检索结果-内蒙古大学图书馆

MAD-BA: 3D LiDAR Bundle Adjustment – From Uncertainty Modelling to Structure Optimization

IEEE Robotics and Automation Letters 2025年第7期10卷 7254-7261页

作者： Krzysztof Ćwian Luca Di Giammarino Simone Ferrari Thomas Ciarfuglia Giorgio Grisetti Piotr Skrzypczyński Institute of Robotics and Machine Intelligence Poznan University of Technology Poznan Poland Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Rome Italy

The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization, with structure refinement either omitted or treated separately using implicit representations. This letter introduces a framework for simultaneous optimization of sensor poses and 3D map, represented as surfels. A generalized LiDAR uncertainty model is proposed to address less reliable measurements in varying scenarios. Experimental results on public datasets demonstrate improved performance over most comparable state-of-the-art methods. The system is provided as open-source software to support further research.

关键词： Laser radar Optimization Uncertainty Simultaneous localization and mapping Point cloud compression Measurement by laser beam Atmospheric modeling Accuracy Trajectory Data models

来源：评论

学校读者我要写书评

暂无评论

Prioritization Method of Test Data for Intelligent Software based on Multi-objective Optimization 9

Prioritization Method of Test Data for Intelligent Software ...

引用

9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Xia, Chunyan Huang, Yifan Huang, Song Zheng, Changyou Wang, Yutong Wang, Yu College of Command and Control Engineering Army Engineering University of Pla China College of Computer and Information Technology Mudanjiang Normal University China Illinois Institute of Technology United States

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the rapid development of the information industry, intelligent software testing has become one of the hot research. This paper studies how to extract useful data from the original test set to test the modified model for the intelligent software. First, the initial test data are obtained according to the label classification of the original structured documents. Second, the non-dominated sorting genetic algorithm is used to prioritize the test data. Finally, the experimental results of intelligent software show that compared with the random method, the accuracy of the penalty_money prediction model is reduced by 0.04% 34.91 %, and the accuracy of the penalty_imprisonment prediction model is reduced by 0.08% 32.63 %, indicating that the proposed method can find more defects of the penalty prediction model earlier, to reduce the cost of intelligent software testing. © 2022 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

引用

8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

来源：评论

学校读者我要写书评

暂无评论

Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)

arXiv

引用

arXiv 2025年

作者： Mehdifar, Farhad Dhullipalla, Mani H. Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Division of Systems and Control The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents’ objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Preprocessing Techniques for Rectal Cancer Diagnosis using MR Images 24

Preprocessing Techniques for Rectal Cancer Diagnosis using M...

引用

13th International Conference on Software and computer Applications, ICSCA 2024

作者： D'Almeida, Sonal Patreena Kamath, Surekha Rajagopal, K.V. Manjunath, K.N. Department Of Instrumentation And Control Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Radiodiagnosis And Imaging Kasturba Medical College Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Computer Science And Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India

ISBN: (纸本)9798400708329

The use of magnetic resonance (MR) Image has become more significant when treating rectal cancer. Rectal cancer can be staged more accurately with MRI, which serves as a great tool for choosing the most suitable course of action. Image pre-processing is used to improve the quality of image and making it better for analysis and further processing. The study carried out in this paper is concentrated on pre-processing methods like Gaussian and Sobel filtering. The filtering techniques were implemented on Pelvic MR images obtained from Kasturba Medical College (KMC), Manipal. The findings suggest that Sobel filtering had a superior output image quality, indicating its potential as a preferred preprocessing technique for enhancing Pelvic MR images in rectal cancer diagnosis and treatment planning. The output image efficiency is calculated by Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE). © 2024 Owner/Author.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Fusing surveillance videos and three-dimensional scene: A mixed reality system

Fusing surveillance videos and three-dimensional scene: A mi...

引用

作者： Cui, Xiaoliang Khan, Dawar He, Zhenbang Cheng, Zhanglin Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Information Technology The University of Haripur Haripur Pakistan

Augmented Virtual Environments (AVE) or Virtual-Reality Fusion systems fuse dynamic videos with static three-dimensional (3D) models of a virtual environment to provide an optimal solution for visualizing and understanding multichannel surveillance systems. However, texture distortion caused by viewpoint changes in such systems is a critical issue that needs to be addressed. To minimize texture fusion distortion, this paper presents a novel virtual environment system in two phases, offline and online phases, to dynamically fuse multiple surveillance videos with a virtual 3D scene. In the offline phase, a static virtual environment is obtained by performing a 3D photogrammetric reconstruction from the input images of the scene. In the online phase, the virtual environment is augmented by fusing multiple videos through two optional strategies. One strategy is to dynamically map images of different videos onto a 3D model of the virtual environment, and the other is to extract moving objects and represent them as billboards. The system can be used to visualize a 3D environment from any viewpoint augmented by real-time videos. Experiments and user studies in different scenarios demonstrate the superiority of our system. © 2022 John Wiley & Sons Ltd.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Enhancing Transformer Tracking Using NF-ResNet and ResNeXT Backbones 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Verma, Sourabh Singla, Rajesh Verma, Om Prakash Sharma, Richa Gupta, Himanshu Sharma, Tarun Muthanna, Ammar Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Computer Science—Internet of Things ABES Institute of Technology Ghaziabad India University Meerut India Saint-Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Residual networks play a foremost role in the domain of tracking, specifically in the extraction of features. The residual networks are using a simple technique of skipping connections to overcome the problem of vanishing gradient. Also, it is using batch normalization after each layer of convolution to accelerate the training by reducing the dependency over the parameters. Though, we have tried to solve these issues by introducing ResNeXt and NF-ResNet one by one into the TransT tracker as feature extractors in the place of ResNet. Finally, we present a ResNeXt-based TransT tracker (named as TransT_NeXt) and NF-Net-based TransT tracker (named as TransT_NFNet). We have evaluated proposed trackers on three large-scale benchmark datasets and four small-scale benchmark datasets but unfortunately, our trackers haven’t performed well. At last, we have discussed about the future work on the basis of this study. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

引用

IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis 21

Personalized Federated Learning with Kolmogorov-Arnold Enhan...

引用

21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Kulevome, Delanyo Kwame Bensah Man, Qiu Mawuli, Ernest Smith Opoku-Mensah, Edward Qilu Institute of Technology Department of Computer and Information Engineering Shandong Jinan250200 China Qilu Institute of Technology Department of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Department of Electrical/Electronic Technology Education Kumasi Ghana HEC Department of Information Technologies Montréal Canada

ISBN: (纸本)9798331519254

Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

引用

3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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：