bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced third-harmonic generation via gold nanospheres localized surface plasmonic resonance
Authors: Wei Haiyuan , Tang Ziying , Shen Yan , Zheng Huying , Wang Yaqi , Wang Runchen , Zhu Hai , Su Shichen , Zhu Yunliang , Zhou Jie , Qiu Zhi Ren 
Published: April 2022

Abstract
In the progress of nanoengineering of noble metals, the extreme nonlinear optics within nanometric volumes is of great research interest in recent years. In particular, plasmonic nanostructures offer unique route for enhancing high-order harmonic generation and other nonlinear optical progress. Here, we report that strong third harmonic emission in zinc oxide (ZnO) microbelts (MBs) via a strong localization of electromagnetic field caused by localized surface plasmon resonances. In MB/Au-nanoparticles composite, it was demonstrated that the intensity of third-harmonic generation (THG) was enhanced by a factor of five compared to bare MB. Additionally, the polarization characteristics of THG in the same system were illustrated in detail. With respect to the bare MB, the polarization degree of THG was improved significantly. The nonlinear polarization features can be attributed to the crystal structure and the ensemble of anisotropic plasmonic hot spots on the surface of Au nanoparticles. Finally, intrinsic enhancement process of THG was explored comprehensively by finite difference time domain method. The simulation results are consistent with our experimental observation.
Kinetic theory of sech2
x electron holes and applications to Kappa-distributed plasmas
Authors: Guo Ran 
Published: April 2022

Abstract
The kinetic theory of sech2
x-type electron holes (EHs) is studied. The potential of the EHs is solved in the weak amplitude limit by the pseudo-potential method. We investigate the existence condition of the sech2
x EHs. It indicates that the derivatives of trapped and untrapped distributions at the separatrix play significant roles in determining the potential profile. The theory is then applied to the Kappa-distributed plasmas. The amplitude and width of the sech2
x EHs are analyzed. Finally, the theoretical results are verified by numerical calculations.
Temperature-dependent asymmetric Prandtl-Ishlinskii hysteresis model for piezoelectric actuators
Authors: Savoie Marc , Shan Jinjun 
Published: April 2022

Abstract
A temperature-dependent asymmetric Prandtl-Ishlinskii (TAPI) model is developed to describe changes in hysteresis curves with respect to temperature found in the displacement curves vs. input voltage of a piezoelectric actuator (PEA). The proposed modeling scheme considers nonlinearities in an idealized capacitor term in the electromechanical model of the PEA to introduce both asymmetry and temperature dependence in the model. The developed model has the advantage of incorporating asymmetric and thermal effects in a hysteresis-free region of the model which simplifies inversion of the model as well as parameter determination. A parameter identification scheme is described to simplify model identification, even for a large number of thresholds, based on the advantages of the classical Prandtl-Ishlinskii model. The TAPI model is verified experimentally and a compensator is designed to demonstrate that the PEA output is effectively linearized throughout the temperature range.
Thermodynamic selection: mechanisms and scenarios
Authors: Babajanyan S G , Koonin E V , Allahverdyan A E 
Published: April 2022

Abstract
Thermodynamic selection is an indirect competition between agents feeding on the same energy resource and obeying the laws of thermodynamics. We examine scenarios of this selection, where the agent is modeled as a heat-engine coupled to two thermal baths and extracting work from the high-temperature bath. The agents can apply different work-extracting, game-theoretical strategies, e.g. the maximum power or the maximum efficiency. They can also have a fixed structure or be adaptive. Depending on whether the resource (i.e. the high-temperature bath) is infinite or finite, the fitness of the agent relates to the work-power or the total extracted work. These two selection scenarios lead to increasing or decreasing efficiencies of the work-extraction, respectively. The scenarios are illustrated via plant competition for sunlight, and the competition between different ATP production pathways. We also show that certain general concepts of game-theory and ecology—the prisoner’s dilemma and the maximal power principle—emerge from the thermodynamics of competing agents. We emphasize the role of adaptation in developing efficient work-extraction mechanisms.
Design and Implementation of Intelligent Security Robot Based on Lidar and Vision Fusion*
Authors: Zhou Bo , Du Minglin , Chen Zhenguo , Liu Yajun , Zhang Yan , Wang Yan 
Published: April 2022

AbstractNowadays, the mainstream security robot is expensive and the single 2D Lidar SLAM robot has large pose error and large matching calculation in navigation. To solve these problems, this paper designs an intelligent security robot based on the ROS system and STM32 microcontroller. Through the fusion of camera, IMU, lidar multi-sensor data, completing its own positioning, surrounding environment mapping and autonomous navigation functions; Binocular camera for face recognition, lock suspicious persons; Combined with temperature and humidity, smoke and other sensors for environmental monitoring, real-time alarm; Security personnel remote control, according to the data uploaded by the robot to complete the patrol. After a lot of experiments, the functions of the robot are better completed, which has a certain practical value.
UAV dynamic obstacle avoidance based on improved reciprocal velocity obstacle
Authors: Gao Jing , Zhang Hongtao , Tan Li , Ren Xueping 
Published: April 2022

Abstract
This paper aims to solve the problem that when the UAV flies from the starting point to the target point in the velocity space, the obstacle avoidance speed provided by the mutual velocity obstacle method will appear too large turning angle in order to avoid dynamic obstacles. In order to better meet the flight performance of UAV, an improved mutual velocity obstacle method is proposed in this paper. This method can not only solve the problem of path jitter caused by the speed obstacle method, but also solve the problem of excessive turning angle when the velocity direction selected by the mutual velocity obstacle method is flying towards the target point. In addition, the UAV will not return to the original path after the obstacle avoidance, but fly along the target point according to the updated best speed direction, so as to reduce the number of turns. The simulation results show that the improved mutual velocity obstacle method can effectively reduce the turning angle of UAV in dynamic environment and meet the flight performance of UAV.
An Active Suspension System Design for a Racing Car
Authors: Shi Kailai 
Published: April 2022

Abstract
This paper investigated the PID controller for an active suspension system for a racing car. A two-wheel half-car model is used and simulated in the Simulink environment. This model allows us to have two outputs and separately change the demand ride height of the front axle and the rear axle. Considering it is a racing car, downforce plays an important part in the suspension system and makes it a bit different from a private car that can only produce little downforce that can be ignored. To improve the control quality, a feedforward controller is added to compensate for the downforce.
Optimal Strategy for Pick-and-Place System with two Robots
Authors: Zhou Min , Jiang Renjie 
Published: April 2022

Abstract
In many situations in industry, a sorting process must be performed to separate different or unnecessary items from those on a moving conveyor belt. In this paper we focus on the problem of how to obtain the optimal pick-and-place (PNP) sequence in a conveyor with two robots. Traditional methods are analyzed and their disadvantages are illustrated, and the algorithm we have developed is presented and accelerated using dynamic planning methods. Furthermore, by building PNPTIME tables, our algorithm considers the complex dynamics and geometry of the different robot models in practice. Our algorithm has sufficient computational speed to be suitable for practical PNP applications requiring high throughput, and extensive testing of the algorithm has shown a 10-20% improvement over traditional methods based on greedy methods.
Synchronous control of the robot for repairing broken strands of overhead ground wires
Authors: Peng Jiaying , Tao Hongfei , Ni Hongyu , Yan Wenxu 
Published: April 2022

Abstract
Abstarct. Overhead ground wire broken strand repair robot is a new type of electric robot, a complex multi-motor system. Therefore, the problem of synchronous control of each mechanism in the robot needs to be solved. A dual-layer sliding mode control strategy based on a cross-coupled synchronous control structure is proposed from the perspectives of mechanical structure synchronization and algorithm synchronization in this paper.
Firstly, a global fast terminal sliding mode controller with single motor is designed for permanent magnet synchronous motor;
Secondly, for the dual-motor system in the pre-twisted wire winding mechanism, a cross-coupling control structure is introduced. And a dual-motor synchronous compensation controller is designed in view of the fast non-singular terminal sliding mode combined with the new approximation law;
Finally, the simulation verification in MATLAB, the results show that it has a certain improvement on the tracking error and synchronization error of the system. The anti-disturbance ability of the pre-twisted wire winding mechanism against uncertain loads is improved.
An improved object detection algorithm based on YOLO*
Authors: Guo Hao , Yang Jiahua 
Published: April 2022

Abstract
Accuracy and speed have always been a measure of the performance of object detection algorithms. The current algorithms have reduced the detection speed on the basis of increasing a certain accuracy due to their complex structure. In response to this problem, this paper uses the RepVGG network to improve the original YOLOv3 structure, which uses a diversified branch structure to enhance the network feature extraction ability during training and transforms the training model into an equivalent VGG-like topology network model during inference. In addition, we use ASFF to deal with the problem of mutually restricted scales. Experiments show that compared with the original algorithm, the improved algorithm increases mAP by 0.51 on the VOC data set, and at the same time the speed increases by 10%.
Target tracking algorithm of intermittently updated template based on reliability evaluation
Authors: Xie Qingsong , Shen Jingwei , An Zhiyong 
Published: April 2022

Abstract
In the siamese network tracking method based on the target matching idea, the tracking performance is closely related to the similarity between the tracking template and the tracking target. The UpdateNet algorithm updates the tracking results of each frame prediction into the template during tracking, which undoubtedly guarantees the real-time correlation between the template and the tracking target. However, tracking targets will encounter tracking challenges such as occlusion and short-term severe deformation. The tracking results predicted under this tracking challenge will be mixed with messy information that has nothing to do with the tracking target. If the UpdateNet algorithm updates the tracking results that are rich in messy information into the template, the purity of the template will decrease. This paper proposes a method of intermittently updating templates by proposing template update constraints, so that the UpdateNet algorithm discards the low-confidence tracking results. This guarantees the purity of the tracking template to a certain extent, which improves the relevance of the tracking template and the tracking target. In addition, this article also conducts optimal training on the UpdateNet algorithm.
SPI-CGAN: Single-pixel imaging based on conditional generative adversarial network
Authors: Lu Yongyun , Tan Benying , Ding Shuxue , Li Yujie 
Published: April 2022

Abstract
Imaging quality at extremely sampling ratios is a key research topic in the field of single-pixel imaging (SPI). Although conventional methods can be used to reconstruct the object images at low sampling ratios, the reconstructed image is still visually unclear. To solve this problem, an SPI model based on a conditional generative adversarial network (SPI-CGAN) is proposed to achieve an end-to-end reconstruction of the object images and improve the image quality of the reconstruction at extremely low sampling ratios. To improve the stability of the training model, the objective function is composed of multiple loss functions. The validity of the model is verified through simulation data, which do not require a long time to collect. The optimized SPI-CGAN can reconstruct a sharp image edge at extremely low sampling ratios. Compared with a linear iterative method and a nonlinear iterative method, the proposed method performs better in terms of the quantitative indicators used.
SETAR Model Research Based on Support Vector Regression Parameter Estimation Method and Empirical Analysis
Authors: Li Yun , Yin Xiangfeng , Wen Xueting 
Published: April 2022

AbstractIn this paper, we use support vector regression (SVR) to estimate the parameters of the SETAR model for predicting the log-return of stock price data. SETAR model is usually estimated using maximum likelihood (ML) and minimize conditional sum-of-squares (CSS), assuming that the data are normal distribution. SVR is a powerful tool for regression estimation. Therefore, this paper proposes to use SVR instead of maximum likelihood and conditional least squares to estimate the parameters of the SETAR model. Empirical analysis on the two stock price data of China Unicom and ICBC, the results show that on the data of China Unicom, the prediction error of the Gaussian kernel SVR-SETAR model is 8% lower than that of the ML-SETAR model and the CSS-SETAR model. And on ICBC data, the prediction error of the Gaussian kernel SVR-SETAR model is 26.32% and 20% lower than that of the ML-SETAR model and CSS-SETAR model, respectively. This suggests that SVR can be used to estimate the parameters of the SESAT model and the SVR-SETAR model has higher predictive power.
Improve the spot-like coding detection of U-net auto-encoder
Authors: Liu Beibei , Hu Weiping , Li Fan 
Published: April 2022

Abstract
The dot-like spray code on the product packaging has always been a difficult problem in industrial inspection due to its complicated background in the printing area, diverse characters in the code, and changeable fonts. With the powerful capabilities of deep learning in the field of computer vision, related algorithms have become popular solutions in the field of computer vision in recent years. The method based on convolutional autoencoders to achieve inkjet detection on food packaging boxes has become a feasible solution. This method takes the mask of the coding character area on the food packaging box as the final goal of network learning. The network inputs the picture with the coding area, and reconstructs the original background of the coding area. At the same time the network uses the residual channel attention mechanism to restore the details of the image, and at the same time introduces the gaussian operator to calculate the loss of network reconstruction. Since the convolutional auto-encoder is an unsupervised learning method, the training data does not require a large amount of manual labeling, and a good solution is proposed for scenarios such as small data sets in industrial production and difficulty in labeling. Through real-time testing of the coding data of the pipeline, it is verified that the method can effectively detect the coding area.
Glass Defect Detection via Multi-Scale Feature Fusion
Authors: Huang Haiying , Zhan Qiugang , Xie Xiurui , Ye Dongsheng , Liu Guisong 
Published: April 2022

Abstract
Glass defect detection is significant in glass industry. However, most of the existing methods for glass defect detection currently still rely on manual screening with high-cost and poor-efficiency. To address this issue, we propose a glass defect detection method using multi-scale feature fusion strategy. Specifically, we first propose an algorithm based on pix2pix to realize the edge extraction. Then, we propose a glass defect detector based on both local and global features. Comprehensive experiments are conducted on collected glass dataset from factories. The experimental results demonstrate that our method outperforms conventional methods including the traditional image processing and Holistically-Nested Edge Detection (HED), with the precision rate (PR) up to 97%, the false precision rate (FP) below 2% and the total accuracy rate (ACC) of glass defect detection up to 98%.
Joint Measurement of Multi-channel Sound Event Detection and Localization Using Deep Neural Network
Authors: Zhou Yuting , Wan Hongjie 
Published: April 2022

Abstract
For joint sound event localization and detection (SELD), a multi-channel sound event method based on deep learning is proposed. This paper uses CRNN model training with datasets of maximum two overlapping sound events. The difficulty of the polyphonic SELD is the combination of SED and DOA estimation in the same network. Using multi-channel audio can better identify these overlapping sound events. The input of the proposed model is a series of continuous spectrograms, which are then output to two branches respectively. As the first branch, SED performs multi-label classification in each time segment. As the second branch, 3-D Cartesian coordinates are used to represent the DOA estimate of each sound event. This paper extracts the phase feature and amplitude feature of the sound spectrum from each audio channel, avoiding feature extraction limited by other microphone arrays.
X-ray security check image recognition based on attention mechanism
Authors: Wu Xiru , Liu Chao 
Published: April 2022

Abstract
In order to improve the detection efficiency and accuracy of the security check process, a detection algorithm that can accurately detect dangerous goods is constructed based on the attention mechanism and combined with the DETR algorithm. Combining the X-ray image data samples collected by the actual security inspection, the image data is trained through the deep learning network and analyzed on the verification data. The results show that the algorithm has higher verification accuracy, and the accuracy is higher than that of traditional detection algorithms, and it can locate dangerous goods more accurately. The method given in this article provides theoretical support and reference for the actual security inspection process.
Remote Sensing Image Retrieval Based on Key Region Detection
Authors: ye Famao , Zhang Rengao , Xing Yuchi , Xin Junwei , Li Dajun 
Published: April 2022

Abstract
Remote sensing (RS) images usually describe large-scale natural geographical scenes with complex and rich background information, which will affect the retrieval performance of image features. How to reduce the background interference and improve the reliability of remote sensing image retrieval (RSIR) features is a problem that needs to be solved. In this paper, a RSIR method based on key region detection was proposed. Firstly, the ground objects of the image are extracted by a famous deep learning object detection model, a YOLO v5 model. Next, we extract the key region of the image according to these ground objects. Then, the image content in the key region is used to extract the retrieval feature by the convolutional neural networks (CNN) model, Resnet. Moreover, the weighted distance based on class probability is used to further improve retrieval performance. Our method utilizes the object detection capability of the YOLO model and the feature extraction capability of RESNET. Our method uses the target detection ability of the YOLO model and the feature extraction ability of RESNET to extract the retrieval feature of RS images. The experimental results on UCMD show that this method can improve the performance of RSIR.
FPGA-based real-time data processing for accelerating reconstruction at LHCb
Authors: Lazzari F. , Baldini W. , Bassi G. , Contu A. , Dorigo M. , Fantechi R. , Giambastiani L. , Morello M.J. , Punzi G. , Sticchi M. , Tuci G. 
Published: April 2022

Abstract
In run-3, beginning in 2022, the LHCb software trigger will start reconstructing events at the LHC average crossing rate of 30 MHz. Within the upgraded DAQ system, LHCb established a testbed for new heterogeneous computing solutions for real-time event reconstruction, in view of future runs at even higher luminosities. One such solution is a highly-parallelized custom tracking processor (“Artificial Retina”), implemented in state of the art FPGA devices connected by fast serial links. We describe the status of a realistic prototype for the reconstruction of pixel tracking detectors that will run on real data during run-3.
Diffusion processes with Gamma-distributed resetting and non-instantaneous returns
Authors: Radice Mattia 
Published: April 2022

Abstract
We consider the dynamical evolution of a Brownian particle undergoing stochastic resetting, meaning that after random periods of time it is forced to return to the starting position. The intervals after which the random motion is stopped are drawn from a Gamma distribution of shape parameter α and scale parameter r, while the return motion is performed at constant velocity v, so that the time cost for a reset is correlated to the last position occupied during the stochastic phase. We show that for any value of α the process reaches a non-equilibrium steady state and unveil the dependence of the stationary distribution on v. Interestingly, there is a single value of α for which the steady state is unaffected by the return velocity. Furthermore, we consider the efficiency of the search process by computing explicitly the mean first passage time. All our findings are corroborated by numerical simulations.
The Effect of Growing Media on the Vitamin C and Chlorophyll Content of Gelinggang Microgreen
Authors: Rohmanna N. A , Mulyawan R. 
Published: April 2022

Abstract

Cassia alata L. (called Gelinggang) was a type of herbal plant used as a skin and diarrhea medicine. However, there has been no further research related to gelinggang microgreens. Therefore, this study utilizes gelinggang as a microgreen. This study aimed to determine the growth media’s effect on chlorophyll and ascorbic acid in gelinggang microgreens. In this study, gelinggang seed was grown in organic soil and rockwool for about 14 days at room temperature (27±1°C). The chlorophyll of acetone extract and ascorbic acid of sodium oxalate extract of this microgreen were determined using UV-Vis spectrophotometric methods. There was a difference between organic soil and rockwool as growing media on the vitamin C and chlorophyll gelinggang microgreen. The study showed that microgreens on organic soil had the highest chlorophyll content, while microgreens on rockwool had the highest vitamin C content. Thus gelinggang microgreens high in phytochemicals compounds could be used as health-promoting food ingredients.