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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68658 条记录，以下是241-250 订阅

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Kinematic Calibration Method Based on Point Cloud Measurement for 3-RPS Parallel Robot 17th

Kinematic Calibration Method Based on Point Cloud Measuremen...

引用

17th international conference on Intelligent Robotics and Applications

作者： Sheng, Lingyun Zhao, Xingwei Fan, Yajun Xue, Lei Tao, Bo Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Wuxi Res Inst Wuhan Peoples R China Shanghai Aircraft Mfg Co Ltd Shanghai Peoples R China

ISBN: (纸本)9789819607709;9789819607716

In order to control the end-effector to achieve accurate and suitable posture for high-precision processes in complex industrial environments, amechanism is often added to make secondary adjustment of the tool end posture. The 3-RPS parallel connection is widely used for target posture control in small workspaces due to its features of high control accuracy, large load, and small size and mass. In order to avoid the influence of process error and assembly error on the control accuracy of the parallel system, it is necessary to identify its parameters with high accuracy before use. However, the traditional non-contact calibration methods, such as position sensors and cameras, have the disadvantages of complex system, difficult to build, lower accuracy and cumbersome operation. To address the above problems, this paper proposes a non-contact measurement calibration method based on facial laser scanner, and establishes a calibration system based on a self-designed and fabricated 3-RPS parallel robot, realizes the high-precision measurement of the overall position and attitude information of the robot through the splicing of multi-segmented point clouds and feature extraction algorithms, establishes an inverse kinematic model of the robot, combines the 3D model with the parameter identification equations, realizes the parallel robot kinematic calibration, and designs the accuracy verification method. The effectiveness of the parameter calibration and the control algorithm of the parallel robot are verified by experiments.

关键词： 3-RPS Parallel Robot Kinematic Calibration computer Vision

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Medium-term wind speed ensemble forecasting using ICEEMDAN and dual attention mechanism 4

Medium-term wind speed ensemble forecasting using ICEEMDAN a...

引用

4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Liu, Jinhua Leng, Zhiyuan Chen, Lu Zhang, Fahong Liu, Fanqian Shen, Qixu Power China Huadong Engineering Corporation Limited Hangzhou310014 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Digital Valley Science and Technology Wuhan430074 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Wind power output has strong randomness, volatility, and intermittency. To maintain the safety and stability of the large power grid under the new power system, high-precision medium-term wind power forecasting is urgently needed. This paper fully leverages the temporal dynamics of the wind power dataset and proposes a medium-term wind power ensemble forecasting model that integrates transfer entropy, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) decomposition, dual attention mechanism, and multiple recurrent neural networks. Firstly, we compare the transfer entropy of wind power and meteorological factors to determine the direction of information flow and select the set of characteristic variables. Next, utilizing the ICEEMDAN signal decomposition algorithm, the wind power sequence is segmented into various intrinsic mode functions, and attention-based LSTM, GRU, and BiLSTM models are established. After aggregation and reconstruction, three sets of predictions are obtained. Finally, the attention mechanism is combined to dynamically weight the three models to achieve ensemble predictions. Actual examples show that compared with several benchmark models, the proposed model notably enhances the predictive accuracy of medium-term wind power forecasting. © 2025 SPIE.

关键词： Wind forecasting

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A Fast Algorithm for Numerical Differentiation from Scattered Data 4th

A Fast Algorithm for Numerical Differentiation from Scatte...

引用

4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Egidi, Nadaniela Giacomini, Josephin Maponi, Pierluigi University of Camerino School of Science and Technology Mathematics Division via Madonna Delle Carceri 9 MC Camerino62032 Italy

ISBN: (纸本)9783031812439

Numerical differentiation from scattered data is a problem often encountered in many fields such as image processing and numerical solution of differential equations. We propose a fast algorithm for numerical differentiation starting from scattered data. The proposed algorithm uses nonuniform Fast Fourier Transform (nFFT) and the explicit expression of the eigensystem of a Fredholm integral operator associated to the differentiation problem. Several numerical experiments are presented to validate the behaviour of the proposed algorithm with exact data and with noise-contaminated data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fredholm integral equations

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Foreground Prediction for Image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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Influence of design parameters on mechanical characteristics of the lenstype space deployable boom with bi-directional bending deformation 24

Influence of design parameters on mechanical characteristics...

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8th international conference on computer science and Application Engineering, CSAE 2024

作者： Zhang, Kangkang Xu, Jiahao Jia, Shunhua Jiang, Yuanzhi Mao, Shaochuan Zhu, Shiyao College of Aerospace Science and Engineering National University of Defense Technology Changsha China Key Laboratory of Spacecraft In-Orbit Fault Diagnosis and Maintenance Xi’an China

ISBN: (纸本)9798400718090

The bi-directional bending deformation of the lens-type space deployable boom (LSDB), with high specific stiffness and high storage ratio, is prone to result in structural damage during folding and unfolding process. Mechanical analysis models of the LSDB are established based on theoretical equivalence and finite element method, and comparative verification is conducted. The influence of design parameters such as lay-up angle, arc radius, and material thickness on the stress and folded strain energy of LSDBs is analyzed. The results show that the simulation analysis is close to the theoretical calculation results, which can basically validate the rationality of models. The stress of LSDB in the folded state decreases with the increase of arc radius, increases with the increase of material thickness, and the influence of lay-up angle on stress has symmetry and is affected by the proportion of arc radius to coller radius. The folded strain energy increases with the increase of material thickness, decreases with the increase of roller radius and arc radius, the influence of lay-up angle on strain energy also has symmetry, and there exists an optimal angle that minimizes the strain energy. The analysis conclusion can provide design references for improving the strength and deployment performance of the LSDB. © 2024 Copyright held by the owner/author(s).

关键词： Bending (deformation)

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Detection of High-Speed Railway Track Bed Foreign Object Based on Contrastive Learning and Adversarial Training 5

Detection of High-Speed Railway Track Bed Foreign Object Bas...

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5th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Fan, Yupeng Dai, Peng Song, Haoran Li, Hongjue School of Astronautics Beihang University Beijing China Railway Infrastructure Inspection Institute China Academy of Railway Science Beijing China

ISBN: (纸本)9798331533694

High-speed railway safety is compromised by track bed foreign objects, highlighting the imperative for efficient inspection. The track bed foreign objects detection is impeded by the rarity and diversity of those objects, resulting in challenges posed by limited and uncharacterized sample sets. This paper proposes a novel detection method based on contrastive learning and adversarial training, preserving the detection speed of one-stage detection while substantially enhancing accuracy. Contrastive learning sharpens feature discrimination, and adversarial training generates robust samples to bolster model performance. Experiments demonstrate that this approach yields a 12.9% increase in mAP.5 and 9.2% increase in mAP.5:.95, all without additional model parameters. © 2025 IEEE.

关键词： Contrastive Learning

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Hyper Spectral Image Analysis Integrating Fuzzy C-Means Clustering and Feature Selection 5th

Hyper Spectral Image Analysis Integrating Fuzzy C-Means Clus...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Rengarajan Goyal, Rohit Khan, N. Zafar Ali Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Hyper spectral picture evaluation is an effective tool for analyzing large volumes of excessive-dimensional facts. In this paper, a brand new method to hyper spectral photo analysis is provided that integrates fuzzy C-means clustering and function choice. The proposed approach permits for significant quantification of the exclusive additives within a hyper spectral image and allows for the identification of key spectral functions. Additionally, the fuzzy C-way clustering approach is used to identify awesome clusters within the photograph, supplying a useful method for studying complicated datasets. The characteristic choice technique facilitates to further lessen the data set and to achieve the maximum meaningful capabilities for further evaluation. The overall performance of the proposed technique is evaluated via the use of an actual-international hyper spectral photo, and the effects exhibit the effectiveness of the proposed technique for hyper-spectral photograph analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image analysis

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Classification of Poikilocytosis Abnormality Using Ensemble Deep Learning Technique 3rd

Classification of Poikilocytosis Abnormality Using Ensemble...

引用

3rd international conference on Emerging Electronics and automation, E2A 2023

作者： Dhar, Prasenjit Singh, Mohit Kumar Ahmed, Tanveer Suganya, Devi K. Medical Imaging Laboratory Computer Science and Engineering Department National Institute of Technology Silchar Assam India

ISBN: (纸本)9789819768011

Red blood cells (RBCs) also known as erythrocytes are an essential component of the human body that play several important roles. RBCs transport oxygen (O2) from the lungs to the body’s tissues, remove carbon dioxide (CO2), regulate pH balance, support the immune system, and provide diagnostic information. The abnormal shapes of RBCs (signified as poikilocytosis) are unable to carry O2 and CO2, leading to decreased O2 delivery to the tissues and an increased workload on the heart and lungs. Anemia, thalassemia, and other blood-related illnesses affect the body for insufficient replenishment (oxygen, protein, nutrients). Hematologists take more time to examine RBC shapes manually using a microscope. In this study ensemble deep learning technique was introduced to examine poikilocytosis abnormality accurately and efficiently. The proposed ensemble model outperforms state-of-the-art approaches with an excellent accuracy and F1-score of 98.77%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Personalized Federated Learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Blockchain-Based Anonymous Authentication Scheme with Traceable Pseudonym Management in ITS 6th

Blockchain-Based Anonymous Authentication Scheme with Trace...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Wang, Mingliang Tan, Haowen Zheng, Wenying School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

With the continuous development of intelligent transportation systems (ITS), the Vehicular Ad-hoc Networks, as a core component of smart transportation, has become a crucial technology for improving traffic safety and transportation efficiency. However, the open communication environment and highly dynamic nature of VANETs make them vulnerable to many security threats. The existing schemes are difficult to get a balance between anonymity and traceability. Therefore, this paper proposes a blockchain-based anonymous authentication scheme with pseudonym management protocol. Using the decentralized nature of blockchain, the scheme incorporates a pseudonym management mechanism based on smart contracts, ensuring that a user’s real identity can only be revealed upon consensus from a certain number of RSU, thereby enhancing identity privacy protection. Additionally, using the immutability of smart contracts, a vehicle authentication protocol is designed to improve system decentralization, data security, and identity traceability. Through security analysis, the proposed scheme demonstrates superior security performance against common threats. The analysis of performance shows that the performance of our proposed scheme is improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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