检索结果-内蒙古大学图书馆

IEEE International Conference on Mechatronics and Automation

作者： QingFeng Li Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China Department of Electrical Engineering Tshwane University of Technology South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Recent studies have explored the applicability of the Transformer model in 3D medical segmentation tasks. The core attention mechanism of transformer, has the property of capturing remote dependencies, which is highly robust when segmenting medical images. Inspired by the emerging visual groupings in Vision Transformers (ViTs), self-attention can be made more robust by improving the intermediate representation. We propose a novel framework called FATUnetr that aims to take advantage of the full attention module to improve accuracy. We also reduce the number of parameters by applying deeply separable convolution at the bottleneck layer. We have verified the effectiveness in the extracranial vault multiple atlas labelling (BTCV) dataset for multi-organ segmentation.

关键词： Image segmentation Visualization Three-dimensional displays Attention mechanisms Accuracy Convolution Transformer cores

来源：评论

学校读者我要写书评

暂无评论

A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation

arXiv

引用

arXiv 2024年

作者： Yuan, Li Cai, Yi Ren, Haopeng Wang, Jiexin School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibits a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and Data are released at https://***/YuanLi95/T5-LMPM. © 2024, CC BY-NC-ND.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Predicting and Visualizing Citrus Color Transformation Using a Deep Mask-Guided Generative Network

引用

Plant Phenomics 2023年第3期5卷 435-446页

作者： Zehan Bao Weifu Li Jun Chen Hong Chen Vijay John Chi Xiao Yaohui Chen College of Informatics Huazhong Agricultural UniversityWuhan 430070China Engineering Research Center of Intelligent Technology for Agriculture Ministry of EducationWuhanChina RIKEN Guardian robot project2-2-2 Hikaridai Seika-choSorakugun619-0288 KyotoJapan Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical EngineeringHainan UniversityHaikou 570100China College of Engineering Huazhong Agricultural University430070 WuhanChina

Citrus rind color is a good indicator of fruit development,and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest *** work presents the complete workflow to predict and visualize citrus color transformation in the orchard featuring high accuracy and fidelity.A total of 107 sample Navel oranges were observed during the color transformation period,resulting in a dataset containing 7,535 citrus images.A framework is proposed that integrates visual saliency into deep learning,and it consists of a segmentation network,a deep mask-guided generative network,and a loss network with manually designed loss ***,the fusion of image features and temporal information enables one single model to predict the rind color at different time intervals,thus effectively shrinking the number of model *** semantic segmentation network of the framework achieves the mean intersection over a union score of 0.9694,and the generative network obtains a peak signal-to-noise ratio of 30.01 and a mean local style loss score of 2.710,which indicate both high quality and similarity of the generated images and are also consistent with human *** ease the applications in the real world,the model is ported to an Android-based application for mobile *** methods can be readily expanded to other fruit crops with a color transformation *** dataset and the source code are publicly available at GitHub.

关键词： Visual Deep network

来源：评论

学校读者我要写书评

暂无评论

Calculation of Salt Heap Volume Based on Point Cloud Surface Reconstruction 4

Calculation of Salt Heap Volume Based on Point Cloud Surface...

引用

4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Liu, Chuang Liu, Jun Zhang, Meijuan Liu, Ruirui Dong, Guangfeng Wang, Zhen Liu, Zhongjian Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China Sdic Xinjiang Luobupo Hoevellite Co. Ltd Technology Center Hami China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Salt heaps in salt-producing areas are large and numerous, and traditional measurement methods cannot efficiently calculate the volume of salt piles. To solve this problem, this paper proposes a method for calculating the volume of salt stacks using airborne LiDAR combined with surface reconstruction. First, the airborne LiDAR was introduced to obtain the surface point cloud of the salt heap from above. Then, the point cloud is preprocessed by denoising, downsampling, and completion, etc. After the three-dimensional model is reconstructed using the greedy projection triangulation algorithm, the volume of the model can be equivalent to the actual volume of the salt heap. The experimental results show that the error between the calculated volume and the actual volume of this method is less than 4%, and the complete three-dimensional model of the salt heap can be reconstructed well, which shows that the method has good reconstruction effect and calculation accuracy. © 2022 IEEE.

关键词： Surface reconstruction

来源：评论

学校读者我要写书评

暂无评论

Design of a Tractor-Trailer Climbing robot for Large Pressure Vessel Inspection 3

Design of a Tractor-Trailer Climbing Robot for Large Pressur...

引用

3rd International Conference on intelligent Design, ICID 2022

作者： Han, Wei Zhang, Wenzhi Guo, Yupeng Beijing Boya Realscence Techology Co. Ltd Beijing China Inner Mongolia University of Technology Inner Mongolia Key Laboratory of special Service Intelligent Robot Huhehaot China

ISBN: (数字)9781665476775

ISBN: (纸本)9781665476775

In-service large pressure vessels need periodic inspection to keep safe operation. Inspection robot for large pressure vessel needs adaption to curved surface, multifunctional and heavy load abilities. In this paper, a tractor-trailer type climbing robot is designed to meet the requirement. The constitutes of robot is introduced. By mutual reference of weld data identified from vision and known weld line distribution map, the approaches of robot self-location and weld line distinguishing are given. An output limited PID controller is designed for weld seam tracking. Experiments show that the robot can carry different tools and automatically track weld lines on the surface of large pressure vessels. © 2022 IEEE.

关键词： Machine design

来源：评论

学校读者我要写书评

暂无评论

Mobility-Aware and Energy-Efficient Task Offloading Strategy for Mobile Edge Workflows

引用

Wuhan University Journal of Natural Sciences 2022年第6期27卷 476-488页

作者： QIN Zhiwei LI Juan LIU Wei YU Xiao School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205.HubeiChina Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology) Wuhan 430205.HubeiChina School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070HubeiChina

With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.

关键词： workflow application task offloading energy saving heuristic algorithm mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Efficient learning of robust quadruped bounding usingpretrained neural networks

引用

IET Cyber-Systems and robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

来源：评论

学校读者我要写书评

暂无评论

Research on Mobile robot Localization Algorithm with Improved ORB Extraction Matching

Research on Mobile Robot Localization Algorithm with Improve...

引用

IEEE International Conference on Mechatronics and Automation

作者： Hongchang Sun Longfei Wei Yongxiang Jiang Yunfei Su Sanpeng Deng Hao Sun Haihang Wang School of Mechanical Engineering Institute of Robot and Intelligent Equipment Tianjin Intelligent Robot Technology and Application Enterprise Key Laboratory TianJin University of Technology and Education Tianjin China School of Artificial Intelligence Engineering Research Center of the Ministry of Education of China in Intelligent Rehabilitation Equipment and Detection Technologies Hebei University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Aiming at the problem of inaccurate positioning accuracy of V-SLAM system caused by poor texture and blurred images generated by mobile robots during rapid movement and large angle rotation, an improved ORB feature extraction matching algorithm is proposed. Firstly, using the statistical form of information entropy to calculate the image features, image enhancement is used to highlight the feature information of poorly textured and blurred image blocks, improve the efficiency of feature point detection, and enhance the robustness of the system. In addition, to improve the accuracy of feature matching, the BEBLID descriptor is used instead of the BRIEF descriptor for feature description. Through experimental verification on the publicly available TUM data set, it was found that the improved algorithm has a higher matching accuracy than the ORB algorithm, and has a significant increase in algorithm time compared to the ORB algorithm. After trajectory calculation, the improved algorithm is more in line with the real trajectory in camera motion and has a good improvement in pose accuracy.

关键词： Visualization Accuracy robot vision systems Feature extraction Odometers Robustness Trajectory

来源：评论

学校读者我要写书评

暂无评论

A Unified Segmentation Network for Multi-Organ Lesion Detection in Two-Dimensional Grayscale Ultrasound Images

A Unified Segmentation Network for Multi-Organ Lesion Detect...

引用

Artificial Intelligence Innovation (ICAII), International Conference on

作者： Hanshuo Xing Yan Jiang Xinyu Cao Yin Fang Peiyan Wu Wenbo Song Xinglong Wu Hubei Key Laboratory of Intelligent Robot School of Computer Science & Engineering Wuhan Institute of Technology Wuhan Hubei China

In recent years, computer-aided diagnostic systems had found wide application in ultrasound image analysis to enhance reliability and alleviate the workload of ultrasound practitioners. The advent of deep learning, particularly the rise of Transformer, has significantly improved the performance of various medical diagnostic tasks, including breast cancer detection, thyroid nodule segmentation, fetal pathology assessment, primary thyroid cancer lymph node metastasis prediction, prostate cancer localization, and brachial plexus nerve system diagnosis. However, most deep learning-based ultrasound lesion segmentation algorithms focus on specific organ types and lack a generalized approach for multi-organ lesion segmentation. This paper presents a unified segmentation network for two-dimensional grayscale ultrasound images, utilizing medical priors to guide the network in learning the relationships between multiple targets. The proposed network introduces a Conv Former module to effectively fuse features at different scales, promoting multi-scale information recognition. Additionally, a self-attention mechanism is introduced to capture internal correlations within the features, thereby reducing reliance on external information. Comprehensive experiments validate the effectiveness and scalability of the proposed framework. Superior performance is achieved compared to the current state-of-the-art segmentation models on a unified ultrasound dataset comprising over 6,000 images and five anatomical sites. This research contributes to the application of computer-aided diagnosis in clinical ultrasound, significantly reducing the workload of healthcare professionals.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Additive manufacturing of heat-resistant aluminum alloys: a review

引用

International Journal of Extreme Manufacturing 2024年第6期6卷 449-504页

作者： Chaoqun Wu Jianyu Wen Jinliang Zhang Bo Song Yusheng Shi Hubei Province Engineering Research Center of Robot&Intelligent Manufacturing School of Mechanical and Electronic EngineeringWuhan University of TechnologyWuhan 430070People's Republic of China State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China

The capability for synergistic advancements in both making and shaping afforded by additive manufacturing(AM) enables the flexible production of high-performance components. Boosted by the growing demand for heat-resistant aluminum alloys in the moderate-temperature weight-critical applications, AM of heat-resistant aluminum alloys constitutes a burgeoning field. Although numerous advances have emerged in recent years, there remains a gap in the review literature elucidating the newly-developed alloy systems and critically evaluating the efficacy. This state-of-the-art review presents a detailed overview of recent achievements on the heat-resistant aluminum alloy development. It begins with the introduction of various AM technologies and the pros and cons of each technique are evaluated. The enhancement mechanisms associated with printability and high-temperature properties of AM aluminum alloys are then delineated. Thereafter, the various additively manufactured aluminum alloy systems are discussed with regard to the microstructure, heat resistance and high-temperature performance. An emphasis is put on the powder bed fusion-laser beam(PBF-LB) as it has garnered significant attention for heat-resistant aluminum alloys and the vast majority of the current studies are based on this technique. Finally, perspectives are outlined to provide guidance for future research.

关键词： additive manufacturing heat-resistant aluminum alloy alloy development microstructure high-temperature mechanical properties

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：