bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graphic Development Environment in Interactive Computer Interior Design
Authors: Yang Yanan , Zou Na 
Published: December 2020

Abstract
In the process of interactive design generally through the interior design graphics to achieve customer needs, interactive design is a new type of green design system, at present, has been widely used in computer web production, industrial product design, furniture manufacturing and other fields. With the continuous development of computer technology, the human-computer interaction problem is gradually reflected. In order to effectively solve this problem, we add the concept of interaction design into the field of architectural design. The interactive design is combined with the characteristics of architectural design to analyze the architectural design. In this paper, the composition, technology platform and operation mode of the interactive design system are analyzed and studied, and the residential building technology is discussed from these three aspects. In addition, it is applied to the actual case, through the use of digital design, numerical control processing simulation experiment, image experiment and other scientific research methods, the residential building space environment design experimental study. This will also lead to reference standards for green interior design of public and industrial buildings.
Research on Crane Sound Clustering of MFCC Based on HHT
Authors: Fu Qinjian , Lv Danjue , Zhang Yan , Xu Haifeng , Yao Wang , Zi Jiali , Liu Jiang 
Published: December 2020

Abstract
Due to the uniqueness of the sound mechanism of birds, they have typical non-stationary and nonlinear characteristics. This paper proposed a new acousic feature, HHT-MFCC, combined the HHT transformation and MFCC method, aiming at the dynamic instantaneousness of bird sounds. This method, firstly, uses the ensemble empirical mode decomposition EEMD to decompose the bird sounds into a number of intrinsic modal functions IMFs, and then adopts the Hilbert transform to obtain the Hilbert marginal spectrum of each IMF, at last applies the mel-scale filter to complete the feature extraction of HHT-MFCC. The experiment extracts HHT-MFCC from 9 kinds of cranes in China to cluster. Three indexes of cluster evaluation are used to evaluate the feautre HHT-MFCC and MFCC. The results show that the HHT-MFCC feature is 10% higher in RI index than MFCC, 9% higher in JC index, and 4% higher in FMI index.
Development status, problems and solutions of speech recognition technology
Authors: Zhang Shuang 
Published: December 2020

Abstract
The purpose of automatic speech recognition technology is to enable the machine to “understand” human voice, and convert human voice into text information, which is the key technology to realize human-computer interaction. This paper briefly introduces the development of speech recognition technology and the acoustic model involved, analyzes the problems of noise interference in the environment, the low recognition rate of dialect speech, and the need to improve the speech recognition effect in the far-field environment, and puts forward solutions such as optimizing the acoustic model, building dialect corpus, accurately modeling the sound transmission environment, the future development is prospected.
Hierarchy-NMS: Merging Candidate Bounding Boxes for Cerebrospinal Fluid Cell Image Segmentation
Authors: Xu Xianwei , Li Fangqi , Wang Shilin , Wang Zhenhai 
Published: December 2020

Abstract
In this article, Hierarchy-Non Maximum Suppression (H-NMS) is proposed to fix the defects of general NMS algorithms in cerebrospinal fluid cell image segmentation. To cope with the confusion caused by the hierarchy of candidate bounding boxes in this scenario, a tree structure is built on the top of all bounding boxes and only leaf nodes are saved as the final output. The experimental results showed that H-NMS outperforms some variants of NMS in the cerebrospinal fluid cell image processing in both image segmentation and cell counting.
SDNet: A Novel Separable Structure for Single Image Deraining
Authors: Chen Xuheng 
Published: December 2020

Abstract
Rain streaks degrade visual quality of images and severely affect the performance of computer vision system. Recently, deep convolutional neural networks (DCNNs) have shown the potential in single image deraining. However, their complex structures with large-scale of parameters lead to strong requirement of large storage, which still limits the applications of deraining systems. In this paper, we propose a separable deraining network (SDNet) which provides a more flexible and simply baseline of deraining network by using a cascaded framework to achieve automatic trade-off between model performance and computational resources. Besides, a novel module called group-atrous spatial pyramid (G-ASP) and a network separation strategy (NSS) are proposed to extract more discriminative multi-scale information with fewer parameters and adapts our model to different levels rain-streaks for reasonable allocation of computational resources automatically. The experimental results on public datasets demonstrate that our SDNet achieves promising performances with much fewer parameters than other methods.
Cloud edge cooperative attack recognition based on CNN
Authors: Ran Qingwen , Ju Xiaoming , Zhang Xibo , Zhang Yan 
Published: December 2020

Abstract
In recent years, the concept of cloud edge collaboration has been proposed, which uses some operations of edge and cloud to complete all kinds of collaboration, and has produced various applications in various fields. CNN is one of the hot spots of scientific research, and has excellent performance in the field of image voice. In view of these advantages, combined with the cooperation of CNN and cloud edge, this paper proposes cloud edge collaborative attack recognition based on CNN. The attack recognition model is trained by using CNN and open source data set, and cloud edge collaboration is realized by data and model transmission between edge and cloud. This method realizes the security collaboration between the cloud and the edge, and reduces the resource pressure of the cloud. At the same time, the cooperation of multiple edge ends increases the security identification ability of the cloud and improves the security coordination between the cloud and the edge.
An exploration of a heart rate sensing garment solution based on rPPG technology
Authors: Liu Meng , Wang Chuanjiang , Wang Ningning , Sun Xiujuan 
Published: December 2020

Abstract
In order to solve the problem of heart rate detection during exercise, this article proposes a solution that combines ordinary clothing with non-contact heart rate measurement technology. The scheme integrates the camera into the shoulder of the jacket, which enables the camera to monitor the neck position in real time and convert the detected neck video image into the corresponding heart rate by the technique of optimized rPPG. In this regard, some experiments designed to verify the accuracy of heart rate detection in resting and exercise states, respectively. The experimental results were compared with a commercially available heart rate oximeter, and the comparative results verified the feasibility of the study.
Research on an improved algorithm for image dehazing in underground coal mine
Authors: Zhang Xiaoyan , Guo Haitao 
Published: December 2020

Abstract
Coal mine images have low illuminance, large dust and high noise intensity, etc. It brings great obstacle to extract effective image information. This paper proposes an image haze removal algorithm for coal mine underground based on dark channel prior. First, histogram equalization method is introduced to enhance the underground image, obtain the dark channel image from the thumbnail of the image, and then restore the pixel value of the image through bilinear interpolation, for shortening the acquisition time of the dark channel image and increasing the speed of the algorithm. Results demonstrate this method greatly reduces the amount of calculation and improves the processing speed of the haze removal algorithm while ensuring the haze removal effect.
A Dynamic Neural Network for Solving Time-varying Shortest Path with Hop-constraint
Authors: Xu Zhilei , Huang Wei , Wang Jinsong 
Published: December 2020

Abstract
This paper proposes a dynamic neural network (DNN) to solve the time-varying shortest path problem with hop-constraint (HC-TSPP). The purpose of HC-TSPP is to find a path with the shortest transmission time and the restricted number of arcs. The proposed DNN is a novel neural network based on dynamic neurons. All neurons on DNN are computing in parallel, and each dynamic neuron is composed of seven parts: input, wave receiver, filter, status memorizer, wave generator, wave sender, and output. Wave is the carrier of neuron communication, and each wave is composed of three parts. The shortest path report is based on the first wave that reaches the destination node and satisfies the hop constraint. The example and experimental results based on the Internet dataset show that the proposed algorithm can arrive at the global optimal solution and outperform the existing algorithm (viz. Dijkstra algorithm).
Recognition method of dairy cow feeding behavior based on convolutional neural network
Authors: Chen Zhefu , Cheng Xiaodong , Wang Xi , Han Mingshu 
Published: December 2020

Abstract
With the continuous development of the Internet of things and artificial intelligence, smart animal husbandry has gained popularity in recent years. It can efficiently solve the labor cost and capital cost, and reduce the loss of pasture and herders. This paper proposes a method based on convolutional neural network to identify the feeding behavior of dairy cows, by detecting the feeding behavior of dairy cows, judging their health status, estimating feed intake, and estimating milk production according to a proportional coefficient. First, this article need go to the ranch to collect activity data and manually calibrate the cows; second, use the sliding window method to upgrade the one-dimensional activity data; finally, feed the two-dimensional data into the CNN, continuously adjust the parameters and lengthen data width, the recognition accuracy rate can reach 89.5%, realizing the recognition of the feeding behavior of dairy cows. The results show that the CNN-based dairy cow feeding behavior recognition method designed and implemented in this paper can realize efficient automatic recognition and has high accuracy.
An Overview of the Attention Mechanisms in Computer Vision
Authors: Yang Xiao 
Published: December 2020

Abstract
Deep convolutional neural network (CNN) plays an important role in the field of computer vision and image processing. In order to further improve the performance of CNN, scholars have conducted a series of new explorations, such as the improvement of activation functions, the construction of new loss functions, the regularization of parameters and the development of new network structures. However, every breakthrough of CNN comes from the innovation of network structure, whose design can be inspired by exploring the cognitive process of human brain. As one of the important features of human visual system, visual attention mechanism is essential in image generation, scene classification, target detection and tracking when applied in the field of computer vision. Focusing on the models of attention mechanisms commonly used in computer vision, their categorizations, principles, and outlook are summarized in this overview.
Modeling and Simulation Research of a Novel Artificial Skin Sensor
Authors: Wang Feilu , Hu Yong , Song Yang , Chen Yufeng , Ye Ruigen 
Published: December 2020

Abstract
In this paper, a novel artificial skin sensor model with special structure based on the flexibility of Polydimethylsiloxane (PDMS) and the piezoresistive effect of Poly Vinylidene Fluoride (PVDF) piezoelectric fiber is proposed to solve the problems of structure design and complicated manufacturing process of skin sensor sensitive units. It has excellent piezoelectric effect and can detect the external force efficiently and precisely. The finite element analysis technology is used to simulate the deformation of the novel artificial skin sensor under external force from different aspects. Firstly, the crossing-mesh sensitive unit model and the three-layer configuration with three-layer of the artificial skin sensor are designed. Secondly, the external forces are applied to the sensitive units with different diameters by finite element analysis, and the sensitive unit with optimum diameter is figured out for the novel artificial skin sensor. After that, with the optimum sensitive unit, the performance of the artificial skin sensor model with the optimum sensitive unit is specifically simulated when it’s subjected to different stress. Finally, through seriously analyzing the experiment results, the mapping relationship between the deformation of the artificial skin sensor and the external force is obtained. Experiment results demonstrate that the novel artificial skin sensor has high sensitivity, not only can truly reflect the approximate linear relationship between sensor deformation and external load, but also can accurately detect the magnitude of external force applied to the sensor.
Research on Motion Attitude Control of Under-actuated Autonomous Underwater Vehicle Based on Deep Reinforcement Learning
Authors: Jiang Jie , Zhang Runqiang , Fang Yuan , Wang Xinlong 
Published: December 2020

Abstract
This paper adopts Deep Deterministic Policy Gradient (DDPG) algorithm in Deep Reinforcement Learning (DRL) to analyse the eca_a9 Autonomous Underwater Vehicle (AUV) motion attitude control based on Robot Operating System (ROS) and Gazebo simulation platform, within UUV Simulator underwater simulation environment. The heel angle ϕ, pitch angle θ heading angle φ are chosen as the agent state input and control variables, and the output angle of the four rudders is selected as the agent action output. The problem of strong coupling caused by X-type rudder and multi-degree-of-freedom control is solved with reinforcement learning and training. In addition, this paper proposes a multi-state space and multi-action space control scheme, which has achieved remarkable results for the AUV’s fixed speed, constant heel, constant pitch, and constant heading motion control.
Study on Deduction Method of Volume Scattering Noise of the Laser Scattering Image on the Ultra-smooth Optical Substrate Surface
Authors: Chen Shiyi , Zhou Jian , Zhang Bin 
Published: December 2020

Abstract
In order to better observe the defects in the laser scattering image on the surface of the ultra-smooth optical substrate, the volume scattering noise needs to be deducted. This paper attempts to use three methods, which are wavelet denoising, wavelet enhancement and gradient model construction, and compares the advantages and disadvantages of the three methods.
Research on reliability data mining technology of electronic components
Authors: Yaru Cui , Kui Zhang , Huaguang Liu , Shengbiao An , Jie Huang 
Published: December 2020

Abstract
This paper deeply analyzed the types of information that can be mined in component reliability data. According to specific needs, the clustering measurement method of reliability data sample similarity is studied, the distance measurement strategy is obtained, the algorithm steps and analysis methods of Apriori association rule data mining are improved, the reliability data prediction and analysis methods based on statistical methods are compared. AR prediction model, MA model and other component reliability information time prediction methods have formed data mining software such as clustering, correlation analysis and prediction, discovered a large amount of hidden reliability information, and realized the comprehensive mining and utilization of electronic component reliability data.
Optimization of Decision Tree Machine Learning Strategy in Data Analysis
Authors: Zhifang Sun , Yi Li 
Published: December 2020

Abstract
Aiming at the problem of too coarse matching of machine learning decision tree models in the field of data mining and low prediction accuracy, the corresponding improved optimization strategies are proposed. First, the field matching degree of data is further improved by discretizing continuous attributes in multiple intervals. Then, the method makes the selection of business attributes more reasonable in the downward splitting process of the model by compensating the weight of feature attributes by business sensitivity indicators. Finally, the data classification rule transformation is used to further improve the data prediction accuracy of the model. The experimental results show that the introduction of the tree model generated by the business sensitivity index is more concise. In addition, the business pertinence and data classification capabilities are stronger. The results show that the transformed and upgraded data classification rules can effectively improve the accuracy of data prediction compared with the traditional optimization algorithm.
Calculation of Fundamental Frequency Amplitude of Transformer Surface Vibration Based on ABC-ELM
Authors: Lu Yu , Wang Xin , Li Zhong 
Published: December 2020

Abstract

The fundamental frequency amplitude of transformer surface vibration is an important indicator for analyzing and diagnosing transformer faults. This article proposed a model for calculating the fundamental frequency amplitude of transformer surface vibration (ABC-ELM) based on Artificial Bee Colony (ABC) optimized Extreme Learning Machine (ELM). Considering the influence factors of transformer vibration, the ELM model was constructed by using the operating voltage, load current and oil temperature as input vectors, and the fundamental frequency amplitude of the transformer surface vibration as output vectors. The data measured by transformer was used for experiments, and the weights and hidden layer biases of the ELM input layer were optimized using ABC. Experimental results showed that ABC-ELM had higher calculation accuracy and smaller error fluctuations than ELM and BP neural networks, which proves the effectiveness of ABC-ELM model in calculating the fundamental frequency amplitude of transformer surface vibration.
Energetic positron propagation from pulsars: an analytical two-zone diffusion model
Authors: Osipov S M , Bykov A M , Petrov A E , Romansky V I 
Published: December 2020

Abstract
We study the cosmic rays (CR) positrons propagation from the near Earth Geminga pulsar wind nebula on the basis of a analytical model of the two-zone spherically symmetric particle diffusion from a central source. We calculate the near Earth spectral distribution of positrons originating from the pulsar. The obtained spectra are compared with the results of another authors considering the problem of the positron excess revealed by PAMELA and AMS-02 experiments.
The measurements of the CMB temperature in diffuse interstellar medium of the Milky-Way and high redshift galaxies based on excitation of CI fine-structure and H2 rotational levels
Authors: Klimenko V V , Ivanchik A V 
Published: December 2020

Abstract
Evolution of the cosmic microwave background (CMB) temperature with redshift

T

CMB

=

T

CMB

0

×
(
1
+
z
)

is predicted by the standard ΛCDM cosmological model and has been confirmed by measurements of the Sunyaev-Zel’dovich effect in Plank data (at z ≤ 1) and excitation of CO rotational levels in quasar spectra (at 1.7 ≤ z ≤ 2.7). Excitation of the fine-structure levels of neutral carbon (CI) is also sensitive to the temperature of the CMB radiation. However collisions and UV pumping lead to a significant degeneracy of the fitting parameters, since poor data on physical conditions. We found that a joint fit to excitation of low H2 rotational and CI fine-structure levels can break this degeneracy and provide a tighter constraints on the T
CMB. We present estimates of the T
CMB derived from excitation of CI fine-structure levels in the Milky Way clouds and high redshift absorption systems.
Signature of geomagnetic superstorm in Earth’s rotation speed
Authors: Trofimov D A , Petrov S D , Movsesyan P V 
Published: December 2020

Abstract
A connection between solar activity and decadal Length of day variations is investigated. Statistical comparison of Length of day with various geomagnetic and Solar activity indices has been done for the last 140 years. A jump in Length of day (LOD) is found at the moment of the geomagnetic storm, the largest one for the last few decades, that happened in late October 2003, the so called Halloween storm. This change in rotational velocity of the Earth can not be explained by any known atmospheric or oceanic influences. Moreover, such jump in LOD is an unique one for the few recent years. It is supposed that this jump can be caused by transfer of angular momentum between Solar wind and the solid Earth. A possible mechanism of such a transfer is proposed.
On stability of toroidal structures in two-tori pulsar wind nebulae
Authors: Ponomaryov G A , Levenfish K P , Petrov A E , Kropotina Yu A 
Published: December 2020

Abstract
The effect of weakly supersonic flow on two-tori pulsar wind nebula is considered. It is shown that the flow going past the nebula does not destroy its two-tori structure, but, on the contrary, contributes to its stability. Under the effect of the flow, the windward and the leeward polar outflows in the nebula operate at drastically different conditions. In the result, two opposite jets of the nebula which form within these polar outflows may differ in their dynamics and appearance. Our work bears implications for the Vela pulsar wind nebula, which interacts with a supersonic flow of Mach number ∼ 1.3 produced by the reverse shock of its parent supernova.
Diffusion as a damping mechanism for neutron-star oscillations
Authors: Kraav Kirill , Gusakov Mikhail 
Published: December 2020

Abstract
We study the effects of diffusion on damping of oscillations in the neutron star cores. This dissipation mechanism is usually ignored in the literature. As we show, the effect of diffusion is always smaller than viscous dissipation if the normal (nonsuperfluid and nonsuperconducting) matter of neutron stars is considered. However, we argue that for superconducting stars the role of diffusion may increase.
Subsecond pulsations in hard X-rays of August 19, 1998 flare according to BATSE/CGRO data
Authors: Shuvalova V I , Sklyarova E M , Charikov Yu E , Shabalin A N 
Published: December 2020

Abstract
Detection of ms-spikes and quasi-periodic subsecond pulsations in the solar flare hard X-rays (HXR) provides useful information about the processes of electron acceleration and transport along the flare magnetic loops. To search for HXR pulsations, we processed the data of BATSE/CGRO spectrometer of high temporal resolution 0.016 s and 0.064 s. BATSE energy range is about 20 keV – 1 MeV. For the powerful solar flare SOL1998:08:19T21:39:21 the FFT and wavelet analysis show the presence of HXR pulsations of ∼ 0.16 – 0.25 s. BATSE data was analyzed for six energy channels 28.8 – 33.1 keV, 37.1 – 41.8 keV, 46.6 – 55.8 keV, 55.8 – 64.6 keV, 64.6 – 74.0 keV, and 74.0 – 99.1 keV. HXR-spikes were detected. The spike shape is practically triangular with FWHM about 100 ms. Simulations of kinetic models of electron transport with short pulses showed that when the duration of an individual spike is longer than ∼ 80 ms, the smearing of pulses practically does not occur. Injection pulses shorter than 30 – 60 ms are not preserved even with a strongly anisotropic distribution of accelerated electrons and a small gradient of the magnetic field. In the isotropic case at the looptop, the decay phase of the X-ray spikes becomes longer compared to the rising phase which is not true for the anisotropic case. The pulse smearing effect is stronger for the softer energy spectra of accelerated electrons.