检索结果-内蒙古大学图书馆

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Leinuo Tang Xiaochun Xu Xin Wang Huibin Feng Nixuan Lin IEEE APCAP 2023 International Digital Economy College Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

Discriminative correlation filter-based trackers have achieved excellent performance. However, they are still suffered from the inherent boundary effect. To alleviate it, this paper proposes an automatic object attention likelihood map correlation filter. Specifically, an automatic object attention searching window is developed to highlight the object more precisely and suppress the background more appropriately, which divides the searching area into three regions and applies different object attention strategies on each area. Furthermore, combining it with the spatially regularized discriminative correlation filter, we propose the automatic object attention likelihood map correlation filter tracker. It further alleviates the boundary effect and adaptively generates a more precise tracking model frame by frame. The experimental evaluations on the popular OT2015 benchmark demonstrate that our proposed tracker gains outstanding tracking performance and offers a satisfying robustness to complex tracking scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Synthesis of Decentralized Variable Gain Robust controllers with Guaranteed L2Gain Performance via Piecewise Lyapunov Functions for a Class of Uncertain Large-Scale Interconnected Systems 48

Synthesis of Decentralized Variable Gain Robust Controllers ...

引用

48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Nagai, Shunya Oya, Hidetoshi Kubo, Tomohiro Matsuki, Tsuyoshi Niihama College Department of Electronics and Control Engineering Ehime Japan Tokyo City University Faculty of Knowledge Engineering Department of Computer Science Tokyo Japan Tokushima University Graduate School of Technology Industrial and Social Sciences Tokushima Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

For a class of uncertain large-scale interconnected systems, a design method of decentralized variable gain robust controllers with guaranteed { mathcal{L}-{{2}}} gain performance based on piecewise Lyapunov functions is shown in this paper. Furthermore, LMI-based sufficient conditions for the existence of the proposed decentralized variable gain robust controller are shown. Finally, the effectiveness of the proposed robust controller is presented through a simple numerical example. © 2022 IEEE.

关键词： Lyapunov functions

来源：评论

学校读者我要写书评

暂无评论

无人飞行器在不确定扰动下的动态目标跟踪

引用

engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

来源：评论

学校读者我要写书评

暂无评论

Deciphering Modern Customer Loyalty: A Machine Learning Approach

Deciphering Modern Customer Loyalty: A Machine Learning Appr...

引用

International Conference on Internet of Things and Machine Learning, IoTML 2023

作者： Li, Kechen Xu, Chang Zhao, Zheyuan Zhu, Mengyu Cui, Xiangxuan Xu, Shurui Zou, Jichen Nantong Normal College Nantong China Ulink College of Suzhou industrial Park Suzhou China Jiangsu Lincheng Technology Co. Ltd. Nantong China School of computer science and technology China University of Mining and Technology Xuzhou China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9781510671805

In the contemporary retail sector, deciphering customer behavior is crucial for businesses vying for a competitive advantage. While customer loyalty has conventionally been gauged through parameters such as repeat purchases and promotional response, the merging of technology, media, and commerce has necessitated a reevaluation of these benchmarks. This study recognizes the inadequacy of solely relying on traditional transactional data to evaluate loyalty. Utilizing Machine Learning (ML), we crafted a novel model to predict customer loyalty, taking into account a plethora of modern factors, including online behavior and social media interactions. This research leveraged a diverse dataset and sophisticated ML algorithms to identify subtle patterns and relationships. Key findings include the significance of online behavior in loyalty prediction, the emerging role of social media interactions, and the nuanced influence of device preferences on brand loyalty. Notably, this manuscript offers a pioneering ML model that accurately reflects the complexities of the modern retail landscape, providing a valuable tool for businesses seeking to foster enduring customer relationships. © 2023 SPIE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

来源：评论

学校读者我要写书评

暂无评论

Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuxin Lin Jing Ma Wei Wang Zhihao Wu Suyu Dong Gongning Luo Kuanquan Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine.

关键词： Databases Biological system modeling Precision medicine Electrocardiography Diffusion models Data models Quality assessment Biomedical monitoring Monitoring Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

引用

Annals of Data Science 2022年第2期9卷 393-428页

作者： Emara, Heba M. Elwekeil, Mohamed Taha, Taha E. El-Fishawy, Adel S. El-Rabaie, El-Sayed M. El-Shafai, Walid El Banby, Ghada M. Alotaiby, Turky Alshebeili, Saleh A. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt KACST Riyadh Saudi Arabia King Saud University Riyadh Saudi Arabia Department of Electrical Engineering King Saud University Riyadh11421 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh21974 Saudi Arabia

Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal processing. The first one is an automatic seizure detection framework based on the utilization of scale-invariant feature transform (SIFT) as an extraction tool. The second one depends on the utilization of the fast Fourier transform (FFT) and an artificial neural network for epileptic seizure prediction. Finally, an automated patient-specific framework for channel selection and seizure prediction is presented based on FFT. The simulation results show the success of the proposed frameworks for automated medical diagnosis. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Recent Technological Advances in Solar Photovoltaic System and Its Applications in Building Integrated Photovoltaic System

Recent Technological Advances in Solar Photovoltaic System a...

引用

International Conference on Sustainable Technology and Advanced Computing in Electrical engineering, ICSTACE 2021

作者： Islam, Noorul Nat, Aslam Khan, Rashid Ahmed Department of Electrical Engineering ZH College of Engineering and Technology Aligarh Muslim University Aligarh202002 India Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan

ISBN: (纸本)9789811943638

Energy has become an integral part of our life. The fossil fuel present in the environment is very much limited. On the other hand, fossil fuel emits harmful gases such as carbon dioxide, Chlorofluorocarbon (CFC), carbon monoxide, and others which harmed flora and fauna. Therefore, the concern for renewable energy application has become more vital due to the depletion of fossil fuels, rising global warming, and increased consumption of energy. Renewable energy aims to reduce pollution levels, enhancing the local economy and community sustainability. Solar energy is the most promising alternative energy source since it has various advantages over other renewable energy sources. The sun is an unquenchable source of energy capable of addressing all energy requirements of humankind. The solar energy concept has been around for a long time, and technological advancements will make it endure for a longer period. The PV-Effect is a mechanism that can produce an electric current when sunlight strikes the limiting layer of semiconductor materials. A photovoltaic (PV) system is a semiconductor device that produces electricity when exposed to luminosity. A PV module is made up of several solar cells that are coupled in parallel and series. This paper provides an extensive study of advancements in energy extraction techniques from a solar panel, analysing solar PV systems under diverse circumstances. PV technology is hastily evolving compared to alternative renewables, consequently leading to the Building Integrated Photovoltaics (BIPV) systems playing a significant role in the generation of electrical energy. BIPV system is a preferred choice as a building integrated solar energy storage system. It has been categorised into three subparts: Grid-connected BIPV system, BIPV system with a solar battery, and PV-Trombe wall. The BIPV mechanism simultaneously acts as a material for the envelope of the building and power extraction from. © 2022, The Author(s), under exclusive license

关键词： Fossil fuels

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation 4

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

引用

4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Source Code Classification and Visualization of Decision Rationales

引用

Procedia computer Science 2024年 246卷 2753-2761页

作者： Shinichi Oeda Seiya Mitsumori Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

In programming education, it is crucial to provide instruction that is tailored to students’ proficiency levels. For this purpose, an objective evaluation of each student’s coding ability is essential. Additionally, it is necessary to understand the characteristics of source code written by both beginners and advanced students. Previous research has successfully converted the structural information of source code into graphs and assessed coding skills using deep learning, achieving high accuracy. However, it remains unclear which specific structural elements significantly influence these assessments. This study addresses this gap by transforming source code into abstract syntax trees and developing a model that uses Graph Convolutional Networks to categorize code as either beginner or advanced users based on learned structural information. Furthermore, we apply Integrated Gradients to visualize the decision-making basis of our model and elucidate the structural characteristics distinguishing source code written by beginner and advanced users.

关键词： Abstract Syntax Tree Deep Learning Explainable AI Graph Convolutional Networks Integrated Gradients Source Code Classification

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：