bluuebunny/crossref_metadata_2025_split huggingface dataset:
Res2-YOLOV4 Object Detection Algorithm for Snakeheads
Authors: Zhang Yifei , Yue Jun 
Published: December 2022

Abstract
Fast and accurate detection of snakeheads is crucial for their aquaculture and behavior analysis. While farming, intra-class overlap causes wrong detection and omission, undermining the accuracy. And at present, there are few effective detection methods for snakehead aquaculture. To address this problem, this paper proposes the Res2-YOLOV4 snakehead object detection method based on the dataset of 2000 snakehead images. Firstly, the Res2net is integrated into the CSPDarkNet53 backbone network based on the YOLOV4 model to enhance the feature extraction capability. Secondly, IOU Soft-NMS, which regards the degree of coincidence between the prediction box and the actual object position as its screening criterion, is put forward to solve the problem that the conventional NMS incorrectly excludes correct prediction boxes in case of intra-class overlap. The experimental results show that AP50 and AP75 reach 97.7% and 94.77% respectively, increasing by 3.9% and 5.61%, 5.6% and 5.87%, and 4.6% and 4.76% respectively compared with YOLOF, VFNET, and YOLOX, which proves the effectiveness of this method.
Mongolian-Chinese Machine Translation Based on Graph Neural Network
Authors: Hao Jia , Tian Yonghong , Zhang Qingqing , Zhang Genmao 
Published: December 2022

Abstract
Most of the current Mongolian-Chinese neural machine translation relies on a sequence-to-sequence-based encoder-decoder, which does not effectively utilize the syntactic information of sentences as well as the hierarchical information of the language. To solve this problem, a graph-to-sequence-based encoder-decoder is constructed by introducing graph neural networks, making better use of syntactic information and semantic knowledge of sentences. We will build a Mongolian dependency tree library and design a densely connected graph convolutional neural network (D-GCN) based on GCN combined with a densely connected network (DenseNet). Experiments are conducted to compare with the sequence-to-sequence based Mongolian-Chinese neural machine translation model and the variant model of graph neural network, and the results verify the advantages of the D-GCN-based Mongolian-Chinese neural machine translation model in translation performance.
Multi-objective Optimal Scheduling of Small Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm
Authors: Huang Fangchen , Chen Yuhan , Lu Wenzhou 
Published: December 2022

Abstract
In order to explore a low-carbon and economical way of supplying energy for buildings and promote the transformation of the energy system, a small integrated energy system including energy supply equipment, energy conversion equipment and energy storage equipment is designed. After comprehensively considering operating cost and carbon emission, a multi-objective optimal scheduling model of the system is constructed. A genetic particle swarm optimization algorithm based on random dynamic inertia weight is proposed to solve the model and analyze the output of each piece of equipment in the system under the optimal operating strategy. An example of a building in a region with hot summer and cold winter is introduced for verification, and the simulation results show that the proposed small integrated energy system model is economical and environmentally-friendly, and the improved particle swarm optimization algorithm has a better optimization effect.
Research on Vehicle Detection Algorithm Based on Improved YOLOv4
Authors: Xu Mingzhi , Cui Wei , Xu Jing , Zhang Wenhan 
Published: December 2022

Abstract
The check of vehicle fact is a significant technology in the intelligent control of transport brightness. A vehicle diagnosis and identification algorithm based on Yolov4 is proposed to encounter the dare of flow vehicle information monitoring technology in exactness, rate and firmness. The K-means++ gathering manner is used to get the mooring frame organize points appropriate for the data set. The CIOU loss function is used to optimize the training process, and then it’s trained through the CSPDarkNet53 network framework to improve the YOLOv4 network structure. The DenseNet module is applied instead of the feature pyramid. The 5-time convolution module in this paper simplifies the feature network. It is found that the experimental results have achieved good results, which can be able to achieve the goal in the practice.
FEM-Based N-Net Control of a Flexible Dexterous Finger
Authors: Xu Jiawei , Li Lvzhou , Dong Xu , Yin Zijian , Jiang Xinyu , Hu Hongwei 
Published: December 2022

Abstract
Owing to their light weight, compactness, and good adaptability, soft robots are promising for applications with safe human-machine interactions. Compared with traditional rigid robots, the distributed actuation of soft robots with soft bodies leads to complex nonlinear deformation, making it challenging to model its dynamics. In this paper, a Finite Element Method (FEM)-based fast neural network open-loop control method is presented to control the flexible dexterous hands in their task-space using the finite element method. The main idea is to use a simple neural network to find the mapping between the end-effector control values and the displacement. In this way, the control of complex structures can be simplified. This model is especially suitable for complicated braking structures that cannot be accurately sensed. Finally, the effectiveness of the proposed method is verified by experiments, and the results are convincing.
Path Planning for AUVs with Energy-Optimal Velocity in Current Fields
Authors: Wang Feng , Chen Wenliang , Li Chenlong 
Published: December 2022

Abstract
This paper presents the energy-optimal velocity to solve the problem of energy-optimal path planning for autonomous underwater vehicles (AUVs) in current fields. In the proposed scheme, a sequence of thrust velocities and steering directions is predicted to enable vehicles to utilize/avoid currents and minimize their energy consumption. Each thrust velocity and steering direction are deduced through a vector analysis method under the expected displacement and corresponding current vector, which is the energy-optimal velocity. The proposed energy-optimal velocity combines the global optimization technique and guarantees the minimization of a vehicle’s energy consumption. Several experiments are performed on strong and turbulent current fields to validate the effectiveness of the proposed scheme.
Simulation and application of the electric power steering system simulation platform based on Simulink
Authors: Gao Senqi , Sun Rui , Fu Ying , Xu Jianguo 
Published: December 2022

Abstract
The principle of the electric power steering system is introduced in this paper, and the simulation platform based on Simulink/Simscape is established. The mechanical structure of the steering gear, the timing scheduling strategy, the hardware control circuit, and the integration of the software control algorithm are modeled and verified in detail. It is proved that the simulation model is reliable and highly precise. The simulation platform is used to analyze the actual problems, and propose an improvement plan. The results show that the platform can improve the intelligence of development and quickly locate, effectively reproduce and analyze actual problems, and reduce the trial-produce cost of the early production line, which has a certain guiding significance for software and hardware development.
Dynamic Trust Continuous Evaluation-based Zero-Trust Access Control for Power Grid Cloud Service
Authors: Lv Pang , Sun Xin , Huang Hui , Qian Jin , Sun Changhua , Dai Hua 
Published: December 2022

Abstract
The development of power grid cloud service puts forward higher requirements for remote secure access. To realize fine-grained access control of remote secure access of power grid cloud service, this paper proposes a zero-trust access control model for power grid cloud service based on dynamic trust continuous evaluation. The experimental results show that the model proposed in this paper can effectively implement continuous trust evaluation for remote access of PowerCloud services, and achieve zero trust access control, with an exception detection accuracy rate of 97.2% and the incremental learning accuracy rate of over 97% when the behavior model of remote access changes.
Workspace Mapping Method Based on Edge Drifting for the Teleoperation System
Authors: Gou Songlin , Zhou Jian , Zhang Hua , Liu Manlu , Yang Shunyao , Zhang Xinglang 
Published: December 2022

Abstract
The workspace mapping method for master-slave isomeric robots is a key research direction. In order to enhance the safety and stability of the operation of such robots, a position-joint hybrid mapping method based on edge drifting is proposed in this paper. First, teleoperation is divided into two phases, that is, task approach and task operation. During the task approach, the edge drifting algorithm is adopted to largely cover the workspace of a robot. Rate mapping is used to quickly control the movement of the slave robot to the target position. During task operation, a hybrid mapping algorithm integrating the Cartesian space and the joint space highly precisely maps both the position and pose of the slave robot to accurately follow the control command of the master end and achieve precise control. Lastly, the proposed method is validated on the experimental platform for master-slave isomeric teleoperation. The experimental results indicate that this mapping method allows the master-slave isomeric teleoperation system to cover a large workspace precisely with desirable precision in position and pose following.
Optimizing Non-diagonal Stiffness Matrix of Compliance Control for Robotic Assembly Using Deep Reinforcement Learning
Authors: Liu Gang , Yao Bitao , Xu Wenjun , Liu Xuedong 
Published: December 2022

Abstract
With the advent of Industrial 4.0, industrial robots have been widely used in various sectors, e.g., in assembly. Peg-in-hole (PiH) assembly is the most typical one among assembly tasks. In PiH tasks, the robot transfers from a non-contact mode to a contact-rich mode. Instead of switching the position/force control mode with a pause when contact is detected, we deploy a non-diagonal stiffness compliance control for planning the adaptive trajectory to improve the task efficiency and ensure contact safety. In this paper, we proposed a deep reinforcement learning (DRL) method to achieve the above compliance. A compliance controller based on a virtual forward dynamic (FD) model is built. A DRL agent is deployed to optimize the parameters of the non-diagonal stiffness matrix for the built compliance controller to generate a trajectory that adapts to the changing contact condition. The experiment shows the proposed method can control the contact force within a safe range and improve the efficiency of assembly tasks.
MI-RRT-Connect Algorithm for Quadruped Robotics Navigation with Efficiently Path Planning
Authors: Zhang Yulong , Jiang Haoyu , Zhong Xungao , Zhong Xunyu , Zhao Jing 
Published: December 2022

Abstract
Autonomous navigation is playing an increasingly important role in quadruped robotic systems. However, providing safe and reliable path planning for robots is still an open problem. In this paper, we propose a sampling-based path planning algorithm fused with a dual-tree structure, here called Multiple Informed RRT-Connect (MI-RRT-Connect). The proposed MI-RRT-Connect can overcome the disadvantage that the Informed RRT* algorithm takes a long time in the initial search path, by using the RRT-Connect algorithm and the target deviation method, and the initial search path can be obtained quickly. Then, for the optimization of the initial search path, the multi-level parent node selection strategy and the method of calculating the path cost for the parent node are used. The simulation and quadruped robot experimental results show that our method can find an optimal path in a shorter time, and it has been well applied in a real quadruped robot.
Tracking of moving targets in complex environments
Authors: Ye Jianchuan , Wang Tingting 
Published: December 2022

Abstract
The tracking of a moving target algorithm based on the traditional visual servo can not introduce image and physical constraints, and does not consider the problems such as the features beyond the camera field of view, the robotic arm’s out-of-motion space, and the robotic arm reaching the joint limit. Moreover, the tracking accuracy and stability of the moving target continue to be poor in complex environments (such as the chaotic background, the illumination changes, and the target is partially occluded), and the recognition of the target image is extremely strict, In this paper, a new method of tracking and grasping moving targets is proposed by combining the pose estimation of targets with the tracking and grasping of industrial robots. Kalman filter is used to compensate for the time delay. This method avoids the influence of traditional methods on target depth estimation and image and physical constraints and has strong robustness.
Research on the Visual Servoing method based on semantic segmentation results of metal objects in a complex lighting environment
Authors: Shi Shunkai , Wang Tingting 
Published: December 2022

Abstract
The problem of feature recognition and servo tracking for semantic segmentation results of metal parts under complex and variable lighting conditions is addressed. In this paper, we propose an algorithm that incorporates template matching and image-based visual servoing based on semantic segmentation. The method adopts a geometry-based template matching method to stably identify the image features of the pixel region obtained from semantic segmentation, introduces a feature pyramid dimensionality reduction method to improve the processing speed, and then introduces the extracted image moment features into the image-based visual servoing to operate the robotic arm to perform the servo process on the target.
PRRT: An Intelligent Planning Algorithm with Memory
Authors: Chen Qinhu , Kang Meilin , Fan Zeming , Yu Xiaojun 
Published: December 2022

Abstract
This paper proposes a practically executable path planning method, namely, Pheromones-RRT(PRRT), for robots with a large joint range in a complex environment. To inter-activate with the real world, the point cloud is utilized as the scene information, while for sampling, the pheromones approach is designed to describe the pheromone content carried by each sampling point. During the sampling process, random sampling nodes are performed with a probability of ε, or those nodes with the highest pheromone content in the current sampling tree are selected with a probability of 1-ε and sampled in their neighborhood. To avoid the local minimum problem, the concept of pheromone volatile factor (PVF) is proposed, while in the expansion, double trees are also generated by PRRT in both cartesian and configuration spaces to improve the speed of the algorithm. The pheromone accumulation enables PRRT to certain learning abilities, reducing the randomness of the sampling process. Simulation results show that the proposed method can effectively plan an optimal obstacle avoidance path with satisfactory performances as compared with the RRT-Connect.
Design of Workshop Control System Based on Digital Twin
Authors: Zheng Liang , Yuan Minghai , Pei Fengque , Mao Kefu 
Published: December 2022

Abstract
For the problems of the traditional workshop control system, such as closed information and untimely delivery, the web-based digital twin workshop control system is designed from the concept of the digital twin. According to the physical entities in the workshop, the corresponding digital twin model is constructed, and the design of the whole process, from the collection of workshop data to the application, is completed. Finally, the system is applied to a small workshop, and the results show that the digital twin-based workshop control system could significantly improve the workshop’s productivity.
2D Mapping of Mobile Robot Based on micro-ROS
Authors: Li Bin , Ma Zhengguang , Zhao Yongguo 
Published: December 2022

Abstract
This paper proposes micro-ROS-based mobile robot system development and 2D mapping. Micro-ROS uses the micro-XRCE-DDS middleware to communicate with ROS2 in an extremely resource-constrained microcontroller environment. The mobile robot control system adopts a layered control method. The x64 PC equipped with Ubuntu ROS foxy is used as the main control platform, the olimex-stm32-E407 equipped with micro-ROS is used as the motion control layer, and the stm32F103 microcontroller is the underlying driver core. The forward and inverse kinematic equations are obtained through the analysis of the robot kinematics model. Through the interaction between the ROS2 nodes in the micro-ROS environment and the ROS2 nodes on the PC side, the cartographer algorithm is used to verify the effectiveness of the micro-ROS-based mobile robot system.
A Variable Stiffness Exosuit for Walking Assistance
Authors: Huang Kaizhen , Xu Jiajun , Lu Yonghua , Xu Linsen 
Published: December 2022

Abstract
Robotic exoskeleton takes an important role in walking assistance for subjects with lower extremity impairment. However, most of the existing robotic exoskeletons are bulky and complicated in the mechanical structure, placing a large load on the wearer. In this paper, a soft exosuit with twisted string actuators (TSAs) is designed for walking assistance. The analytic model of the TSA is derived to prove its characteristic of variable stiffness, benefited from which, flexible and compliant assistive torque can be generated to knee joints whilst walking. Furthermore, a human-robot coupling dynamic model is established, and an admittance control strategy is utilized to improve the trajectory tracking performance. Finally, simulations are conducted to verify the proposed robot system.
Designing Control System of Palletizing Robot Based on RobotStudio
Authors: Chen Xiaokang , Zhou Yin , Yang Bin , Miao Xinghua , Li Yanlin , Zhang Miaorong 
Published: December 2022

Abstract
According to the operational task of the palletizing robot in the intelligent manufacturing system, the layout and control process of the palletizing system are first designed. And then, the Smart Component of RobotStudio is used to design the system signal with I/O control logic, conveyor control logic, and a suction cup tool. Finally, the rationality, efficiency, and convenience of the function of the palletizing robot control system are verified by building a palletizing simulation system.
Assembly Status Detection Based on Step-by-step Strategy for Robotic Assembly
Authors: Cui Jia , Bie Haodong , Yao Bitao , Xu Wenjun 
Published: December 2022

Abstract
Assembly status detection is of great significance in the automated assembly of industrial robots. However, in addition to the traditional manual inspection, the existing methods for assembly status detection are mainly through methods such as torque sensors, etc., which are difficult to implement in practical assembly and lack the detection of the entire assembly cycle. To solve this problem, this paper proposes a method for assembly statute detection based on a step-by-step strategy. The first step is to use fast bounding box collision detection to determine whether the robot enters the correct assembly neighborhood. The second step is to use accurate pose information to determine whether the product is assembled correctly. The experiments show that the proposed method can accurately detect the assembly status for the entire assembly cycle with high assembly efficiency.
Multi-robot Coordination for Environment Exploration and Patrol
Authors: Yang Jianxin 
Published: December 2022

Abstract
With the development of manufacturing, people benefit from using multiple mobile robots to complete tasks in various scenarios, and multi-robot planning coordination has become a hot research topic in recent decades. This paper introduces two popular multi-robot system applications: multi-robot exploration and multi-robot patrol. First, the two kinds of problems are introduced and compared. Then, typical methods to figure out the problems are presented, including the frontier-based and market-based methods for exploration and the v-Min algorithm and v-Neighbor algorithm for patrol. Finally, this paper gives the experiment results and conclusions of different methods.
Study on the determination of variable characteristics of axial piston pumps
Authors: Yuan Chenhui , Xing Keli , Jin Xiajie 
Published: December 2022

Abstract
Because the variable characteristics of axial piston variable pump can be applied to different working conditions, this paper simulates the actual working conditions of axial piston pump in excavator, theoretically analyzes its variable working principle, and focuses on how to adjust the difference between pump outlet pressure and load sensitive port pressure stably. Finally, an axial piston pump variable characteristic test system is built based on LabVIEW software, and a field verification test is conducted, and an effective and reasonable test scheme is selected through analysis and comparison, which provides a certain engineering reference value for the variable characteristic test of axial piston pump.
A Hybrid Model Based on Jensen’s Inequality Theory for 3D Printed Lattice Structures Maximum Stress Prediction
Authors: Zhang Zhiwei , Zhang Yuyan , Wen Yintang , Ren Yaxue 
Published: December 2022

Abstract
Inherent defects in 3D-printed metal lattice structures are inevitable and significantly affect the structural part’s maximum stress. To solve this problem, a hybrid model based on Jensen’s Inequality Theory to predict the maximum stress of 3D printed lattice structures is proposed in this study. Jensen’s inequality theory means that the average of the model predictions will not be lower than the predictions of the individual prediction models. Therefore, the optimal number of hybrid models is discussed and designed, and the optimal base estimators are trained and selected on the same dataset. A hyperparametric approach is used to optimise the base estimator parameters. The experimental results demonstrate that the hybrid model performs better than a single estimator’s prediction, with an R2 of 0.83. This research offers a novel approach for the prediction of 3D-printed lattice structures using machine learning.
Mechanism and regulation of high temperature and high salt tolerance polymer polymerization behavior
Authors: Song Liping , Zhao Xingze , Wu Yining , Ren Hao 
Published: December 2022

Abstract
In this study, the molecular dynamics simulation of imidazole-modified polyacrylamide (VPAM) and polyacrylamide (PAM) system showed that the VPAM system containing imidazole-modified polyacrylamide (VPAM) had better cohesion-bonding effect than the PAM system without imidazole. Based on the imidazole-containing VPAM system, the cohesion properties of the functional monomers and the mechanism of the functional monomers in the process of cohesion were analyzed at high temperature and high salt, and the structure-activity relationships between the chemical structure of the functional monomers and the ratio of polymer monomers and the apparent cohesion properties were obtained.