bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Review of Hyperspectral Image Classification Based on Joint Spatial-spectral Features
Authors: Qu Shenming , Li Xiang , Gan Zhihua 
Published: February 2022

Abstract
Hyperspectral image classification technology is a basic work in the application of hyperspectral images. In recent years, with the innovation and development of hyperspectral image classification technology, the method and performance of hyperspectral image classification based on joint spatial-spectral features have made breakthroughs, and have gradually become the focus of researchers. In order to further promote the development of the spatial-spectral feature union class method and improve the classification accuracy of hyperspectral images, this paper summarizes the commonly used spatial-spectral union algorithms. Firstly, the introduction briefly outlines the background and research status of this field. Some common problems in the process of hyperspectral image classification are listed. Finally, some current hyperspectral image classification methods based on joint spatial-spectral features are introduced. The main roles and existing problems of spatial-spectral joint features in the field of hyperspectral image classification are summarized in detail, and the future research directions are prospected.
Design of Diesel Oxidation Catalyst Temperature Control System Based on Fuzzy Adaptive PID
Authors: Wang Ning , Sun Yao , Hu Yunfeng , Zhao Jinghua , Gong Xun 
Published: February 2022

Abstract
This paper presents a temperature control framework of Diesel Oxidation Catalyst (DOC) of after-treatment system (ATS) which aims to increase the control precise of DOC temperature under different exhaust flow rates. To start with, a control-oriented DOC temperature model is established. Considering the sensitivity of DOC to different exhaust flow rate, a fuzzy adaptive PID temperature control system framework under the Smith predictor is designed. Finally, the controller is verified in the Matlab/Simulink. The results show that when tracking the desired temperature, the overshoot is reduced and the response speed is faster at different exhaust flow rates compared with the traditional PID control system.
A Cloud Computing Platform Technology Based on Space-based Intelligent Information Network
Authors: Ding Yueli , Shao Yingzhao , Zhang Jianhua , Nie Xiaokai , Yang Pengfei , Lv Wenkai 
Published: February 2022

Abstract
Based on large-scale storage and networked computing resources, cloud computing technology provides a strong computing platform which could support for massive data storage and big data intelligent application in the different field. Space-based information infrastructure has matured over the years, but most of the satellite systems are independent, which cause the resources of the satellite computing platform are isolated from each other. In this case, storage and computing resources cannot be utilized fully, which also cannot effectively support space-based intelligent application platform[1]. In this paper, a space-based embedded cloud computing platform is designed by combining the application requirements of space-based intelligent computing and ground-based cloud computing system, which includes sharing and using large-scale computing resources, concurrent monitoring and management of multi-tasks, fault-tolerant migration of tasks, and distributed storage retrieval. The results of this paper are committed to building an intelligent information network computing and storage platform, which could support for space-based intelligent infrastructure application.
Design of Quadrotor System for Transmission Line Inspection
Authors: Lin Xumei , Miao Fangrong , Chen Yige , Zai Wenqiang , Ding Yan , Pan Jiaxin 
Published: February 2022

Abstract
A novel robust adaptive control is proposed for target detection and recognition in transmission line inspection, this paper proposes a quadrotor inspection system based on robust adaptive control of multi-sensor fusion. Robust adaptive control ensures the stability and anti-disturbance ability of the quadrotor during the flight. It is measurable for the distance between the quadrotor and cables by laser sensor and ultrasonic sensor. It can be used to detect and identify the target objects on cable lines by OpenMV. According to the proposed Lyapunov function, the asymptotically stable conditions is obtained, in which all trajectories converge in finite time to the desired location. Finally, the effectiveness of inspections is validated experimentally, and the system has strong anti-interference ability.
A Simple Data Privacy and Security Protection Algorithm Based on Federated Learning
Authors: Wang Jingtian , Chen Shenbo , Li Boyuan , Wang Guanghui 
Published: February 2022

Abstract
Although federated learning can break the data island and enhance privacy, it is not absolutely secure. External eavesdroppers can intercept the model when clients upload their models to the server, which might be used to infer the data information. A natural solution is to add noise to the uploaded model. However, this solution has its drawback, because the added noise cannot be eliminated when the models are aggregated at the server, which leads to poor performance. In order to tackle this problem, we propose a simple but effective algorithm, FedNoise. In the vanilla federated learning algorithm, a gradually decreasing learning rate is applied at the client side. While in our algorithm, we divide the parameter update into client-side and server-side. The stochastic gradient descent update with constant learning rate is performed at the client side and a gradually decreasing learning rate is performed at the server. Our algorithm can not only ensure the data security, but also maintain comparable system performance. Simulation results validate our conclusion.
Augmented Value Iteration Networks with Artificial Potential Fields for Path Planning
Authors: Jin Xiang , Lan Wei , Yu Pengyao , Wang Tianlin 
Published: February 2022

Abstract
Value iterative networks (VINs), as a differentiable path planning method, taking environmental images as input, can solve path planning problems in new and unseen environments with powerful generalization capabilities. But the planning success rate decreases rapidly as the planning space scales. We propose the potential field augmented VIN by replacing the environmental map with the potential map as input and implementing this process in the form of dilated convolutions, giving more a priori information to the subsequent planning computation while hardly increasing the computational cost. Experiments on 2D grid-world show that the improved method has higher path planning success rates and smaller difference of predicted paths from shortest paths, as well as smaller performance degradation with increasing map size.
Dynamical Modelling and Vibration Analysis of the Non-coplanarity of the Orthogonal Support of Gyrowheel
Authors: Liu Xiaokun , Tan Minglang , Yang Guanglie , Mo Chaoming , Gu Dongqing , Song Bin , Wu Shufan 
Published: February 2022

Abstract
In the engineering implementation of gyrowheel, the influence of orthogonal support non-coplanar is inevitable. However, when the gyrowheel is used for satellite inertial angular velocity measurement, this non-ideal factor will inevitably have an adverse impact on the accuracy of angular velocity measurement. Therefore, the dynamic model of gyrowheel with non-coplanar orthogonal support is established, and the influence of the non-coplanarity support on the vibration frequency is analysed. Finally, the correctness of this research is verified by simulation.
An Optimal Evader-Pursuer Strategy based on Machine Learning and Differential Game
Authors: Wang Jingjie , Chen Xiangwei , Yu Yongjia , Yuan Bingchang , Jin Chufeng , Zhu Ren , Li Heng 
Published: February 2022

Abstract
Based on the special differential game model of evader and pursuers, in this paper, we propose an optimal escape and containment strategies of evader and pursuers, and gives the escape boundary conditions of evader. Through the training of DDPG and A3C algorithms, the evader have the ability to escape autonomously, and the evaluation system is established with the reward value, the number of rounds, the success rate of escape and the ratio of escape speed as the main indicators. Taking different pursuer-evader speed ratios under DDPG and A3C as key indicators, a quantitative evaluation system was established, and the evaluation results verify the effectiveness of the proposed approach.
Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding
Authors: Gan Ling , Xiao Yang 
Published: February 2022

Abstract
Most current knowledge base question answering models mainly use RNN and its various derivative versions such as BiLSTM to model the problem, which limits Parallel computing capabilities of the model. In response to this problem, we try to use TransformerEncoder instead of BiLSTM to model and encode the problem, and hope to improve the parallel computing efficiency of the model. At the same time, to solve the problem of insufficient relative position information obtained by using absolute position coding in TransformerEncoder, it is proposed to use relative position coding instead of absolute position coding. According to the experimental results, our model effectively reduces a certain amount of training time and has achieved certain results on the WebQuestions benchmark data set.
A Novel Brain Tumor Segmentation Method Based on Im-proved Spatial Attention Mechanism and Multi-path Deep Neural Network
Authors: Wang Guizeng , Lu Huimin , Wang Yifan , Zhao Chengcheng 
Published: February 2022

Abstract
Preoperative Magnetic Resonance Image (MRI) brain tumor diagnosis is an effective technical approach. To accurately segment tumor regions, we propose a novel brain tumor segmentation method based on improved Spatial Attention mechanism and Multi-path neural network (SAMPU-Net). Firstly, we propose a multipath input method to extract feature information of different scales by using convolution kernels of different sizes, so as to fully extract MRI feature information. Secondly, we improve the spatial attention mechanism by adding convolution layer of pyramid structure to it to obtain the features of different receptive fields. In the convolution layer of this pyramid structure, the larger the convolution kernel is, the more global features will be extracted; conversely, the smaller the convolution kernel is, the more local features will be extracted. Thirdly, we use more multi-mode MRI information to segment the brain tumor images. In practical application, due to the fuzzy tumor regions in some MRIs, we use the method of restricted contrast adaptive histogram equalization to perform local enhancement of images. The proposed model and several other mainstream segmentation methods are trained and tested on the BraTS2019 public dataset. Experimental results indicate that using our method, the Dice coefficient of tumor core and tumor enhancement region is increased by 2.4% and 1.3% respectively, and our proposed method has better segmentation effect than other methods.
The Research of RoboCup2D Player Shooting Technique Based on Fuzzy Control
Authors: Yu Junfeng , Bahitbek Ardah , Yang Yingze 
Published: February 2022

Abstract
Multi-agent collaboration is the core task of multi-agent system (MAS) research. In order to explore a kind of multi-agent collaboration way with dynamic environment, this paper proposes a method based on fuzzy learning and applies it to RoboCup2D. In the RoboCup2D simulation competition, eleven players need to cooperate to win, so score becomes extremely critical. Therefore, this paper focus on shooting technique based on fuzzy control. In order to increase the scoring rate, We speculate through observation and testing that finding the correct shooting position and angle is the key. For solving the problem, this paper firstly design fuzzy controller for selecting the shooting path; and then, this paper purpose fuzzy neural network for optimizing shooting time; At last, this paper do lots of experiments to verify its effectiveness and find it does work on increasing the scoring rate. After optimization on the fuzzy control, we are fortunate to have won the national second prize in 2021 RoboCup China Open, and the goal accuracy has increased by 21.36%.
A Closed-Loop Assembly Network Optimization Based on Chance Constraint with Robust Approximation
Authors: Wei Dongyuan , Wang Yue , Li Xinchao , Lu Shan 
Published: February 2022

Abstract
In this paper, an optimization algorithm of forward-reverse cyclic assembly network under uncertainty is studied. A chance constrained algorithm based on robust approximation is adopted, in which the size of uncertain set is used to describe the violation probability of constraint. Further, some parameters are set to describe the confidence probability of the model. By implementing the proposed algorithm, we can reduce the manufacturing cost of the final forward-reverse cycle assembly network by 3.17% while ensuring a certain confidence probability, and relax the lower bound of the probability of the model, which makes the model more adaptable. By calculating the scheduling data and comparing with flexible robust optimization, the effectiveness of the algorithm is proved.
Distributed Cruise Control and Comfort Evaluation for High-speed Trains Under Moving Block Mode
Authors: Zhou Feng , Tao Kewu , Zheng Ge 
Published: February 2022

Abstract
This paper investigates the problems of distributed cooperative cruise control and train running comfort for high-speed trains with input constraints under moving block mode. Firstly, inspired by the mode of hitting a hard wall for moving block, a distributed cooperative cruise control algorithm based on the position and speed state information of adjacent trains is proposed. Under this control algorithm, all trains can track the given speed and realize speed cruise. In this algorithm, the relationship between speed and displacement is used to improve the position term, so as to achieve the minimum safe distance between trains by adjusting the speed. Secondly, aiming at the problem of the excessive acceleration of each train in the moving block mode, the hyperbolic tangent function is introduced to limit the acceleration of each train in an appropriate range by limiting the control input. Finally, the stability of the closed-loop system in error state is proved by using Lyapunov stability theorem, and the feasibility of the algorithm is verified by simulation.
A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm
Authors: Wu Peiliang , Zhang Yan , Li Yao , Mao Bingyi , Chen Wenbai , Gao Guowei 
Published: February 2022

Abstract
Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently.
Research on Control Strategy of Grid-connected Inverter with Active Filter Function
Authors: Jia Die , Zhong Jianwei , Yang Yongchao , Tian Shidong , Zheng Wenli 
Published: February 2022

Abstract
Grid-connected inverters incorporate distributed energy sources such as photovoltaics and wind energy into the micro-grid. Due to the volatility of distributed energy generation and the presence of a large number of non-linear loads in the micro-grid, harmonic wave seriously affect power quality. The grid-connected inverter with active filtering function is formed by adding the harmonic wave suppression function to the grid-tie inverter, which not only realizes the inverter grid connection but also achieves the purpose of harmonic wave compensation. The traditional model predictive control (MPC) has the characteristics of strong robustness and fast dynamic response, but its high switching frequency causes serious heating of the inverter. In response to the above problems, combined with hysteresis control theory, a model control strategy based on hysteresis is proposed in this paper, which improves the accuracy of grid-connected current of the grid-connected inverter with active filter function and inhibits the harmonic component of the system, verifying the effectiveness of the strategy.
Study on time measurement for CSA-based readout electronics in STCF ECAL
Authors: Luo L. , Jia Z. , Shen Z. , Zhang Y. , Liu S. 
Published: February 2022

Abstract
Super Tau-Charm Facility (STCF) will provide unique support
for particle physics research in China owing to its high luminosity
and large dynamic range. Its electromagnetic calorimeter (ECAL) is
designed as a new type of calorimeter which can perform
high-precision energy and time measurement at the same
time. Considering the high event rate and pile up induced by high
luminosity, and the large channel number scale of ECAL, the readout
electronics needs to adopt a high-precision, unconventional, and
real-time processing method to reduce the data amount in
transmission. Based on the charge-sensitive front-end electronics in
energy measurement, an online time measurement method based on
waveform fitting algorithm has been studied and implemented in this
work. The parameters which would affect the time resolution are
analyzed according to simulation and experimental test. After the
optimization of electronics, a time resolution of 165 ps at the
equivalent deposition energy of 1 GeV is achieved, which indicates
that the waveform fitting method based on charge-sensitive readout
electronics can meet the future requirements.
The straw tracking detector for the Fermilab Muon g-2 Experiment
Authors: King B.T. , Albahri T. , Al-Kilani S. , Allspach D. , Beckner D. , Behnke A. , Bowcock T.J.V. , Boyden D. , Carey R.M. , Carroll J. , Casey B.C.K. , Charity S. , Chislett R. , Eads M. , Epps A. , Foster S.B. , Gastler D. , Grant S. , Halewood-Leagas T. , Hardin K. , Hazen E. , Hesketh G. , Hollywood D.J. , Jones T. , Kenziora C. , Keshavarzi A. , Kiburg M. , Kinnaird N. , Kintner J. , Lancaster M. , Lucà A. , Lukicov G. , Luo G. , Mapar L. , Maxfield S.J. , Mott J. , Motuk E. , Mourato H. , Pohlman N. , Price J. , Roberts B.L. , Sathyan D. , Shenk M. , Sim D. , Stuttard T. , Sweetmore G. , Thayer G. , Thomson K. , Turner W. , Vasilkova D. , Velho J. , Voirin E. , Walton T. , Warren M. , Welty-Rieger L. , Whitley M. , Wormald M. 
Published: February 2022

Abstract
The Muon g-2 Experiment at Fermilab uses a gaseous straw
tracking detector to make detailed measurements of the stored muon
beam profile, which are essential for the experiment to achieve its
uncertainty goals. Positrons from muon decays spiral inward and pass
through the tracking detector before striking an electromagnetic
calorimeter. The tracking detector is therefore located inside the
vacuum chamber in a region where the magnetic field is large and
non-uniform. As such, the tracking detector must have a low leak
rate to maintain a high-quality vacuum, must be non-magnetic so as
not to perturb the magnetic field and, to minimize energy loss, must
have a low radiation length. The performance of the tracking
detector has met or surpassed the design requirements, with adequate
electronic noise levels, an average straw hit resolution of
(110 ± 20) μm, a detection efficiency of 97% or higher,
and no performance degradation or signs of aging. The tracking
detector's measurements result in an otherwise unachievable
understanding of the muon's beam motion, particularly at early times
in the experiment's measurement period when there are a
significantly greater number of muons decaying. This is vital to the
statistical power of the experiment, as well as facilitating the
precise extraction of several systematic corrections and
uncertainties. This paper describes the design, construction,
testing, commissioning, and performance of the tracking detector.
A new technique to enhance the position resolution of large area plastic scinitillators to reconstruct the cosmic muon tracks
Authors: Sehgal R. , Dey R. , Behera S.P. , Netrakanti P.K. , Mishra D.K. , Mulmule D. , Jha V. , Pant L.M. 
Published: February 2022

Abstract
In this paper, we present a study to use thick plastic
scintillators to reconstruct the cosmic muon tracks, that can be
used for the applications like Muon Tomography. At Bhabha Atomic
Research Centre (BARC), India, a plastic scintillator array —
`ISMRAN (Indian Scintillator Matrix for Reactor Anti-Neutrinos),'
with a total weight of 1.0 ton has been configured for neutrino
physics study. Using the ISMRAN scintillators matrix, we present a
technique of the position calibration of thick plastic scintillators
using cosmic muons as a probe. The position resolution obtained from
the cosmic muons based calibration method is compared with the one
obtained from the traditional calibration method using the
radioactive source. Finally, the accuracy of reconstructed cosmic
muon tracks from the two position calibration techniques is compared
using the χ2/ndf distribution of the fitted cosmic muon
tracks.
Fabrication and Characterization of the self support enriched 107, 109Ag thin targets for heavy ion induced transfer reaction
Authors: Vinayak A. , Abhilash S.R. , Hosamani M.M. , Hiremath G.B. , Umapathy G.R. , Khandelwal D. , Madhavan N. , Kabiraj D. , Badiger N.M. 
Published: February 2022

Abstract
The enriched, self-supported, and thin isotopic targets of
107Ag and 109Ag were fabricated at the target development
laboratory, IUAC New-Delhi, using a vacuum deposition facility. The
targets were fabricated by the thermal evaporation technique with
minimum material consumption. The simple and efficient method for
the fabrication of thin targets with high quality without major
impurities was discussed in detail. The prepared targets were
characterised by using Rutherford Backscattering Spectrometry, X-Ray
Diffraction, Total-Reflection X-Ray-Fluorescence and
Scanning-Electron-Microscope techniques. From the characterizations,
it was found that the fabricated targets were quite stable, had the
same crystalline nature, the absence of any other elements, and were
suitable for nuclear reaction experiments.