检索结果-内蒙古大学图书馆

SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

3D Robotic Control of Micro-Scale Optical Swarms at an Interface

引用

IEEE Robotics and Automation Letters 2025年第7期10卷 6712-6719页

作者： Carlisle, Nicholas Nock, Volker Williams, Martin A.K. Whitby, Catherine P. Chen, Jack L.Y. Avci, Ebubekir University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand MacDiarmid Institute for Advanced Materials and Nanotechnology Wellington New Zealand Massey University School of Fundamental Sciences Palmerston North New Zealand Auckland University of Technology School of Science Auckland New Zealand

Optical force-induced assembly is a promising yet scarcely explored approach for developing functional tools and objects at the microscale, with a wide range of potential applications. Our previous work was the first to investigate the manipulation of these assemblies in the XY plane. Here, we expand on these techniques by systematically exploring optical trap manipulation with the addition of Z-axis control. Manipulation of the Z-axis is referred to as axial displacement and is a viable approach for actively manipulating the assembly morphology. Experiments are conducted for the first time to explore and detail the response of the assembly during active 3D trap manipulation, informing the development of an autonomous control algorithm over the 2D area of the assembly during motion. This control presents techniques to increase assembly stability or alter the area of the assembly for tasks such as passing through constrictions. This work aims to develop the control techniques required to create a unique micromanufacturing approach inspired by the Kilobot thousand-robot swarm. © 2016 IEEE.

关键词： Microrobots

来源：评论

学校读者我要写书评

暂无评论

Portfolio selection based on market states acquired via price and non-price data

引用

Discover Data 2025年第1期3卷 1-21页

作者： Chung, Andy Tanaka-Ishii, Kumiko Yairi, Takehisa Department of Advanced Interdisciplinary Studies Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Computer Science and Engineering School of Fundamental Science and Engineering Waseda University Tokyo Japan

Financial markets exhibit quasi-stationary patterns, known as market states, which previous studies have identified through the correlation structure of asset returns using clustering techniques. In this paper, we propose a novel approach to the portfolio selection problem by leveraging market states through an unsupervised method that extends beyond conventional correlation analysis. We introduce market distance metrics for both price and non-price data to estimate market states. Our proposed model allocates assets based on portfolio choices optimized within specified market states. In addition to using asset return correlations, we experiment with two non-price data sources: (1) a text-based market distance metric derived from Federal Open Market Committee (FOMC) policy statements, and (2) the correlation of multivariate time series search volumes from Google Trends. Using a multi-asset portfolio encompassing major U.S. asset classes, we trained our model from 2016 to 2019 and evaluated its out-of-sample performance from 2020 to 2023. Our findings demonstrate the superiority of our ensemble solution, incorporating multiple market distance metrics with price and non-price data, over established baselines with statistically significant results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Exploring 2D representation and transfer learning techniques for indoor localization

引用

Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Queue-Based Analytical Modeling for Capacity Estimation of Wearable eHealth Systems

引用

IEEE Transactions on Network science and engineering 2025年第3期12卷 2033-2042页

作者： Pathak, Nidhi Mukherjee, Anandarup Misra, Sudip Indian Institute of Technology Kharagpur Advanced Technology Development Center Kharagpur721302 India University of Cambridge Institute for Manufacturing CambridgeCB3 0FS United Kingdom Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

The heterogeneity in IoT-based health monitoring devices and communication protocols leads to diverse system designs and configurations resulting in uncertain outcomes. Most present-day eHealth approaches focus on optimizing the performance of already designed systems (hardware, network, and software) rather than optimizing the system design itself. A reliable eHealth monitoring system must use appropriate protocols and hardware to avoid data loss and delays. Identifying the magnitude of performance changes when integrating new devices into an already functional eHealth system is also critical. This work addresses these issues by adopting a queuing theory-based analytical model to characterize an eHealth wearable system. This approach acts as a guiding framework for system and network capacity estimation by simulating the dynamics of an eHealth wearable system with selected communication protocols before deployment. The simulation tool identifies the system's limits in terms of delays, the number of simultaneously supported wearables, and packet loss. We identify and examine some well-known wireless connectivity protocols as possible candidates for eHealth wearable systems. The output of the analytical model is compared vis-à-vis data from a real-life proof-of-concept eHealth wearable system. The proposed approach estimates the numbers of served packets and blocked packets with approximate errors of 0.02% and 0.2%, respectively. © 2013 IEEE.

关键词： Queueing theory

来源：评论

学校读者我要写书评

暂无评论

Dialogue-to-Video Retrieval via Multi-Grained Attention Network

引用

IEEE Access 2025年 13卷 82302-82311页

作者： Yu, Yi Zhang, Jiwei Tang, Suhua Li, Wei Hiroshima University Graduate School of Advanced Science and Engineering Higashihiroshima739-0046 Japan KDDI Research Inc. Human-Centered AI Laboratories Fujimino356-8502 Japan Wakayama University Department of Systems Engineering Wakayama640-8441 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Chofu182-8585 Japan Fudan University School of Computer Science Shanghai200433 China

Dialogue-to-video retrieval is an interesting while challenging task, which exploits an AI agent to retrieve the video that matches and aligns with the conversational context between users. In particular, given a history of dialogue exchanges, the agent is expected to identify the most fitting video content that complements the ongoing conversation. The computational complexity posed by the processing of videos within deep neural networks encourages us to adapt CLIP, a cutting-edge large multi-modal models (LLMs), into the dialogue-video domain. On this basis, we propose a multi-grained attention network (MGAT), integrating query-scoring, dual-softmax and query-bank normalization techniques. We design a multi-grained attention module to optimize the inadequate modeling of conversation semantics in existing pre-trained models, dynamically assign weights during conversation feature extraction, and introduce conversation context features in multimodal alignment. Most importantly, fine-grained similarity by per-round query-frame scoring and coarse-grained similarity by high-level semantics of all rounds of dialogues for each video are respectively calculated, which are further learned via multi-grained attention mechanism. This approach effectively transfers CLIP's text-image multimodal knowledge into the dialogue-video retrieval system, alleviating the need for resource-intensive and costly fine-tuning dialogue-video procedures. Extensive experiments on multiple datasets demonstrate that our method outperforms state-of-the-art approaches. © 2013 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Distributed Estimation and Motion Control in Multi-Agent Systems Under Multiple Attacks

引用

IEEE Transactions on Automation science and engineering 2025年 22卷 12548-12559页

作者： Jenabzadeh, Ahmadreza Shu, Zhan Huang, Tingwen Zhu, Quanmin Shang, Yilun Cui, Yukang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Peng Cheng Laboratory Shenzhen518000 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China University of the West of England School of Engineering BristolBS16 1QY United Kingdom Northumbria University Faculty of Engineering and Environment Department of Computer and Information Sciences Newcastle upon TyneNE1 8ST United Kingdom

This paper addresses the problem of distributed estimation and motion control (DEMC) in multi-agent systems (MASs) with both linear and Lipschitz nonlinear dynamics. Unlike conventional DEMC methods designed for MASs under ideal conditions, this work investigates scenarios where all agents are vulnerable to various forms of attacks. The considered attacks comprise false-data injection (FDI) attacks and denial of service (DoS) attacks that affect the communication channels among agents to destabilize the MAS. Also, the unbounded actuator attacks which exist in practical environments to intentionally degrade the MAS performance is considered. To cope with these kinds of attacks, two novel resilient approaches are established aimed at estimating and following a mobile target under attacks. The proposed distributed attack-resilient control strategies are designed based on a dual-layer structure, guaranteeing effective DEMC with an ultimately bounded error. The results from two simulation examples are provided to validate the presented algorithms. © 2004-2012 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Optimization of Parameters of the OWC Wave Energy Converter Device Using a Machine Learning Approach 5

Optimization of Parameters of the OWC Wave Energy Converter ...

引用

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Karnavat, Kush Vadodaria, Nilansh Trivedi, Kshma Patel, Prashant School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Advanced Sciences VIT-AP University Department of Mathematics Amaravati India

ISBN: (纸本)9798350356236

In this study, the performance of an L-shaped front wall OWC (Oscillating Water Column) device is investigated in the presence of irregular incident waves. To efficiently explore the complex interactions between input features and the target variable, two supervised machine learning models: (i) a decision tree model and (ii) an XGBoost model are considered. The input parameters selected for this analysis are: OWC Front wall Draft, OWC Duct Length, Chamber Length, ripple Number, and ripple amplitude of the sinusoidal seabed. The output attribute is the maximum power extracted by the OWC device. The XG Boost model is used to identify critical input regions where power extraction peaks, while the decision tree model further aids in understanding the parameter influences on power extraction. A dataset of 3125 samples was generated for training, validation, and testing. By employing accumulated local effect plots with the XG Boost model, five specific regions in the input space were identified that maximize annual average power extraction. The results indicate that optimal power extraction is achieved for a specific value of each parameter, thereby validating the influence of each on OWC device performance. © 2025 IEEE.

关键词： Wave energy conversion

来源：评论

学校读者我要写书评

暂无评论

Transformer-based Models for Visual and Textual Data Extraction

Transformer-based Models for Visual and Textual Data Extract...

引用

Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Meet Patel Manisha J Nene Department of Computer Science and Engineering Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In an era driven by digital transformation, the demand for intelligent and automated data processing has increased significantly. This research explores a unified approach to extracting both visual and textual information from images, enabling better analysis and interpretation in multiple domains. As a case study, we focus on CCTV surveillance, where traditional monitoring methods are often limited to object detection and manual inspection. Our proposed framework enhances this process by generating detailed image descriptions using transformer-based models, extracting multilingual textual data through Optical Character Recognition (OCR), and translating it into English for broader accessibility. By unifying these components, the frame-work offers a more comprehensive understanding of surveillance footage, improving both real-time monitoring and post event investigations. Fine-tuned on a domain-specific CCTV dataset, the system surpasses smaller state-of-the-art models in generating accurate and context-rich captions. Additionally, its lightweight design allows for deployment on edge devices, making it suitable for real-time monitoring and security applications without relying on high-end computational resources. This research demonstrates the potential of multimodal data extraction to improve situational awareness and decision making in security and other real-world scenarios.

关键词： Visualization Adaptation models Translation Accuracy Surveillance Optical character recognition Transformers Data models Data mining Security

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：