检索结果-内蒙古大学图书馆

Neuromorphic Computing (ICNC), International Conference on

作者： Shuo Lian Jun Wang Min Han Jianchao Fan School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Department of Computer Science and Department of Data Science City University of Hong Kong Kowloon Hong Kong China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

Offshore aquacultural raft information extraction from synthetic aperture radar (SAR) images is crucial for large-scale marine resource exploitation and environmental protection. In this paper, a deep learning model named Detail-Enhancing Generative Adversarial Network (DEGAN) is proposed for SAR image segmentation to monitor aquacultural rafts. DEGAN incorporates several key modules to enhance segmentation performance, including $f$ -divergence to improve the discrim-inator's ability to handle noisy data, and the Convolutional Block Attention Module (CBAM) for more effective spatial and channel attention. Within CBAM, multi-scale feature fusion is employed to capture both fine and coarse details across different resolutions. Additionally, the Simple, Parameter-Free Attention Module (SimAM) is introduced to enhance feature refinement by deriving neuron importance through an energy function. These modules, when integrated into the GAN framework, work together to improve noise suppression and detail preservation, critical for accurate segmentation in challenging SAR images. Experimental results demonstrate that DEGAN outperforms five baseline models in four performance metrics, making it highly effective for aquacultural raft monitoring in complex marine environments.

关键词： Deep learning Image segmentation Generative adversarial networks Feature extraction Radar polarimetry Noise measurement Spatial resolution Monitoring Synthetic aperture radar Aquaculture

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Methodology of Field-Oriented control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

引用

IEEE Access 2025年 13卷 89524-89541页

作者： Busarello, Tiago Davi Curi Bubshait, Abdullah Varaprasad, Oruganti V. S. R. Alsaleem, Abdulhakeem Simoes, Marcelo Godoy Federal University of Santa Catarina Department of Control Automation and Computation Engineering Florianopolis88040-900 Brazil King Faisal University College of Engineering Department of Electrical Engineering Al Ahsa31982 Saudi Arabia Group Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada Qassim University College of Engineering Department of Electrical Engineering Buraydah52571 Saudi Arabia University of Vaasa School of Technology and Innovations Vaasa65200 Finland

This paper proposes a comprehensive methodology for Field-Oriented control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness. © 2013 IEEE.

关键词： Vector control (Electric machinery)

来源：评论

学校读者我要写书评

暂无评论

Application of Image Processing and Machine Learning Technologies for Home Care Bed

Application of Image Processing and Machine Learning Technol...

引用

2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Suponenkovs, Artjoms Lacis, Matiss Markovics, Zigurds Kovalovs, Mihails Platkajis, Ardis Kalnins, Andrejs Process Engineering and Equipment Manufacturing Company PERUZA Riga Latvia Riga Technical University Department of Computer Control and Computer Networks Riga Latvia Riga Technical University Faculty of Computed Science and Information Technology Riga Latvia Riga Stradins University Department of Radiology Riga Latvia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The problem of analysing a bed bound patient (that is, a patient who is confined to their bed) is a relevant factor in whether or not they will receive timely medical care. This paper aims to investigate the challenges and possible solutions to such problems. It describes an approach and methods for such analysis, as well as for home care bed control. This paper also addresses the challenges of Image Processing and Machine Learning. The experimental part describes important aspects regarding analysing brain activity, facial expression, blood flow, and human body pose. These experimental results and the proposed approach based upon them can be helpful for ensuring timely medical care. In addition, the results of this work can be useful for home care bed development. © 2022 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Identifiability and Solvability in Inverse Linear Quadratic Optimal control Problems

引用

Journal of Systems Science & Complexity 2021年第5期34卷 1840-1857页

作者： LI Yibei WAHLBERG Bo HU Xiaoming Optimization and Systems Theory Department of MathematicsKTH Royal Institute of TechnologySE-10044StockholmSweden Division of Decision and Control School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologySE-10044StockholmSweden

In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is *** the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.

关键词： Inverse optimal control linear quadratic regulators model identifiability

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum

Machine Learning-Based Underwater Communication System Using...

引用

CLEO: Science and Innovations, S and I 2022

作者： Jarrett, William A. Avramov-Zamurovic, Svetlana Esposito, Joel M. Nelson, Charles Weapons Robotics and Control Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States Electrical and Computer Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States

ISBN: (纸本)9781557528209

An underwater communication system using light carrying orbital angular momentum is evaluated using a convolutional neural network through simulated and experimental thermally-generated underwater optical turbulence. 100% classification is demonstrated in no or weak turbulence. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Optical communication

来源：评论

学校读者我要写书评

暂无评论

Modelling, Analysis and control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV

arXiv

引用

arXiv 2023年

作者： Aboudorra, Youssef Gabellieri, Chiara Brantjes, Ralph Sablé, Quentin Franchi, Antonio Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

This paper introduces for the first time the design, modelling, and control of a novel morphing multirotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-directional thrust (UDT) configuration can be used, e.g., during standard point-to-point displacements. Fully-actuated (FA) and omnidirectional (OD) configurations can be instead used for full pose tracking, such as, e.g., constant attitude horizontal motions and full rotations on the spot, and for full wrench 6D interaction control and 6D disturbance rejection. Morphing is obtained using a single servomotor, allowing possible minimization of weight, costs, and maintenance complexity. The actuation properties are studied, and an optimal controller that compromises between performance and control effort is proposed and validated in realistic simulations. Preliminary tests on the prototype are presented to assess the propellers' mutual aerodynamic interference. © 2023, CC BY.

关键词： Maneuverability

来源：评论

学校读者我要写书评

暂无评论

A survey on efficient vision transformers: Algorithms, techniques, and performance benchmarking

arXiv

引用

arXiv 2023年

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene Zhou, Luping The Department of Computer Control and Management Engineering Sapienza University of Rome 00185 Italy The School of Electrical and Information Engineering Faculty of Engineering The University of Sydney NSW2006 Australia

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution. Generally speaking, it is challenging to employ these architectures in real-world applications due to many hardware and environmental restrictions, such as processing and computational capabilities. Therefore, this survey investigates the most efficient methodologies to ensure sub-optimal estimation performances. More in detail, four efficient categories will be analyzed: compact architecture, pruning, knowledge distillation, and quantization strategies. Moreover, a new metric called Efficient Error Rate has been introduced in order to normalize and compare models' features that affect hardware devices at inference time, such as the number of parameters, bits, FLOPs, and model size. Summarizing, this paper firstly mathematically defines the strategies used to make Vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios. Toward the end of this paper, we also discuss open challenges and promising research directions. © 2023, CC BY.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

来源：评论

学校读者我要写书评

暂无评论

Derivative-Free Data-Driven control of Continuous-Time Linear Time-Invariant Systems

arXiv

引用

arXiv 2024年

作者： Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Department of Electrical Electronic and Information Engineering University of Bologna Italy Institute for System Theory and Automatic Control University of Stuttgart Germany Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. © 2024, CC BY.

关键词： Continuous time systems

来源：评论

学校读者我要写书评

暂无评论

Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Federica D’Annunzio Giulia Fiscon Gaia Bertolotti Raffaele Dello Ioio Paola Paci Department of Translational and Precision Medicine Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering "A. Ruberti" National Research Council of Rome Sapienza University of Rome Institute for Systems Analysis and Computer Science "A. Ruberti" Rome Italy Department of Biology and Biotechnology "Charles Darwin" Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering "A. Ruberti" Sapienza University of Rome Karolinska Institutet Stockholm Sweden Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes’ relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots’ orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems.

关键词： Correlation Buildings Biological systems Predictive models Phylogeny Complexity theory Bioinformatics

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：