检索结果-内蒙古大学图书馆

6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Benchmarking Derivative-Free Global Optimization Methods on Variable Dimension Robotics Problems 13

Benchmarking Derivative-Free Global Optimization Methods on ...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Küdela, Jakub Juříček, Martin Parǎk, Roman Tzanetos, Alexandros Matoušek, Radomil Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Jönköping University Department of Computing Jönköping Sweden

ISBN: (纸本)9798350308365

Several real-world applications introduce derivativefree optimization problems, called variable dimension problems, where the problem's dimension is not known in advance. Despite their importance, no unified framework for developing, comparing, and benchmarking variable dimension problems exists. The robot arm controlling problem is a variable dimension problem where the number of joints to optimize defines the problem's dimension. For a holistic study of global optimization methods, we studied 14 representative methods from 4 different categories, i.e., (i) local search optimization techniques with random restarts, (ii) state-of-the-art DIRECT-type methods, (iii) established Evolutionary Computation approaches, and (iv) state-of-the-art Evolutionary Computation approaches. To investigate the effect of the problem's dimensionality on the solution we generated 20 instances of various combinations among the number of predefined and open decision variables, and we performed experiments for various computational budgets. The results attest that the robot arm controlling problem provides a proper benchmark for variable dimensions. Furthermore, methods in-corporating local search techniques have dominant performance for higher dimensionalities of the problem, while state-of-the-art EC methods dominate in the lower dimensionalities. © 2024 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Model Predictive Control for Three-Intersection Traffic Control Including Bus-Only Lanes

Model Predictive Control for Three-Intersection Traffic Cont...

引用

2023 China automation Congress, CAC 2023

作者： Huang, Zhentao Dai, Li Lin, Shu Sun, Zhongqi Zhan, Yufeng Xia, Yuanqing School of Automation Beijing Institute of Technology Beijing China Jiaxing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350303759

In this paper, we propose a model predictive control (MPC) method based on an enhanced urban traffic model. The aim is to enhance intersection access efficiency and alleviate congestion. The model incorporates two main improvements: 1) the separate modeling of ordinary vehicles and buses on urban roads, taking into account dedicated lanes and stops for buses, and 2) the inclusion of three intersections in the traffic network to capture the interactive influence of two road sections. Building upon this improved model, we design an MPC controller to regulate the traffic light status at the intersections and effectively control traffic flow. We provide simulation results of the controller implemented in the traffic network and compare with other scheme to validate the effectiveness of the model and control strategy. © 2023 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Clinical Learning Order-Guided Deep Neural Network for Brain Tumor Segmentation

引用

IEICE Transactions on Information and Systems 2025年第6期E108.D卷 615-628页

作者： Zhang, Pengfei Wang, Jinke Cheng, Yuanzhi Tamura, Shinichi School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China School of Automation Harbin University of Science and Technology Harbin150080 China Weihai Research Institute Harbin University of Science and Technology Weihai264300 China School of Information Science and Technology Qingdao University of Science and Technology Shandong Qingdao266061 China Graduate School of Medicine Osaka University Suita-shi565-0871 Japan NBL Technovator Co. Ltd. Sennan590-0522 Japan

In this paper, we present an innovative multi-label local-to-global learning segmentation (MLLGL-Seg) neural network model for brain tumor segmentation. Our framework is grounded on a clinical learning order from the whole tumor (WT) to the tumor core (TC) and then to the enhanced tumor (ET). Thus, we first propose a multi-label segmentation network to embed the clinical learning sequence of WT-TC-ET during training. We then introduce a local-to-global learning algorithm and integrate it into multi-label to construct the MLLGL-Seg model. In addition, considering the hierarchical structure of the output tumor region, we further perform hierarchical consistency transformation on the network output to ensure that it complies with hierarchical constraints. The novelty of this paper lies in the proposal of the MLLGL-Seg by introducing curriculum learning based on category space into segmentation, in the construction of a learning sequence based on boundary difficulty and category similarity based on clinical experience, and in the device of learning sequences based on anti-similarity and label noise. Experimental results show that the proposed method achieves one of the most competitive results in brain tumor segmentation accuracy on three publicly available datasets. Copyright © 2025 The institute of Electronics, Information and Communication Engineers.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Scene text recognition via dual character counting-aware visual and semantic modeling network

引用

Science China(Information Sciences) 2024年第3期67卷 313-314页

作者： Ke XIAO Anna ZHU Brian Kenji IWANA Cheng-Lin LIU School of Computer Science and Artificial Intelligence Wuhan University of Technology Human Interface Laboratory Kyushu University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Scene text recognition(STR) is drawing increasing attention nowadays due to its wide application in real life. Character counting information, as auxiliary information, has been shown to be effective in boosting text ... 详细信息

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Unifying Generation and Prediction on Graphs with Latent Graph Diffusion 38

Unifying Generation and Prediction on Graphs with Latent Gra...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Cai Wang, Xiyuan Zhang, Muhan Department of Automation Tsinghua University China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute for Artificial Intelligence Peking University China School of Intelligence Science and Technology Peking University China

In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one formulation. We first formulate prediction tasks including regression and classification into a generic (conditional) generation framework, which enables diffusion models to perform deterministic tasks with provable guarantees. We then propose Latent Graph Diffusion (LGD), a generative model that can generate node, edge, and graph-level features of all categories simultaneously. We achieve this goal by embedding the graph structures and features into a latent space leveraging a powerful encoder and decoder, then training a diffusion model in the latent space. LGD is also capable of conditional generation through a specifically designed cross-attention mechanism. Leveraging LGD and the "all tasks as generation" formulation, our framework is capable of solving graph tasks of various levels and types. We verify the effectiveness of our framework with extensive experiments, where our models achieve state-of-the-art or highly competitive results across a wide range of generation and regression tasks. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HGGAN: Visible to Thermal Translation Generative Adversarial Network Guided by Heatmap

HGGAN: Visible to Thermal Translation Generative Adversarial...

引用

2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Liu, Tong Liu, Yufeng Xu, Wenda Pu, Yuandong Hao, Yuqi Zuo, Wei School of Automation Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

The realization of multi-modal image fusion requires sufficient cross domain data. Translating the visible images is an effective method to obtain thermal-visible paired images from the visible image domain to the thermal image domain. The current translation methods have some disadvantages, such as unreasonable distribution of thermal radiation intensity, blurred edges, spatial distortion and feature loss. So they are not friendly to downstream tasks. Based on the generation and reconstruction strategy of CycleGAN, we propose an image to image translation network guided by heatmap which is called HGGAN. We use the heatmap of object that detected by network to encode the heatmap code, and combine the image edge code to improve the image generation performance. We test the image standard of the generated image, and use the object detection network to verify. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A Vectorization Method Induced By Maximal Margin Classification For Persistent Diagrams

A Vectorization Method Induced By Maximal Margin Classificat...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, An Pan, Yu Zhou, Fuqi Yan, Jinghui Liu, Chuanlu Zhejiang A&f University College of Mathematics and Computer Science Hangzhou China Beijing Institute of Technology School of Mathematics and Statistics Beijing China Hangzhou Dianzi University School of Electronics and Information Hangzhou China Chinese Academy of Sciences Institute of Automation Beijing China Beijing Institute of Technology School of Computer Science & Technology Beijing China

ISBN: (纸本)9798350386226

Persistent homology is an effective method for extracting topological information, represented as persistent diagrams, of spatial structure data. Hence it is well-suited for the study of protein structures. Attempts to incorporate Persistent homology in machine learning methods of protein function prediction have resulted in several techniques for vectorizing persistent diagrams. However, current vectorization methods are excessively artificial and cannot ensure the effective utilization of information or the rationality of the methods. To address this problem, we propose a more geometrical vectorization method of persistent diagrams based on maximal margin classification for Banach space. We evaluated our vectorization method using a binary classification task on proteins and compared it with the statistical methods that exhibit the best performance among thirteen commonly used vectorization methods. The experimental results indicate that our approach surpasses the statistical methods in both robustness and precision. © 2024 IEEE.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Vision-Based Fixed-Time Uncooperative Aerial Target Tracking for UAV

引用

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1322-1324页

作者： Peng Sun Siqi Li Bing Zhu Zongyu Zuo Xiaohua Xia the Seventh Research Division Beihang UniversityBeijing 100191China the School of Automation Beijing Institute of TechnologyBeijing 100081China the Department of Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoria 0002South Africa

Dear Editor,In this letter,a vision-based fixed-time control is proposed for an unmanned aerial vehicle(UAV)with actuator saturation to track an uncooperative *** fixed-time control is designed in backstepping *** rel... 详细信息

关键词： Fixed cooperative letter

来源：评论

学校读者我要写书评

暂无评论

Notice of Removal: Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI

引用

IEEE/CAA Journal of Automatica Sinica 2023年 1-8页

作者： Kim, Chung Song Sun, Jinwei Liu, Dan Wang, Qisong Paek, Sung Gyun School of Electrical Engineering and Automation Harbin Institute of Technology 92 West Dazhi Street Harbin 150001 China and also with the Department of Automation Kimchaek University of Technology Pyongyang 950003 Democratic People U+02BC s Republic of Korea School of Electrical Engineering and Automation Harbin Institute of Technology 92 West Dazhi Street Harbin 150001 China School of Computer Science and Technology Harbin Institute of Technology 92 West Dazhi Street Harbin 150001 China and also with the Institute of Information Technology Kimchaek University of Technology Pyongyang 950003 Democratic People U+02BC s Republic of Korea

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：