检索结果-内蒙古大学图书馆

A novel active-passive protection strategy endows carbonyl iron with excellent intelligent anti-corrosion and microwave absorption dual functional characteristics

引用

Nano Research 2025年第1期18卷 487-500页

作者： Wei Tian Quanyuan Feng Linbo Zhang Yinfan Liu Xian Jian Longjiang Deng Institute of Microelectronics School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 611756China National Engineering Researching Centre of Electromagnetic Radiation Control Materials Key Laboratory of Multi-Spectral Absorbing Materials and Structures of Ministry of EducationState Key Laboratory of Electronic Thin Films and Integrated DevicesSchool of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China The Yangtze Delta Region Institute(Huzhou)&School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaHuzhou 313001China

Carbonyl iron absorbers(CI)face significant challenges in practical applications,such as corrosion,interface bonding failure,detachment,and high maintenance ***,we have developed intelligent self-healing technology based on proactive/passive mechanisms via in situ synthesis of self-healing factors(polydopamine/benzotriazole(PDA/BTA))and physical barrier layers(SiO_(2)/1,1,1,3,3,3-hexamethyl disilazane,SiO_(2)/HMDS)to enhance corrosion resistance,while also being compatible with efficient microwave absorption *** unique multiscale structure gives full play to the utilization of the roles of each functional layer,including the intelligent self-healing features of PDA/BTA,physical shielding and spatial confinement characteristics of SiO_(2)/HMDS,and magnetic-dielectric synergistic mechanism resulted from the good impedance matching characteristics,the conduction loss,the interfacial polarization loss and natural *** asfabricated composites achieved an exceptional minimum reflection loss value of-55.4 dB at 10.7 GHz and the effective absorption band of 7.6 ***,it still exhibits obvious self-healing and corrosion resistance characteristics after 360 h corrosion treatment,ascribed to the self-healing mechanism of PDA/BTA and the blocking intervention effect of SiO_(2)/*** work is considered to pave the way for the synthesis of high-performance magnetic absorbers,especially in enhancing their intelligent self-healing ability in corrosive environments.

关键词： microwave absorption intelligent self-healing technology carbonyl iron multiscale structure magneticdielectric synergistic mechanism

来源：评论

学校读者我要写书评

暂无评论

Extending Token Computation for LLM Reasoning 24

Extending Token Computation for LLM Reasoning

引用

8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Li, Bingli Vargas, Danilo Vasconcellos Laboratory of Intelligent Systems The Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Fukuoka Japan

ISBN: (纸本)9798400718182

Large Language Models (LLMs) are pivotal in advancing natural language processing but often struggle with complex reasoning tasks due to inefficient attention distributions. In this paper, we explore the effect of increased computed tokens on LLM performance and introduce a novel method for extending computed tokens in the Chain-of-Thought (CoT) process, utilizing attention mechanism optimization. By fine-tuning an LLM on a domain-specific, highly structured dataset, we analyze attention patterns across layers, identifying inefficiencies caused by non-semantic tokens with outlier high attention scores. To address this, we propose an algorithm that emulates early layer attention patterns across downstream layers to re-balance skewed attention distributions and enhance knowledge abstraction. Our findings demonstrate that our approach not only facilitates a deeper understanding of the internal dynamics of LLMs but also significantly improves their reasoning capabilities, particularly in non-STEM domains. Our study lays the groundwork for further innovations in LLM design, aiming to create more powerful, versatile, and responsible models capable of tackling a broad range of real-world applications. © © 2024 Copyright held by the owner/author(s).

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Reinforcement-Learning-Based Finite Time Fault Tolerant Control for a Manipulator With Actuator Faults

引用

IEEE Transactions on Cybernetics 2025年第6期55卷 2621-2632页

作者： Yang, Pengxin Zhang, Shuang Yu, Xinbo He, Wei University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Institute of Artificial Intelligence School of Intelligence Science and Technology Beijing100083 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing Advanced Innovation Center for Materials Genome Engineering Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Beijing100083 China Liaoning Academy of Materials Institute of Materials Intelligent Technology Shenyang110004 China Beijing Information Science and Technology University School of Automation Institute of Artificial Intelligence Beijing102206 China University of Science and Technology Beijing School of Intelligence Science and Technology Institute of Artificial Intelligence Beijing100083 China

This study introduces a novel finite time fault tolerant controller integrating nonsingular terminal sliding mode (NTSM) and reinforcement learning (RL) strategies for manipulator systems with actuator faults. Leveraging an actor–critic network architecture, the RL algorithm facilitates the computation of the cost function and the approximation of unknown nonlinear dynamics. The inherent properties of NTSM mitigate the effects of parameter uncertainties, thereby enhancing system robustness. Furthermore, an adaptive law is crafted to counteract the deleterious effects of actuator faults. Through the direct Lyapunov function approach, it is demonstrated that the closed-loop system achieves semi-global practical finite-time stability. This control strategy diminishes the dependence on precise model accuracy and augments the system’s fault tolerance. The viability of the proposed algorithm is corroborated by simulation results, and its efficacy is further validated through experiments conducted on the 6-DOF Kinova Jaco 2 platform. © 2013 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Supervisory Control of Discrete Event systems Modeled With Labeled Petri Nets for Diagnosability Enforcement

引用

IEEE Transactions on Automation science and engineering 2025年 22卷 13855-13870页

作者： Li, Chengzong Chen, Yufeng Mostafa, Almetwally M. Khalgui, Mohamed Qinghai University School of Energy and Electrical Engineering Xining810000 China Macau University of Science and Technology Institute of Systems Engineering Taipa China King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia German International University Faculty of Engineering Berlin13507 Germany

This paper proposes an active fault diagnosis method to enforce the diagnosability of discrete event systems using labeled Petri nets by constructing a diagnostic supervisor. For a non-diagnosable net model, its diagnosability is viewed as a control specification and addressed by using supervisory control techniques. First, an event-based monitor and a Petri net structure, referred to as data isolation arcs, are introduced to apply control specifications for labeled Petri nets. Then, a diagnostic supervisor reachability graph is generated to estimate the current state of the diagnoser. By analyzing the diagnostic supervisor reachability graph, an integer linear programming model is formulated to design a diagnostic supervisor, which can prevent the system from entering into any indeterminate cycle only by observing the occurrence of events. Finally, by the obtained diagnostic supervisor, the resulting net model is shown to be diagnosable. Some examples are presented to demonstrate the proposed method. © 2004-2012 IEEE.

关键词： Supervisory personnel

来源：评论

学校读者我要写书评

暂无评论

Flexible Inverse Design of Microwave Filter Customized on Demand With Wavelet Transform Deep Learning

引用

IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 806-817页

作者： Xu, Kuiwen Wang, Jianguo Cai, Jialin Ma, Xuetiao Lv, Qinyi Chen, Shichang Liu, Jie Liu, Jun Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology The Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou310018 China Chang'an Campus Northwestern Polytechnical University School of Electronic Information Xi'an710060 China University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China Information Science Academy China Electronics Technology Group Corporation Beijing100846 China

Artificial intelligence (AI) techniques are increasingly being used for the inverse design of microwave devices. However, several challenges, including intensive computation costs for training samples, high-dimensional data, nonuniformity, and low-quality samples in the design space, can negatively impact the final modeling performance. To alleviate these issues, a high-quality sampling inverse design scheme incorporating wavelet transform deep learning (HQS-WTDL) is proposed to achieve customized, automated microwave filter design. In the forward simulation-based sampling, particle swarm optimization (PSO) is used to tentatively select rule-defined samples. Multilabel synthetic minority over-sampling technique (MLSMOTE) is then applied to enlarge the training samples and improve their uniformity in the design space. An inverse modeling approach using neural networks to map the nonlinear relationship between a given set of S-parameters and required filter structural parameters is presented. Given that the dimension of the S-parameters is much higher than that of the structure parameters, the corresponding neural network used in this approach is deep and complex, with multiple layers and neurons. To reduce the number of input variables, the S-parameters are subjected to wavelet transformation, allowing for more efficient representation by the neural network. The proposed method is validated using a band-pass microstrip hairpin filter as an example. Results demonstrate that the proposed approach achieves better modeling effectiveness and inverse design efficiency than conventional methods. In addition, the proposed method allows for fast customization of device parameters, such as center frequencies and bandwidths with good prediction accuracy. © 1982-2012 IEEE.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation

引用

IEEE Transactions on Circuits and systems for Video technology 2025年第5期35卷 4846-4856页

作者： Yu, Li Zhong, Hongchao Zou, Longkun Chen, Ke Gao, Pan Nanjing University of Information Science & Technology School of Computer Science Nanjing210044 China Nanjing China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Peng Cheng Laboratory Shenzhen China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy-free. Therefore, representations of those ideal synthetic point clouds have limited variations in the geometric perspective and can gain good performance on a number of 3D vision tasks such as point cloud classification. In the context of unsupervised domain adaptation (UDA), representation learning designed for synthetic point clouds can hardly capture domain invariant geometric patterns from incomplete and noisy point clouds. To address such a problem, we introduce a novel scheme for induced geometric invariance of point cloud representations across domains, via regularizing representation learning with two self-supervised geometric augmentation tasks. On one hand, a novel pretext task of predicting translation distances of augmented samples is proposed to alleviate centroid shift of point clouds due to occlusion and noises. On the other hand, we pioneer an integration of the self-supervised relational learning on geometrically-augmented point clouds in a cascade manner, utilizing the intrinsic relationship of augmented variants and other samples as extra constraints of cross-domain geometric features. Experiments on the PointDA-10 dataset demonstrate the effectiveness of the proposed method, achieving the state-of-the-art performance. © 1991-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

引用

Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Lu, Benxue Gao, Kaizhou Duan, Peiyong Slowik, Adam School of Computer Science Liaocheng University Liaocheng252000 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Information and Automation Engineering Qilu University of Technology Jinan250353 China Koszalin University of Technology Department of Electronics and Computer Science Koszalin75-453 Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is formalized, followed by the establishment of a mathematical model. Second, enhanced version of the artificial bee colony (ABC) algorithms is proposed for tackling the Bi-HFSP_CS. Then, fourteen local search operators are employed to search for better solutions. Two different O-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process. Finally, the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm, its three variants, and three effective algorithms in resolving 95 instances of 35 different problems. The experimental results and analysis showcase that the enhanced ABC algorithm combined with O-learning (QABC1) demonstrates as the top performer for solving concerned problems. This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength, offering beneficial perspectives for exploration and research in relevant domains. © 2021 TUP.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

MCFF-Net: Multi-Scale Contextual Feature Fusion Network for Polyp Segmentation

引用

Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Ma, Lei Yan, Jiangkai Shao, Dangguo Li, Jingtao Wang, Jiawei Yan, Yukun Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Provincial Key Laboratory of Artificial Intelligence Kunming650500 China

In clinical practice, technologies for automatically segmenting polyps might greatly improve the speed and accuracy of medical diagnosis, significantly reducing the chances of patients developing colorectal cancer. Nevertheless, the segmentation of polyps presents a considerable challenge due to their low contrast, presence of occlusion, and intricate background disturbances. In this paper, we propose an innovative approach for segmenting polyps, called multi-scale contextual feature fusion network for polyp segmentation (MCFF-Net). Specifically, MCFF-Net includes three modules: feature enhancement module (FEM), global-local feature fusion module (GLFF), and cross-scale feature fusion module (CFFM). Among them, FEM is used to improve the features at each scale. Building on this, the CFFM enables comprehensive interaction among the information across various scales, enhancing the diversity of the feature scales extracted. Moreover, the GLFF fuses global-local features to generate a rough object localization map. This map further modulates the fusion features from the CFFM, facilitating more precise segmentation. On five publicly available datasets, our approach not only segments various polyp types but also surpasses existing methods in terms of accuracy and generalizability. © Shanghai Jiao Tong University 2024.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Smart fiber-optic masks: real-time respiratory monitoring and health analysis

引用

science China(information sciences) 2025年第4期68卷 216-227页

作者： Qi YU Ya-Nan ZHANG Jiaqi ZHANG Shuang YANG Lingxiao JIANG Xuegang LI College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Affiliated Hospital of North Sichuan Medical College

In this study, we design a smart mask based on an ultrathin retractable flexible fiber optic sensor with high sensitivity(0.56 V/m-1), ultra-light mass（0.24 g）, and excellent tensile properties. The sensor was integrated with a medical mask to recognize multiple breathing patterns（standard, fast, slow, shallow, deep, breath-holding）, coughing, and speaking,as well as to study gender characteristics and physiological differences in the respiratory system. By comparing the nasal-oral respiratory waveforms and frequencies, guidance for respiratory correction was provided. In addition, affixing flexible fiber optic sensors to the outside of the mask reduces the risk of cross-infection and significantly reduces temperature and humidity disturbances. Studies have shown that nine sensor locations placed on the outside of the mask responded significantly to respiratory waveforms. For personalized health management, the system is also equipped with a communication module,web and mobile apps to support data downloads, real-time monitoring, and exception alerts.

关键词： smart mask respiratory monitoring fiber optic sensor health management anti-interference

来源：评论

学校读者我要写书评

暂无评论

A Novel Nectarine Fruit Maturity Detection and Classification Counting Model Based on YOLOv8n

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2025年第1期3卷 144-155页

作者： Ji, Baofeng Zhao, Jingming Tao, Fazhan Zhang, Ji Zhang, Gaoyuan Wang, Nan Zhang, Ping Fan, Huitao Henan University of Science and Technology College of Information Engineering Luoyang471023 China Longmen Laboratory Luoyang471000 China Henan Academy of Sciences Zhengzhou450046 China College of Information Engineering Henan University of Science and Technology Luoyang471023 China Henan University of Science and Technology School of Mathematics and Statistics Luoyang471023 China

Fruit yield assessment is an important aspect of orchard management. In this context, target detection of fruit is of paramount importance. However, due to complex factors in real orchard environments, such as fruit occlusion, insufficient lighting, and overlapping fruits, traditional detection and counting methods often suffer from low detection accuracy and inadequate classification precision, failing to meet the requirements of practical applications. To address this issue, we focus on nectarine fruit and propose an improved YOLOv8n-based object detection algorithm model, YOLOv8n-global feature extraction enhancement (GFE). We integrate the effective squeeze-and-excitation attention mechanism into the YOLOv8n model. This integration allows our approach to adaptively adjust the weight of each channel, which enhances both detection efficiency and target recognition accuracy. Then, we introduce focal distance-intersection over union loss to address the misjudgment of hard samples. This further contributes to improving detection accuracy. In addition, we incorporate the gather-and-distribute mechanism from GOLD-YOLO, replacing the traditional feature pyramid network structure. This enhancement improves the information fusion capability in the neck of the model, leading to a higher mean average precision (mAP@0.5). In addition, the output of the improved model can be used as an input to DEEPSORT to classify and count nectarine fruit. This functionality can be used for estimating fruit maturity and yield in orchards. Experimental results demonstrate that the YOLOv8n-GFE model achieves a mAP@0.5 of 92.5%, which is an improvement of 3.2% over the original YOLOv8n model, meeting the required accuracy for recognizing nectarine fruit maturity in practical applications. © 2023 IEEE.

关键词： YOLO Lighting Feature extraction Attention mechanisms Shape Technological innovation Neck Head Detection algorithms Data models

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：