bluuebunny/crossref_metadata_2025 huggingface dataset:
Noise modeling and variance stabilization of a computed radiography (CR) mammography system subject to fixed-pattern noise
Authors: Borges Lucas R , Brochi Marco A C , Xu Zhongwei , Foi Alessandro , Vieira Marcelo A C , Azevedo-Marques Paulo M
Published: September 2020

In this work we model the noise properties of a computed radiography (CR) mammography system by adding an extra degree of freedom to a well-established noise model, and derive a variance-stabilizing transform (VST) to convert the signal-dependent noise into approximately signal-independent. The proposed model relies on a quadratic variance function, which considers fixed-pattern (structural), quantum and electronic noise. It also accounts for the spatial-dependency of the noise by assuming a space-variant quantum coefficient. The proposed noise model was compared against two alternative models commonly found in the literature. The first alternative model ignores the spatial-variability of the quantum noise, and the second model assumes negligible structural noise. We also derive a VST to convert noisy observations contaminated by the proposed noise model into observations with approximately Gaussian noise and constant variance equals to one. Finally, we estimated a look-up table that can be used as an inverse transform in denoising applications. A phantom study was conducted to validate the noise model, VST and inverse VST. The results show that the space-variant signal-dependent quadratic noise model is appropriate to describe noise in this CR mammography system (errors< 2.0% in terms of signal-to-noise ratio). The two alternative noise models were outperformed by the proposed model (errors as high as 14.7% and 9.4%). The designed VST was able to stabilize the noise so that it has variance approximately equal to one (errors< 4.1%), while the two alternative models achieved errors as high as 26.9% and 18.0%, respectively. Finally, the proposed inverse transform was capable of returning the signal to the original signal range with virtually no bias.
Probing spatiotemporal fractionation on the preclinical level
Authors: Telarovic Irma , Krayenbuehl Jerome , Grgic Ivo , Tschanz Fabienne , Guckenberger Matthias , Pruschy Martin , Unkelbach Jan
Published: September 2020

In contrast to conventional radiotherapy, spatiotemporal fractionation (STF) delivers a distinct dose distribution in each fraction. The aim is to increase the therapeutic window by simultaneously achieving partial hypofractionation in the tumour along with near uniform fractionation in normal tissues. STF has been studied in silico under the assumption that different parts of the tumour can be treated in different fractions. Here, we develop an experimental setup for testing this key assumption on the preclinical level using high-precision partial tumour irradiation in an experimental animal model. We further report on an initial proof-of-concept experiment. We consider a reductionist model of STF in which the tumour is divided in half and treated with two complementary partial irradiations separated by 24 h. Precise irradiation of both tumour halves is facilitated by the image-guided small animal radiation research platform X-RAD SmART. To assess the response of tumours to partial irradiations, tumour growth experiments are conducted using mice carrying syngeneic subcutaneous tumours derived from MC38 colorectal adenocarcinoma cells. Tumour volumes were determined daily by calliper measurements and validated by CT-volumetry. We compared the growth of conventionally treated tumours, where the whole tumour was treated in one fraction, to the reductionist model of STF. We observed no difference in growth between the two groups. Instead, a reduction in the irradiated volume (where only one half of the tumour was irradiated) resulted in an intermediate response between full irradiation and unirradiated control. The results obtained by CT-volumetry supported the findings of the calliper-derived measurements. An experimental setup for precise partial tumour irradiation in small animals was developed, which is suited to test the assumption of STF that complementary parts of the tumour can be treated in different fractions on the preclinical level. An initial experiment supports this assumption, however, further experiments with longer follow-up and varying fractionation schemes are needed to provide additional support for STF.
EXIRAD-HE: multi-pinhole high-resolution ex vivo imaging of high-energy isotopes
Authors: Nguyen Minh Phuong , Goorden Marlies C , Beekman Freek J
Published: September 2020

We recently developed a dedicated focusing multi-pinhole collimator for a stationary SPECT system that offers down to 120 µm (or 1.7 nL) spatial resolution SPECT images of cryo-cooled tissue samples (EXIRAD-3D). This collimator is suitable for imaging isotopes that are often used in small animal and diagnostic SPECT such as 125I (27 keV), 201Tl (71 keV), 99mTc (140 keV), and 111In (171 and 245 keV). The goal of the present work is to develop high-resolution pinhole imaging of tissue samples containing isotopes with high-energy photon emissions, for example, therapeutic alpha and beta emitters that co-emit high energy gammas (e.g. 213Bi (440 keV) and 131I (364 keV)) or 511 keV annihilation photons from PET isotopes. To this end, we optimise and evaluate a new high energy small-bore multi-pinhole collimator through simulations. The collimator-geometry was first optimised by simulating a Derenzo phantom scan with a biologically realistic activity concentration of 18F at two system sensitivities (0.30% and 0.60%) by varying pinhole placements. Subsequently, the wall thickness was selected based on reconstructions of a Derenzo phantom and a uniform phantom. The obtained collimators were then evaluated for 131I (364 keV), 213Bi (440 keV), 64Cu (511 keV), and 124I (511 + 603 keV) with biologically realistic activity concentrations, and also for some high activity concentrations of 18F, using digital resolution, mouse knee joint, and xenograft phantoms. Our results show that placing pinhole centres at a distance of 8 mm from the collimator inner wall yields good image quality, while a wall thickness of 43 mm resulted in sufficient shielding. The collimators offer resolutions down to 0.35 mm, 0.6 mm, 0.5 mm, 0.6 mm, and 0.5 mm when imaging 131I, 213Bi, 18F, 64Cu, and 124I, respectively, contained in tissue samples at biologically achievable activity concentrations.
Strain and stress effects on single crystal-supported titania and related nanostructures
Authors: Pang Chi Lun
Published: September 2020

The properties of materials can be altered by introducing strain. For instance, the critical temperature of superconductors has been raised as has the Curie temperature of ferroelectric materials. As TiO2 is the most studied single crystal metal oxide, it is highly desirable to understand how strain might be used to tune its properties. Theoretical calculations indicate that the band gap of anatase TiO2 may be lowered, which would remove one of the roadblocks to its wider use as a photocatalyst. Surface defects on rutile TiO2 are responsible for a large part of its reactivity and the nature of these may also be affected by strain according to calculations. In this review, the introduction of strain and its effect on reactivity will be examined. The strain in the most part arises from lattice mismatch in the growth of TiOx films on various substrates although cases where TiO2 is the substrate are also included as is the implantation of Ar clusters in the subsurface of TiO2.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
An Assessment Method of Structure Strength Analysis on Full-Scale LNG Independent Type C Tank
Authors: Liu Ge , Xie Zheng , An Qianglin , Pei Fei
Published: September 2020

The sloshing induced by partial loaded in LNG (Liquified Natural Gas) independent type C tank will threat the structure of tank. The strength check of the tank plays an important role in the design of LNG tank, which can also improve the safety of the cargo containment system. Hence, an assessment method of the structure of LNG independent type C tank is presented, in which the method of one-way fluid-structure interaction is used. Sloshing load is considered with a numerical technique based on CFD theory, which can reduce large computational expense and remain a good accuracy. The dangerous conditions of sloshing pressure are determined by frequency domain analysis of the tank and ship. By computation of sloshing condition of 1000 m3 fuel bunker vessel, the feasibility of this method is explained and suggestions on structure design of tank is given.
A Data Simulation Method of Bank Fraud Transaction Based on Flow-Based Generative Model
Authors: Wang Xiaoguo , Zhang Yin
Published: September 2020

In order to solve the problem of category imbalance caused by the shortage of bank fraud transaction data, this paper proposes a bank fraud transaction data simulation method based on flow-based generative model. On the basis of the flow-based generative model and the real fraudulent transaction data of the bank, this method designs a generative model suitable for the bank data, and learns the distribution of the real data through the generators G and G-1. The experimental results show that mixing the generated simulated data and real business data in a certain proportion to train the fraud detection model can improve the detection effect of the model to a certain extent.
Securing the Private Key in Your Blockchain Wallet: A Continuous Authentication Approach Based on Behavioral Biometric
Authors: Hu Teng , Liu Xiaolei , Niu Weina , Ding Kangyi , Wang Yanping , Zhang Xiaosong
Published: September 2020

As blockchain is increasingly valued, the application of blockchain and its security has become a research hotspot. Not only that, more and more ordinary users are paying attention to the blockchain and beginning to trade commodities in cryptocurrencies. The blockchain wallet is a program for users to manage and stores their private keys. Furthermore, the private key is the user’s unique credential to the cryptocurrency in the blockchain. But for now, blockchain wallets are only using passwords to authenticate users, which poses a potential risk to private key security. To mitigate this risk, this paper proposes a continuous authentication approach based on user mouse behavioral biometrics. The approach was experimentally shown to be able to authenticate once per second with an average FAR and FRR of 6.92% and 6.18%, respectively.
Examination Paper Image Segmentation with Adversarial Network
Authors: Hu Siquan , Zhang Muliang , Shi Zhiguo , Zhang Min
Published: September 2020

Examination paper analysis is important for students to improve their learning efficiency. Traditional paper examination papers are difficult to sort out, which makes collecting mistakes for future review both time-consuming and laborious, either by handwriting or existed software tools. To easy the process, we propose a layout analysis method combined with the conditional generative adversarial network (CGAN). The traditional semantic segmentation structure is improved and used as a generator in the network, while a discriminator is designed to make the segmentation results more accurate. The motivation is that the discriminator can judge the authenticity of the image, so it can help reduce the unreasonable phenomenon in the semantic segmentation results of the generator. The experimental results show that by this method the examination paper image can be welly split into various components, which provides convenience for further sorting and analysis of the examination paper images.
Fresh Agricultural Products Cold Chain Location Selection in Context of Big Data
Authors: Weng Lili
Published: September 2020

In the domestic fresh market, agricultural products are one of the major consumer goods. The rational optimization of the fresh agricultural products cold chain distribution center location is of great significance in enhancing the efficiency of the entire cold chain logistics. Therefore, in the context of big data, this paper chooses the grey forecasting method with a higher prediction accuracy, applied GM(1,1) prediction model for fresh agricultural products demand forecast under the premise of comparing different forecast methods’ accuracy; Basing on the demand forecast, this paper formulates fresh agricultural products distribution center location model with the minimum total cost objective. In this objective, the thesis considers the cost of cargo damage and the penalty cost of violating the time window in order to this order to improve customer satisfaction and loyalty, which is considered by a few researchers. Finally, a specific case study is designed for Q enterprise to solve the optimal the result and test the validity of the model algorithm.
Research on Health Management Platform Based on Data Analysis
Authors: Li Qin , Lan Jian , Li Gang
Published: September 2020

In order to solve the problem of economic loss and human casualties caused by aging and damage of resistant material lining of torpedo tank during steelmaking, this paper proposes a health management platform of torpedo tank based on data analysis combining cloud platform and big data analysis. The platform has completed the acquisition, transmission, storage, analysis, mining and visualization of real-time temperature data of torpedo tank truck. The fault prediction of refractory lining of torpedo tank truck based on temperature data is realized, which provides data basis for torpedo tank overhaul. This platform can be used for reference for remote monitoring and health management of similar objects.
Key Technologies of Intelligent Manufacturing of Protection and Control Equipment in Ubiquitous Power Internet of Things
Authors: Wang Xiang , Zhao Jianfen , Wang Zhiguo
Published: September 2020

As the most basic and lowest data node of ubiquitous power Internet of things, power system protection and control equipment plays a vital role in the stable operation of ubiquitous power Internet of things. The current status and problems of power system protection and control equipment manufacturing are discussed. Aiming at the National Equipment Manufacturing Industry Standardization and Quality Improvement Plan, the production intelligent manufacturing standards and related test verification standards of power system protection and control equipment are put forward. Four key points of intelligent manufacturing of power system protection and control equipment are studied. Technical features: intelligent design verification, intelligent production scheduling verification, intelligent production, power control and protection equipment life cycle data management. Through the gradual implementation of these technologies, the level of intelligent manufacturing of protection and control equipment for ubiquitous power network interconnection has been improved, hoping to promote the implementation and application of national equipment manufacturing standards in the power industry.
Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors
Authors: Li Zhe , Pei Haodong , He Liang , Liu Jiaming , Hu Jiaxin , Wang Dongji
Published: September 2020

How to construct an effective sample affinity matrix is an important problem for subspace clustering, and most existing subspace clustering algorithms pursue the affinity matrix in a single space. In this paper, we propose a novel computational framework for subspace clustering, called Complementary Subspace Clustering (CSC) at first, where the affinity matrix is constructed in a pair of complementary spaces which provide different and complementary constraints on the affinity matrix. Many existing structural priors on self representation and dimensionality reduction can be seamlessly integrated into the CSC framework. Then under this framework, we explore a simple and effective subspace clustering algorithm by respectively introducing two basic priors - sparse representation and spatial closeness - into the referred pair of spaces. Moreover, a kernel variant of the proposed clustering algorithm is present. Extensive experimental results demonstrate that although only basic priors are involved, the explored algorithms from the CSC framework can improve the clustering performances significantly when the number of the sample classes is relatively big.
Analysis on the Influencing Factors of the Reform Design of the Teaching Building of Design College Based on ISM
Authors: Xie Guanyi , Chang Xiaowen
Published: September 2020

Background The renovation design of the teaching building of Design College of South China University of Technology needs to achieve the design objectives of function replacement, space reorganization and improvement of space quality. Problem The space utilization rate of the existing teaching building is low, which cannot meet the teaching efficiency; and because the teaching building was originally used as a public classroom which is a teaching space without college characteristics. Technology This paper summarizes the influencing factors by means of literature inquiry, field investigation and interviews, and uses Interpretative Structural Model (ISM) to analyze the hierarchical relationship among the factors obtained in the investigation, so as to help people understand the logical relationship within the complex system at a more hierarchical level through this method. Target With the help of this method, the key factors affecting the renovation design of teaching buildings are summarized, and the ISM is constructed to provide guidance for an effective solution strategy. Conclusion the ISM method is used to extract the relevant design elements and form the design model of the system, which provides a useful reference for the smooth implementation of the reform project.
An Anti-collision Early Warning System for Mine Trucks Based on RBF Network and WIFI
Authors: Xiao Dong , Li Hongzong , Ji Zhiwen , Xu Enjie , Luo Bendong , Chen Jinghuang
Published: September 2020

Heavy-duty trucks in open-pit mines are of huge size and blind areas, so it is difficult for drivers to see other vehicles around the blind areas. In addition, due to the dullness of transportation in mines, drivers are prone to distraction and other phenomena, so collisions between vehicles occur from time to time. In existing technologies, such as radar and infrared ranging, it is difficult to detect vehicles on the other side of the bend at the bend, and it is vulnerable to dust and weather, resulting in false alarm. Aiming at the above problems, a collision prevention and warning scheme for heavy truck in open pit mine based on RBF network and WIFI is proposed. That is to say, a WIFI ranging module is installed in the middle of each mining truck. When the distance between the two trucks is less than the defined range, an early warning signal will be sent, indicating that there are other vehicles near the driver and paying attention to driving safety. The measurement error of WIFI ranging is easy to fluctuate in a long distance, but WIFI ranging has the advantages of long measurement distance, not easily affected by weather and dust, low cost and so on. For the system modeling, the author went to Deerni Copper Mine to collect data, such as the main test parameters: ranging, signal intensity and so on. The model between WIFI signal intensity and distance is constructed. The results show that the system has better measurement accuracy, and thus realizes the early warning function. It realizes the automatic collection and identification of wireless information when vehicles approach, which is of great significance for reducing and eliminating the collision accidents of open-pit transport vehicles and improving the level of safety management.
A Study of the Operation Parameters of a Tube Chain Conveyor Based on Discrete Element Simulation
Authors: Yao Yanping
Published: September 2020

Computer simulation design can be utilized to determine operation parameters for tube chain conveyors (TCCs), such as a reasonable material filling rate and running speed of the scraper chain; this approach can shorten the developing cycle of TCCs and reduce developing costs. This study chose three material filling rates (75%, 60%, and 50%) and the corresponding running speeds of the chain (0.37 m/s, 0.47 m/s and 0.57 m/s) as research variables. Using simulation software implementing the engineering discrete element method (EDEM), we obtained the chain tension values during material conveyance. By comparing these values with those obtained in a field experiment measurement under the aforementioned conditions, we conclude that the EDEM simulation results are consistent with the experimental measurement results. This study demonstrates that it is plausible to simulate different operation parameters of TCCs using EDEM simulation, which provides a computer design method for rationally determining the operation parameters of TCCs.
Architecture Design of Teenager Mental Health Evaluation System Based on High Concurrency and High Availability
Authors: Zhang Yongxiong , Huang Xiangzheng , Wang Liangming
Published: September 2020

In order to solve the problem of high concurrency and stability of real-time collection and analysis of teenager mental health data, this paper proposes a high concurrency and high availability system architecture based on Docker container, choreography and deployment technology and artificial fish swarm algorithm. To solve the problem of low efficiency of Kubernetes default scheduling algorithm, this paper designs a parallel scheduling algorithm using artificial fish swarm algorithm. The experimental results show that the parallel scheduling algorithm is effective. For the complexity of container redundant operation, this paper designs a microservice rank scheme. Combined with AFS-FCM algorithm, the microservice rank of the system platform is determined, so that the redundant equipment can be automatically optimized for high availability.
Design of Photovoltaic Power Generation System Based on Single Chip Microcomputer
Authors: Chen Gang , Chen Guangzhou , Chen Dongsheng
Published: September 2020

This paper describes the design of photovoltaic power generation system based on SCM (single chip microcomputer). This system adopts the SCM with photoresistor sensor as the detective devices. By using the CSM with PID and the dual-axis servo, it can achieve the aim of automatic sun tracking, so that the solar panel will face sunlight at any time. Finally, the voltage data is shown to evaluate the proposed system by LabVIEW software, which ensured the measurement accuracy. What’s more, this system uses solar energy as the energy, which is economical and environment friendly. It accords with the human’s needs of environmental protection.
Shared Massage Chair Application in the Context of IoT: Take “Lemobar” as an Example
Authors: Jiang Qingquan , Lin Qiaozhen , Zhang Rui , Huang Honggang
Published: September 2020

The increase in productivity brought about by every technological change in history will also profoundly affect and change people’s lifestyles and habits. The increasing maturity and wide application of the Internet of Things technology has brought unlimited possibilities for business. The further development of artificial intelligence will make big data integrated through the Internet of Things and create greater value. The “sharing economy” has deeply affected people’s lives in recent years and been widely reported and triggered by many scholars’ research. This paper explores the shared massage chair that is rarely studied from a technical point of view, compared to the popular shared bicycle, shared power bank, and takes Lemobar as an example, which is so far the benchmarking in the shared massage industry. This paper innitially concludes the development process of shared massage that is changing with technological progress, and furthermore analyzes the shared massage application including software & hardware support, operation management, system integration and different kind of scenarios combination. The application of Lemobar is a remarkable case of human and machine interaction in the context of Internet of Things and big data.