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检索条件"丛书名=Lecture notes in artificial intelligence,"

共 57438 条记录，以下是611-620 订阅

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Design and Envelope Grasping Analysis of Deployable Manipulator for Space Capture 17th

Design and Envelope Grasping Analysis of Deployable Manipula...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Yang, Hui Han, Yi Li, Chuanyang Wang, Yan Liu, Rongqiang Yanshan Univ Parallel Robot & Mechatron Syst Lab Hebei Prov Qinhuangdao 066004 Hebei Peoples R China PLA Rocket Force Univ Engn Lab Intelligent Control Xian 710025 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

ISBN: (纸本)9789819607709;9789819607716

This paper discusses a space manipulator for grasping non-cooperative objects and an envelope grasping planning method for objects of different shapes and sizes. A novel space deployable manipulator is proposed in this paper, which consist of a finger mechanism and a wrist mechanism. Then, a novel envelope planning method is proposed to make the manipulator grasp objects of different sizes more efficiently and stably. The static analysis of grasping process is carried out, the factors affecting grasping balance are analyzed, and the mathematical model of grasping balance is established. An envelope grasping optimization model with the driving rope length as variable is proposed to obtain the best grasping posture. Finally, the envelope planning simulation is carried out, which verifies that the novel manipulator proposed in this paper has good shape adaptability and size adaptability, and verifies that the envelope grasping method is feasible.

关键词： Space manipulator Enveloping grasp planning Static analysis Grasping simulation

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A Novel Building Construction Inspection Method Based on Naive Bayes Model by Fusing BIM and Lidar Point Cloud 17th

A Novel Building Construction Inspection Method Based on Nai...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Jiang, Boyu Fan, Liting Zhang, Yang Han, Yu Cheng, Zhongjiang Shenyang Jianzhu Univ Mech Engn Sch Shenyang 110168 Peoples R China Shenzhen Technol Univ Sino German Coll Intelligent Mfg Shenzhen 518118 Peoples R China Shenzhen Technol Univ Mech Ind Key Lab Intelligent Robot Technol 3C Shenzhen 518118 Peoples R China

ISBN: (纸本)9789819607945;9789819607952

As the construction industry progresses and develops, construction inspection of the built environment plays a vital role in the success of a building project. This paper introduced a building construction quality monitoring method that combined building information modeling and LiDAR point cloud technology. The method first collected the point cloud data of the building environment, and preprocessed point cloud data including removing outliers and alignment;then, the point cloud generated by the BIM model was compared and analyzed with the actual point cloud to obtain the relative relationship between the two point clouds;finally, the results were determined by a Naive Bayes classification model to assess the construction quality. This method provided a complete set of solutions for construction quality verification, and promoted the construction industry to move towards intelligent and precise management.

关键词： Construction monitoring Point cloud comparison BIM technology Naive bayes classifier

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Standing on the Shoulders of Giants 34th

Standing on the Shoulders of Giants

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34th Brazilian Conference on Intelligent Systems

作者： Cardoso, Lucas F. F. Filho, Jose de Sousa Ribeiro Santos, Vitor C. A. Frances, Regiane S. Kawasaki Alves, Ronnie C. O. Fed Univ Para UFPA Belem Para Brazil Fed Inst Para IFPA Belem Para Brazil Vale Technol Inst ITV Belem Para Brazil

ISBN: (纸本)9783031790317;9783031790324

Although fundamental to the advancement of Machine Learning, the classic evaluation metrics extracted from the confusion matrix, such as precision and F1, are limited. Such metrics only offer a quantitative view of the models' performance, without considering the complexity of the data or the quality of the hit. To overcome these limitations, recent research has introduced the use of psychometric metrics such as Item Response Theory (IRT), which allows an assessment at the level of latent characteristics of instances. This work investigates how IRT concepts can enrich a confusion matrix in order to identify which model is the most appropriate among options with similar performance. In the study carried out, IRT does not replace, but complements classical metrics by offering a new layer of evaluation and observation of the fine behavior of models in specific instances. It was also observed that there is 97% confidence that the score from the IRT has different contributions from 66% of the classical metrics analyzed.

关键词： Machine Learning Classification Item Response Theory

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A Short Analysis of Hybrid Approaches in COVID-19 for Detection and Diagnosing 19th

A Short Analysis of Hybrid Approaches in COVID-19 for Detect...

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19th International Conference on Hybrid artificial intelligence Systems

作者： Simic, Dragan Bankovic, Zorana Villar, Jose R. Luis Calvo-Rolle, Jose Simic, Svetislav D. Simic, Svetlana Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Frontiers Media SA Paseo Castellana 77 Madrid Spain Univ Oviedo Campus Llamaqu Oviedo 33005 Spain Univ A Coruna Dept Ind Engn Ferrol A Coruna 15405 Spain Univ Novi Sad Fac Med Hajduk Veljkova 1-9 Novi Sad 21000 Serbia

ISBN: (纸本)9783031741821;9783031741838

The epidemic spread model of infectious diseases is a traditional epidemiological and mathematical problem with crucial practical value. The first transmission of coronavirus to humans started in Wuhan city of China and took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19). Many papers and studies have been written from 2019 until today. The aim of this paper is to present and short-analyse hybrid AI techniques which have been applied: k-nearest neighbours algorithm, Support Vector Machine, Naive Bayes, Random Forest, k-means clustering, hierarchical clustering, fuzzy logic, Logistic Regression, Decision Tree, artificial Neural Networks, Deep Learning, Convolutional Neural Network to extract significant features and classify various health conditions, detection and diagnoses of COVID-19 patients. The hybrid algorithm overcomes the lack of exploiting individual AI techniques and algorithms;and simultaneously, the obtained statistical results prove the efficiency and robustness compared over other state-of-the-art approaches.

关键词： artificial intelligence infectious diseases COVID-19 machine learning X-ray images detection diagnosing severity level

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Learning Adaptive Edge Dual-Graph Convolutional Network for Robust Point Cloud Analysis 17th

Learning Adaptive Edge Dual-Graph Convolutional Network for ...

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17th International Conference on Intelligent Robotics and Applications

作者： Li, Jimin Zhang, Zhiqiang Qilu Inst Technol Jinan 250353 Shandong Peoples R China Shanghai Univ Shanghai 200444 Peoples R China

ISBN: (纸本)9789819607884;9789819607891

Graph convolutional networks (GCNs) provide a promising yet effective way to process 3D point clouds. However, two critical issues have significantly influenced the learning of robust GCNs, and thus limited the applications of most existing approaches for point cloud analysis: 1) local shapes are always sensitive to noisy points due to the cluttered background and missing cloud point parts, and 2) local geometric structure cannot be well preserved during the typical down-sampling procedure. We present a novel adaptive edge dual-graph convolutional networks (AE-DualGCN) to deal with these issues, which includes two key components: 1) adaptive-gate edge convolution on dual graph, which dynamically adjusts the edge features of the feature graph and position graph to preserve the topology of local shape, and 2) local attention graph sampling, which performs down-sampling according to local geometric structure. Our proposed AE-DualGCN can pass on the most important points with intrinsic 3D structure to the next layers, and at the same time, make the down-sampling of GCN in an end-to-end fashion. We demonstrate the robust of AE-DualGCN for different scenarios in point cloud

关键词： Graph Convolutional Networks Adaptive-gate Robust Method Local Attention Graph Sampling Dual Loacal Graph

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Dual-Mode Contrastive Learning-Enhanced Knowledge Tracing 21st

Dual-Mode Contrastive Learning-Enhanced Knowledge Tracing

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21st Pacific Rim International Conference on artificial intelligence

作者： Huang, Danni Yu, Jicheng Mao, Shun Li, Jiawei Jiang, Yuncheng South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China South China Normal Univ Sch Artificial Intelligence Foshan 528225 Peoples R China South China Normal Univ Sch Software Foshan 528225 Peoples R China

ISBN: (纸本)9789819601158;9789819601165

Knowledge Tracing (KT) aims to model learners' dynamic knowledge states and predict their future response performance. Most of the existing KT models overlook the importance of advanced exercise representations and other beneficial exercise information. Besides, the relationship between exercises and learners is neglected, and difficulty and discrimination differ from exercises to exercises. Therefore, we propose a Dual-mode Contrastive Learning-Enhanced Knowledge Tracing (DEKT) model, which can obtain fine exercise representations and generate better predictions. Specifically, our model incorporates three additional types of heterogeneous nodes: learner, difficulty, and discrimination to explicitly exploit the exercise information. Furthermore, we employ the dual-mode contrastive learning (path-based mode and schema-based mode) to strengthen and complement exercise representations with semantic and structural information. Finally, through the dual-temporal attention mechanism architecture (one for tracing learners' knowledge states and the other for predicting), we acquire the long-term dependencies between exercises between time step 1 and t, as well as between time step 1 and t + 1, helping us to capture learners' knowledge states in a more effective manner.

关键词： Knowledge Tracing Exercise Representation Enhancement Heterogeneous Contrastive Learning

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Adaptive Anti-swing Control for a Ship Board Boom Crane 17th

Adaptive Anti-swing Control for a Ship Board Boom Crane

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17th International Conference on Intelligent Robotics and Applications

作者： Li, Zhi Hou, Chuanjing Shandong Jianzhu Univ Jinan 250101 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

This paper proposes an adaptive feedback control for an offshore boom crane. The offshore crane is a typical nonlinear under-actuated mechanical system, which has three independent control input (i.e., the boom luffing and rotating control torques and the cable length control force) and more than four degrees of freedom (DOFs). These characteristics make the control of an offshore boom crane a difficult issue. The offshore crane is suffering from the persistent ship movement induced disturbances which make the control problem more challenging. Furthermore, in practice, the offshore crane would hoist payloads with different mass and the payload mass is usually unknown. Such that, it is more suitable to design robust anti-swing control methods which do not need the payload mass information. In this paper, the adaptive method is used to approximate the payload mass online and then an energy-based feedback anti-swing control method is proposed to regulate the payload to desired positions and suppress the residual swing. The closed-loop stability is analyzed by using the Lyapunov theory. The effectiveness of the proposed control method is verified via the simulation results.

关键词： under-actuated system adaptive control nonlinear control

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StereoNeRF: Learning Radiance Fields from Stereo Observation for Driving View Synthesis 17th

StereoNeRF: Learning Radiance Fields from Stereo Observation...

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17th International Conference on Intelligent Robotics and Applications

作者： Qi, Dexin Zhang, Zhihong Tao, Tao Mei, Xuesong Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China State Key Lab Mfg Syst Engn Xian Peoples R China Shaanxi Key Lab Intelligent Robots Xian Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9789819607730;9789819607747

Synthesizing driving views is crucial for extending training data in autonomous driving scenes. Recently, Neural Radiance Fields (NeRF) have achieved impressive results in novel view synthesis tasks for bounded scenes. However, due to implicit inconsistency, most existing NeRF-like models face performance degradation when reconstructing autonomous driving scenes. In this paper, we present StereoNeRF, which leverages the characteristics of stereo cameras to mitigate the negative effects of geometric uncertainty in volume rendering and enhance the performance of synthesized views. Firstly, we propose a novel loss term to regularize implicit geometric consistency by exploiting the photometric consistency between image pairs captured by stereo cameras. Furthermore, we introduce a data augmentation method to generate views across image pairs from stereo cameras based on the tracks of key points provided by Structure-from-Motion (SfM), which helps address performance degradation caused by shape-radiance ambiguity. Experiments on the KITTI-360 dataset demonstrate that our approach synthesizes photo-realistic novel views in autonomous driving scenes.

关键词： Novel View Synthesis Visual Perception Machine Learning

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MDLIRE-Net: Semantic Segmentation of Point Clouds Based on Multidimensional Local Information Re-encoding 17th

MDLIRE-Net: Semantic Segmentation of Point Clouds Based on M...

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17th International Conference on Intelligent Robotics and Applications

作者： Huang, Shijian Wang, Qi Min, Huasong Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Wuhan 430081 Peoples R China

ISBN: (纸本)9789819607730;9789819607747

With the development of fields such as autonomous driving, smart factories, and augmented reality, the demand for scene understanding and environmental perception of point clouds is increasing. Point cloud semantic segmentation technology is a key research focus in this area. However, in large-scale point cloud scenes, some objects' spatial geometric structures do not differ significantly from each other, and relying solely on spatial geometric information can lead to errors in segmentation boundaries. Therefore, this paper synchronizes and encodes the spatial position and color information of point clouds, combined with semantic information, proposing a method of multidimensional local information re-encoding, which enhances the accuracy of segmentation in areas where geometric structures are not significantly different. Subsequently, a residual connection and dense connection module were designed to alleviate the problem of segmentation errors at the boundaries of different categories of objects. Finally, the network performance was evaluated on the S3DIS and Toronto-3D datasets, demonstrating higher overall accuracy compared to PointNet++, RandLA-NET, and BAF-LAC.

关键词： Point Cloud Semantic Segmentation Re-encoding Residual Connection Dense Connection

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S<SUP>2</SUP>NeRF: Neural Radiance Fields Training with Sparse Points and Sparse Views 17th

S<SUP>2</SUP>NeRF: Neural Radiance Fields Training with Spar...

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17th International Conference on Intelligent Robotics and Applications

作者： Zhang, Zhihong Wang, Wenjun Qi, Dexin Mei, Xuesong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Shaanxi Key Lab Intelligent Robots Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9789819607730;9789819607747

Neural volume rendering methods, especially NeRF, have demonstrated remarkable performance in novel view synthesis. However, NeRF relies solely on image data and lacks explicit geometric information, necessitating a large number of posed images and a computationally intensive ray sampling strategy to learn accurate scene representations. This poses challenges and may result in incomplete or locally optimal scene geometry when views are sparse or incomplete, as the limited views may not provide sufficient constraints to determine a unique geometry solution for complex scenes. Meanwhile, sparse point clouds provide an attractive source of scene information, especially for geometry, to complement images in neural scene representations, particularly when input views are sparse. To overcome these limitations, we propose (SNeRF)-Ne-2, a novel Neural Radiance Field that simultaneously incorporates features from both point clouds and images for volume rendering. Specifically, (SNeRF)-Ne-2 extracts patch-wise point features from point clouds and raywise image features from adjacent views. Then the scene feature volume is constructed by implicitly fusing these point and image features through self-attention. Finally, the volume feature is utilized to render novel views of the scene. Experimental results on the challenging TartanAir dataset demonstrate that, thanks to the integration of feature volume from point clouds and images, (SNeRF)-Ne-2 achieves state-of-the-art performance in novel view synthesis.

关键词： Machine Learning Computer Vision Volume Rendering

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