bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wheat Canopy Cover Estimation by Optimized Random Forest and UAV Multispectral imagery
Authors: Zhang Tianxiang , Liu Yan , Li Jiangyun 
Published: February 2022

Abstract
Canopy cover estimation is widely applied to reflect crop status in agriculture research and management. In particular, an accurate CC estimation is beneficial for crop model calibration, providing high-accuracy observations. Recent solutions on CC are drawn by experimental regression or basic machine learning classifier because CC estimation can be treated as a wheat/non-wheat segmentation task. However, the appearance of hyperparameters in such machine learning algorithms impairs the segmentation performance. In this paper, by the means of UAV multispectral imagery, Bayesian optimization based Random Forest approach is selected to tune the uncertain hyperparameters accurately and robustly, providing a novel way on CC estimation. Experimental results collected in Yangling experiment field by the RedEdge camera on DJI M100 UAV are to evaluate the proposed method. Comparative studies show that the overall accuracy can reach up to 99.9%, promoting 0.2% in comparison with basic Random Forest. Therefore, integrating optimized Random Forest and UAV multispectral imagery can be applied in CC estimation at farmland scales.
Semi-supervised Deep Closest Point Method for Point Cloud Registration
Authors: Wu Yong , Shen Lingfeng , Li Ping 
Published: February 2022

Abstract
Point cloud registration is one of the key issues in fields that need 3D scenes with global vision, including 3D scene reconstruction in robot technology, high-accuracy 3D map reconstruction in automatic driving, 3D reconstruction of real-time monitoring underground tunnel. Recently, point cloud registration methods based on deep neural networks have made great progress by learning from a large amount of well-aligned point cloud pairs. However, these large-scale labelled samples are usually difficult to obtain in the real-world applications such as visual simultaneous location and mapping of mobile robots which obtains unlabelled scene images in real time. To solve this problem, we propose a deep point cloud registration method based on the semi-supervised learning. Specifically, for the problem of less supervised data, we use Iterative Closest Point (ICP) algorithm to generate pseudo labels for the point cloud samples with unknown rigid transformations, effectively increasing the number of supervised data; in order to amend the pseudo labels, we design an alternating optimization algorithm to jointly learn the pseudo labels and deep point cloud registration model. The deep point cloud registration model and ICP are used alternately to continuously improve the precision of pseudo labels and the performance of the deep model. Experimental results show that this method can still obtain a reliable point cloud registration model even when just observing a small amount of point cloud samples with ground truth labels.
Modeling and Simulation of Flexible Spacecrafts Based on Dual Quaternions
Authors: Zhang Wenzheng , Zhang Jiawei , Zhang Canheng , Chen Dianyin , He Yijie 
Published: February 2022

Abstract
This paper proposes a description of flexible spacecrafts’ motion and rotation uniformly with dual quaternions. The dynamic equations are derived by using Lagrange equations. Recursive Newton-Euler (RNE) method is introduced to calculate the nonlinear terms in the equations. The simulation results demonstrate the coupling effects during the actuating speed switch of the solar panel.
Research on A Low Computational Cost Vision-aided Inertial Navigation Method for Precision Landing on Asteroid
Authors: Lin Boyu , Cui Hutao 
Published: February 2022

Abstract
Asteroid exploration missions demand more autonomous navigation system that are precise and in real-time. Visual-aided inertial navigation systems combine the advantages of autonomy of inertial navigation systems and accuracy of visual navigation systems. However, both increase the computational burden for the information processing. With the goal of decreasing the computational load, this paper proposes a low cost vision-aided inertial navigation method which can calculate increment position, velocity and attitude directly in inertial frame by pre-integrating IMU measurements. For visual information, specific combinations of the features are selected according to information entropy theory to reduce computation. Simulation results show that the standard deviations of the spacecraft’s estimated position error is less than 1 m, the velocity less than 0.002 m/s and the attitude angle less than 0.005 deg, compatible with precision landing on asteroids.
Cycle-slip Detection Method Considering Clock Jump for GNSS Raw Observation of Smartphones
Authors: Liu Xing , Li Chunhua , Li Cailing , Gao Pan 
Published: February 2022

Abstract
Using low-cost sensors to achieve high-precision positioning has been a hot research topic in autonomous driving technology, and smartphones not only have the advantages of low cost and easy installation, but also can provide high-precision navigation and positioning services. In this paper, we focus on the cycle-slip detection method based on the original Global Navigation Satellite System (GNSS) observations of smartphones. In response to the problem that the phase value of smartphone GNSS has serious cycle slip and the existence of frequent clock jumps will affect the cycle slip detection, a cycle slip detection method that integrates clock jumps and single and dual frequency observations is proposed. The experiments show that the method can effectively detect the continuous and as small as 0.5 week of cycle slip for the phase observations with the sampling rate of 1 Hz. The root mean square error (RMSE) in the L1 and L5 phase residuals after the cycle slip repair is 7.0 mm and 8.3 mm, respectively, with an accuracy improvement of 18.16% and 9.62%, which indicates that this method is effective and feasible.
Improving the Transferability of Adversarial Examples by Using Generative Adversarial Networks and Data Enhancement
Authors: Xie Pengfei , Shi Shuhao , Xie Wanjing , Qin Ruoxi , Hai Jinjin , Wang Linyuan , Chen Jian , Hu Guoen , Yan Bin 
Published: February 2022

Abstract
Deep neural networks (DNNs) can be attacked by adversarial examples that are undetectable by humans. Generation-based approaches have recently gained popularity because they directly translate the input distribution to the distribution of adversarial instances, making them more effective and efficient. However, existing techniques are susceptible to overfitting on the substitute model, limiting the transferability of adversarial examples. In this paper, we introduce data augmentation into AdvGAN, called AdvGAN-DE, which can dramatically improve the transferability of adversarial examples. Experiments demonstrate that we enhance the success rate by 31.09% for defense models and 10.23% for typically trained models on average when compared to AdvGAN.
Research on Mosaic Method of UAV Low-altitude Remote Sensing Image based on SIFT and SURF
Authors: Zhu Hang , Jiang Yu , Zhang Cui , Liu Shu 
Published: February 2022

Abstract
Unmanned aerial vehicle (UAV) low-altitude remote sensing image stitching is a new technology to promptly grasp the lodging situation of rice. The effect of image stitching depends on different application scenarios, so that it is necessary to explore low-altitude remote sensing image stitching algorithm suitable for rice lodging monitoring. The research adopts SIFT (Scale invariant feature transform) and SURF (Speeded up robust features) feature detection algorithms to conduct mosaic experiments based on drone images of a rice field in Dehui City, Jilin Province. The results demonstrate that the image stitching technology based on surf algorithm possesses better real-time performance, and the panorama obtained can well reflect the lodging condition of rice field. This research can provide technical reference for the actual lodging monitoring of rice field.
YOLOX-Mobile: A Target Detection Algorithm More Suitable for Mobile Devices
Authors: Ma Songzhe , Lu Huimin , Wang Yifan , Xue Han 
Published: February 2022

Abstract
With the continuous development of network, the increasing popularity of embedded technology, and the continuous improvement of mobile terminal computing ability, how to reduce the size of the model without affecting the accuracy as much as possible, and how to deploy the algorithm in embedded devices has become the current research hotspot. In this paper, we propose a new target detection algorithm called YOLOX-Mobile. Firstly, in order to further reduce the power consumption of embedded devices while maintaining the performance, we replaced the SiLU activation function in the original YOLOX with Mish activation function. Because SiLU activation functions occupy a certain amount of computing and storage resources, and the cost of calculating the Sigmoid type functions on mobile devices is much higher. Therefore, we use Mish functions that are smoother, non-monotonic, unbounded upper and lower bounds are used as activation functions to better meet the low power requirements of embedded devices. Secondly, we used Focal Loss as the Loss function of obj_output to achieve the balance of positive and negative samples, as well as hard-to-classify and easy-to-classify samples. Thirdly, we introduce the Involution operator and use it as the convolution kernel of 3×3. We validated the proposed algorithm on public dataset VOC2012 against the current mainstream YOLOX, YOLOX-M, and YOLOX-S algorithms. The mAP of our proposed algorithm is 78.22% and the detection speed is 55.26 FPS. Compared with the original YOlOX, YOLOX-M and YOLOX-S algorithms, the average FPS is improved by 1.99% and 4.13 FPS, and the average Params Size is reduced by 28.80%. Experimental results show that our proposed algorithm improves the accuracy and speed of detection on top of greatly reducing the network parameters and computation, making it a more suitable target detection algorithm for mobile devices.
Path Planning of Cleaning Robot for Cement Silo Inner Wall
Authors: Tian Jianying , Liu Shuchen , Sun Liwen , Jia Lianqin 
Published: February 2022

Abstract
This paper studies the design and control method of the cement silo wall cleaning robot system, analyzes the overall control requirements of the robot, and uses PLC as the control processor to design a cement silo wall cleaning robot control system, including action motor, pneumatic components and other components selection, which designs and completes working motors and sensors wiring schematic diagram. According to PLC input and output wiring diagram design, timing analysis and the human-computer interface of the upper computer, the design of the inner wall cleaning robot control system was completed, which can adapt to different circumstances, and has the characteristics of automatic cleaning, continuous operation and efficient completion. On this basis, aiming at the functional needs of the robot cleaning the inner wall of cement silo, cleaning path planning and control has been designed with better conditions.
Calibration of RGB-D Camera Using Depth Correction Model
Authors: Zhou Yang , Chen Danqing , Wu Jun , Huang Mingyi , Weng Yubin 
Published: February 2022

Abstract
This paper proposes a calibration method of RGB-D camera, especially its depth camera. First, use a checkerboard calibration board under auxiliary Infrared light source to collect calibration images. Then, the internal and external parameters of the depth camera are calculated by Zhang’s calibration method, which improves the accuracy of the internal parameter. Next, the depth correction model is proposed to directly calibrate the distortion of the depth image, which is more intuitive and faster than the disparity distortion correction model. This method is simple, high-precision, and suitable for most depth cameras.
Survey of Object Tracking Algorithm Based on Siamese Network
Authors: Zuo Mengle , Zhu Xuyang , Chen Yongchao , Yu Junyang 
Published: February 2022

Abstract
The network model using deep learning have been widely used in the sphereof visual object tracking for the past few years. The Siamese network can utilize the model based on deep learning to achieve a balance between the tracking accuracy and speed in the visual object tracking. This work mainly introduces the development process of the visual target tracking field and traditional target tracking algorithms. It focuses on the Siamese network structure and the improved the Siamese algorithm, and compares tracking results of related algorithms. Aiming at the deficiencies of existing Siamese object tracking algorithms, the future development trend is prospected.
A Review of Image Inpainting Automation Based on Deep Learning
Authors: Sun Qi , Zhai Rui , Zuo Fang , Zhong Yuhao , Zhang Yutao 
Published: February 2022

Abstract
The purpose of image inpainting is to automatically repair damaged areas using relevant information from preserved areas. Recent years, with the advancement of deep learning, significantly improved performance of image drawing has been achieved. In this paper, we are committed to reviewing the key techniques for automating image inpainting research. The article briefly describes conventional methods while focusing on deep learning-based inpainting methods, including model classification, strengths and weaknesses, range of usage, and performance comparison. Finally, the current issues and tendencies of image inpainting automation are discussed and predicted.
Adaptive Control Strategy with Threshold of Virtual Inertia and Virtual Damping for Virtual Synchronous Generator
Authors: Deng Wei , Zhong Jianwei , Huang Ming , Zhang Jixue , Zhang Zhekai 
Published: February 2022

Abstract
Virtual Synchronous Generator (VSG) control strategy can provide inertia and damping support for the power system by simulating the operating characteristics of synchronous generator, and improve the frequency response of the power system. First, analyse the influence of various parameters on the output characteristics of the system by establishing a mathematical model of VSG. Secondly, to fully utilize the flexible and controllable characteristics of VSG control strategy, the basic principle of VSG parameter adaptation is obtained through the analysis of the angular frequency oscillation characteristics of synchronous generators, and an adaptive control strategy of virtual inertia and virtual damping parameter is proposed, which can better track changes in frequency, and set the action threshold for adaptive control. Finally, compare the frequency and active power response between the traditional VSG control strategy and the adaptive one and verify the effectiveness of the proposed control strategy.
Application of Deep Learning in Wireless Networks for Channel Estimation: A Survey
Authors: Peng Guangyao , Zhu Zhengfa , Liang Yuanhai 
Published: February 2022

Abstract
The quality of channel estimation (CE) is critical to the performence of wireless communication systems. In addition to the conventional model based CE methods appeared in the past few decades, deep learning (DL) based CE methods, which is introduced to learn the statistical characteristics of wireless channels, has emerged as a promising methods in recently years. In this survey, different DL models applied to solve CE problems are discussed firstly, then DL-based CE problems in different wireless communication systems are investigated.
An Improved Algorithm for Target Detection in Low Light Conditions
Authors: Yin Dan , Tang Wengsheng , Chen Peng , Yang Bo 
Published: February 2022

Abstract
The accuracy of pig target detection is not ideal due to insufficient light in a real pig farm environment. An improved enhanced network helps to increase the accuracy for pig target detection. The ResNet-Attention-RetinexNet algorithm (RA-RetinexNet) is proposed to solve the problems that YOLO V4 has low accuracy in pig image detection under low light and Mosaic data enhancement method cannot improve the brightness of low light image. In this model, we build residual connection and add attention mechanism, decreasing data loss of RetinexNet and enhancing the brightness information of images. The experimental results show that the model achieves better performance for pig target detection under low light.
Wheel’s Slip Ratio and Sinkage Estimation for Planetary Rovers Moving on Deformable Terrain
Authors: Yang Huaiguang , Ding Liang , Gao Haibo , Deng Zongquan 
Published: February 2022

Abstract
Planetary rover plays a significant role in the lunar and Mars exploration missions and wheel’s move status such as sinkage and slip ratio are the critical Factor to estimate the moving ability of the Mars rover. A method is proposed to estimate wheel’s sinkage and skid angle based on terramechanic model and estimate slip ratio based on rover’s dynamic model using Extended Kalman Filter algorithm. The proposed method can avoid the errors caused by Visual Odometry and improves the estimation accuracy of rover state through the fusion with other sensors’ data. The effectiveness of the estimation method for planetary rovers is verified by comparing with the results obtained from dynamic simulation of Mars rovers.
Survey of Image Augmentation Based on Generative Adversarial Network
Authors: Yue Fei , Zhang Chao , Yuan MingYang , Xu Chen , Song YaLin 
Published: February 2022

Abstract
On the visual side of computer science, image data is very important in the training of neural network models. Sufficient training data can alleviate the over-fitting problem of the model during training and help the model obtain the optimal solution. However, in many computer vision assignments, it is not easy and costly to obtain sufficient training samples. Therefore, image augmentation has become a commonly used method to increase training samples. Generative Adversarial Network (GAN) is a generative method of machine learning that can generate realistic images and provide a new solution for image augmentation. This article first introduces image augmentation and its commonly used four types of methods. Secondly, the basic principles of GAN and its direct and integrated methods in image augmentation are introduced, and the typical methods used to calculate whether the images from the networks meets the requirements; then the research status of GAN in image augmentation is analyzed. Finally, the problems and development trends of GAN model in image augmentation are summarized and prospected.
Adaptive Particle Filter with Abnormal Detection for Wearable Indoor Pedestrian Navigation
Authors: Chen Ying , Chen Liang , Huang Lixiong , Liu Xiaoyan , Ma Zhen , Pan Yinghua 
Published: February 2022

Abstract
Currently, indoor location-based services (ILBSs) have increasing requirements in people’s daily life. In the meanwhile, the wearable devices are becoming more popular. In this paper, we studied a wearable system for indoor localization mainly based on INS/UWB. In order to achieve high-precision, stable, and continuous positioning, a sensor fusion method with anomaly detection is proposed. In the method, the sensor fusion method is derived from Bayesian estimation and a particle filter is developed to solve the nonlinearity problem and non-Gaussian errors for indoor positioning. In addition, the anomaly detection eliminates effects of NLoS and multipath effects significantly with the Mahalanobis distance. Two field experiments are conducted, and the results demonstrate that the 90% error of the proposed adaptive particle filter is 0.53 m, which is a 40% decrease compared with the PDR-only and UWB-only and classic PF, indicating better robustness and stability.
Application of Particle Swarm Optimization Algorithm in Photovoltaic Constant Pressure Water Supply Electrical Control System
Authors: Zhu Xianping , Li Shaowu , Cheng Hang , Fan Zhipeng 
Published: February 2022

Abstract
The traditional constant water supply system has problems, low operation efficiency, and the strong nonlinear and large time delay of the control system. It did not meet the needs of water supply in remote and desert areas. To solve these problems, the PV constant voltage water supply system is designed to optimize the Proportion Integration Differentiation (PID) controller method based on the particle swarm optimization (PSO) algorithm. The programmable logic controller (PLC) and frequency converter are used as the core control mechanism of the system, the water pump motor as an actuator, the pressure sensor and the level sensor be used as a feedback mechanism, Photovoltaic power generation equipment is used in power supply agencies. In this paper, the objective utilizes the PSO algorithm to optimize the parameters of the PID controller, and the principle and control process of the PSO-PID algorithm are analyzed. Simulation results by MATLAB verify that the PSO-PID algorithm has better dynamic and steady-state performance than the traditional PID algorithm. Finally, the practicability and effectiveness of the water supply system are proved by the PLC of the OMRON-CP1H series and the touch screen of the WEINVIEW series.
Simulated Troubleshooting of Civil Aircraft Based on Operation and Maintenance Database
Authors: Lyu Huizi , Ma Zhenyuan 
Published: February 2022

Abstract
The characteristics of aircraft maintenance training are that the teaching hardware resources are few, the maintenance training items are many, and the students are difficult to master. To solve this problem, we developed a simulator for aircraft maintenance training by studying the key technologies of maintenance training devices (MTD). Based on the classic control theory to design the autopilot control law, using big data from aircraft operation and scheduled maintenance to achieve high simulation MTD, to solve the difference between traditional MTD teaching and actual aircraft maintenance makes CMTD more effective and vital.
Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm
Authors: Zhang Zhizhuo , Zheng Change 
Published: February 2022

Abstract
There are many kinds of inverse kinematics solutions for robots. Deep reinforcement learning can make the robot spend a short time to find the optimal inverse kinematics solution. Aiming at the problem of sparse rewards in the process of deep reinforcement learning, this paper proposes an improved PPO algorithm. Firstly, built a simulation environment for the operation of the robotic arm. Secondly, use a convolutional neural network to process the data read by the camera of the robotic arm, obtaining a network about Actor and Critic. Thirdly, based on the principle of inverse kinematics of the robotic arm and the reward mechanism in deep reinforcement learning, design a hierarchical reward function containing motion accuracy to promote the convergence of the PPO algorithm. Finally, compare the improved PPO algorithm with the traditional PPO algorithm. The results show that the improved PPO algorithm has improved both the convergence speed and the operating accuracy.
Research on Grasping Strategy Based on Residual Network
Authors: Li Shaobo , Bai Qiang , Yang Jing , Yu Liya 
Published: February 2022

Abstract
At present, the research on rectangular grasp strategy is generally based on object detection algorithm, which limits the improvement of model accuracy and generalization performance. This paper studies the semantic segmentation model based on residual network, and uses it to generate grasp strategies. The improved algorithm model not only achieves excellent rectangular grasping strategy prediction, but also has good generalization performance.