy, registration speed and robustness of the ICP algorithm has become an important research hotspot at present. However, existing algorithms have low computational efficiency under large-scale point cloud data, and when the overlap between point clouds is small, the registration accuracy is low. To deal with these problems, this paper proposes a semi-dense ICP algorithm based on SIFT feature points neighbourhood. First, the algorithm selects the SIFT feature point matching algorithm as the rough registration method of ICP, because the SIFT feature points have the advantage of rotation invariance, illumination invariance, and scale invariance; then based on the SIFT feature points, the algorithm selecting its neighbourhood as the matching range. The experimental result shows that this algorithm has the better registration accuracy and computational efficiency.
Description and Application Research of Multiple Regression Model Optimization Algorithm Based on Data Set Denoising
Authors: Kang Hao , Zhao Hailong 
Published: September 2020

Abstract
Multiple regression model is based on a large number of data sample set in the prediction process, and the noise data in the data set will have a great impact on the results of the fitting equation, which makes the results unreliable and unreliable. This paper forwards fuzzy least square method based on fuzzy set theory, it can optimize the regression model algorithm and reduce the influence of noise data on the fitting equation. This algorithm is applied to the real estate price forecast, it would obtain the final fitting equation after repeating iterative calculation, which makes the price prediction and eliminates the influence of bad data on the fitting results and improves the reliability and availability of the forecast results.
Scarf Defect Detection Method Based on Periodicity of Braided Texture
Authors: Meng Xiangwei , Li Wei , Kong Xianghong , Ma Zhixiang , Chi Haoyu 
Published: September 2020

Abstract
The pure color scarf image is a typical periodic texture image. Based on the periodicity of scarf image texture, in order to eliminate the influence of periodical change of gray value of scarf image on scarf defect detection caused by periodic texture change, a defect detection method based on scarf knitting texture periodicity is proposed. Firstly, autocorrelation function is applied to determine the texture period of the averaged image, and the texture period is taken as the parameter to improve the quality of scarf defect detection Row down sampling and bilinear interpolation are used to eliminate the periodic change of gray value caused by texture. After that, the method based on maximum inter class variance is used to cut the image to complete the defect detection of scarf image. Through a large number of experiments, it is proved that this method has good effect on scarf defect detection, and has the characteristics of stable effect and strong practicability.
A Transfer Learning Method for Deep Networks with Small Sample Sizes
Authors: Zheng Xin , Lin Luyue , Liang Shouzhi , Rao Bo , Zhan Ruidian 
Published: September 2020

Abstract
Transfer learning is that a machine learning model learns knowledge from more than one domain, and it is applied to the context of small sample size. Some of approaches concentrate on the correlation determination among all domains while some pay more attention on knowledge transfer among all domains. In this paper, on the basic of SVM with hinge loss, a new regularized transfer learning deep network with a specific regularization is proposed, in which a deep network learns high level representation with respect to the given samples. And a part of parameters in SVM are shared such that the similarity of data distribution can be well captured. Besides, a modified regularized SVM is exploited such that the gradient based method is feasible, which yields a parallel implementation of the proposed method. After that, in the experiment part, the comparison of our approach with state-of-the-art approaches manifests the competitive performance and the feasibility in classification.
Applicable Scene Text Detection Based on Semantic Segmentation
Authors: Gu Shifu , Zhang Fusheng 
Published: September 2020

Abstract
In the past few years, scene text detection problem has attracted much attention and widely studied for many applications. Segmentation based methods are quite popular in this filed, it was not only for the simple post-processing procedure, but also for the ability of handle the various shape of text. In this manuscript, a method named CFPM-IDB was proposed which based on a new decoder module Consolidated Feature Pyramid Module (CFPM) and a light weight segmentation head Improved Differentiable Binarization (IDB). The CFPM module originated from Feature Pyramid Networks and achieved an idea trade-off between local details and macro semantic information. IDB module performed binarization process by threshold map and probability map. Combining them results in CFPM-IDB, which improved the performance of scene text detection, both precision and efficiency. Experimental results on two different datasets proved the promising advance in the accuracy and speed of this model.