检索结果-内蒙古大学图书馆

Nonsingular fast predefined time convergence sliding mode control for construction robot

ISA Transactions 2025年 161卷 109-121页

作者： Yin, Chun-Wu Ding, Yun-Peng Sun, Hou-Jun College of Information and Control Engineering Xi'an University of Architecture and Technology Shaanxi Xi'an710055 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China

To solve the practical engineering problem that the angle of a construction robot with an arbitrary initial value completely and accurately track the desired trajectory, this paper presents a control strategy for extending the desired trajectory, and a sliding mode controller with predefined time convergence is designed for an uncertain construction robot. Firstly, the special characteristics of the construction robot's trajectory tracking control are analyzed, an extended desired trajectory control strategy including the arrival phase and the precise tracking phase is proposed, and the design method of the extended desired trajectory in the arrival stage is given. Then, based on the improved non-singular fast sliding mode surface and a novel stability criterion of predefined time convergence, a predefined time convergence sliding mode controller based on a state observer was designed for the construction robot, which enables the angle tracking error of the construction robot to converge to zero within the specified time, ensures the angle completely and accurately tracking the original desired trajectory. Comparative simulation results show that the angle tracking error based on the proposed algorithm reaches 0.0004 rad, with an accuracy improvement of 33 %, and solves the problem of control torque chattering. © 2025 ISA

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Dual User Association for Large Scale IRS-Aided mmWave Communication Networks

引用

China Communications 2025年第1期22卷 182-195页

作者： Chen Guolin Deng Yiqin Huang Xiaoxia Fang Yuguang School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Control Science and Engineering Shandong UniversityJinan 250100China Department of Computer Science City University of Hong KongHong Kong 999077China

The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention *** the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS ***,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and *** problem is efficiently addressed in two ***,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE *** reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and *** results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

关键词： energy efficiency intelligent reflecting surface,millimeter-wave communication

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal and Retrieval-Augmented Modelling for Chest X-Ray Report Generation

引用

IEEE Transactions on Medical Imaging 2025年第5期44卷 2244-2255页

作者： Yang, Yan You, Xiaoxing Zhang, Ke Fu, Zhenqi Wang, Xianyun Ding, Jiajun Sun, Jiamei Yu, Zhou Huang, Qingming Han, Weidong Yu, Jun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Tsinghua University Department of Automation Beijing100084 China School of Intelligence Science and Engineering 518055 China University of Chinese Academy of Sciences School of Computer and Control Engineering Beijing101408 China Zhejiang Cancer Hospital Department of Colorectal Medical Oncology Hangzhou310022 China

Chest X-ray report generation has attracted increasing research attention. However, most existing methods neglect the temporal information and typically generate reports conditioned on a fixed number of images. In this paper, we propose STREAM: Spatio-Temporal and REtrieval-Augmented Modelling for automatic chest X-ray report generation. It mimics clinical diagnosis by integrating current and historical studies to interpret the present condition (temporal), with each study containing images from multi-views (spatial). Concretely, our STREAM is built upon an encoder-decoder architecture, utilizing a large language model (LLM) as the decoder. Overall, spatio-temporal visual dynamics are packed as visual prompts and regional semantic entities are retrieved as textual prompts. First, a token packer is proposed to capture condensed spatio-temporal visual dynamics, enabling the flexible fusion of images from current and historical studies. Second, to augment the generation with existing knowledge and regional details, a progressive semantic retriever is proposed to retrieve semantic entities from a preconstructed knowledge bank as heuristic text prompts. The knowledge bank is constructed to encapsulate anatomical chest X-ray knowledge into structured entities, each linked to a specific chest region. Extensive experiments on public datasets have shown the state-of-the-art performance of our method. Related codes and the knowledge bank are available at https://***/yangyan22/STREAM. © 1982-2012 IEEE.

关键词： X ray production

来源：评论

学校读者我要写书评

暂无评论

SKINC-NET: an efficient Lightweight Deep Learning Model for Multiclass skin lesion classification in dermoscopic images

引用

Multimedia Tools and Applications 2025年第13期84卷 12531-12557页

作者： Asif, Sohaib Qurrat-ul-Ain Khan, Saif Ur Rehman Amjad, Kamran Awais, Muhammad School of Computer Science and Engineering Central South University Hunan Changsha China School of Public Health Central South University Hunan Changsha China School of Control Science and Engineering Central South University Hunan Changsha China School of Minerals Processing and Bioengineering Central South University Hunan Changsha China

Diagnosing skin cancer through visual image examinations is time-consuming and error-prone, as the similar appearance and variations within each type of skin cancer challenge the efficiency and accuracy. computer-aided techniques, notably convolutional neural networks (CNNs), demand substantial computational resources and exhibit a high level of time complexity, rendering them impractical for real-world applications necessitating rapid and precise skin cancer detection. To address this issue, this research introduces SKINC-NET, a streamlined deep learning (DL) model specifically designed to achieve precise diagnoses of various skin cancer types while minimizing computational resource requirements. SKINC-NET can classify seven types of skin lesions using dermoscopic images, designed with dense layers, batch normalization (BN), and LeakyReLU activation functions. Numerous experiments were conducted on the HAM10000 dataset, using a data augmentation approach to tackle the class imbalance problem. The study compared the SKINC-NET model’s performance with four transfer learning models, namely ResNet50, VGG16, MobileNetV2, and EfficientNetB0. Several ablation studies were conducted to identify the optimal hyperparameters that lead to precise skin cancer diagnosis. The SKINC-NET model demonstrates its superiority by achieving the highest overall classification accuracy of 98.54% with minimal computation. The experimental findings affirm that the SKINC-NET model showcases remarkable robustness and presents several significant benefits, including lower computational complexity, a smaller number of trainable parameters, and superior performance compared to existing state-of-the-art models. SKINC-NET can assist dermatologists in making appropriate decisions regarding the early detection of skin cancer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Data-driven output regulation control for constrained linear systems

引用

Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

来源：评论

学校读者我要写书评

暂无评论

Frequency Attacks Based on Invertible Neural Networks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第2期6卷 292-300页

作者： Shao, Ming-Wen Yang, Jian-Xin Meng, Ling-Zhuang Hu, Zhi-Yong China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China China University of Petroleum College of Control Science and Engineering East China Qingdao266580 China

Adversarial attacks reveal the vulnerability of classifiers based on deep neural networks to well-designed perturbations. Most existing attack methods focus on adding perturbations directly to the pixel space. However, the perturbations generated by these methods may be easily perceived by humans. To alleviate the aforementioned problem, we propose a novel high-frequency attack based on invertible neural networks (HA-INN) that relies on INNs to adds perturbations to the high-frequency space of the image instead of the pixel space. In this way, we can fool the classifier while the perturbations are not easily detected by humans. Specifically, we introduce INNs to separate the high-frequency and low-frequency components of the image. And the low-frequency components are guaranteed to be reconstructed as the original image, while the high-frequency components are replaced with resampled high-frequency latent variables with additional adversarial information. Then, the low-frequency components and the high-frequency components with adversarial information are inversely fed into the INN to generate effective adversarial examples. Extensive experiments on two datasets (CIFAR-10 and CIFAR-100) show that our method can generate misleading and transferable cross-architectural adversarial examples with greatly reduced computational resource requirements. Under the white-box setting, the attack success rate of CIFAR10 and CIFAR100 is 99.8% and 99.74%, respectively. Further, under the black-box setting, the adversarial examples generated by our method are more effective than the other methods. © 2020 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Improving graph collaborative filtering with network motifs

引用

Neural Computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Aspect Sentiment Triple Extraction Model Integrating with Syntactic Information 24

Aspect Sentiment Triple Extraction Model Integrating with Sy...

引用

5th International Conference on computer Science and Management Technology, ICCSMT 2024

作者： Zhao, Jingsheng Qu, Weilong Zheng, Jiashang Yu, Chen Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Shandong Qingdao China School of Computer Science and Technology Soochow University Jiangsu Suzhou China

ISBN: (纸本)9798400709999

The current mainstream aspect level sentiment analysis methods focus on modeling the relationship between word representations in text and their context, fail to fully utilize the syntactic structure information of the *** paper proposes a sentiment triplet extraction model IS-ASTE(Integrating with Syntactic information model for Aspect Sentiment Triplet Extraction, IS-ASTE).Firstly, the graph attention layer is employed to obtain contextual context ***, based on the syntactic dependency tree to obtain the text element's dependency, lexical and distance information, which is combined with the attention mechanism to augment the sentiment ***, graph convolutional neural networks are integrated to aggregate the feature information of nodes for sentiment *** experimental results indicate that, across four representative datasets, the F1 values are all better than those of the baseline model, demonstrating better performance in sentiment analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Invariant Set Analysis and control Synthesis of Multi-Equilibrium Switched Systems under control Constraints 13

Invariant Set Analysis and Control Synthesis of Multi-Equili...

引用

13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Han, Yuejiang Zhang, Lixian Ding, Yihang Dong, Yifei Liang, Ye School of Astronautics Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798331516147

This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, accompanied by criteria formulated as linear matrix inequalities, ensuring that the state remains within the mode-dependent invariant sets. Furthermore, considering the control constraints in practical scenarios, a multi-equilibrium switched controller with control constraints (MESCC controller) is designed. Compared to existing works, the proposed method incorporates mode-dependent dwell time conditions and mode-dependent invariant sets, thereby offering enhanced design flexibility. An example of an aero-engine control system is presented to demonstrate the effectiveness and potential of the theoretical results. © 2025 IEEE.

关键词： control system synthesis

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：