检索结果-内蒙古大学图书馆

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bing Song Gang Xiong Zhen Shen Fenghua Zhu Yisheng Lv Peijun Ye The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heavy metal budgets in two forested catchments with heavy atmospheric deposition pollution in China

引用

Science of the Total Environment 2025年 988卷 179815页

作者： Lv, Dongwei Yang, Dongxing Zhang, Jiawei Zhang, Yuchun Mulder, Jan Duan, Lei State Key Laboratory of Regional Environment and Sustainability School of Environment Tsinghua University Beijing100084 China Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Tsinghua University Beijing100084 China

To investigate heavy metals (HMs) migration in forests with decreasing atmospheric HM deposition in China, the zinc (Zn), lead (Pb) and cadmium (Cd) fluxes through hydrological pathways were observed in a coniferous forest at Tieshanping (TSP) near Chongqing, a mega city in Southwest China, and a broad-leaved forest at Gaozhuang village (GZV) near Jiyuan, an industrial city with large-scale smelters. The throughfall fluxes of Pb and Cd at GVZ were 109 and 9.57 mg·m−2·yr−1, respectively, far exceeding those at TSP, indicating severe pollution by atmospheric deposition. The increasing HM fluxes from rainfall to throughfall highlighted the contribution of dry deposition with coniferous forests exhibiting stronger airbone-HM adsorption. The litter layer on forest floor retarded 40.7 % ~ 70.0 % HMs, with broad-leaved litterfall exhibiting lower retention rates due to faster decomposition. Soil stored 56.0 mg·m−2·yr−1 of the three HMs at TSP and 77.2 mg·m−2·yr−1 at GZV, resulting in little HM concentration in outflows. GZV's soil with higher pH made it a more effective HM sink than TSP. Despite HM retention rates exceeding 90 % at both forests, HMs in surface soil began to move downward. This study provided the first evidence that following China's substantial reduction of atmospheric HM pollution, polluted forests exhibited vertical HM migration through soil profiles. Fortunately, HMs were further retained by deep soil, preventing watershed-scale export. © 2025 Elsevier B.V.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Path Planning Based on Segmented Bezier Curves and A* Algorithm for Mobile Robot

Path Planning Based on Segmented Bezier Curves and A* Algori...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Xiangrui Meng Shuang Liang Zhiqiang Cao Zhonghui Li Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Information Science Academy China Electronics Technology Group Corporation Beijing China Beijing Engo Technology Co. Ltd Beijing China

ISBN: (纸本)9781665405362

Path planning is an important problem for mobile robot in complex environments. In this paper, an optimized path planning method based on segmented Bezier curves and A* algorithm is proposed. The result of A* algorithm is used to guide the direction of segmented Bezier curves, which fit the A* path. The segmented Bezier curves are generated to meet the requirements of fitting index with the consideration of constraints from the environment and robot itself. The set of the optimized Bezier curves for each segmented A* path is connected end to end and regarded as the result of path planning for the mobile robot. The effectiveness of the proposed method is verified through the experiments.

关键词： Smoothing methods Conferences Biomimetics Fitting Path planning Mobile robots Indexes

来源：评论

学校读者我要写书评

暂无评论

A Parallel Framework for Scene Understanding

A Parallel Framework for Scene Understanding

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wenwen Zhang Jiangong Wang Kunfeng Wang School of Software Engineering Xi'an Jiaotong University Xi'an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investigate the current state of scene understanding from different aspects relying on different computer vision tasks and propose an effective framework based on the theory of parallel vision to combine different tasks for scene understanding. With the maintained artificial scene throughout time from the proposed framework, we can understand the scene in an elegant way. To explain our proposed framework, a case study is given.

关键词： Computer vision Digital twin Conferences Task analysis

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Learning from Demonstrations for Long-Horizon Tasks

Hierarchical Learning from Demonstrations for Long-Horizon T...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Boyao Li Jiayi Li Tao Lu Yinghao Cai Shuo Wang China Academy of Launch Vehicle Technology Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

Although reinforcement learning (RL) has achieved great success in robotic manipulation skills learning, it is still challenging for long-horizon tasks. Combining RL with demonstrations is an effective solution. In this paper, we propose a novel hierarchical learning from demonstrations method for long-horizon tasks, which leverages (i) object-centered segmentation of demonstrations to automatically segment the teaching trajectories into episodes. (ii) a bi-level hierarchical imitation learning method with a parallel training mechanism to train the two-level policies simultaneously. Experimental results on three challenging long-horizon tasks with sparse rewards show that our proposed method significantly outperforms state-of-art approaches in terms of both sample-efficiency and success rate. Moreover, our method is the only one which achieves satisfactory performance in tasks of multi-object stack and multi-object push&stack.

关键词： Deep learning Training Learning systems Automation Conferences Reinforcement learning Trajectory

来源：评论

学校读者我要写书评

暂无评论

An Island Remote Sensing Image Segmentation Algorithm Based on A Fusion Network with Attention Mechanism 3

An Island Remote Sensing Image Segmentation Algorithm Based ...

引用

2020 3rd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Wu, Peng Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, in view of the problems that remote sensing images contain richer ratio information and complex background interference, we propose an island remote sensing image segmentation algorithm based on a fusion network with attention mechanism, called AFU-Net. The network is built on the basis of FC_U-Net [1]. An attention mechanism is added to pre-weight the shallow features before deep-shallow layer feature fusion, in order to enhance the response capability of the target features, suppress the background interference and improve the segmentation accuracy of the network. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of AFU-Net are greatly improved compared to FC_U-Net, and are also superior to other three state-of-the-art methods, U-Net, FCN, SegNet, which indicates the effectiveness of our method. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

arXiv

引用

arXiv 2022年

作者： Liu, Tianying Zhang, Lu Wang, Yang Guan, Jihong Fu, Yanwei Zhao, Jiajia Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai201804 China The Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200438 China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Shanghai200433 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

An Island Remote Sensing Image Segmentation Algorithm Based on FC_U-Net Network

An Island Remote Sensing Image Segmentation Algorithm Based ...

引用

2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Du, Junrong Wu, Peng Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194813

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, for remote sensing images with rich details and different target scales, there are still some problems such as insufficient segmentation accuracy. To handle these problems, we propose an island remote sensing image segmentation algorithm based on FC_U-Net network. In this network, U-Net and FCN (Fully Convolutional Network) are improved respectively to form two new modules, and then the semantic features and detail features from the two modules are fused by a fusion module. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of FC_U-Net are better than other three state-of-the-art methods, U-Net, FCN (Fully Convolutional Network), SegNet, which indicates the effectiveness of our method. © 2020 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Impact of corticosteroids on initiation and half-year durability of humoral response in COVID-19 survivors

引用

Chinese Medical Journal Pulmonary and Critical Care Medicine 2024年第1期2卷 48-55页

作者： Yeming Wang Li Guo Guohui Fan Yang Han Qiao Zhang Lili Ren Hui Zhang Geng Wang Xueyang Zhang Tingxuan Huang Weiyang Wang Lan Chen Lixue Huang Xiaoying Gu Xinming Wang Jingchuan Zhong Ying Wang Hui Li Jiapei Yu Zhibo Liu Chaolin Huang Bin Cao Jianwei Wang National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesChina-Japan Friendship HospitalBeijing 100029China National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 102629China Key Laboratory of Respiratory Disease Pathogenomics Chinese Academy of Medical SciencesBeijing 100029China School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeState Key Laboratory of Respiratory Health and MultimorbidityKey Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College)Ministry of EducationBeijing 100730China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesDepartment of Clinical research and Data managementCenter of Respiratory MedicineChina-Japan Friendship HospitalBeijing 100029China Jin Yin-tan Hospital WuhanHubei 430023China Department of Pulmonary and Critical Care Medicine West China HospitalSichuan UniversityChengduSichuan 610041China Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences&Peking Union Medical CollegeNational Clinical Research Center for Dermatologic and Immunologic Diseases(NCRC-DID)Ministry of Science&TechnologyState Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College Hospital(PUMCH)Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijing 100730China Department of Pulmonary and Critical Care Medicine Center of Respiratory MedicineChina-Japan Friendship HospsitalInstitute of Respiratory MedicineChinese

Background:The impact of corticosteroids on humoral responses in coronavirus disease 2019(COVID-19)sur-vivors during the acute phase and subsequent 6-month period remains *** study aimed to determine how the use of corticosteroids influences the initiation and duration of humoral responses in COVID-19 survivors 6 months after infection ***:We used kinetic antibody data from the lopinavir-ritonavir trial conducted at Jin Yin-Tan Hospital in January 2020,which involved adults hospitalized with severe COVID-19(LOTUS,ChiCTR2000029308).Anti-body samples were collected from 192 patients during hospitalization,and kinetic antibodies were monitored at all available time points after ***,plasma samples were collected from 101 COVID-19 survivors for comprehensive humoral immune measurement at the half-year follow-up *** main focus was comparing the humoral responses between patients treated with systemic corticosteroid therapy and the non-corticosteroid ***:From illness onset to day 30,the median antibody titre areas under the receiver operating characteristic curve(AUCs)of nucleoprotein(N),spike protein(S),and receptor-binding domain(RBD)immunoglobulin G(IgG)were significantly lower in the corticosteroids *** AUCs of N-,S-,and RBD-IgM as well as neutralizing antibodies(NAbs)were numerically lower in the corticosteroids group compared with the non-corticosteroid ***,peak titres of N,S,RBD-IgM and-IgG and NAbs were not influenced by *** 6-month follow-up,we observed a delayed decline for most binding antibodies,except N-IgM(𝛽−0.05,95%CI[−0.10,0.00])in the corticosteroids group,though not reaching statistical *** significant difference was observed for ***,for the half-year seropositive rate,corticosteroids significantly accelerated the decay of IgA and IgM but made no difference to N-,S-,and RBD-IgG or ***,corticosteroids group showed a trend towards d

关键词： COVID-19 Corticosteroid Immunity Antibody Humoral response

来源：评论

学校读者我要写书评

暂无评论

A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution Computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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：