检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Guo, Xiao-Yu Li, Yi-Fan Liu, Yuan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Protein design has become a critical method in advancing significant potential for various applications such as drug development and enzyme engineering. However, protein design methods utilizing large language models with solely pretraining and fine-tuning struggle to capture relationships in multi-modal protein data. To address this, we propose ProtDAT, a de novo fine-grained framework capable of designing proteins from any descriptive protein text input. ProtDAT builds upon the inherent characteristics of protein data to unify sequences and text as a cohesive whole rather than separate entities. It leverages an innovative multi-modal cross-attention, integrating protein sequences and textual information for a foundational level and seamless integration. Experimental results demonstrate that ProtDAT achieves the state-of-the-art performance in protein sequence generation, excelling in rationality, functionality, structural similarity, and validity. On 20,000 text-sequence pairs from Swiss-Prot, it improves pLDDT by 6%, TM-score by 0.26, and reduces RMSD by 1.2 Å, highlighting its potential to advance protein design. © 2024, CC BY-NC-SA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Differentiator-Based Adaptive H8 Tracking control of Fully Actuated Systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

引用

3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

来源：评论

学校读者我要写书评

暂无评论

FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

arXiv

引用

arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

GPC-LIO-SAM:Lidar inertial odometry and mapping method for mobile robot with ground plane constraint

GPC-LIO-SAM:Lidar inertial odometry and mapping method for m...

引用

第43届中国控制会议

作者： Chong Cao Gang Peng Dingxin He Yihao Cao Yue Xu Zhang Deng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan Lisai Technology Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Due to the limitation of the vertical angle resolution of the lidar sensor,the observation information in the vertical direction is reduced,resulting in the elevation cumulative drift phenomenon of many lidar SLAM algorithms in large-scale environment,which reduces the accuracy of pose estimation and the quality of *** order to solve this problem,based on LIO-SAM,this paper proposes a lidar SLAM algorithm with ground plane *** method can effectively extract the ground point cloud and obtain the plane normal vector,and construct the constraint relationship with the prior plane normal *** optimal state estimation is solved by nonlinear optimization method,which effectively reduces the elevation drift caused by the long-term operation of the system in large-scale *** paper compares the improved algorithm with A-LOAM,LeGO-LOAM and original LIO-SAM algorithms in challenging datasets such as KITTI,M2 DGR and *** results show that this method effectively improves the accuracy of pose estimation and the quality of mapping,and limit the elevation drift of mobile robot in ground operation,which has high application value.

关键词： Ground plane constraint elevation drift LIDAR simultaneous localization and mapping(SLAM)

来源：评论

学校读者我要写书评

暂无评论

Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Du, Zhenbang An, Jiayu Tu, Yunlu Hong, Jiahao Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Open Set Domain Adaptation (OSDA) copes with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target domain. Most existing OSDA approaches, depending on the final image feature space of deep models, require manually-tuned thresholds, and may easily misclassify unknown samples as known classes. Mixture-of-Experts (MoE) could be a remedy. Within an MoE, different experts handle distinct input features, producing unique expert routing patterns for various classes in a routing feature space. As a result, unknown class samples may display different expert routing patterns to known classes. This paper proposes Dual-Space Detection, which exploits the inconsistencies between the image feature space and the routing feature space to detect unknown class samples without any threshold. A Graph Router is further introduced to better make use of the spatial information among the image patches. Experiments on three datasets validated the effectiveness and superiority of our approach. © 2020 IEEE.

关键词： Mixture-of-Experts Open Set Domain Adaptation Routing Feature

来源：评论

学校读者我要写书评

暂无评论

An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

引用

SPIE 12th International Symposium on Multispectral image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

引用

Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

来源：评论

学校读者我要写书评

暂无评论

Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

引用

5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

来源：评论

学校读者我要写书评

暂无评论

Model Predictive control Based on Amplitude Decaying Aggregation Strategy for Hydrogen Gas Turbine

Model Predictive Control Based on Amplitude Decaying Aggrega...

引用

2022 Chinese Automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

引用

2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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：