bluuebunny/crossref_metadata_2025_split huggingface dataset:
Threshold recovery policy for the machine interference repair problem with server vacations
Authors: Ojobor S A , Ogini N O 
Published: February 2022

Abstract
In this paper, we consider the threshold recovery policy for the machine interference problem (MIP) with server vacations, having a non-dependable server which operates a threshold recovery policy, i.e., service starts after a fixed threshold level q ≥ 1. In the threshold recovery policy, M machines of similar nature are subject to fail or break down with a single server responsible for maintaining or repairing the failed machines. When the server is working, it is subject to breakdown with exponential distribution rate α; it is not possible to repair any server until the number of failed computers exceeds a predetermined threshold, q where 1 ≤ q ≤ M. The repaired rate is exponentially distributed with rate β. The server breaks down with a constant failure rate. The repair and vacation time of server is exponentially distributed. We developed the difference differential equations for the transient state probabilities for the system. We calculate the threshold recovery strategy for the MIP with server vacation using the ODE45 (Runge-Kutta method for solving ordinary differential equations) in the MATLAB computer language. We develop a variety of operational quality metrics for the system.
A proposed Method for the Solution of One-Dimensional Gas Dynamic Equation with Legendre and Chebyshev polynomials
Authors: Ojobor S A , Inonoje S O E , Mamadu E J 
Published: February 2022

Abstract
We have considered a proposed method to efficiently handle the piecewise formulation in the generation of approximate solutions for gas dynamic equation via the Legendre and Chebychev basis functions. Two cases of the gas dynamic equation were considered for numerical illustrations aided by MAPLE 18 software. We did obtained some fascinating results. To be precise, applying the proposed method to the homogeneous gas dynamic via the Legendre polynomial, we attained a maximum error of order 10−4 as against that of VIM with a maximum error of order 10−3 at t = 0.0001. Also, the proposed method via Chebyshev polynomials at t = 0.0001 attained a maximum error of order 10−4 as against the VIM which has a maximum error of order 10−2. In like manner, applying the proposed method to the nonhomogeneous gas dynamic via the Legendre polynomials we attained a maximum error of order 10−4 at t = 0.0001 as against that of VIM, which does not show convergent. Also, proposed method via Chebyshev polynomials at t = 0.0001 attained a maximum error of order 10−4 as against the VIM which does converge at every grid points. Thus, it is obvious that the proposed method is a better convergent iterative scheme than the in as much as t decreases.
IRNet: A Real-time Intersection Recognition Network Based on Graph Classification and 3D LiDAR Point Cloud for UGVs
Authors: Tang Hanbo , Wu Tao , Dai Bin , Cai Ying , Cao Kai , Li Shi 
Published: February 2022

Abstract
The intersections are junctions of roads. Recognizing intersections based on LiDAR accurately and quickly is a key task for UGVs. In this paper, we propose an end-to-end real-time intersections recognition network (IRNet) with graph attention convolution based on graph classification and 3D LiDAR point cloud. Multiple evaluations on KITTI and Tunnel dataset demonstrate that our model performs better than other competitive models and meets the real-time recognition. We research the performance of our model under different number of input points, and certify that neither of two spatial transformation networks is effective for our model. Ablation experiments certify effectiveness of the proposed features skip connection.
Simulation and Implementation of Robot Obstacle Avoidance Algorithm on ROS
Authors: Zhang Jiaonan , Zhang Tengyu , Niu Yi , Guo Yuxuan , Xia Jiahao , Qiu Yang , Yuan Zhengyi , Yang Yingze 
Published: February 2022

Abstract
Robots often collide in the process of simulation soccer match. The collision will block the robot’s own motion trajectory, which is not conducive to the robot’s ball grasping and defense. Considering that the obstacles have the characteristics of dynamics, antagonism and uncertainty, the robot’s motion trajectory, path selection and obstacle avoidance are very important. Taking ROS as the research platform, the study focuses on the path planning of simulated soccer robot. Meanwhile, we have applied some obstacle avoidance algorithms to the Middle Size Simulation League of soccer.
Machine Learning Approaches to Estimate Flow Rate of Drip Irrigation Emitter Based on Structure Parameters and Pressure
Authors: Chen Xueli , Wei Zhengying , Wei Caixiang , He Kun 
Published: February 2022

Abstract
The agricultural water-saving irrigation systems need to be adapted to local conditions, and irrigation emitters with different flow rates need to be designed to meet actual needs. In this study, machine learning approaches were used to predict the flow rate of the labyrinth emitters. The structural parameters of the emitter [number of channel units (N), channel unit length (L), channel width (W), tooth height (H) and channel depth (D)] and working pressure (P) were considered as independent variables. The applied machine learning models included k-nearest neighbor (KNN), multi-layer perceptron (MLP), support vector machine (SVM) and radial basis function artificial neural network (RBF-ANN). The accuracy of the machine learning model was evaluated by the mean square error (MSE) and coefficient of determination (R
2). The results show that the MSE and R2 of the RBF-ANN model were higher than the corresponding parameters of the other three models, indicating the RBF-ANN model can predict the flow rate of the labyrinth emitter more accurately. The research provides a reference for the rapid design of emitters with different flow rate. It is helpful to realize the automation of agricultural irrigation, and then realize the construction of a smart agricultural irrigation system.
Design and test of a distributed control system of weeding robot based on multi-STM32 and CAN bus
Authors: Pan Dong , Gao Qiaoming , Zhao Pengfei , Zeng Junhao , Xu Peng , Xiang Hao 
Published: February 2022

Abstract
Aiming at the problems of a single control mode, complex signal line, serious interference and poor system stability of a traditional weeding machine, a distributed control idea was proposed. Based on STM32 as the main control chip, each control unit realizes motion control, detection control, command control, and the terminal was displayed in a centralized manner. Based on ISO11783 protocol, according to the actual requirements of the weeding robot, the CAN bus communication protocol was improved and formulated to form a distributed control system. The system can completely realized all the actions of the weeding robot. 1. Motion controls: robot forwards, backwards, turning and weeding knife up or down; 2. Detection feedback: a power chassis climbing angle calculation and weeding knife speed self-adjustment, battery power, engine fuel capacity display; 3. Control command input functions. The test results had shown that the control system is stable, responsive solid, design specifications. It could meet the requirements of practical application. Meanwhile, the real-time accuracy and reliability of a CAN bus application in the agricultural production control system were verified.
Droplet Distribution of an Autonomous UAV-based Sprayer in Citrus Tree Canopy
Authors: Meng Yanhua , Zhong Wanqiang , Liu Yue , Wang Meimei , Lan Yubin 
Published: February 2022

Abstract
Although Unmanned Aerial Vehicles (UAVs) are developed for military missions originally, they have been used widely in civil activities for several decades globally. In agricultural, UAVs have been developed as an efficient sprayer for pesticide application since 1987. UAV-based sprayers are popular for the prevention and control of pests and diseases in field crop in many countries recently. Some of the UAV-based sprayers are developed to be equipped with fruit tree mode aiming at solving droplet penetration in the inside and bottom part of the fruit tree canopy. In this study, a newly released UAV-based sprayer (i.e., T30) equipped fruit tree mode is chosen as spraying platform to optimize the spraying parameters for practical application. The flight velocity and application rate are the variables, while droplet coverage, density, size, and penetration are the observed metrics. Three treatments with different flight velocities (2 m s-1 or 3 m s-1) or application rates (60 L ha-1 or 75 L ha-1) are arranged to collect the droplets for assessment. Water Sensitive Papers (WSPs) are placed in the outside, bottom, and inside layers of the canopy to collect droplets. The results show that the treatment combined a flight velocity of 2 m s-1 and an application rate of 60 L ha-1 obtains the most droplets among all the variables based on the values of droplet coverage and density. The treatment with a flight velocity of 2 m s-1 and an application rate of 75 L ha-1 has the best penetration, while the treatment with a velocity of 2 m s-1 and an application rate of 60 L ha-1 takes the second place according to the percentages of droplet deposition in the three layers. Overall consideration of the total droplet distribution and penetration of the application parameters, a flight velocity of 2 m s-1 and an application rate of 60 L ha-1 are recommended to get an ideal droplet distribution in tree canopy when UAV-based sprayer T30 equipped with fruit tree mode flies at 1.6∼2 m above the citrus tree canopy.
Spatial-Temporal Semantic Neural Network for Time Series Forecasting
Authors: Zhang Rui , Xie Fei , Shi Jianjun , Zhao Jing , Yang Jiquan , Ling Xu 
Published: February 2022

Abstract
Time series forecasting play an important role in many applications, and combining neural networks to forecast time series features can uncover many potential application situations. For example, forecasting traffic flow time series data is important for urban traffic planning and traffic management. Accurate forecasting of traffic flow provides the basis for road traffic control, which in turn improves traffic efficiency and reduces congestion. In this paper, STSeNN is proposed, which combines graph neural network and TCN to dynamically capture the spatial correlation, temporal correlation and semantic correlation of data. Experiments show that the prediction performance of STSeNN on the traffic flow datasets is more accurate compared with existing methods.
A Data Augmentation Method to Optimize Neural Networks for Predicting SOH of Lithium Batteries
Authors: Fan Yuanliang , Wu Han , Chen Weiming , Jiang Zeyu , Huang Xinghua , Chen Si-Zhe 
Published: February 2022

Abstract
Neural Network is an excellent methodology for predicting lithium battery state of health (SOH). However, if the data amount is insufficient, the neural network will be overfitted, which decreass the prediction accuracy of SOH. To solve this issue, a data augmentation method based on random noise superposition is proposed. The original dataset is expanded in this approach, which enhances the neural network’s generalization ability. Moreover, random noises simulate capacity regeneration, capacity dips and sensor errors during the actual operation of lithium batteries, which also improves the adaptive and robustness of the SOH prediction method. The proposed method is validated on mainstream neural networks, including long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. In terms of the results, the proposed data augmentation method effectively improves the neural network generalization ability and lithium battery SOH prediction accuracy.
Application of Particle Swarm Optimization Combined with Long and Short-term Memory Networks for Short-term Load Forecasting
Authors: Han Mingchong , Tan Aiguo , Zhong Jianwei 
Published: February 2022

Abstract
In this paper, we apply the Long Short-Term Memory (LSTM) network to short-term load forecasting, and use the TensorFlow deep learning framework to build a Particle Swarm Optimization (PSO) model to optimize the parameters of the LSTM. Optimization (PSO) model to optimize the parameters of LSTM. In this paper, we use the meteorological data and historical load data of a certain place as the input of LSTM before and after optimization, and compare the model with the BP Neural Network before and after optimization, and the results show that the PSO-LSTM model has higher reliability and prediction accuracy.
Model Predictive Control : A Reinforcement Learning-based Approach
Authors: Pan Xia , Chen Xiaowei , Zhang Qingyu , Li Nannan 
Published: February 2022

Abstract
This article proposes a method of model predictive control, which combine the excellent data-driven optimization ability of reinforcement learning and model predictive control to design the controller. Different from the off-line design of MPC, reinforcement learning is based on the adaptation of on-line data to achieve the purpose of control strategy optimization. The reinforcement learning-based model predictive control can improve the control performance effectively. And the numerical simulations are given to demonstrate the effectiveness of the proposed approach.
YOPCB: A Single-stage Multi-attention Detection Network for PCB Surface Defects
Authors: Ge Yi , Zhou Yang , Yang Jiale , Zhang Yue , Xie Fei , Tang Xiaobo 
Published: February 2022

Abstract
With the miniaturization of electronic components, printed circuit boards (PCB) as a important component of them has become more and more complex and exquisite. Different defects in PCB can be found in modern industrial manufacture, such as lack of weld, lacking components, continuous welding, and the color ring resistance, resulting in low production. Accordingly, it is necessary to achieve the high rate of precision and efficiency when defecting during PCBs industrial production process. Considering that existing detection of PCB defects have a low efficiency, we propose an improved model YOPCB defecting PCB surface defects. Our improved model can achieve the precision of 97.6% mAP, which means our method is more efficient.
The Control Principle and Mathematical Model of Intelligent Solar Panels
Authors: Chen Wenxing , Dai Shuyang , Zheng Baojuan , Tang Zhuyan , Lin Hao 
Published: February 2022

Abstract
Energy is an important material that promotes social development and maintains human life. Clean energy has become a scientific research theme of open world which appeal to many scientists to research it. This paper inspired by the experience of sunflower growth. So we proposed a mathematical model for automatic adjustment of the maximum luminous flux for a limited solar panel, which can help to adjust the best light position. The physical model added to the light detector in the practical application, real-time comparison of the difference in illumination at different positions to adjust the direction of solar panel, thereby enhancing the utilization of light energy. In addition, in order to save space and prevent severe weather attacks, we designed a autofolding circle disc and retractable rectangular solar panels. The intelligent system judgement and decision making based on the electrical signals fed back by the wind sensor or the vibration sensor. When the wind speed reaches the threshold, it can be folded or contracted automatically, reducing manual maintenance. The fully automatic solar panels are designed in this paper, the research results show that theis model has a high utilization, strong sensitivity, safety and durability, and easy control. This is also a process of theoretical exploration of interdisciplinary services for the energy system.
Advances in the DTT poloidal interferometer/polarimeter design
Authors: Fiorucci D. , Fassina A. , Filippi F. , Mazzotta C. , Rocchi G. , Rossi R. , Terranova D. , Tudisco O. , Andreoli P. , Cipriani M. , Consoli F. , Gaudio P. , Gelfusa M. , Giudicotti L. , Innocente P. , Belpane A. , Mauro G. , Mascali D. , Naselli E. , Orsitto F.P. , Torrisi G. 
Published: February 2022

Abstract
A multi-channels poloidal interferometer/polarimeter is under development for the Divertor Tokamak Test (DTT) facility, a new tokamak device whose construction is starting in Italy. The aim of the diagnostics is the simultaneous measurement of the line integrated electron density, Faraday rotation and Cotton Mouton effect. The measurement of two polarimetric signals together with the interferometric one would allow for a robust electron density estimate, for the internal magnetic field measurement as well as for the magnetic equilibrium reconstruction. In this work, we present the advances in the diagnostics design whose characteristics are constrained by scientific requirements as well as the DTT structural aspects. In this regards, we have analyzed the possibility of accommodating up to 16 lines of sight in the available space and the contribution of different chords positions in the magnetic equilibrium reconstruction. In particular, we have used the VMEC and V3FIT codes to evaluate the expected interferometric/polarimetric signals and their contribution in the reconstruction of the q profile. These results have been used to optimize the chords positions. Eventually, we present a realistic CAD-driven design of the diagnostic, with details of the most critical components (e.g. the corner cube retroreflectors in the high field side), which would allow for the selected chords configurations implementation.
Decoupling between ecosystem photosynthesis and transpiration: a last resort against overheating
Authors: Krich Christopher , Mahecha Miguel D , Migliavacca Mirco , De Kauwe Martin G , Griebel Anne , Runge Jakob , Miralles Diego G 
Published: February 2022

Abstract
Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of photosynthesis and transpiration would represent an effective strategy (‘known as leaf or canopy cooling’) to prevent lethal leaf temperatures. At the ecosystem scale, continued transpiration might dampen the development and propagation of heat extremes despite further desiccating soils. However, at the ecosystem scale, evidence for the occurrence of this decoupling is still limited. Here, we aim to investigate this mechanism using eddy-covariance data of thirteen woody ecosystems located in Australia and a causal graph discovery algorithm. Working at half-hourly time resolution, we find evidence for a decoupling of photosynthesis and transpiration in four ecosystems which can be classified as Mediterranean woodlands. The decoupling occurred at air temperatures above 35 ∘C. At the nine other investigated woody sites, we found that vegetation CO2 exchange remained coupled to transpiration at the observed high air temperatures. Ecosystem characteristics suggest that the canopy energy balance plays a crucial role in determining the occurrence of a decoupling. Our results highlight the value of causal-inference approaches for the analysis of complex physiological processes. With regard to projected increasing temperatures and especially extreme events in future climates, further vegetation types might be pushed to threatening canopy temperatures. Our findings suggest that the coupling of leaf-level photosynthesis and stomatal conductance, common in land surface schemes, may need be re-examined when applied to high-temperature events.
Electrically tunable toroidal Fano resonances of symmetry-breaking dielectric metasurfaces using graphene in the infrared region
Authors: Chen Yankai , Wang Yi 
Published: February 2022

Abstract
The magnitudes of coupling strength play an important role in various resonant phenomena such as Fano resonances (FRs). However, the coupling strength within the FRs using dielectric metasurfaces cannot be easily manipulated once they have been made. In this paper, toroidal FR is excited using the silicon metasurface with symmetry-breaking nanocylinders. Inserting a graphene layer with an ion-gel top gate onto the silicon metasurface, actively tunable response of a toroidal FR resulting from the manipulated coupling strength and the phase shift between two states. The hybrid graphene-silicon metasurface realize tunable Fano parameter (q) from −1.38 to −1.85 with applied voltage ranging from 0 to 2 v. Theoretical results predicted that higher q values are reachable relying on the hybrid graphene-silicon metasurface. The high-quality(Q)-factor (∼444) tunable FR of metasurface in the near-infrared region is observed. By applying a bias voltage to graphene obtain a blueshift of resonant wavelength (∼4 nm) with a maximum change of transmission spectrum peak up to 30%. These results have potential in high-efficient tunable electro-optic modulators, near-infrared optical switches, etc.
A DFT study of transition metal doped two-dimensional Bismuth (Bismuthene) for spintronics applications
Authors: Saini Shipra , Choudhary Sudhanshu 
Published: February 2022

Abstract
The effect of substitutional doping of transition metals such as Y, Zr, Nb, Mo, Tc and Ru on the electronic and magnetic properties of two-dimensional bismuth (bismuthene) is investigated by using density functional theory (DFT) based simulations. The geometry optimisation results in covalent bonds between transition metal atoms and bismuthene atoms due to orbital hybridisation. The results suggest that semiconducting and non-magnetic behaviour of bismuthene can be altered by magnetically active dopants. The Nb, Tc and Ru doped bismuthene shows half metallic properties, and Zr and Mo doped bismuthene shows non-magnetic metal and magnetic semiconductor characteristics, respectively. Furthermore, Y doped bismuthene behaves as non-magnetic semiconductor like pristine bismuthene.
Software mitigation of coherent two-qubit gate errors
Authors: Lao Lingling , Korotkov Alexander , Jiang Zhang , Mruczkiewicz Wojciech , O'Brien Thomas E , Browne Dan E 
Published: February 2022

Abstract
Two-qubit gates are important components of quantum computing. However, unwanted interactions between qubits (so-called parasitic gates) can be particularly problematic and degrade the performance of quantum applications. In this work, we present two software methods to mitigate parasitic two-qubit gate errors. The first approach is built upon the Cartan’s KAK decomposition and keeps the original unitary decomposition for the error-free native two-qubit gate. It counteracts a parasitic two-qubit gate by only applying single-qubit rotations and therefore has no two-qubit gate overhead. We show the optimal choice of single-qubit mitigation gates. The second approach applies a numerical optimisation algorithm to re-compile a target unitary into the error-parasitic two-qubit gate plus single-qubit gates. We demonstrate these approaches on the CPhase-parasitic iSWAP-like gates. The KAK-based approach helps decrease unitary infidelity by a factor of 3 compared to the noisy implementation without error mitigation. When arbitrary single-qubit rotations are allowed, recompilation could completely mitigate the effect of parasitic errors but may require more native gates than the KAK-based approach. We also compare their average gate fidelity under realistic noise models, including relaxation and depolarising errors. Numerical results suggest that different approaches are advantageous in different error regimes, providing error mitigation guidance for near-term quantum computers.