bluuebunny/crossref_metadata_2025_split huggingface dataset:
Testing the quantum effects near the event horizon with respect to the black hole shadow *
Authors: Xu Zhaoyi , Tang Meirong 
Published: April 2022

Abstract
In recent years, the study of quantum effects near the event horizon of a black hole (BH) has attracted extensive attention. It has become one of the important methods to explore BH quantum properties using the related properties of a quantum deformed BH. In this work, we study the effect of a quantum deformed BH on the BH shadow in two-dimensional Dilaton gravity. In this model, quantum effects are reflected by the quantum correction parameter m. By calculation, we find that: (1) the shape of the shadow boundary of a rotating BH is determined by the BH spin a, the quantum correction parameter m, and the BH type parameter n; (2) when the spin

, the shape of the BH shadow is a perfect circle; when

, the shape is distorted; if the quantum correction parameter

, their shapes reduce to the cases of a Schwarzschild BH and Kerr BH, respectively; (3) the degree of distortion of the BH shadow is different for various quantum correction parameters m; with an increase in the parameter m, the boundary of the BH shadow expands; (4) the size of the BH shadow varies greatly with respect to various quantum deformed BHs (n), and the change in BH shadow shape caused by parameter n is similar to that caused by parameter m, which indicates that there is a "degenerate phenomenon" between the two parameters. Because the value of m in actual physics should be very small, the current observations of the event horizon telescope (EHT) cannot distinguish quantum effects from the BH shadow. In future BH shadow measurements, it will be possible to distinguish quantum deformed BHs, which will help to better understand the quantum effects of BHs.
Dynamic adaptive security path planning based on A* algorithm
Authors: Lu Fan , Han Cao , Wu Ganwenxiang , Lu Mingrui , Yang Jikun , Miao Baorui , Ma Yingjie 
Published: April 2022

Abstract
Aiming at the problems of large memory consumption, long operation time and low security of traditional A * algorithm, a dynamic adaptive improved algorithm based on A * algorithm is proposed. Firstly, according to the heuristic function of the proportion of obstacles in the map, the adaptive dynamic weighting is carried out to shorten the search time. Secondly, the jump point search (JPS) algorithm is integrated to remove a large number of redundant points and improve the running speed of the algorithm. Finally, according to the relative position of the starting point and the target point, the obstacles are expanded dynamically to keep a safe distance between the planned path and the obstacles. In order to verify the effect, the improved algorithm is tested in different sizes and different proportion of obstacles. The results show that the improved algorithm is efficient and safe in global path planning.
Neural Network Based Model for Friction Potential Estimation under Longitudinal and Lateral Excitations
Authors: Todorovic Smiljana , Wagner Andreas , Müller Sven , Neubeck Jens 
Published: April 2022

Abstract
Information about the maximum tire-road friction coefficient secures safety, increases the performance of ADAS functions, and enables higher levels of automation. An important parameter representing the relation between maximal longitudinal or lateral forces and normal force is friction potential. This paper presents a new approach for friction potential estimation based on Neural Networks by using data of 4WD vehicle. The regression model built in this paper can estimate the friction potential under both, longitudinal and lateral excitations. The goal was to build an estimation model, which does not require a tire model and can work under different types of excitations. Furthermore, this paper confirms, that the neural network approach requires lower excitations for friction potential estimation in comparison to traditional approaches. Data for training and validation was collected using a research vehicle called FlexCar, which was designed, built, and automated in the project with the same name. Experiments were conducted on three different friction levels.
Joint segmentation of optic cup and optic disc using deep convolutional generative adversarial network
Authors: Yu Linfang 
Published: April 2022

Abstract
Glaucoma, as one of the three major blinding ophthalmic diseases in the world, is usually accompanied by changes in the structure of the patient’s optic disc, such as optic disc atrophy and depression. Clinical ophthalmologists tend to use the cup-disc ratio as an evaluation index to realize the screening and diagnosis of glaucoma. Therefore, the accurate measurement of optic cup (OC), optic disc (OD) and other parameters is of great clinical significance for early screening of glaucoma. Inspired by game theory, this paper combines deep convolutional neural networks (DCNN) with generative adversarial networks (GAN), and proposes a model for the joint segmentation of OC and OD. Specifically, the generator is composed of a deep convolutional encoder-decoder network to jointly segment the OC and OD, and the discriminator is composed of an eight layer full convolutional neural network. The discrimination results adjust the parameters in the structure of the generator and discriminator network through back propagation to achieve the effect of autonomous learning and optimization of the model. When the proposed network and the existing networks are evaluated on the public dataset Drishti-GS1, the research results demonstrate that the proposed network can achieve a significant improvement in the overall performance.
Lightweight spatial pyramid pooling network for real-time semantic segmentation
Authors: Jiang Peilong , Ke Xiao 
Published: April 2022

Abstract
In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed.
Design and implementation of the whole process collaborative system for software development
Authors: Kai Wang 
Published: April 2022

Abstract
With the increasingly urgent and arduous task of software development, software engineering development tools that can efficiently respond to the continuous changes in software requirements, rationally plan the software development process, and control the development cost will be widely used in software engineering. In response to actual needs, the supporting tool is designed to meet the system requirements, software requirements and software design and development. First, we use visualization modeling optimization technology of structured analysis and design method to build demand analysis models and system design models; Secondly, the dynamic verification technology of data model logical consistency is adopted to verify the logical consistency of data model in evolution; finally, the verification of the system model is achieved through hierarchical flow diagrams, fully expanded flow diagrams and statistical views. The vehicle cruise control system is selected for application verification, and the tool meets the requirements of software development and improves the software quality.
Underwater color restoration and dehazing based on deep neural network
Authors: Wang Nan 
Published: April 2022

Abstract
In real-world underwater environment, exploration of seabed resources, underwater archaeology, and underwater fishing rely on a variety of sensors, vision sensor is the most important one due to its high information content, non-intrusive, and passive nature. However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images. To address this problem, firstly, we proposed an underwater unsupervised generative adversarial network call UWGAN for generating realistic underwater fake images from paired images in air and their corresponding depth maps based on an improved underwater imaging model. Secondly, U-Net, which is trained efficiently using synthetic underwater dataset, is adopted for color restoration and dehazing. Our model directly reconstructs underwater clear images using end-to-end autoencoder networks, while maintaining the structural similarity of underwater scenes. The results obtained by our method were compared with existing methods qualitatively and quantitatively. Experimental results obtained by the proposed model demonstrate well performance on open real-world underwater datasets, and the processing speed can reach up to 125FPS running on one NVIDIA 1060 GPU.
Research on Anomaly Detection of Train Communication Network Based on Long and Short-term Memory Network
Authors: Guo Jianing 
Published: April 2022

Abstract
Aiming at the intrusion detection algorithm based on traditional machine learning, which can not effectively deal with the problems of large quantity, strong timing and high dimension in the train communication network system, this paper proposes an intrusion detection algorithm based on adaptive momentum estimation optimization of long and short-term memory networks. This article first introduces the principles and characteristics of the long and short-term memory network, and then preprocesses the train communication network information according to the characteristics of the train communication network mode, proposes a train communication network anomaly detection model based on the long and short-term memory network, and studies different parameters The impact on the performance of anomaly detection, and the comparison of multiple intrusion detection performance indicators with other traditional shallow machine learning methods, proves the effectiveness of the algorithm proposed in this paper.
Design and Mechanical Performance Analysis of Vibration Reduction and Isolation Platform
Authors: Yang Hongping , Che Xiaowei , Yang Chen , Wang Wenwen , Pu Jiao 
Published: April 2022

Abstract
Vibration reduction and isolation platform is a device that can effectively reduce or isolate vibration in space environment. It is widely used in precision equipment fields such as mechanical structure, precision machining, optical instruments. A three-stage mechanical active vibration reduction and isolation platform is designed. The static analysis is carried out using professional software. The research results show that when 3000N concentrated load is applied on the working platform, the maximum deformation is 0.0073mm, the maximum stress is 2.82MPa. And when 4000N asymmetric load is applied, the maximum deformation is 0.048mm, the maximum stress is 200MPa. The modal analysis results show that the first frequency is 40Hz and the sixth frequency is 180Hz. The product has certain engineering application value.
Research on Generating Method of On-board Equipment Test Sequence in ATO System of High Speed Railway
Authors: Shen Xiangyin , Kuang Wenzhen 
Published: April 2022

Abstract
With the development of railway technology, the Train control system of Beijing-Zhangjiakou high-speed railway, which was opened in 2019, applies CTCS-3+ATO (Automatic Train Operation) technology to realize automatic driving at 350km/h, including automatic train opening, automatic and precise parking, and the linkage between train doors and platform doors. To ensure the safety and reliability of train control system, it is necessary to test the system. In this paper, the test cases used in the testing work are concatenated into test sub-sequences according to certain principles, and the concatenation problems of test sub-sequences are converted into TSP (Travelling Salesman Problem) for solving. PSO (Particle Swarm Optimization) algorithm was used to obtain the test sequence of ATO system in high-speed railway, which provided guidance for the test work.
Magnetic coupled ultra-low frequency piezoelectric energy harvester for self-powered sensors
Authors: Li Yuanhua , Han Minglei , Jiang Lei 
Published: April 2022

Abstract
Harvesting human motion energy to power various sensors has attracted more and more attention of researchers. Aiming to harvest the ultra-low frequency vibration energy generated by human motion, this paper proposes a magnetic coupled scheme which consists of two flextensional transducers with two endmost magnets, a center magnet, and a tube. Through magnetic coupling effect, the ultra-low frequency vibration energy is amplified and effectively harvested, and the output voltage amplitude at 5Hz reaches 22V under the initial distance of 48mm and the acceleration of 1g. The output voltage amplitude of the harvester is related to the initial distance and excitation acceleration which are theoretically analyzed in this paper.
Design of the Gradually Defect Patch Fractal Array Antenna for Unmanned Aerial Vehicle
Authors: Huang Yuting , Lin Bin , Zhang Zhiwei , Chen Haoyuan , Zheng Meijie , Wen Shuyao 
Published: April 2022

Abstract
This paper creatively proposes a new fractal iterative structure of a gradual defect patch and combines it with a rectangular array structure. Aiming at the performance requirements of the UAV system for multi-band compatible antennas, a gradual defect patch fractal array is designed. The radiation performance of the fractal array antenna was simulated by the method of moments, and antenna samples were made and the radiation performance was tested. Both simulation and actual measurement results show that this antenna has a working bandwidth of 5.5 GHz or more, a minimum return loss as low as -44 dB and excellent omnidirectional radiation capability. This antenna is compatible with the GPS system frequency band, Beidou system frequency band, all working frequency bands of the second to fifth generation mobile communication systems and the three working frequency bands of the RFID system, and is expected to be widely used as an unmanned aerial vehicle system antenna.
Hydrodynamic Performance and Vibration and Noise of Machine-pump Integration Device
Authors: Zhang Chi , Qiao Mingzhong , Lu Xihao 
Published: April 2022

Abstract
As a new type of drainage equipment or underwater propeller, the machine pump integrated device composed of permanent magnet motor and pump blade of Halbach structure has many advantages and wide application prospects. Starting from the hydrodynamic analysis method of the machine pump integrated device, this paper reviews the research progress in this field at home and abroad in recent years. The results are summarized and analyzed. Finally, according to the current problem needs and the shortcomings of the research status, some ideas are put forward for the vibration and noise suppression method of the machine pump integrated device, which provides a reference for the follow-up related research.
Unmanned Aerial Vehicle conflict detection based on spatiotemporal data grid
Authors: Yang Jianhua , Hu Minghua , Su Fangzheng 
Published: April 2022

Abstract
—The increasing number of unmanned aerial vehicles (UAVs) and the complexity of the low-altitude airspace operation environment have also increased. How to efficiently solve the pre-flight UAV conflict detection problem is an important link in the low altitude airspace operation environment. This paper combines the global airspace grid coding system and the relational spatio-temporal data structure to propose a method based on spatio-temporal data grid, establishes the grid size applicable to conflict detection, and verifies the feasibility and effectiveness of the proposed UAV conflict detection type in different environments through simulation experiments. The results also show that the seventh level grid of the global airspace grid is the most suitable basic grid size, and the time consumption is reduced by about 50-75% compared with the traditional method in different scenarios.
Research on mode-coupled multi-mode Fiber mode-division in multiplexing transmission system
Authors: Zhou Yang , Su Han 
Published: April 2022

Abstract
The mode coupling and random loss between different modes have important effects on the reliability of the multimode fiber-based mode division multiplexing transmission system. Using the VPI optical communication simulation platform, a short-distance analog-division multiplexing transmission system based on multimode fiber was designed and built. By setting the relevant parameters of mode coupling and random loss caused by bending, the quality of the eye diagram of the received signal is analyzed by the photoelectric signal analyzer at the receiving end. The experimental results show that the mode coupling has great influence on the anti-noise performance and anti-inter-symbol interference performance of the multimode fiber-based transmission system, and the random loss has great influence on the anti-noise performance of the system, this effect is more obvious in high-order linear polarization mode fiber.
Calculation and Structure Optimization of Electric Field Distribution of The 10kV Solid-sealed Polar Pole
Authors: Wang Shuo , Xuan Yingying , Lu Yu , Zhang Yan , Zheng Ziyao , Zhang Ji 
Published: April 2022

Abstract
As one of the core modules of the ring - net switch cabinet, the solid-sealed polar pole was used to seal the three-station isolation ground switch and vacuum arcing chamber together. The electric equipment was concentrated, which was prone to electric field distortion. In this paper, a three-dimensional simulation model was established based on the drawings of the solid-sealed polar pole. The electric field distribution under the initial structure was calculated based on the finite element method, and it was optimized by adjusting the thickness of the moving contact and epoxy layer, changing the radius of the metal pull rod in the control mechanism and adding the equalizing ring on the shell surface. The results showed that when the inner diameter of the moving contact was 20mm, the outer diameter of the air layer was 43mm, and the radius of the metal tie rod in the control mechanism was 18mm, the field intensity in the solid-sealed polar pole was relatively reasonable, but the maximum E-field intensity in the arc-extinguishing chamber gap between the contacts was up to 5041V/mm. It was necessary to carry out pressure withstand test to ensure insulation safety. The results of this paper could provide reference for the optimal design of the solid-sealed polar pole.
Control and realization of intelligent fishing system based on PLC and fuzzy PID algorithm
Authors: Hao Zongrui , Li Chao , Zhang Hao , Ren Wanlong 
Published: April 2022

Abstract
In this paper, based on the frequency conversion constant speed control principle theory, to control the stability of the number of fish pipe to fish for the technical means, to control the number of fish to ensure catch efficiency and damage rate as the main purpose, research and design based on the fuzzy PID controller of intelligent pump suction fishing control system, determine the system platform, sucking fish overall frame structure of the control system is given, A complete electrical control system is designed. The purpose of this paper is to obtain a fully functional fish suction equipment control system, specifically realize the intelligent catch count, control fish suction switch, lossless fish transfer speed control system switch and automatic cycle control.
The application of an integrated monitoring system for power distribution and consumption
Authors: Gu Zhanlei , Wang Jun , Han Linfeng 
Published: April 2022

Abstract
In view of the fact that the traditional integrated power distribution and consumption monitoring system and power information acquisition system are independent of each other, and the data between the two systems are not shared and reused, which brings great obstacles to the construction of new power systems, this paper proposes an integrated power distribution and consumption monitoring system. The system can simultaneously collect distribution monitoring data and power consumption information data, and accurately perceive the panoramic data of power systems such as high voltage power line, distribution transformer area, low voltage power line, metering tank and so on. Through big data statistical analysis and accurate prediction of fault events, realize the two-way interaction between distribution side and power consumption side, reduce power maintenance costs, improve power supply quality, it provides a strong guarantee for the safe operation of power distribution and electrical equipment.
Research on Coordinated DC voltage Control Strategy of DC Microgrid based on Photovoltaic Power Generation System
Authors: Zhou Ke , Qiu Xiaoying , Song Yi , Jin Qingren , Mo Zhiyue 
Published: April 2022

Abstract
DC microgrid system is a flexible and commonly used renewable power generation system. Among them, the energy storage system (ESS) can reduce the waste light of the photovoltaic (PV) power generation system and provide stable power output for the power grid. However, a reasonable control strategy is needed to realize the energy balance of PV, grid-connected converter and ESS, so as to ensure the safe and stable operation of DC microgrid. Therefore, this paper proposes a DC voltage coordination control strategy without communication. The control strategy can automatically coordinate the working state of each converter of DC microgrid, which according to the change of DC voltage and the ESS’s state of charge (SOC), At the same time, the inertia of DC voltage in each working state is analyzed, and the stability of DC microgrid is improved through the backup inertia support of ESS. Finally, the effectiveness of the proposed method is verified by simulation and experiments.
A store-and-forward neural network to solve multicriteria optimal path problem in time-dependent networks
Authors: Liu Jin , Chen Li , Zhang Honghao , Huang Wei , Jiang Kaiwen , Zhang Hongmin 
Published: April 2022

Abstract
This paper introduces the constrained multi-objective optimal path problem in time-dependent networks. In the existing literatures, the constraints are all imposed on the objective function while the problem constraints are related to the non-objective function. It is the difference that makes the traditional algorithm unable to get a better solution quality. In this light, we propose a store-and-forward neural network (SFNN) that finds the better result. In the design of SFNN, the topology of neural network is the same as that of time-varying network, and each node is designed as store-and-forward neuron. Each neuron transmits information to other neurons by sending signals. The experimental results show that compared with the traditional methods, the accuracy is significantly improved when the calculation time is acceptable.