检索结果-内蒙古大学图书馆

Fusing Geometrical and Visual information via Superpoints for the Semantic Segmentation of 3D Road Scenes

Tsinghua Science and Technology 2020年第4期25卷 498-507页

作者： Liuyuan Deng Ming Yang Zhidong Liang Yuesheng He Chunxiang Wang the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China the Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China the Research Institute of Robotics Shanghai Jiao Tong UniversityShanghai 200240China

This paper addresses the problem of the semantic segmentation of large-scale 3D road scenes by incorporating the complementary advantages of point clouds and *** make full use of geometrical and visual information,this paper extracts 3D geometric features from a point cloud using a deep neural network for 3D semantic segmentation and extracts 2D visual features from images using a Convolutional Neural Network(CNN)for 2D semantic *** order to bridge the features of the two modalities,this paper uses superpoints as an intermediate representation to connect the 2D features with the 3D features.A superpoint-based pooling method is proposed to fuse the features from the two different modalities for joint *** evaluate the approach,the paper generates 3D scenes from the Virtual KITTI *** results of the experiments demonstrate that the proposed approach is capable of segmenting large-scale 3D road scenes based on the compact and semantically homogeneous superpoints,and that it achieves considerable improvements over the 2D image and 3D point cloud semantic segmentation methods.

关键词： scene understanding point cloud semantic segmentation multi-modal information fusion deep learning

来源：评论

学校读者我要写书评

暂无评论

CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs 39

CAD-GPT: Synthesising CAD Construction Sequence with Spatial...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Siyu Chen, Cailian Le, Xinyi Xu, Qimin Xu, Lei Zhang, Yanzhou Yang, Jie School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Institute of Cyber Science and Technology Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China University of Minnesota Twin Cities Saint PaulMN United States

ISBN: (纸本)157735897X

Computer-aided design (CAD) significantly enhances the efficiency, accuracy, and innovation of design processes by enabling precise 2D and 3D modeling, extensive analysis, and optimization. Existing methods for creating CAD models rely on latent vectors or point clouds, which are difficult to obtain and costly to store. Recent advances in Multimodal Large Language Models (MLLMs) have inspired researchers to use natural language instructions and images for CAD model construction. However, these models still struggle with inferring accurate 3D spatial location and orientation, leading to inaccuracies in determining the spatial 3D starting points and extrusion directions for constructing geometries. This work introduces CAD-GPT, a CAD synthesis method with spatial reasoning-enhanced MLLM that takes either a single image or a textual description as input. To achieve precise spatial inference, our approach introduces a 3D Modeling Spatial Mechanism. This method maps 3D spatial positions and 3D sketch plane rotation angles into a 1D linguistic feature space using a specialized spatial unfolding mechanism, while discretizing 2D sketch coordinates into an appropriate planar space to enable precise determination of spatial starting position, sketch orientation, and 2D sketch coordinate translations. Extensive experiments demonstrate that CAD-GPT consistently outperforms existing state-of-the-art methods in CAD model synthesis, both quantitatively and qualitatively. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： High level synthesis

来源：评论

学校读者我要写书评

暂无评论

GP-based LMPC for Nonlinear system with Stability Guarantee

GP-based LMPC for Nonlinear System with Stability Guarantee

引用

第32届中国过程控制会议(CPCC2021)

作者： Tongqiang Zhang Shaoyuan Li Yi Zheng Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education of China

The Gaussian process model inferred from the Bayesian framework is a powerful modeling *** provides not only the predictive value,but also the uncertainty measure for the predictive *** this paper,we combine the GP models with LMPC to achieve stable control for system of unknown dynamics,and especially use its capability of uncertainty quantification to assist in the stability guarantee under certain *** reference control law the Lyapunov constraint is designed by approximate numerical computation and imposed conditionally with an infeasible level set of states,to realize stable control according to Lyapunov stability *** the simulation on a chemical process case is executed and the results verify our proposed GP-based LMPC method.

关键词： Gaussian process models unknown dynamics MPC Lyapunov Stability

来源：评论

学校读者我要写书评

暂无评论

ACLFs-based MPC for Nonlinear systems with Uncertainty

ACLFs-based MPC for Nonlinear Systems with Uncertainty

引用

第32届中国过程控制会议(CPCC2021)

作者： Yanye Wang Shaoyuan Li Yi Zheng Qiangyu Li Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education of China

In this paper,we propose the framework to design a stabilized adaptive MPC for nonlinear systems with unknown *** problem is how to design the stability *** firstly design a piece-wise continuous control law and a parameter update law based on the Adaptive control Lyapunov Functions(ACLFs),which can stabilize the closed-loop system and is used to limit the MPC control *** this ACLFs-based constraint,the stability of the closed-loop system can be *** the application,this constraint can update according to new arrival data at each sampling *** prior information of the actual parameters and the rigorous model parameters mismatch are not ***,to improve the performance related with the control processes and requirements,we can use any methods,*** learning method,to get an accurate prediction model employed to calculate the cost *** updating of the prediction model will not affect the feasibility and the stability of the closed-loop *** proposed MPC scheme simplifies the control design for systems with difficulty in estimation of the unknown parameters since only one additional constraint is *** results show the effectiveness of the proposed scheme.

关键词： Model Predictive control Adaptive control Lyapunov-based control

来源：评论

学校读者我要写书评

暂无评论

Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes

arXiv

引用

arXiv 2023年

作者： Zhong, Jiquan Huang, Xiaolin Yu, Xiao Department of Automation Xiamen University Xiamen China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Automation Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen China

Monocular depth estimation is a fundamental task in computer vision and multimedia. The self-supervised learning pipeline makes it possible to train the monocular depth network with no need of depth labels. In this paper, a multi-frame depth model with multi-scale feature fusion is proposed for strengthening texture features and spatial-temporal features, which improves the robustness of depth estimation between frames with large camera ego-motion. A novel dynamic object detecting method with geometry explainability is proposed. The detected dynamic objects are excluded during training, which guarantees the static environment assumption and relieves the accuracy degradation problem of the multi-frame depth estimation. Robust knowledge distillation with a consistent teacher network and reliability guarantee is proposed, which improves the multi-frame depth estimation without an increase in computation complexity during the test. The experiments show that our proposed methods achieve great performance improvement on the multi-frame depth estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Model-free False Data Injection Attack Strategy in Networked control systems

A Model-free False Data Injection Attack Strategy in Network...

引用

IEEE Conference on Decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Jianping He The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology and Institute of Cyberspace Research Zhejiang University China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.

关键词： Networked control systems Computational modeling Current measurement Linear programming Real-time systems Data models Time measurement

来源：评论

学校读者我要写书评

暂无评论

Modeling, Prediction and Risk Management of Distribution system Voltages with Non-Gaussian Probability Distributions

arXiv

引用

arXiv 2024年

作者： Gao, Yuanhai Xu, Xiaoyuan Yan, Zheng Shahidehpour, Mohammad Yang, Bo Guan, Xinping The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion Shanghai Jiao Tong University Shanghai200240 China The Shanghai Non-carbon Energy Conversion Utilization Institute Shanghai200240 China The Galvin Center for Electricity Innovation Illinois Institute of Technology ChicagoIL60616 United States The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China

High renewable energy penetration into power distribution systems causes a substantial risk of exceeding voltage security limits, which needs to be accurately assessed and properly managed. However, the existing methods usually rely on the joint probability models of power generation and loads provided by probabilistic prediction to quantify the voltage risks, where inaccurate prediction results could lead to over or under estimated risks. This paper proposes an uncertain voltage component (UVC) prediction method for assessing and managing voltage risks. First, we define the UVC to evaluate voltage variations caused by the uncertainties associated with power generation and loads. Second, we propose a Gaussian mixture model-based probabilistic UVC prediction method to depict the non-Gaussian distribution of voltage variations. Then, we derive the voltage risk indices, including value-at-risk (VaR) and conditional value-at-risk (CVaR), based on the probabilistic UVC prediction model. Third, we investigate the mechanism of UVC-based voltage risk management and establish the voltage risk management problems, which are reformulated into linear programming or mixed-integer linear programming for convenient solutions. The proposed method is tested on power distribution systems with actual photovoltaic power and load data and compared with those considering probabilistic prediction of nodal power injections. Numerical results show that the proposed method is computationally efficient in assessing voltage risks and outperforms existing methods in managing voltage risks. The deviation of voltage risks obtained by the proposed method is only 15% of that by the methods based on probabilistic prediction of nodal power injections. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Performance-Based Model Recovery Anti-Windup Design for Linear systems Subject to Actuator Saturation

A Performance-Based Model Recovery Anti-Windup Design for Li...

引用

American control Conference (ACC)

作者： Wenxin Lai Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Char-lottesville VA U.S.A.

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we investigate the problem of reference tracking for a class of linear systems subject to actuator saturation and propose a performance-based model recovery anti-windup strategy. We adopt the classic model recovery anti-windup framework and regard the states or one of the outputs of the anti-windup compensator as the tracking error, by which the difference between the unconstrained system and the saturated system is quantified. Then, instead of employing the commonly used $\mathcal{L}_system$ gain, we present the prescribed performance functions (PPFs) to characterize the real-time tracking error such that the system performance can be captured more specifically. In particular, to avoid singular issues arising from the occurrence of saturation, we modify the existing PPFs with an auxiliary system when saturation occurs. Based on these modified performance functions, we follow a modified prescribed performance control approach and design the remaining output of the anti-windup compensator. Such a design procedure is constructive, making it more promising for extension to nonlinear systems. Theoretical results establish the boundedness of all signals in the closed-loop system. Simulation results verify the effectiveness of our design strategy.

关键词： Linear systems Actuators Simulation system performance Stability analysis Real-time systems Trajectory

来源：评论

学校读者我要写书评

暂无评论

Incremental Online Learning of Randomized Neural Network with Forward Regularization

arXiv

引用

arXiv 2024年

作者： Wang, Junda Hu, Minghui Li, Ning Al-Ali, Abdulaziz Suganthan, Ponnuthurai Nagaratnam The Key Laboratory of System Control and Information Processing Ministry of Education of China China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Electrical and Electronic Nanyang Technological University Singapore The KINDI Computing Research Center College of Engineering Qatar University Qatar

Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Networks (Randomized NN), a framework facilitating continuous improvements to Randomized NN performance in restrictive online scenarios. Within the framework, we further introduce IOL with ridge regularization (-R) and IOL with forward regularization (-F). -R generates stepwise incremental updates without retrospective retraining and avoids catastrophic forgetting. Moreover, we substituted -R with -F as it enhanced precognition learning ability using semi-supervision and realized better online regrets to offline global experts compared to -R during IOL. The algorithms of IOL for Randomized NN with -R/-F on non-stationary batch stream were derived respectively, featuring recursive weight updates and variable learning rates. Additionally, we conducted a detailed analysis and theoretically derived relative cumulative regret bounds of the Randomized NN learners with -R/-F in IOL under adversarial assumptions using a novel methodology and presented several corollaries, from which we observed the superiority on online learning acceleration and regret bounds of employing -F in IOL. Finally, our proposed methods were rigorously examined across regression and classification tasks on diverse datasets, which distinctly validated the efficacy of IOL frameworks of Randomized NN and the advantages of forward regularization. Copyright © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：