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Autonomous trajectory tracking control method for an agricultural robotic vehicle

International Journal of Agricultural and Biological engineering 2024年第1期17卷 215-224页

作者： Jin Yan Wenguang Zhang Yong Liu Wei Pan Xiaoyu Hou Zhiyu Liu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Information Engineering Xuzhou University of TechnologyXuzhou 221018JiangsuChina National Key Laboratory of Transient Physics Nanjing University of Science and TechnologyNanjing 210094China School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing 210094China

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground ***,this paper presents the design and implementation of a lightweight,modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster *** vehicle comprises drive wheel modules,passive wheel modules,battery modules,a vehicle frame,a sensor system,and a control ***,a novel robust trajectory tracking method was proposed,utilizing an improved pure pursuit ***,an Online Particle Swarm Optimization Continuously Tuned PID(OPSO-CTPID)controller was introduced to dynamically search for optimal control gains for the PID *** results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control *** validate the performance,the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural *** outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in *** proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error(RMSE)of the curve and straight lines,given a suitable set of parameters,meeting the requirements of agricultural *** findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots.

关键词： trajectory tracking autonomy control agricultural robotic vehicle online PSO continuously tuned PID dynamic pure pursuit algorithm

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Plasma sprayed thermal barrier coatings with outstanding ultrahigh temperature thermal shock resistances through gradient porosity level optimizations

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Ceramics International 2025年第13期51卷 17484-17491页

作者： Liu, Liqiang Xu, Wenhu Shi, Junmiao Zhang, Xian-Cheng Tu, Shan-Tung Key Laboratory of Pressure Systems and Safety Ministry of Education School of Mechanical and Power Engineering East China University of Science and Technology Shanghai200237 China School of Mechanical and Electrical Engineering Changchun University of Science and Technology Changchun130022 China

In this study, the YSZ thermal barrier coatings (TBCs) with gradient porosity was developed through design the spraying parameters. The effect of the gradient porosity on the bond strength and thermal shock life was investigated, and the failure mechanism of the TBCs was explored. This article prepared three coatings with different porosities, tested the bonding strength of different porosities using the tensile method, and tested the thermal shock resistance of the coatings using water quenching after thermal shock. By simulating the residual stress inside the coatings after spraying, the failure mechanism of the coatings after thermal shock was analyzed. With the decrease of the porosity from TBC I to TBC III, the residual stress concentration after the spraying raised. Subsequently, the bond strength of the TBCs decreased form 37 MPa–31 MPa owing to the residual stress evolution. All of the three TBCs underwent three thermal shock test without macroscopic spalling. During the thermal shock test, tensile horizontal stress formed and promoted the formation of vertical cracks. Tensile vertical stress concentrated at the top - coat/bond - coat (TC/BC) interface significantly, inducing horizontal crack adjacent to the interface. Additionally, the TC layer surface zone sintered under the oxygen-acetylene flame heating. Then tensile vertical stress concentration was formed in the sintering zone region, and horizontal crack was detected in the TC layer. The connection of the vertical cracks and horizontal cracks was responsible for the spalling and failure of the TC layer. © 2025

关键词： Spalling

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Visual Analytics Technologies for Situation Awareness 8

Visual Analytics Technologies for Situation Awareness

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8th International Conference on Big Data and information Analytics, BigDIA 2022

作者： Meng, Xiangxia Rao, Jiaren Fang, Yujie Li, Tingting Diao, Lianwang Xie, Ke Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

Visual analytics technologies are very important for mining information and knowledge from big data. Its goal is to generate images that people can obtain information intuitively. A good visual analytics method is an important way to promote human-machine interaction, and it is also an important technology in the current era of big data and artificial intelligence. Situation awareness is a typical application of mining information from data. By reviewing the role of visual analytics on human cognition process, it is proposed that visual analytics also plays an important role in situation awareness. Combining the visual analytics technologies and situation awareness process, three models are studied in this paper. Starting from the information transformation model of situational awareness, how the situation information are transformed is illustrated. Then the situation awareness model with visual analytics is proposed, in which the process flow with analytics models and visualization technologies is illustrated to enhance human's situation awareness. Finally, the processing flow from the perspective of data state is proposed, which provides a basis for the development of better human-machine interaction situation awareness methods. © 2022 IEEE.

关键词： Big data

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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ZIF-derived N-doped carbon nanorods supporting bimetallic CoFe single-atoms/nanoclusters as bifunctional oxygen electrocatalysts for stable Zn-air batteries

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Rare Metals 2024年第11期43卷 5769-5780页

作者： Hong-Shuang Fan Fei-Xiang Ma Zi-Hao Liu Wen-Hui Wang Zheng-Qi Liu Xiong-Yi Liang Yue Du Yang-Yang Li Liang Zhen Cheng-Yan Xu Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China School of Civil and Environmental Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China Peng Cheng Laboratory Shenzhen 518055China MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing Harbin Institute of TechnologyHarbin 150080China

High-performance bifunctional oxygen electrocatalysts that simultaneously boost the sluggish oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)need to be developed for advanced rechargeable Znair battery *** this work,a zeolitic imidazolate framework(ZIF)-phase conversion associated with a subsequent thermal fixing strategy was developed to fabricate bimetallic CoFe single atoms/clusters embedded in N-doped carbon(denoted as CoFe-N-C)nanorods,which can serve as efficient bifunctional ORR/OER *** observation and X-ray absorption spectroscopy analysis confirm the co-existence of highly active Co/Fe-N_(x) dual sites and CoFe alloy nanoclusters.X-ray photoelectron spectroscopy(XPS)results prove that implanting secondary Fe atoms into Co-N-C matrix nanorods can induce electronic redistribution of atomic Co/Fe active sites and generate synergistic effects,which would optimize the adsorption energy of the reaction intermediates and thus enhance the bifunctional ORR/OER *** bimetallic CoFe-N-C nanorods exhibit significantly enhanced bifunctional ORR/OER activity and stability than the monometallic Co/Fe-N-C nanorods in alkaline electrolytes in terms of a very positive half-wave potential of 0.90 V(*** hydrogen electrode(RHE))for ORR,and an overpotential of 440 mV to reach current density of 10 mA·cm^(-2)for OER,yielding a small overpotential gap of 0.77 ***,the rechargeable Zn-air batteries using bimetallic CoFe-N-C nanorods as air-cathode catalyst demonstrates peak power density of200.7 mW·cm^(-2)and robust cycling stability of up to200 h,corresponding to 1200 discharge-charge cycles.

关键词： Bimetallic electrocatalyst Single-atom catalysts Oxygen reduction reaction Oxygen evolution reaction Zn-air batteries

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Effect of diamond crystal structure on anode heat transfer rate of x-ray source 6

Effect of diamond crystal structure on anode heat transfer r...

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6th Conference on Frontiers in Optical Imaging and technology: Novel Technologies in Optical systems

作者： Liu, Yanyang Li, Xinwei Qin, Xulei Li, Ye Changchun University of Science and Technology China Jilin Quantum Information Technology Engineering Laboratory China

ISBN: (数字)9781510679672

ISBN: (纸本)9781510679665

The composite photovoltaic anode, leveraging the unique characteristics of a composite structure and high thermal conductivity materials, is progressively replacing traditional sample anodes in X-ray sources. This paper elucidates the structure of the diamond-based X-ray source anode (referred to as the "composite anode"). By incorporating a diamond layer, the composite anode aims to achieve rapid heat conduction and high-temperature durability. The characteristics and thermal conductivity weight of the diamond layer are explored. Furthermore, finite element analysis (FEA) methodology is employed to simulate and compare different thicknesses of the diamond layer. The findings illustrate that the diamond layer in the composite anode structure exhibits a high thermal conductivity rate and high melting point characteristics. Additionally, the thickness variation of the diamond layer directly impacts the overall heat conduction capacity of the composite anode. Consequently, the relationship between the thermal conductivity coefficient of the composite anode and the thickness of the diamond layer is ascertained, provided that the experimental conditions allow for reproducibility. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Anodes

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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Development of a toroidal soft x-ray imaging system and application for investigating three-dimensional plasma on J-TEXT

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Plasma science and technology 2024年第3期26卷 94-99页

作者：赵传旭李建超张晓卿王能超丁永华杨州军江中和严伟李杨波毛飞越任正康 the J-TEXT Team International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic Engineering and School of PhysicsHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205People's Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China

A toroidal soft x-ray imaging(T-SXRI)system has been developed to investigate threedimensional(3D)plasma physics on *** T-SXRI system consists of three sets of SXR *** sets are newly developed and located on the vacuum chamber wall at toroidal positionsφof 126.4°and 272.6°,respectively,while one set was established previously atφ=*** set of SXR arrays consists of three arrays viewing the plasma poloidally,and hence can be used separately to obtain SXR images via the tomographic *** sawtooth precursor oscillations are measured by T-SXRI,and the corresponding images of perturbative SXR signals are successfully reconstructed at these three toroidal positions,hence providing measurement of the 3D structure of precursor *** observed 3D structure is consistent with the helical structure of the m/n=1/1 *** experimental observation confirms that the T-SXRI system is able to observe 3D structures in the J-TEXT plasma.

关键词： SXR imaging J-TEXT tokamak three-dimensional measurement MHD

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Unveiling size-fluorescence correlation of organic nanoparticles and its use in nanoparticle size determination

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Aggregate 2024年第1期5卷 331-336页

作者： Wu-Jie Guo Shixiang Ma Hui Wang Lu Qiao Lei Chen Chenyu Hong Bin Liu Xiaoyan Zheng Hui-Qing Peng State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijingChina Key Laboratory of Cluster Science of Ministry of Education Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion MaterialsKey Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information TechnologySchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijingChina

Quantitatively establishing the correlation between nanoparticle size and fluorescence is essential for understanding the behavior and functionality of fluorescent nanoparticles(FNPs).However,such exploration focusing on organic FNPs has not been achieved to ***,we employ the use of supramolecular polymeric FNPs prepared from tetraphenylethylene-based bis-ureidopyrimidinone monomers(bis-UPys)to relate the size to the fluorescence of organic *** an equal concentration of bis-UPys,a logarithmic relationship between them is built with a correlation coefficient higher than *** calculations indicate that variations in fluorescence intensity among FNPs of different sizes are attributed to the distinct molecular packing environments at the surface and within the interior of the *** leads to different nonradiative decay rates of the embedded and exposed bis-UPys and thereby changes the overall fluorescence quantum yield of nanoparticles due to their different specific surface *** established fluorescence intensity-size correlation possesses fine universality and reliability,and it is successfully utilized to estimate the sizes of other nanoparticles,including those in highly diluted dispersions of *** work paves a new way for the simple and real-time determination of nanoparticle sizes and offers an attractive paradigm to optimize nanoparticle functionalities by the size effect.

关键词： aggregation-induced emission fluorescence detection fluorescent nanoparticle nanoparticle size supramolecular polymer

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Copy-move detection method based on Decoupled Edge Supervision and multi-domain cross correlation modeling

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Multimedia Tools and Applications 2025年第18期84卷 20079-20101页

作者： Jing, Niantai Nie, Jie Wang, Jingyu Wang, Xiaodong Liang, Xinyue Gao, Xuesong Faculty of Information Science and Engineering Ocean University of China QingDao China Qingdao Hisense State Key Laboratory of Digital Multimedia Technology QingDao China

Copy move forgery detection is defined as the process of moving one region in the image (the source region) to another region in the image (the tampered region). Common classification methods include copy move forgery location (CMFL, which does not distinguish between source regions and tampered regions) while existing copy move source target detection (CMSTD) use limited information to distinguish the source and region. Since the edges of the tampered region able to act as the important clues such as blurring, bringing the challenge of detecting the copy-move forgery target with the complete structure. Therefore, we propose a Copy-move Detection Method based on Decoupled Edge Supervision and Multi-domain Cross Correlation (DM-Net), including Multi-scale Similar Region Detection module (MSD), Decoupled Edge Supervision module (DEM), and Multi-domain Correlation Modeling module (MCM), which can overcome the problem that the tamper trace is fuzzy caused by the post-processing operation. Specifically, the MSD module is proposed to extract coarse similar regions by multi-scale method. The DEM module is proposed to extract the tamper region by the method of decoupling edge supervision, which avoids information redundancy while using shallow edge features. The MCM module conducts cross-correlation modeling between the tampered, source and similar region, further optimizes detection targets of similar region by mining the correlation among multiple domains. By adding edge information, we can improve the efficiency of distinguishing source and target regions by 2%. We performed experiments on USC-ISI data set, and the accuracy was improved by 0.21% compared with CNN-T GAN method, and the F1-score index was improved by 0.87% compared with DOA-GAN. The accuracy of CASIA v2.0 data set is 2.89% higher than that of Busternet method, and the precision index is 3.98% higher than that of CMSD-STRD method on source. The accuracy of CoMoFoD data set is improved by 0.93% compared with

关键词： Classification (of information)

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