bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic Modeling of Protein Field with a Delayed Feedback
Authors: Bratsun D A , Buzmakov M D 
Published: June 2021

Abstract
Protein fields synthesized by genes play a principal role in the functioning of living systems. The processes of gene regulation determine the properties of these fields. Since the number of nucleotides usually is not large, a deterministic description of the field dynamics is insufficient. In this work, we consider a special kind of protein field, the dynamic behavior of which is described by the non-Markov process. Generally, the dynamics of complex organic compounds is time-dependent and spatially extended, and it may depend on all the previous evolution of the system. We consider a time-delayed repressilator as a model system. We study this system numerically using a modified Gillespie algorithm. New dynamic phenomena, which are visible only within a stochastic description, are reported. We show that synchronization in a gene expression occurs much faster due to the non-linear interaction of noise and delay. It results in almost regular oscillations even below the neutral curve derived within the deterministic analysis. We apply a hybrid approach to study the spatial dynamics of the repressilator proteins. This approach includes a deterministic calculation of the diffusion fluxes between cells and the stochastic simulation of gene regulation processes. We found that the combined action of time-delay, noise, and spatial signaling can lead to pattern formation even when the deterministic description predicts the absolute stability of the system.
On the Origin of Geostrophic Liquid Cylinders in Rotating Sphere with Oscillating Inner Core
Authors: Subbotin S 
Published: June 2021

Abstract
The effect of non-axisymmetric inertial waves on the zonal flow structure in a rotating spherical shell is studied experimentally. The wave source is the inner core, which performs circular oscillations in the equatorial plane. The case of positive frequencies that corresponds to the advanced core motion is considered. As a result of non-linear effects, a quasi-two-dimensional axisymmetric steady flow with a complex distribution of angular velocity is generated. The extrema in the velocity profile are equivalent to the nested liquid geostrophic cylinders, which appearance is due to the interaction of the inertial waves within a viscous boundary layer. One of the extrema is associated with the critical latitude at the core boundary, where the inertial waves are excited. With an increase in the oscillation frequency, the position of this extremum gradually shifts to the rotation axis. Additional geostrophic circulation occurs close to the points where the inertial wave reflects from the cavity boundary.
Plasma Treatment of Vascular Implant
Authors: Chudinov V S , Shardakov I N , Litvinov V V , Galkin S V , Kondyurin A V 
Published: June 2021

Abstract
This paper examines a carbon layer on a blood vessel implant. The carbon layer is created by high energy ions bombarding an inner surface of the polyurethane implant. An analysis of the molecular structure of the layer was carried out, demonstrating the appearance of free radicals stabilized by aromatic clusters, as well as new carbon-carbon, oxygen-containing and nitrogen-containing groups in the inner surface of the blood vessel implant. The covalent adsorption of the protein on the carbon layer inside the implant has been proven. The antithrombogenicity of the carbon layer with a covalently bound protein on polyurethane implant of blood vessels in an experiment on rabbits has been shown.
A monocular vision positioning method based on topology pattern of artificial landmark
Authors: Gu Yang , Zhong Weibo , Chen Ruiyu 
Published: June 2021

Abstract
Image-based positioning accuracy is usually affected by image rotation and/or distortion due to the changes of the sensor orientation. In this paper, the artificial landmark is composed of color block with different shapes, and its topology pattern is composed of the number of the block shapes, colors, shape and color of each block and topological relation among the blocks, which are robust for the artificial landmark rotation and/or tilting. The positioning is based on artificial landmark pattern matching and the corresponding relation between artificial landmark and its position. In our experiments, 196 different artificial landmarks are layered on the 3 × 3M plane uniformly. There are four blocks in an artificial landmark and each block has six different shapes and four different colors. The experiment results show that the attributions of the block were calculated accurately, the topology pattern construction and recognition method for the artificial landmark are feasible, reliable and can be used in monocular vision positioning.
Research on Image Super-resolution Technology Based on Sparse Constraint SegNet Network
Authors: Lin Ruikai , Wang Liyuan , Xia Tao 
Published: June 2021

Abstract
Aiming at the problems of long time-consuming and low accuracy in extracting buildings with traditional machine learning methods. In this paper, the SegNet semantic segmentation models on deep learning is used to improve the algorithm, and a high-resolution remote sensing image building extraction algorithm based on sparse constrained SegNet are proposed. First, regular terms and Dropout are added to the SegNet model, which greatly reduces the occurrence of model over-fitting; secondly, in order to extract richer semantic features for the model, the algorithm introduces a pyramid pooling module; finally, the Lorentz function sparse constraint factor is introduced to the SPNet model, Construct a new semantic segmentation model LSPNet. In order to verify the reliability and applicability of the proposed algorithm, the optimized LSPNet model is used to identify and extract the buildings in the high-resolution data set. Experimental results show that compared with traditional machine learning methods, this method has the advantages of fast convergence and high accuracy, and has a good application prospect.
High-Precision Indoor Robot Dynamic Obstacle Detection with Laser and Camera
Authors: Hu Chuanqi 
Published: June 2021

Abstract
Indoor robot is a very common robot, and its main navigation method is laser SLAM. In the process of detecting dynamic obstacles with laser SLAM, the clustering effect is not obvious, the accuracy is low, and the real-time performance is poor. To solve this problem, a sensor fusion method of lidar and monocular camera is proposed, which improves the accuracy of detecting the position of moving obstacles. When processing lidar data, first preprocess the data, then use the BDSCAN algorithm to cluster the point cloud, and finally use the Kalman filter to predict and calculate the location of the moving obstacle. When processing the data of the monocular camera, the YOLOv3 algorithm is used to quickly detect the target and return the position of the monocular camera. After obtaining the camera and laser data, according to the installation position of the radar and camera, map the position calculated by the radar to the coordinates of the camera. If the coordinate difference after mapping is small, it can be considered that the two sensors have detected the same target. The result is returned to the display interface. Using this method, the efficiency and accuracy of indoor robots in detecting moving obstacles have been greatly improved.
Research on Computer Image Processing Technology in Navigation and Navigation
Authors: Ke Jinding 
Published: June 2021

Abstract
In view of the existing navigation systems used on ships, the Baidoo satellite signal access recognition rate is low, and the target recognition accuracy is poor. The paper proposes a computer image recognition method for the positioning target of the ship navigation system based on the Baidoo satellite. In view of the large resource consumption and low calculation rate of the traditional method in the calculation process of the Baidoo satellite signal, the compressed sensing image processing algorithm of single-layer wavelet transform is introduced to perform directional exclusive calculation on the Baidoo satellite signal. The research in the thesis found that the Baidoo satellite ship navigation system positioning target accurate recognition method based on the single-layer wavelet transform-based compressed sensing image processing algorithm can quickly, effectively and accurately lock and identify the set target in the Baidoo satellite navigation.
Mathematical Analysis of the Inhibitory Effects of Ten Kinds of Fungicides Applied on Apple
Authors: Liu Wenyu , Han Lin , Chi Wenjuan 
Published: June 2021

Abstract
Apple ring rot is one of the three major diseases of apple, which seriously affects the yield and quality of apple. At present, chemical agents are mainly used to prevent and control apple ring rot. In this study, 10 kinds of low virulence fungicides were selected. The inhibitory effects of 10 kinds of fungicides on Botryosphaeria dothidea were determined. The results indicated that 50% carbendazim and 70% thiophanate methyl had the best mycelial growth inhibition effect among 10 kinds of fungicides. The inhibitory effect of 50% carbendazim on hyphae was 91.63% under the concentration of 400 times dilution, the inhibitory effect of 70% thiophanate methyl on hyphae was 90.31% under the concentration of 500 times dilution, and there were recommended to use.
Research on High Resolution Optical Remote Sensing Image Technology Based on Internet Architecture
Authors: Xiao Gengyi 
Published: June 2021

Abstract
Target detection technology in high-resolution optical remote sensing images is of great significance in both civil and military fields. Based on the Internet architecture, this paper proposes a multi-class target detection method for high-resolution and high-resolution optical remote sensing images. The fused multi-layer features are used for detection. In view of the challenges of complex background and deformation of the target, the deformable convolution network can be used for reference to extract the characteristics of the target itself and reduce the background interference In order to reduce the storage space required by the deep convolution neural network model and increase the portability of the network, I also put forward a method of lightening the network. Experiments show that our proposed method is feasible. Compared with the popular target detection method based on deep convolution neural network, our method has great advantages in precision and recall rate.
Study on cyclic voltammetric electrochemical fingerprint method for origin traceability of rice
Authors: Sha Min , Tang Zhilong , Zhang Ding , Zhang Zhengyong , Liu Jun 
Published: June 2021

Abstract
The origin traceability of geographical indication rice has always been highly concerned, this paper focused on the perspective of non-equilibrium samples, and the feasibility of using the cyclic voltammetric electrochemical fingerprint of rice to identify the origin of rice was studied. Jiansanjiang, Fangzheng and Xiangshui rice, whose production places were close, were selected, and the fingerprint collection method and data preprocessing method were explored respectively. Fingerprints with rich information, significant features were obtained by fast detection. Principal component analysis combined with support vector machine analysis were used to establish the discrimination model, the recognition accuracy of the prediction set was over 90.0%, showing a good application prospect. The electrochemical fingerprint of the non-equilibrium sample measured by real-time tracking of the reaction process could not only reflect the differences in the types and contents of the sample components, but also show the differences in the chemical properties of the components, providing a new idea for easy and accurate identification of food.
Image de-blurring Model based on Machine Learning
Authors: Li Junsheng , Xu Silu , Wang Cong 
Published: June 2021

Abstract
Due to the influence of haze in winter, outdoor images usually lose contrast and fidelity. In view of the fact that most de-fog algorithms are not effective for images with large sky areas, an improved dark channel a priori de-fog method is proposed. First of all, the sky region is segmented according to the image gradient information, and on the basis of sky segmentation, the atmospheric light value is reasonably estimated by setting the discriminant formula combined with the high brightness and smoothness of atmospheric light reference pixels. Secondly, according to the different dark channel values, the piecewise linear function is used to dynamically modify the adjustable parameters to solve the local shadow caused by excessive defog. Then, the transmittance estimated by the bright channel model and the improved dark channel prior model are fused, and the edge is optimized by guided filtering. Finally, combined with the atmospheric scattering model, the defog image is obtained by brightness compensation and contrast stretching. The experimental results show that the improved method can effectively improve the image distortion, enhance the image contrast and details, especially in maintaining the visual authenticity of the sky region.
Detection of Fruit Surface Defects Based on Machine Vision
Authors: Cao Yayue 
Published: June 2021

Abstract
With the continuous improvement of social level, the perfection of fresh fruit surface has become a potential requirement for people to purchase. Aiming at the problems of high intensity and low efficiency in manual inspection, a method combining computer machine vision technology with MATLAB application was designed to detect the surface defects of fruits. Experiments show that this method has advantages in speed and precision in detecting fruit surface, and has certain application value.
Optimization of Drone Quantity Based on Empirical Analysis in Bushfire Extinguishment
Authors: Zhou Zilong , Lin Fu , Gong Xinyi 
Published: June 2021

Abstract
With the deterioration in the environment, extreme weather is more likely to occur. The wildfire is becoming severer in turns of frequency and magnitude, causing disastrous damage to our fragile ecosystem. In this paper, we purpose a model to identify the optimal number of drones in case of any potential threat posed by bushfire in Victoria, Australia. In this paper, we use satellite data from space agencies such as NASA and the European Space Agency to determine the initial conditions and parameters of the model. including land cover, topography, traffic development, potential reinforcements nearby, distance to water and historical wildfires. We extract the data of several modes and determine the coefficients that affect the forest-mass. According to our simulation results, the optimal expected number of SSA UAV and relay UAV is 53 and 85 respectively.
Survey of Target Detection Based on Neural Network
Authors: Deng Bao , Lv Hao 
Published: June 2021

Abstract
Target detection is a hot topic in the field of artificial intelligence, which is widely used in robot, UAV, aerospace and other fields. In this paper, the research background and significance of target detection are summarized, and two categories of target detection algorithms based on deep learning, i.e., candidate region based and regression based, are described. For the first category, a series of region with convolutional neural network (r-cnn) algorithms are introduced, this paper introduces the researchers’ research on the basis of r-cnn algorithm: the improvement of feature extraction network, pooling layer of region of interest and non-maximum suppression algorithm. The second algorithm is divided into Yolo (you only look once) series, SSD (single shot multibox detector) algorithm and its improvement. According to the current target detection algorithm in the development of more efficient and reasonable development trend, the research hotspot of target detection algorithm in the future is prospected, including unsupervised and unknown class object detection.
Online Machine Learning System of Power Quality Information Technology Based on Big Data
Authors: Wang Wei 
Published: June 2021

Abstract
Power quality monitoring has the characteristics of large amount of collected data and high collection frequency. Long-term monitoring will form power quality big data, while conventional data storage and processing methods are difficult to effectively use big data and explore the value of big data. This paper proposes an online machine learning recognition and classification method for power quality disturbances based on inherent modal function singular value decomposition and least square support vector machines. Aiming at the characteristics of power quality data in distribution networks, this thesis proposes a power quality big data storage scheme based on Mongo DB + Redis, designs a power quality big data processing process, and builds an online machine learning system based on the Apache Spark power quality big data calculation framework.
The Method of Recognizing Bank Card Number Based on MATLAB
Authors: Wang Di 
Published: June 2021

Abstract
In this paper, a quick recognition method of bank card number based on MATLAB is designed, which can identify bank card number automatically. Through the gray processing, edge cutting processing, card number positioning processing, template matching card number recognition and other processing of the bank card image, an accurate and clear bank card number is obtained, which has a certain use value in e-commerce and the Internet. it also provides some reference for image processing and character recognition.
A Research of Vespa Mandarinia through Visualization Technology and Convolution Neural Network
Authors: Duan Jionghui , Cheng Junwei , Cheng Yitai 
Published: June 2021

Abstract
After being discovered in September 2019, Vespa mandarinia, a fierce alien invader, quickly got public’s attention. The inhabitants of Washington State flooded to report the suspected cases around them. However, most of them were proved to be false information. How to interpret the provided data and determine the priority of dealing with the problem is of vital importance. Based on this situation, this paper develops a model that robustly and accurately predicts the spread of Vespa mandarinia and the facticity of provided reports. By adopting the idea of mining common behavior patterns, this paper took full use of other wasps’ data to make up for our lacking data on Vespa mandarinia. Though few samples of Vespa mandarinia been provided, this paper took complete utilizing of other data. To evaluate our external model, this paper gave 2019 data of the Vespa mandarinia to propagate then the prediction of 2020 turned to be perfectly accurate. After this, this paper combined the image, location and time information together to predict the sample. This paper got 87.6% accuracy on positive samples and 98.7% accuracy on negative samples. The calculated renewal frequency was proved to be fit for Vespa madarinia’s actual living habits. And the MSE of our enhanced Logistic function was 10–4, which can simulate the growth of Vespa mandarinia well and also taken human eradication into consideration.
Research on Vision Perception Technology of Auto Fueling Robot on opencv-based gas tank cap recognition
Authors: Guo Li , Liu Kun , Zhang Liuli , Zhang Yuyi , Tian Yuqi , Zhang Jindong 
Published: June 2021

Abstract
Designing a suitable algorithm to recognize the fuel tank cap and perform automatic refueling by manipulating a robotic arm. In exploring a new recognition algorithm for automatic vehicle refueling technology, for motor vehicle fuel tank lid, through the difference of images taken by binocular camera, combined with the basic calculation principles such as the imaging principle of binocular camera and the conversion relationship between coordinate systems, the recognition of ROI area is carried out automatically, then a closed-loop system is formed for real-time tracking, and finally the accurate positioning of fuel tank lid position is completed. The matching recognition and tracking technology of the target combines the comprehensive discipline research of mathematical analysis, image processing, object kinematics, artificial intelligence, automation control and other related fields, and has a very important status and research value in military aerospace, industrial production, intelligent medical care, security monitoring, intelligent driving, human-computer interaction and other fields.
A review of commercial vehicle cab NVH research
Authors: Su Jintao , Lou Jianping , Jiang Xiaolu 
Published: June 2021

Abstract
With the development of social economy, people’s demands for vehicle functions are constantly changing. The comfort, intelligence, electric and energy saving of vehicles have gradually become the core technology of modern vehicle design. NVH (Noise Vibration Harness, NVH) performance, as the core index of driving comfort, is gradually paid attention to by automobile design manufacturers and users. How to reduce the interior noise and improve the interior sound quality has always been the focus of research in the field of vehicle NVH. This paper summarizes the research status of commercial vehicle NVH.
Research on Autonomous Grasping of Target Based on Machine Vision
Authors: Lu Chengya , Zeng Haisen , Xiong Yongchuan , Lang Wenjiang , Wang Kaiping 
Published: June 2021

Abstract
In order to improve the work efficiency and performance of the robotic arm in the product sorting process, a robot autonomous grasping control method based on visual recognition feedback control is proposed. Aiming at the limitations of traditional robot teaching control or grasping object recognition based on a single feature, an improved template matching target identification method based on contour Hu moment feature for contour matching combined with color and shape features is proposed. Through carrying out autonomous grasping experiments and analysis, the experimental results show that the visual recognition feedback control method effectively improves the accuracy of robot grasping.
Systematic Research on the Heat Dissipation Channel of High Power LED Street Lamps
Authors: Wang Zhao , Bao Qijun , Li Yuefeng , Zou Jun , Zheng Hao , Yang Jiexiang , Su Xiaofeng , Wang Wei 
Published: June 2021

AbstractThis article mainly studies the thermal characteristics of light source of the street lamp, and systematically analyzes the effective ways of heat transfer and heat dissipation of street lamp. Based on experiments and finite element simulations, the heat dissipation channels of street lights were studied, and the weak links of heat transfer and heat dissipation were analyzed, and the heat dissipation structure of street lamp was optimized. The experimental results show that the junction temperature of the chip is lower than 90 °C in the steady state, and the luminous efficiency decreases by 5.93%. In order to ensure the efficiency and reliability of the street lamp, it is recommended that the junction temperature of the street lamp be controlled below 85 °C. After analyzing the arrangement of the chips, silicone grease with different thermal conductivity and aluminum substrate, it is found that the substrate is the bottleneck affecting effective heat transfer, and the use of diamond like carbon metal core printed circuit board (DLC-MCPCB) can reduce the junction temperature by 17.04 °C. Through the grid design of shell of the street lamp, the heat dissipation effect of the street lamp surface can be enhanced, and the junction temperature can be reduced by 7.04 °C. Based on these studies, the optimal design model of street lamp was obtained, which provides guidance for practical applications.