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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Xue, Zhengbo Feng, Yong Li, Yingna Wang, Jianguo Key Laboratory of Computer Technology Application Yunnan Kunming China China Copper Corporation Limited Yunnan Kunming China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374315

Building digital twin factories can achieve intelligent, efficient, and sustainable production methods for factory operations. The premise of implementing digital twin technology is the combination of virtual and real, which requires digital coding of industrial equipment and the development of a unified method for equipment identification image segmentation and recognition, providing relevant information for workers to control and maintain equipment. We use the standard QR codes for digital encoding of industrial equipment. We design and use the MSA U-Net image segmentation neural network to segment the equipment identification image, and then decode the identification image through a QR code decoder to obtain basic information such as equipment numbers, and then querying the database to obtain information on industrial equipment. Multiple experiments have been conducted on the dataset, and the results show that the proposed method improves the accuracy of segmentation and recognition of industrial equipment identification images, which is higher than the comparative advanced work. These findings substantiate the viability of our proposed framework in advancing the realization of digital twin factories. © 2024 IEEE.

关键词： Image segmentation

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Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Kinetic study on microwave-enhanced direct reduction of titanomagnetite concentrate with coal

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Journal of Iron and Steel Research International 2023年第3期30卷 429-445页

作者： Peng Liu Si-yu Gong Yu-wen Chao Bing-guo Liu Li-bo Zhang En-hui Wu State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization Kunming University of Science and TechnologyKunming 650093YunnanChina Yunnan Provincial Key Laboratory of Intensification Metallurgy Kunming 650093YunnanChina National Local Joint Laboratory of Engineering Application of Microwave Energy and Equipment Technology Kunming 650093YunnanChina Faculty of Metallurgical and Energy Engineering Kunming University of Science and TechnologyKunming 650093YunnanChina Key Laboratory of Unconventional Metallurgy Kunming University of Science and TechnologyKunming 650093YunnanChina Panzhihua University Panzhihua 617000SichuanChina

Titanomagnetite concentrate is one of the important titanium *** apparent activation energy(Ea)of the direct reduction of titanomagnetite concentrate was composed of two parts(average activation energy:E_(a)=E_(a-L)+E_(a-Step1),where E_(a-L) is the lattice energy of titanomagnetite concentrate,and is the activation energy of step 1 for the reduction of titanomagnetite concentrate in the route ***^(3+)→^(Step1)Fe^(2+)→^(Step2)Fe_(2)O^(2)→^(Step3)Fe^(0)).Ea(583.43 kJ/mol),(426.4 kJ/mol),and(157.0 kJ/mol)were calculated by the model-free methods based on thermogravimetry and Dmol3 *** with the analysis of activation energy fluctuation and the shifting trend of related mechanism functions,the reduction kinetic system with three main characteristics,namely nucleation,diffusion and concentration fluctuation,was *** addition,the scanning electron microscopy comparison analysis of the samples from microwave reduction and conventional reduction shows that microwave heating could realize the microstructure Ti-Fe separation and reduce the lattice energy of the titanomagnetite concentrate,thus enhancing the reduction process by 7.68%from the perspective of activation energy.

关键词： Titanomagnetite Kinetics Lattice energy Reduction Microwave Titanium

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Robust Fixed-time Composite Controller Design with the Unknown System Dynamics Estimator for Nonlinear Robotic Manipulator Systems 24

Robust Fixed-time Composite Controller Design with the Unkno...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Li, Siyan Xing, Yashan Na, Jing Li, Qi Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Yunnan China

ISBN: (纸本)9798400710681

In this paper, a robust fixed-time composite control scheme based on an Unknown System Dynamics Estimator (USDE) is proposed. The proposed control method guarantees the tracking error convergence in a fixed-time for nonlinear robotic manipulators with unknown dynamics and disturbance. To address the existence of uncertainties, first, a simple structure of USDE by a low-pass filtering operation is constructed without using acceleration signals, Which is adjusted for only one filter coefficient. Then, the USDE compensate the estimated value into the robust fixed-time composite controller. The composite controller contains a fixed time control term, a robust control term, and a computed torque control term. By the fixed-time control term, the upper bound on the convergence time is independent of the initial conditions of the system, which ensures fast-tracking tasks for the robot manipulator. And the robust control term is used to reduce the impact of USDE estimation errors. The stability of the closed-loop system is demonstrated through Lyapunov criteria. The validity of the proposed method is verified by simulation. © 2024 Copyright held by the owner/author(s).

关键词： Stability criteria

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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Synergetic monitoring of pressure and temperature stimulations in multisensory electronic skin based on time decoupling effect

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Carbon Energy 2025年第1期7卷 52-63页

作者： Zhiyi Gao Ye Zhang Zhenyu Hu Dongdong Zhang Shengbin Li Huiyun Xiao Ziyin Xiang Dan Xu Haifeng Zhang Yuanzhao Wu Yiwei Liu Jie Shang Runwei Li CAS Key Laboratory of Magnetic Materials and Devices Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina Yunnan Key Laboratory for Micro/Nano Materials&Technology School of Materials and EnergyYunnan UniversityKunmingChina Ningbo Institute of Digital Twin Eastern Institute of TechnologyNingboChina Institute of Micro/Nano Materials and Devices Ningbo University of TechnologyNingboChina

The progress from intelligent interactions requires electronic skin(E-skin)to shift from single-functional perception to multisensory ***,the intuitive and interference-free reading of multiple sensory signals without involving complex algorithms is a critical ***,we propose a flexible multisensory E-skin by developing a highly homogeneous dispersion of BaTiO_(3)nanoparticles in polydimethylsiloxane dielectric *** E-skin is sensitive to externally applied pressure as well as temperature and can distinguish dual synergetic stimuli by the time decoupling *** pressure and temperature perception was achieved in an individual device,which greatly reduced the structural complexity compared with multifunctional integrated *** sensitivity of E-skin for pressure detection is as high as 0.0724 kPa^(−1)and the detection range reaches as wide as 15.625-10 *** sensitivity to temperature detection is as high as−1.34℃^(−1)and the detection range reaches 20-200℃.More importantly,by equipping with a multilayer neural network,the evolution from tactile perception to advanced intelligent tactile cognition is demonstrated.

关键词： E-skin intelligent tactile cognition machine learning multifunctional sensing time decoupling effect

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

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China Communications 2024年第1期21卷 310-322页

作者： Zhiwei Wang Nianhua Yang Qingqing Chen Wei Shen Zhiying Zhang School of Computer Sciences Nanjing University of Posts and TelecommunicationsNanjing 210023China Dept.of Intelligent Science and Information Law East China University of Political Science and LawShanghai 201620China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal UniversityHaikou 571158China State Key Laboratory of Public Big Data Guizhou UniversityGuizhou 550025China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Research Institute of Beijing University of Aeronautics and AstronauticsKunming 650233

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be *** owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the *** prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file *** also provide security analysis in the context of four typical attacks.

关键词： blind batch encryption data sharing onetime adaptive access public ledger security and privacy

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Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

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computers, Materials & Continua 2024年第11期81卷 2517-2545页

作者： Wenming Wang Zhiquan Liu Shumin Zhang Guijiang Liu School of Computer and Information Anqing Normal UniversityAnqing246133China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210023China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and EconomicsKunming650221China

Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among ***,malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle *** mechanisms serve as an effective solution to this *** recent years,many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes,incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the *** this paper,we propose a distributed vehicular network scheme based on trust ***,the designed architecture partitions multiple vehicle regions into ***,cloud supervision systems(CSSs)verify the accuracy of the information transmitted by ***,the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation *** proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among *** theoretical and experimental analysis show that our scheme outperforms the compared schemes.

关键词： Distributed trust mechanism vehicular network privacy protection

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