bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonlinearity Correction of Fourier Transform Spectrometer in Solar Observation Using Simulated Annealing
Authors: Dong Ze-Yin , Bai Xian-Yong , Feng Song , Deng Yuan-Yong , Yu Zi-Hang , Ao Wen-Tong , Yang Xiao , Zhang Zhi-Yong , Feng Zhi-Wei 
Published: March 2023

Abstract
A Fourier transform spectrometer (FTS) has been used to observe solar activities due to its ultra-high spectral resolution. However, the FTS in-band spectra are usually distorted and some artifacts appear in out-of-band regions due to nonlinear effects. Therefore, the FTS nonlinear problem must be corrected. In this study, we proposed a novel method to correct the nonlinear effects using simulated annealing. We simulated several nonlinear spectra to evaluate the performance of our method. The calculated quadratic coefficients are extremely close to the given values, demonstrating that the method is effective and accurate. The proposed method is further used to correct the blackbody and solar spectra with nonlinearity obtained by Bruker IFS-125HR installed at the Huairou Solar Observing Station, which is a pathfinder for the accurate infrared magnetic field measurements of the Sun project. To the blackbody spectra, the nonlinearity in low- and high-frequency regions are corrected by 89.09% and 60.84%. The nonlinear correction of the solar spectra in the low- and high-frequency regions have reached 65.34% and 81.04%, respectively. These results prove that our method can correct the nonlinear problem to improve the data accuracy.
Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques
Authors: Esas Mustafa Yasin , Latifoğlu Fatma 
Published: March 2023

Abstract

Objective. Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease in the community draws an increasing graph from the past to the present. While the ADHD diagnosis is basically made with the psychiatric tests, there is no active clinically used objective diagnostic tool. However, some studies in the literature has reported development of an objective diagnostic tool that facilitates the diagnosis of ADHD. Approach. In this study, it was aimed to develop an objective diagnostic tool for ADHD using electroencephalography (EEG) signals. In the proposed method, EEG signals were decomposed into subbands by robust local mode decomposition and variational mode decomposition techniques. These subbands and the EEG signals were fed as input data to the deep learning algorithm designed in the study. Main results. As a result, an algorithm has been put forward that distinguishes over 95% of ADHD and healthy individuals through using a 19-channel EEG signal. In addition, a classification accuracy of over 87% was obtained by the proposed approach of EEG signal decomposition followed by data processing in the designed deep learning algorithm. Significance. The findings of the current research enrich the literature based on originality and proposed method can be used as a clinical diagnostic tool in the near future.
Review of Human-exoskeleton Control Strategy for Lower Limb Rehabilitation Exoskeleton
Authors: Long Yi , Guo Hexiao , Chi Yutian , Mo Duncheng 
Published: March 2023

AbstractThe research on exoskeleton robots has been widely carried out for many years around the world, especially the development of new-style lower limb exoskeletons for rehabilitation and assistance is one of the key research directions. The focus on the control system of lower limb exoskeletons for rehabilitation is discussed. Based on the public literature in recent years, it is summarized from three aspects, i.e., movement mode switching, human gait recognition and human-exoskeleton interaction control. Finally, the technical issues of the current lower limb rehabilitation exoskeleton control strategy are discussed. The future development prospects and research directions of the lower limb rehabilitation exoskeleton are prospected, and some suggestions on how to achieve a more efficient and accurate control are given.
A Task Allocation Strategy based on Hungarian Algorithm in RoboCup Rescue Simulation
Authors: Wang Zijin , Wang Yibin , Chen Ming , Yuan Tingyu , Chen Haibo , Yang Yingze 
Published: March 2023

Abstract
In the multi-agent cooperation of RoboCup Rescue Agent Simulation, as one of the three agents, the agent with the task of clearing roadblocks plays a significant role. The clearing behavior help other agents to work promptly and provide a highly efficient operation to the entire simulation system. However, considering the cost of all agents in a limited time, it is not easy to carry out flexible task allocation, which leads to the current strategy is not perfect, and finally leads to the low efficiency of rescue operations. Therefore, it is necessary to make sure the high efficiency of clearing behavior. In order to maximize the resources of agents, this article puts forward a task allocation strategy based on Hungarian algorithm, which has substantially enhanced the agents’ efficiency through experimental verification at last.
Classification of CMT Additive Manufacturing Defects based on Statistical Features
Authors: Wang Hongmin , Wu Longhua , Jiang Meng , Liang Yanyang , Wang Tianlei , Pan Zengxi , Ye Haohuai , Song Yingying 
Published: March 2023

Abstract
Aiming at the problem of the CMT(cold metal transfer) additive manufacturing process defect monitoring method being susceptible to environmental influence, this paper proposes a classification method for additive manufacturing process defects with current as the research object. By setting up three sets of experiments, experimenting and collecting the current parameters of each group, using the statistical feature method to pretreat the current, and then classifying the pretreated data into random forests, to highlight the superiority of statistical characteristics plus random forests, two sets of control groups are also set up for comparison. It was concluded that the principal component analysis method could not classify the non-defective group and the non-protective gas group, while the use of random forest could achieve two by two classifications of the three working conditions; After the statistical feature method treatment, the random forest classification is compared with the original current direct random forest classification, and the accuracy of the training set, verification set, and test set has been improved.
Latent-Variable-Modeling-Based Fault Diagnosis for Unknown Nonlinear Distributed Parameter Systems with Application to Snap Curing Oven
Authors: Luo Zhao-dong , Li Han-Xiong 
Published: March 2023

Abstract
A novel fault detection method based on time/space separation and latent variable model is proposed for unknown nonlinear distributed parameter systems in sensor-constrained environments. By performing time/space separation, the augmented matrix formed by the spatio-temporal distribution data of the DPSs can be split into a spatial basis function (SBFs) and a time series model, and the dimensionality reduction capability of the SBF is further utilized to obtain a low-order temporal model. Then, the temporal model is further extracted by using a dynamic latent variable modeling method to obtain the dominant time components and establish the corresponding monitoring statistics. Utilizing the appropriate kernel density function, confidence bounds are selected for the monitoring statistics when the system is normal. As a data-based fault diagnosis method, it requires only the past data records of the system and no reliance on complex mathematical models. Two sets of experiments performed on a snap curing oven verified the effectiveness of the proposed method.
Modeling and High-performance Trajectory Optimization of the Industrial Robot
Authors: Huang Shihui , Lin Yamei , Jiang Aipeng , Qiu Guangsheng 
Published: March 2023

Abstract
Rapidity, accuracy and energy cost are the key performance indexes for of the work of industrial robot. In this paper, the shortest time and the minimum energy consumption are taken as the goal for the comprehensive optimization. The control vector parameterization method is used to reasonably refine the time grid of the optimization problem. Then, an efficient control strategy for non-uniform adaptive mesh refinement is proposed. According to different changing trends, time nodes are merged or inserted into the time grid. While reducing the number of discrete grids, it also ensures the accuracy of the solution. Taking the Manutec R3 industrial robot as the object, the trajectory optimization simulation calculation is carried out. According to the verification of the simulation computing, the proposed method can significantly improve the time grid refinement. Compared with the traditional CVP method, not only the calculation time is shortened, but also more accurate results can be obtained.
Non-communicating Decentralized Multi-robot Collision Avoidance in Grid Graph Workspace based on Dueling Double Deep Q-Network
Authors: Liu Yuanxin , Tian Delei , Zheng Bin 
Published: March 2023

Abstract
This paper designs a motion rule suitable for grid environment and proposes a multi-robot autonomous obstacle avoidance method based on deep reinforcement learning. The training and validation of the method are done under the Stage simulation platform based on ROS operating system. During the training process, the robot uses Lidar to obtain the surrounding state information and generates actions based on the state information to obtain rewards, and the robot is guided by the rewards to optimize the strategy. Based on the D3QN algorithm, a new reward function is designed, a proximity penalty is introduced to reduce the collision between robots, a distance reward is added to guide the robot to complete the task, a step reward is added to improve the efficiency of the robot to complete the task, and an illegal action penalty is added to avoid the robot to choose an illegal action; the input is 5 frames of Lidar data, and in the network structure, the agent can better learn the correlation between the data by introducing Long Short Term Memory(LSTM) layer, and introducing Convolutional Block Attention Module(CBAM), a hybrid attention mechanism to allow the robot to pay more attention to the information of the surrounding robots. By designing experiments, we demonstrate that the learned strategy can effectively guide the robot through obstacle avoidance and complete the task.
Research on Vehicle Re-identification for Vehicle Road Collaboration
Authors: Sun Chenyang , Wang Yang , Deng Yanfei , Li Huafu , Guo Junqi 
Published: March 2023

Abstract
Vehicles and roads cooperate to perceive traffic targets, which can reduce the perception blind spots of vehicles and improve driving safety. In this paper, we proposes a vehicle re-identification method oriented to vehicle-road coordination. This method first designs a lightweight vehicle re-identification network based on ShufflenetV2 to solve the computational efficiency problem of vehicle-road coordination scenarios, which can efficiently complete vehicle feature extraction; then, due to the real-time requirements of scenario communication, an adaptive feature conversion mechanism is designed in combination with the LSH algorithm, which can make the re-identification module to dynamically perform binary bit feature conversion and adjust the dimension according to the communication channel state; finally, a loss function for the conversion of vehicle re-identification features is designed, which can greatly reduce the accuracy loss rate of converting floating-point features to bit features. Experiments show that our method can efficiently complete the information extraction and comparison of vehicle re-identification features in the vehicle-road coordination scenario, and can improve the perception efficiency of vehicle-road coordination while taking into account performance and bandwidth.
A Cloud-based State of Health Estimation Method for Lithium-ion Batteries Using Sparse Charging Data
Authors: Guo Wenchao , Yang Lin , Deng Zhongwei 
Published: March 2023

Abstract
Under the current background of the national standard requirements for electric vehicle remote management and the companies’ needs for their digital platform services, automobile factories habitually transmit battery data to a cloud in a sparse way for state of health (SOH) estimation. Compared with the dynamic discharge condition of the vehicle, the charging process has been noticed in recent years due to its relatively stable advantages. In this paper, a cloud-based SOH estimation method for lithium-ion batteries using sparse charging data is proposed. A HI feature that can be extracted from the sparse data is derived and only the constant voltage charging process is considered. By using the experimental data deployed on the cloud computing system, the correlation of battery health with the HI is verified and SOH can be efficiently estimated by taking as this feature an input to three data-driven methods. The results show that all methods can achieve a low test error of 2% under 10s sparsity, which is promising to be applied in cloud sparse charging conditions.
Cell State Recognition of Cytopathic Effect with YOLO Detector
Authors: Xu Zixi , Lou Wange , Lu Wenxin , Wei Wenxia 
Published: March 2023

Abstract
Cell state recognition and evaluation of its life cycle are the fundamental and critical procedure to identify the pathological mechanism of viruses and analyze pharmacodynamic effectiveness. Modern life science research is based on large-scale biological experiments so that it is inefficiency to estimate and decide the physicochemical results on petri dishes. With the development of target recognition in computer science, especially YOLO (You Only Look Once) approach, we are able to incorporate unified, real-time object detection into cell state recognition in this paper, which realizes automotive process of detection and classification of CPE (cytopathic effect) states. In our work, we build up photomicrograph datasets of Vero cell in CPE state and train our YOLO model with it. Finally, our model learns characteristic pixel structure of cell rounding, syncytium and inclusion bodies, acting as end-to-end detector in cell infected experiments.
Detection Method of River Floating Objects based on Edge Computing
Authors: Li He , Jia Huimei , Zhang Rui , Yang Shuaipeng , Qi Qinglei , Liu Tianyang 
Published: March 2023

Abstract
For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network.
MPPT Study of Outdoor Sensing Terminal PV System Based on Improved PSO Algorithm
Authors: Xiao Yu , Zhu Li , Tan Jianjun , Zhun Yunyu , Yang Chen 
Published: March 2023

Abstract
With the popularity of wireless sensor use, the problem of supplying energy to its outdoor nodes is becoming more and more prominent. An outdoor wireless sensing terminal photovoltaic system is proposed to address the problem of outdoor wireless sensing terminal node power supply. The maximum power tracking (MPPT) is achieved by Adaptive weight adjustment Particle swarm optimization (AwaPSO) to achieve fast tracking and stable and reliable output, so that the Buck main circuit output of the PV system can meet the working requirements of the outdoor sensor terminal. The simulation model is built by Matlab/Simulink, and the simulation results show that the improved PSO algorithm can track the maximum power quickly and output stably and reliably, with the stabilization time shortened to 0.041s and the tracking error up to 0.625%, and its speed and stability can meet the outdoor wireless sensing terminal PV power supply requirements.
A Toolpath Optimization Algorithm for Layered 3D Printings based on Solving the TSP
Authors: Hu Kaiyu , Li Hailong , Xi Kou 
Published: March 2023

Abstract
In order to optimize the tool path of 3D printing such that the efficiency is improved, by summerizing the pros and cons of existing methods, we proposed a noval tool path optimization algorithm for layered 3D printings based on solving the Traveling Salesman Problem. Our algorithm first adjusts the major printing direction using Principal Components Analysis, and then applies the greedy strategy and generates multiple printing paths by interleavingly appending the filling segments and contour segments along the major printing direction. Thereafter, by considering the multiple printing paths as “cities”, and elaborately defining the distances between them, we successfully model the problem of minimizing the flying distances as a kind of Traveling Salesman Problem. Af-ter converting the accurate solution of TSP to our problem, we are able to get the global optimized tool path with the given multiple printing paths. Experiments demonstrate that the proposed algorithm not only miminizes the flying distances, but also reduces the switches between printing status and flying status, and boosts the percentage of long segments in the whole tool path as well, thus the printing efficiency is significently imporved without sac-rifying the printing quality.
Design of Sweeping Robot Based on STM32 Single Chip Microcomputer
Authors: Long Tao 
Published: March 2023

Abstract
As a new product in the field of service robots, the sweeping robot has made some achievements, but there are still many key technical problems to be solved or improved. This paper focuses on the precise obstacle avoidance and highly sensitive execution of the sweeping robot, and designs a sweeping robot based on STM32 microcontroller. The infrared ranging and ultrasonic ranging are combined to achieve accurate data acquisition. When the measurement distance is relatively close, infrared ranging is used. Otherwise, the ultrasonic distance measurement is adopted. In addition, PID algorithm is used to speed up the response of the system, and the motion state of the sweeping robot is adjusted through prediction data to ensure the accuracy of STM32 execution. It provides reference for the development of sweeping robot industry.
Partial Discharge Detection Method for Distribution Network Based on Feature Engineering
Authors: Liu Hongxiao , Xiang Mian Xiang , Zhou Bingtao , Zhu Li , Duan Yaqiong , Zhang Xiaoyan 
Published: March 2023

Abstract
Partial discharge phenomenon of overhead lines in distribution network is usually caused by the concentration of local electric field inside or on the surface of electrical equipment. According to partial discharge problem, based on the characteristics of the project the use of a machine learning is proposed for distribution network overhead line partial discharge detection model, first using the characteristics of engineering to extract the signal characteristics of different sides characterization, then respectively using K neighbor algorithm and back propagation algorithm, support vector machine (SVM) classification algorithm test. Experimental results show that when machine learning algorithm is used to classify time domain characteristic signals based on the feature engineering selected in this paper, k-nearest Neighbor algorithm has better classification and recognition effect than back propagation algorithm and support vector machine algorithm, with accuracy rate of 97.20%, recall rate of 96.30% and F value of 96.73%. In the frequency domain feature recognition and classification, the k-nearest Neighbor algorithm has 98.95% accuracy, 99.42% recall rate and 97.61% F value. Compared with the back propagation algorithm and support vector machine algorithm, the K-nearest Neighbor algorithm has the highest detection accuracy in the frequency domain feature detection of partial discharge on overhead lines of distribution network.
Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved MRAS Algorithm
Authors: Liu Yi , Ran Quan , Zhao Shiping 
Published: March 2023

Abstract
During the operation of a permanent magnet synchronous motor, the electrical parameters of the motor will be affected by the temperature and the saturation of the magnetic chain, which will lead to a decrease in the effectiveness of the control algorithm for motor control. In this paper, we address the problem that the traditional MRAS algorithm has under-ranked equations when identifying motor parameters, and the three parameter identification values are coupled with each other and affect each other, which leads to scattered identification results and slow identification speed. In this paper, we propose a segmentation identification algorithm that combines the traditional MRAS algorithm with the recursive least squares method. In this paper, the inductance value is first identified by the recursive least squares method, and then the inductance value is substituted into the MRAS to identify the stator resistance and rotor magnetic chain, which effectively solves the problems of equation under-ranking and slow identification speed in the traditional MRAS, thus improving the stability of the permanent magnet synchronous motor operation.
Probabilistic forecasting of tropical cyclones intensity using machine learning model
Authors: Meng Fan , Yao Yichen , Wang Zhibin , Peng Shiqiu , Xu Danya , Song Tao 
Published: March 2023

Abstract
This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation of this uncertainty should be provided. Previous studies construct this uncertainty through ensemble or statistical methods, neither of which can directly characterize this uncertainty and suffer from problems such as excessive computational effort. And for this reason, we propose to assess the forecast without this uncertainty through the forecast distribution. Meanwhile, none of the previous studies on TC intensity forecasting by artificial intelligence methods characterize the uncertainty, so this study is a new supplement to data-driven TC forecasting. During the 2010–2020 evaluation period, the model’s point forecast can outperform the current state-of-the-art operational statistic-dynamical model results, and can obtain forecast intervals to provide reliable probabilistic forecasts, which are critical for disaster warnings.