检索结果-内蒙古大学图书馆

Hybrid Optimization Algorithm for Handwritten Document Enhancement

computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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

Phy-CoCo: Physical Constraint-Based Correlation Learning for Tropical Cyclone Intensity and Size Estimation 27

Phy-CoCo: Physical Constraint-Based Correlation Learning for...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Hanting Mu, Pan Huang, Cheng Zhang, Jinglin Bai, Cong College of Computer Science Zhejiang University of Technology China School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781643685489

Tropical Cyclone (TC) estimation aims to estimate various attributes of TC in real-time to alleviate and prevent disasters caused by violent TCs. As artificial intelligence technology advances, various deep learning-based multi-task estimation approaches have been proposed. However, most of them only focus on extracting common features of tasks, disregarding potential negative transfer and task interactions between different tasks. This paper is thus motivated to propose a Physical Constraint-based Correlation (Phy-CoCo) learning framework from the perspective of Multi-Task Learning (MTL). Specifically, for task-specific feature learning, we introduce Correlation Modeling (CoM) based on Centrally Expanded Pooling (CEP). Furthermore, for cross-task interaction, we propose a Multi-Domain Recurrent Convolution (MDRC) module to incorporate physical constraints into MTL. These physical constraints enable the transformation of different task features by simulating the physical relations among different attributes of TC. Lastly, in combination with a task-shared network that leverages the hybrid fusion of multi-modal data, our MTL framework accurately estimates various TC attributes. Extensive experiments conducted on our constructed dataset demonstrate that the proposed Phy-CoCo outperforms previous methods in TC estimation in terms of estimation error, verifying the potential of the physics-incorporated MTL model. © 2024 The Authors.

关键词： Tropical cyclone

来源：评论

学校读者我要写书评

暂无评论

SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

引用

Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Multi Modal EEG-Based Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Hilbert Huang Transform

Multi Modal EEG-Based Classification of Alzheimer's Disease ...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Gopu, G. Sathish, S. Shanmathan, T.S. Suthakar, G. Sivakumaran, N. Geetha Devasena, M.S. Sri Ramakrishna Engineering College Electronics And Communication Engineering Coimbatore India National Institute Of Technology Instrumentation And Control Engineering Tiruchirappalli India Sri Ramakrishna Engineering College Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

This study investigates whether high-resolution brain-functional connectivity, which is measured by using Electroencephalography (EEG) combined with Hilbert Huang Transform (HHT) can be useful in Alzheimer Disease Diagnosis. The major issue with Alzheimer's disease (AD) is that it leads to cognitive decline and affects millions of people worldwide. Early prediction of the disease is mandatory and crucial for timely intervention and effective care. EEG signals, a non-invasive and cost-effective method to assess brain activity, have shown potential in predicting this disease. In this study, we propose an approach that utilizes EEG data combined with Hilbert-Huang Transform (HHT) and machine learning algorithms for classification which is a novelty of the study that enables early prediction of Alzheimer's disease. Our methodology was tested on a dataset comprising EEG signals recorded using 19 electrodes from both healthy individuals and patients with Alzheimer's and the Mild Cognitive Impairment (MCI). The results were promising, demonstrating high accuracy, sensitivity, and specificity. Machine learning techniques can be employed for categorizing EEG signals, but it essential to validate their effectiveness using large and diverse datasets. This research highlights the potential of HHT and EEG data in the prediction of this disease. Principal Component Analysis (PCA) is used for dimensional reduction of the dataset. Notably EEG signals combined with Hilbert Huang Transform and Principal Component Analysis gives better accuracy of 98.6% for KNN. This research highlights the potential of EEG analysis combined with advanced techniques for accurate classification of dementia-related disorders. © 2024 IEEE.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

UAV 3D Trajectory Planning Based on Improved A* Algorithm and Differential Evolution

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第4期8卷 1150-1163页

作者： Yang, Li Tan, Liang Zhang, Fuquan Department of information engineering Sichuan Vocational and Technical College of Communications Chengdu611130 China Thai-Chinese International College Assumption University of Thailand Bangkok100027 Thailand College of Computer and Control Engineering Minjiang University Fuzhou350108 China

Track planning technology, as a key technology for UAV mission planning, has always been a major research hotspot in the field of UAVs. In real-life applications, due to the complex and constrained UAV flight environment, how to establish an accurate environment model and how to select an efficient planning algorithm become the elements to solve the trajectory planning problem. In this work, after studying existing trajectory planning algorithms, a method using a combination of improved A* algorithm and differential evolution algorithm is proposed for 3D trajectory planning in complex environments. Firstly, the mathematical modelling of the UAV track planning related problems, including the UAV model, manoeuvrability constraints and equivalent digital maps, is carried out. Secondly, an improved sparse A* algorithm is proposed for solving the winding path problem of the sparse A* algorithm, which reduces the winding path of the trajectory by extracting path feature points and quadratic optimisation. Finally, the improved sparse A* algorithm is combined with the differential evolutionary algorithm and applied to the UAV real-time trajectory planning, where the improved sparse A* algorithm is used for the UAV global trajectory planning and the differential evolutionary algorithm is applied to the emergent threat avoidance, i.e. local trajectory planning. The results of MATLAB simulations show that the proposed method can solve the patharound problem of the sparse A* algorithm and can be flexible in complex environments to achieve dynamic trajectory planning, thus effectively guaranteeing the flight safety of UAVs in practical environments. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Rice Variety Classification Based on Optimized Near-Infrared Spectral Classification Model

引用

Rice science 2024年第1期31卷 6-9,I0009-I0012页

作者： YANG Sen WANG Zhenmin ZHANG Houqing SONG Wenlong College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China The 49th Research Institute of China Electronics Technology Group Corporation Harbin 150000China

Accurate classification of rice variety is essential to ensure the brand value of high-quality rice *** the impact of sample state on modeling optimization algorithms,rice samples after grinding and sealing were *** enhance the accuracy of rice variety classification,we introduced a spectral characteristic wavelength selection method based on adaptive sliding window permutation entropy(ASW-PE).

关键词： sealing Infrared grinding

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

Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

引用

Big Data Mining and Analytics 2023年第2期6卷 185-200页

作者： Jiancheng Zhong Zuohang Qu Ying Zhong Chao Tang Yi Pan College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences ShenzhenGuangzhou 518055China

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential ***,gene expression data are prone to significant fluctuations due to noise interference in topological *** this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise *** then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression *** the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential *** strategy exhibited a high recognition rate and good *** validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy *** with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and *** also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene *** experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

关键词： Protein-Protein Interaction(PPI)network continuous and discrete similarity coefficient essential proteins

来源：评论

学校读者我要写书评

暂无评论

Object Helps U-Net Based Change Detectors

引用

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 548-550页

作者： Lan Yan Qiang Li Kenli Li College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change *** detection is fundamental to many computer vision *** existing solutions based on ... 详细信息

关键词： Net computer networks

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：