检索结果-内蒙古大学图书馆

Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Xiaohui Yan Liang Yu Guiwei Shao Shuyi Shao Jing Zhang School of Artificial Intelligence and Big Data Hefei University Hefei P. R. China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei P. R. China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing P. R. China Science Island Branch of Graduate School USTCHefeiChina Hefei P. R. China

This paper proposes a robust trajectory tracking control strategy based on the high-order fully actuated system approach and disturbance observer. In spite of the strong coupling characteristic and high nonlinearity of the quadrotor unmanned aerial vehicle (QUAV) system, the linear time-invariant system with desired eigenstructures can be obtained by designing a greatly simple robust controller via the direct parametric method. Meanwhile, a disturbance observer (DO) is designed to estimate the external disturbances, and the output of the DO can be adapted for the controller design to compensate for the affect of external disturbances on control performance. Eventually, the numerical simulation results of the QUAV trajectory tracking demonstrate the effectiveness of the developed robust control scheme.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CPTNet: Cascade Pose Transform Network for Single Image Talking Head Animation 15th

CPTNet: Cascade Pose Transform Network for Single Image Talk...

引用

15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhang, Jiale Xian, Ke Liu, Chengxin Chen, Yinpeng Cao, Zhiguo Zhong, Weicai Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huawei CBG Consumer Cloud Service Search Product and Big Data Platform Department Xi’an China

ISBN: (纸本)9783030695378

We study the problem of talking head animation from a single image. Most of the existing methods focus on generating talking heads for human. However, little attention has been paid to the creation of talking head anime. In this paper, our goal is to synthesize vivid talking heads from a single anime image. To this end, we propose cascade pose transform network, termed CPTNet, that consists of a face pose transform network and a head pose transform network. Specifically, we introduce a mask generator to animate facial expression (e.g., close eyes and open mouth) and a grid generator for head movement animation, followed by a fusion module to generate talking heads. In order to handle large motion and obtain more accurate results, we design a pose vector decomposition and cascaded refinement strategy. In addition, we create an anime talking head dataset, that includes various anime characters and poses, to train our model. Extensive experiments on our dataset demonstrate that our model outperforms other methods, generating more accurate and vivid talking heads from a single anime image. © 2021, Springer Nature Switzerland AG.

关键词： Animation

来源：评论

学校读者我要写书评

暂无评论

An Automated and Interpretable Computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming

引用

IEEE Transactions on Evolutionary Computation 2024年

作者： Yu, Kunjie Lian, Jintao Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie Zhengzhou University School of Electrical and Information Engineering Zhengzhou China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang China Longmen Laboratory Luoyang China Zhongyuan University of Technology School of Electronic and Information Zhengzhou China Henan Institute of Technology School of Electrical Engineering and Automation Xinxiang China Victoria University of Wellington School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Wellington New Zealand

Malignant melanoma is a very deadly form of skin cancer and early diagnosis can significantly reduce the mortality rate. Many computer-aided diagnosis (CAD) systems have been developed as second opinion diagnostic aids to assist dermatologists in diagnosing malignant melanoma. However, traditional CAD systems often require domain knowledge for feature extraction, while neural network-based CAD systems require specialized knowledge for designing network structures and often have poor interpretability. In this paper, we propose a new skin cancer CAD system based on genetic programming (GP) to automatically learn effective features for classification with strong interpretability. The approach can automatically evolve variable-length models to extract informative features for describing skin cancer images based on a relatively simple program structure, a new function set, and a terminal set. In addition, compared with other GP methods, this approach employs a newly proposed duplicate subtree removing mechanism, which can effectively prevent the duplication of features, thereby simplifying the model and enhancing its interpretability. The proposed approach has been examined on five real-world skin cancer classification tasks. The results suggest that the proposed approach achieves better performance than GP-based, neural network-based feature learning comparison methods and traditional comparison methods in most cases. Further analysis shows that the proposed approach has employed a smaller tree structure and can automatically evolve/learn models with potentially high interpretability. © 1997-2012 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligent pneumonia image recognition and classification method based on improved convolutional neural network 4

Intelligent pneumonia image recognition and classification m...

引用

4th IEEE International Conference on automation, Electronics and Electrical Engineering, AUTEEE 2021

作者： Liu, Jiahang Zhao, Wenkang Li, Zongyi Song, Zeyang Toe, Teoh Teik Artificial Intelligence of Automation University of Science and Techonology Beijing Beijing China Data Science and Big Data Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications Telecommunications Engineering With Management Beijing China Nanjing Tech University Mechanical Engineering Nanjing China Nanyang Technological University NTU Business AI Lab Singapore Singapore

ISBN: (纸本)9781665428392

This paper is an attempt to apply neural networks to classify pneumonia images. The classifying model is built based on thousands of the actual X-ray images, which relatively belong to both pneumonia patients and normal people. The classification neural network, a popular way of machine learning, will serve as a supplementary means to help diagnose pneumonia and perform pre-screening. The paper analyzes and contains details of the algorithms we used. By analyzing the structure of the network, we come up with several useful methods to improve the model, so as to achieve a better model structure and application to classification. The new structure we propose is demonstrated in this paper. The final training accuracy is over 95% and the test accuracy is approximately 92%. © 2021 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

TOWARDS EFFECTIVE ADVERSARIAL ATTACK AGAINST 3D POINT CLOUD CLASSIFICATION

TOWARDS EFFECTIVE ADVERSARIAL ATTACK AGAINST 3D POINT CLOUD ...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Ma, Chengcheng Meng, Weiliang Wu, Baoyuan Xu, Shibiao Zhang, Xiaopeng NLPR Institute of Automation Chinese Academy of Sciences China Zhejiang Lab China The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9781665438643

In the domain of 3D point cloud classification, deep learning based classifiers have made significant progress, while they have been also proven to be vulnerable on the adversarial attack at the same time. Some recent works employ the attack methods that devised for image classification such as projected gradient descent (PGD) to attack the 3D classifiers, but their performances seem quite limited when faced with statistical operations including point cloud denoising and point cloud upsampling. In this paper, we propose 'SmoothAttack', a new attack that can craft adversarial point clouds robust to statistical operations. SmoothAttack can be easily applied in both global constraint and pointwise constraint. Besides, we analyze the directions of perturbations onto the point cloud during the iteration process, where SmoothAttack can somehow stabilize the direction and make full use of the adversarial budgets. Experiments validate that our 'SmoothAttack' can raise the attack success rates against statistical defenses up to 98% for untargeted attack and 91% for targeted attack on ModelNet40 database when fooling the classifiers PointNet and DGCNN. © 2021 IEEE

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biomarker Detection Using Machine Learning Algorithms

Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biom...

引用

Communication, Control and Intelligent Systems (CCIS)

作者： P. Balaji D. Devasena P. Rithick T. Karthi K. Rishmitha R. Roopa Dept. of. Electronics and Instrumentation Engineering Sri Ramakrishna Engineering College Coimbatore Tamilnadu Dept. of Robotics and Automation Engineering Sri Ramakrishna Engineering College Coimbatore Tamilnadu Dept. of Automobile Engineering Kongu Engineering College Erode India Dept. of Artificial Intelligence and Data Science Arjun College of Technology Coimbatore Tamilnadu

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

The detection of Glial Fibrillary Acidic Protein (GFAP) is a crucial biomarker for diagnosing neurological conditions such as traumatic brain injuries (TBI) and astrocytoma. This paper presents the design and analysis of a novel microfluidic Lab-on-a-Chip (LoC) platform for GFAP biomarker detection. The system integrates microfluidic channels with embedded gold nanoparticle-functionalized electrochemical sensors for high-sensitivity biomarker capture. The microfluidic chip is designed using soft lithography and PDMS (Polydimethylsiloxane), allowing for precise fluid handling and minimal sample consumption. The electrochemical sensors generate voltage signals corresponding to GFAP concentration levels, which are processed using digital filtering techniques to mitigate noise and improve signal quality. For accurate biomarker quantification, machine learning algorithms, including Random Forest ,KNN and Support Vector Machine (SVM), are employed to classify sensor output data into clinically significant concentration ranges. The model is trained on both simulated and experimentally collected sensor data, achieving an accuracy of over 93 percent in distinguishing low (below 50 ng/mL) and high (above 50 ng/mL) GFAP concentrations. The combination of microfluidic precision, sensitive electrochemical detection, and data analysis enables rapid and reliable detection of GFAP , providing a scalable and portable solution for early diagnosis of brain injuries and neurodegenerative diseases. This system highlights the potential of integrating microfluidics and machine learning in next-generation point-of-care diagnostic devices.

关键词： Support vector machines Soft lithography Machine learning algorithms Accuracy data analysis Lab-on-a-chip Sensor systems Biosensors Microfluidics Brain injuries

来源：评论

学校读者我要写书评

暂无评论

Unified Domain Adaptive Semantic Segmentation

arXiv

引用

arXiv 2023年

作者： Zhang, Zhe Wu, Gaochang Zhang, Jing Zhu, Xiatian Tao, Dacheng Chai, Tianyou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Computer Science Wuhan University China The College of Computing & Data Science Nanyang Technological University Singapore Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled and shifted target domain. The majority of existing UDA-SS works typically consider images whilst recent attempts have extended further to tackle videos by modeling the temporal dimension. Although two lines of research share the major challenges – overcoming the underlying domain distribution shift, their studies are largely independent. It causes several issues: (1) The insights gained from each line of research remain fragmented, leading to a lack of holistic understanding of the problem and potential solutions. (2) Preventing the unification of methods and best practices across two scenarios (images and videos) will lead to redundant efforts and missed opportunities for cross-pollination of ideas. (3) Without a unified approach, the knowledge and advancements made in one scenario may not be effectively transferred to the other, leading to suboptimal performance and slower progress. Under this observation, we advocate unifying the study of UDA-SS across video and image scenarios, enabling a more comprehensive understanding, synergistic advancements, and efficient knowledge sharing. To that end, we explore the unified UDA-SS from a general domain augmentation perspective, serving as a unifying framework, enabling improved generalization, and potential for cross-pollination, ultimately contributing to the practical impact and overall progress. Specifically, we propose a Quad-directional Mixup (QuadMix) method, characterized by tackling intra-domain discontinuity, fragmented gap bridging, and feature inconsistencies through four-directional paths designed for intra- and inter-domain mixing within an explicit feature space. To deal with temporal shifts within videos, we incorporate optical flow-guided feature aggregation across spatial and temporal dimensions for fine-grained domain alignment, which is extendable to image scenarios

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Constrained Coverage of Unknown Environment Using Safe Reinforcement Learning

Constrained Coverage of Unknown Environment Using Safe Reinf...

引用

IEEE Conference on Decision and Control

作者： Yunlin Zhang Junjie You Lei Shi Jinliang Shao Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Artificial Intelligence Henan University Zhengzhou China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

Achieving a connected, collision-free and time-efficient coverage in unknown environments is challenging for multi-agent systems. Particularly, agents with second-order dynamics are supposed to efficiently search and reach the optimal deployment positions over targets whose distribution is unknown, while preserving the distributed connectivity and avoiding collision. In this paper, a safe reinforcement learning based shield method is proposed for unknown environment exploration while correcting actions of agents for safety guarantee and avoiding invalid samples into policy updating. The shield is achieved distributively by a control barrier function and its validity is proved in theory. Moreover, policies of the optimal coverage are centrally learned via reward engineering and executed distributively. Numerical results show that the proposed approach not only achieves zero safety violations during training, but also speeds up the convergence of learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification

A Cross-Feature Mutual Learning Framework to Integrate Funct...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Min Zhao Rongtao Xu Dongmei Zhi Shan Yu Vince D Calhoun Jing Sui Brainnetome Center Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Cognitive Neuroscience Beijing Normal University Beijing China Laboratory of Brain Atlas and Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center Georgia State University Atlanta GA USA IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Time courses (TC) and functional network connectivity (FNC) features, derived from functional magnetic resonance imaging, show considerable potential in the study of brain disorders. Despite significant advancements, most deep learning approaches tend to either directly concatenate complementary MRI features at the input level or ensemble decisions after separately learning each feature, whereas an end-to-end, mixed feature learning framework is still lacking. To bridge this gap, we introduce a cross-feature mutual learning (CFML) to enable collaborative learning of TC-specific and FNC-specific models and facilitate mutual knowledge transfer to distill shared and robust characteristics from the high-level representations of TC and FNC, thereby enhancing brain disorder classification performance. Specifically, we first develop a recurrent neural network-based TC-specific encoder to capture temporal dynamic dependencies within TCs, alongside a transformer-based FNC-specific encoder to discern global high-order functional dependencies among independent components in FNCs. Subsequently, we design a cross-modal module for the adaptive integration of TC-specific and FNC-specific features. Additionally, the CFML strategy is proposed to collaboratively train these modules, incorporating feature-specific loss, feature-exchange loss, and joint loss. Empirical results reveal that CFML achieves an accuracy of 85.1% in differentiating healthy controls (HC) from schizophrenia (SZ) patients, surpassing 12 comparative models by a margin of 3.0-9.2% accuracy using either static FNC or TCs or both. These findings underscore the efficacy of CFML in classifying brain disorders, highlighting its potential in advancing this field.

关键词： Representation learning Deep learning Neuroimaging Accuracy Recurrent neural networks Mental disorders Transformers Brain modeling Object recognition Knowledge transfer

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：