检索结果-内蒙古大学图书馆

Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

引用

Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Behavioural classification of network devices using graph structure of private networks 23

Behavioural classification of network devices using graph st...

引用

23rd Conference Information Technologies - Applications and Theory, ITAT 2023

作者： Outrata, Vojtěch Hlaváč, Jaroslav Kopp, Martin TD&R Data Science Cisco Systems Karlovo nám. 10 Praha 2120 00 Czech Republic Czech Technical University Faculty of Electrical Engineering Karlovo nám. 13 Praha 2120 00 Czech Republic Charles University Faculty of Mathematics and Physics Ke Karlovu 3 Praha 2121 16 Czech Republic

Classifying computer network devices by their behaviour is crucial for keeping track of today's dynamically changing network environments. In the field, it is mainly done either manually or using heuristics. The commonly used solutions completely overlook the computer network's inherent graph structure. In this paper, we show that leveraging the graph structure using graph convolutional neural networks is worth the added computational burden. Our evaluation on real-world networks with user-defined classes includes three baseline models and a graph neural network. The experimental results highlight the models' proficiency in learning diverse device classes, with the graph-based models exhibiting superior performance. We also show that the graph-based models struggle to adapt to the ever-changing structure of the network and measure the cost of their retraining. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation

arXiv

引用

arXiv 2023年

作者： Cai, Danwei Li, Ming The Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States The Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China

This paper focuses on the application of Conformers in speaker verification. Conformers, initially designed for Automatic Speech Recognition (ASR), excel at modeling both local and global contexts within speech signals effectively. Previous research has established that ASR and speaker verification tasks can naturally complement each other. Building on this synergistic relationship, this study introduces three strategies for leveraging ASR-pretrained Conformers in speaker verification: (1) Transfer learning: We use a pretrained ASR Conformer encoder to initialize the speaker embedding network, thereby enhancing model generalization and mitigating the risk of overfitting. (2) Knowledge distillation: We distill the complex capabilities of an ASR Conformer into a speaker verification model. This not only allows for flexibility in the student mode's network architecture but also incorporates frame-level ASR distillation loss as an auxiliary task to reinforce speaker verification. (3) Parameter-efficient transfer learning with speaker adaptation: A lightweight speaker adaptation module is proposed to convert ASR-derived features into speaker-specific embeddings, without altering the core architecture of the original ASR Conformer. This strategy facilitates the concurrent execution of ASR and speaker verification tasks within a singular model. Experiments were conducted on VoxCeleb datasets. The results are compelling: models employing ASR pretraining and knowledge distillation significantly outperform standard Conformers. Specifically, the best model using the ASR pretraining method achieved a 0.43% equal error rate (EER) on the VoxCeleb1-O test trial, while the knowledge distillation approach yielded a 0.38% EER. Furthermore, by adding a mere 4.92 million parameters to a 130.94 million-parameter ASR Conformer encoder, the speaker adaptation approach achieved a 0.45% EER, enabling parallel speech recognition and speaker verification within a single ASR Conformer encoder.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection

arXiv

引用

arXiv 2024年

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Online Hand Movement Recognition System with EEG-EMG Fusion Using One-Dimensional Convolutional Neural Network

Online Hand Movement Recognition System with EEG-EMG Fusion ...

引用

IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Haozheng Wang Hao Jia Zhe Sun Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Department of Engineering University of Vic-Central University of Catalonia Vic Spain Faculty of Health Data Science Juntendo University Urayasu Chiba Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Upper limb amputees face significant challenges in their daily lives due to the loss of hand or arm functionality. Researchers have developed upper limb prostheses to restore normal hand movements for them. Most hand movement recognition systems of prostheses use electromyography (EMG) as the input signal source, but ignore the interrelationship with electroencephalography (EEG), which may contain valuable movement-related information as well. In order to enhance the accuracy of hand movement classification, we proposed a hand movement recognition system based on a one-dimensional convolutional neural network (1D-CNN) that combines EEG and EMG as the input signal sources to increase the quantity of accessible information. In this work, we collected the EEG and EMG of five subjects during the hand movements and used a 1D-CNN based model to classify the preprocessed signals. The average accuracy of using EEG-EMG fusion is 96.59±2.63%, significantly higher than 74.99±8.24% of using single EEG and 90.31±7.16% of using single EMG. Then, we applied the model trained by offline experiment for online recognition, and controlled the Pepper robot to complete the corresponding hand movements. The average accuracy of online recognition can reach 93.00±4.85% by using majority voting method. The results indicate that the method of EEG-EMG fusion can effectively enhance the performance of hand movement recognition system, which promote the development of upper limb prostheses and contribute to the rehabilitation of upper limb amputees.

关键词： Hands Accuracy Muscles Brain modeling Fatigue Electromyography Electroencephalography Real-time systems Convolutional neural networks intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Frequency-Domain Refinement of Vision Transformers for Robust Medical Image Segmentation Under Degradation

Frequency-Domain Refinement of Vision Transformers for Robus...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Sanaz Karimijarbigloo Sina Ghorbani Kolahi Reza Azad Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Department of Radiology Northwestern University Chicago USA Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Medical image segmentation is crucial for precise diagnosis, treatment planning, and disease monitoring in clinical settings. While convolutional neural networks (CNNs) have achieved remarkable success, they struggle with modeling long-range dependencies. Vision Transformers (ViTs) address this limitation by leveraging self-attention mechanisms to capture global contextual information. However, ViTs often fall short in local feature description, which is crucial for precise segmentation. To address this issue, we reformulate self-attention in the frequency domain to enhance both local and global feature representation. Our approach, the Enhanced Wave Vision Transformer (EW-ViT), incorporates wavelet decomposition within the self-attention block to adaptively refine feature representation in low and high-frequency components. We also introduce the Prompt-Guided High-Frequency Refiner (PGHFR) module to handle image degradation, which mainly affects high-frequency components. This module uses implicit prompts to encode degradation-specific information and adjust high-frequency representations accordingly. Additionally, we apply a contrastive learning strategy to maintain feature consistency and ensure robustness against noise, leading to state-of-the-art (SOTA) performance in medical image segmentation, especially under various conditions of degradation. Source code is available at GitHub.

关键词： Degradation Wavelet transforms Image segmentation Computer vision Frequency-domain analysis Noise Contrastive learning Transformers Noise measurement Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Compensation Control of PMSMs Based on a High-order Sliding Mode Observer with Inertia Identification

引用

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Li, Ke Ding, Jie Sun, Xiaodong Lei, Gang Dianov, Anton Demidova, Galina Prakht, Vladimir Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia

This article proposes a new sliding mode control (NSMC) reaching law to improve the speed tracking and anti-interference performance of permanent magnet synchronous motors (PMSMs). The NSMC improves the sliding mode control (SMC) in approaching speed and reducing chattering. Firstly, considering that the external load interference will adversely affect the control system, a high-order sliding mode observer (HOSMO) is designed to observe the load torque, and the observed value is compensated to the output of the speed controller. Then, combined with the Landau algorithm, a variable adaptive gain algorithm is introduced to decrease the impact of inertia mismatch on the system control performance. The algorithm identifies the rotational inertia online, reduces the effect of load disturbance on the identification performance, and updates the identification results to the observation in processors and controllers. Experimental results show that the proposed control method reduces the speed and current fluctuations of the system when load torque and inertia vary and improves the system's robustness. © 2013 IEEE.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

T3: Multi-modal Tailless Triple-Flapping-Wing Robot for Efficient Aerial and Terrestrial Locomotion

arXiv

引用

arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

来源：评论

学校读者我要写书评

暂无评论

MLRS-CNN-DWTPL: A New Enhanced Multi-Label Remote Sensing Scene Classification Using Deep Neural Networks with Wavelet Pooling Layers

MLRS-CNN-DWTPL: A New Enhanced Multi-Label Remote Sensing Sc...

引用

2021 International Telecommunications Conference, ITC-Egypt 2021

作者： El-Khamy, Said E. Al-Kabbany, Ahmad El-Bana, Shimaa Alexandria University Faculty of Engineering Alexandria21544 Egypt Arab Academy for Science Technology and Maritime Transport Intelligent Systems Lab Alexandria21937 Egypt Alexandria21311 Egypt

ISBN: (纸本)9781665445733

Aerial scene classification using multi-label remote sensing (MLRS) is a remote sensing challenge task. Conventional techniques in this research area have mainly focused either on the simplified single-label case or on pixel-based approaches, which cannot efficiently handle high-resolution images. Deep learning (DL) and convolutional neural networks (CNNs) have defined the state-of-the-art in many vision problems in recent years. CNNs often adopt pooling layers to enlarge the receptive field, which can lower computational complexity. On the other hand, Conventional pooling methods can result in data loss, degrading subsequent operations such as feature extraction, image retrieval, and scene analysis. Inspired by this drawback, we propose a new CNN model by investigating the impact of discrete wavelet transform pooling (DWTPL) on the performance of this model. Wavelet pooling allows us to utilize spectral information, which is crucial in multi-label remote sensing tasks. We show consistent improvements in precision and F1-score on a widely adopted AID dataset compared to other models from the recent literature. © 2021 IEEE.

关键词： Remote sensing

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：