检索结果-内蒙古大学图书馆

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Yuan Han, Xu Li, Xianzhi Yu, Qiao Xu, Jinfeng Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology China South China University of Technology China School of computer science and technology Huazhong University of Science and Technology China Guangdong Intelligent Robotics Institute China Guangdong HUST Industrial Technology Research Institute China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou 510640 China Pazhou Laboratory Guangdong Guangzhou 510640 China

ISBN: (纸本)157735897X

Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs has yet to be replicated in 3D understanding. In this paper, we rethink this issue and propose a new task: 3D Data-Efficient Point-Language Understanding. The goal is to enable LLMs to achieve robust 3D object understanding with minimal 3D point cloud and text data pairs. To address this task, we introduce GreenPLM, which leverages more text data to compensate for the lack of 3D data. First, inspired by using CLIP to align images and text, we utilize a pre-trained point cloud-text encoder to map the 3D point cloud space to the text space. This mapping leaves us to seamlessly connect the text space with LLMs. Once the point-text-LLM connection is established, we further enhance text-LLM alignment by expanding the intermediate text space, thereby reducing the reliance on 3D point cloud data. Specifically, we generate 6M free-text descriptions of 3D objects, and design a three-stage training strategy to help LLMs better explore the intrinsic connections between different modalities. To achieve efficient modality alignment, we design a zero-parameter cross-attention module for token pooling. Extensive experimental results show that GreenPLM requires only 12% of the 3D training data used by existing state-of-the-art models to achieve superior 3D understanding. Remarkably, GreenPLM also achieves competitive performance using text-only data. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features

Plant Leaf Diseases Classification Using SVMs, XGBoost, and ...

引用

IEEE International Conference on Artificial Intelligence in Engineering and technology (IICAIET)

作者： Abdullah Kamal Sami Khamael Al-Dulaimi Computer Science Dept. Al-Nahrain University Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary Pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models.

关键词： Plant diseases Accuracy Temperature Shape Computational modeling Machine learning Color Feature extraction Object recognition Diseases

来源：评论

学校读者我要写书评

暂无评论

Shape Sensing of a Soft Robot Using Fiber Bragg Gratings for Single-port Transvesical Radical Prostatectomy

Shape Sensing of a Soft Robot Using Fiber Bragg Gratings for...

引用

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Liu, Zefeng Li, Ru Cao, Yongfeng Feng, Fan Xie, Le Shanghai Jiao Tong University School of Materials Science and Engineering The Institute of Medical Robotics Shanghai200240 China Quanzhou Normal University School of Mathematics and Computer Science Quanzhou362000 China Shanghai Jiao Tong University Institute of Medical Robotics Institute of Forming Technology & Equipment Shanghai200240 China

ISBN: (纸本)9781665481090

Robot-assisted laparoscopic radical prostatectomy is a common treatment for prostate cancer. Traditional rigid instruments, on the other hand, have operating blind areas and lack real-time position and shape feedback, which may result in additional trauma during the surgery. To this end, this work describes the development of a soft robot for single-port transvesical radical prostatectomy (STRP) using a multicore fiber with Fiber Bragg Gratings (FBGs). The shape sensing method is proposed in the meanwhile to rebuild the soft robot's shape from the wavelength data of FBGs. The experiments are carried out to confirm that the suggested strategy is workable. © 2022 IEEE.

关键词： Fiber Bragg gratings

来源：评论

学校读者我要写书评

暂无评论

Temporally Robust Multi-Agent STL Motion Planning in Continuous Time

Temporally Robust Multi-Agent STL Motion Planning in Continu...

引用

American Control Conference (ACC)

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Signal Temporal Logic (STL) is a formal language over continuous-time signals (such as trajectories of a multiagent system) that allows for the specification of complex spatial and temporal system requirements (such as staying sufficiently close to each other within certain time intervals). To promote robustness in multi-agent motion planning with such complex requirements, we consider motion planning with the goal of maximizing the temporal robustness of their joint STL specification, i.e. maximizing the permissible time shifts of each agent's trajectory while still satisfying the STL specification. Previous methods presented temporally robust motion planning and control in a discrete-time optimization scheme. In contrast, we parameterize the trajectory by continuous Bézier curves, where the curvature and the time-traversal of the trajectory are parameterized individually. We show an algorithm generating continuous-time temporally robust trajectories and prove soundness of our approach. Moreover, we empirically show that our parametrization realizes this with a considerable speed-up compared to state-of-the-art methods based on constant interval time discretization.

关键词： Redundancy Formal languages Robustness Encoding Trajectory Planning Logic

来源：评论

学校读者我要写书评

暂无评论

A Clustering-Based Support Vector Classifier for Dynamic Time-Linkage Optimization

A Clustering-Based Support Vector Classifier for Dynamic Tim...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Gao, Meng Liu, Xiao-Fang Zhan, Zhi-Hui Zhang, Jun Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

ISBN: (纸本)9781665430654

Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time. © 2023 IEEE.

关键词： dynamic optimization evolutionary computation support vector machine time-linkage

来源：评论

学校读者我要写书评

暂无评论

Robust Policy Learning for Multi-UAV Collision Avoidance with Causal Feature Selection

arXiv

引用

arXiv 2024年

作者： Zhuang, Jiafan Han, Gaofei Xia, Zihao Wang, Boxi Li, Wenji Wang, Dongliang Hao, Zhifeng Cai, Ruichu Fan, Zhun College of Engineering Shantou University China School of Computer Science Guangdong University of Technology China University of Electronic Science and Technology of China China International Cooperation Base of Evolutionary Intelligence and Robotics

In unseen and complex outdoor environments, collision avoidance navigation for unmanned aerial vehicle (UAV) swarms presents a challenging problem. It requires UAVs to navigate through various obstacles and complex backgrounds. Existing collision avoidance navigation methods based on deep reinforcement learning show promising performance but suffer from poor generalization abilities, resulting in performance degradation in unseen environments. To address this issue, we investigate the cause of weak generalization ability in DRL and propose a novel causal feature selection module. This module can be integrated into the policy network and effectively filters out non-causal factors in representations, thereby reducing the influence of spurious correlations between non-causal factors and action predictions. Experimental results demonstrate that our proposed method can achieve robust navigation performance and effective collision avoidance especially in scenarios with unseen backgrounds and obstacles, which significantly outperforms existing state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2

引用

PeerJ computer science 2025年 11卷 1-22页

作者： Saxena, Pranshu Singh, Sanjay Kumar Rashid, Mamoon Alshamrani, Sultan S. Alnfiai, Mrim M. School of Computer Science Engineering and Technology Bennett University Greater Noida India University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information Technology College of Computers and Information Technology Taif University Taif Saudi Arabia

Background: Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes. Methods: This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 dataset and compared with VGG11 and SqueezeNet1_1, demonstrating modified ResNet-34’s optimal balance between depth and performance. FastAI-2 enhances computational efficiency, enabling rapid convergence with minimal training time. Results: The proposed system achieved 99.78% accuracy, confirming the effectiveness of automated lung cancer histopathology classification. This study highlights the potential of artificial intelligence (AI)-driven diagnostic tools to assist pathologists by improving accuracy, reducing workload, and enhancing decision-making in clinical settings. Copyright 2025 Saxena et al.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Drone to Address Smart City Security Issues 37th

AI-Powered Drone to Address Smart City Security Issues

引用

37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Adaptive Geodesic Flow Kernel Transfer for Many-Task Optimization

Adaptive Geodesic Flow Kernel Transfer for Many-Task Optimiz...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Dai, Yang-Tao Liu, Xiao-Fang Zhan, Zhi-Hui Zhong, Jinghui Zhang, Jun Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

ISBN: (纸本)9781665430654

Many-task optimization problems (MaTOP) involve more than three tasks, which can be solved simultaneously via knowledge transfer by utilizing complementary information of different tasks. Due to the biases between tasks, relevant tasks are usually selected for knowledge transfer to avoid negative effects. There are two challenging issues, i.e., source task selection and inter-task knowledge transfer. To address these issues, this paper proposes an adaptive geodesic flow kernel transfer method (AGFKTM) for MaTOP. In AGFKTM, multiple source tasks are selected based on both the similarity between tasks and the performance of tasks. In this way, similar and well-performed tasks are selected with a high priority. In addition, an adaptive geodesic flow kernel is constructed to implement knowledge transfer, in which the adopted subspaces along the geodesic flow path are adaptively controlled. Particularly, the transferred solutions are used to generate new ones using mutation operators. Integrating the AGFKTM into differential evolution, a new algorithm named AGFKT-DE is put forward. Experimental results on GECCO20MaTOP benchmark show that the new algorithm outperforms state-of-the-art algorithms. © 2023 IEEE.

关键词： differential evolution evolutionary computation geodesic flow kernel knowledge transfer many-task optimization

来源：评论

学校读者我要写书评

暂无评论

Fabric computing: Concepts, opportunities, and challenges

引用

The Innovation 2022年第6期3卷 67-76页

作者： Min Chen Jia Liu Pan Li Hamid Gharavi Yixue Hao Jingyu Ouyang Jiayu Hu Long Hu Chong Hou Iztok Humar Lei Wei Guang-Zhong Yang Guangming Tao Wuhan National Laboratory for Optoelectronics and School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China National Institute of Standards and Technology(NIST) GaithersburgMD 20899-8920USA School of Optics and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Faculty of Electrical Engineering University of LjubljanaTrzaska cesta 251000 LjubljanaSlovenia School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Medical Robotics Institute Shanghai Jiao Tong UniversityShanghai 200030China State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

With the advent of the Internet of Everything,people can easily interact with their environments *** idea of pervasive computing is becoming a reality,but due to the inconvenience of carrying silicon-based entities and a lack of fine-grained sensing capabilities for human-computer interaction,it is difficult to ensure comfort,esthetics,and privacy in smart *** by the rapid developments in intelligent fabric technology in the post-Moore era,we propose a novel computing approach that creates a paradigm shift driven by fabric computing and advocate a new concept of non-chip sensing in living *** discuss the core notion and benefits of fabric computing,including its implementation,challenges,and future research opportunities.

关键词： computing computer smart

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：