Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献_第2页

Design and Control of Dual-Body Negative Pressure Ground-Wall Transition Robot 双体负压地墙过渡机器人的设计与控制

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-03-08 DOI: 10.1002/aisy.202400467

Huan Shen, Kai Cao, Jiajun Xu, Wenjun Yan, Xuefei Liu, Huan Wang, Youfu Li, Linsen Xu, Aihong Ji

{"title":"Design and Control of Dual-Body Negative Pressure Ground-Wall Transition Robot","authors":"Huan Shen, Kai Cao, Jiajun Xu, Wenjun Yan, Xuefei Liu, Huan Wang, Youfu Li, Linsen Xu, Aihong Ji","doi":"10.1002/aisy.202400467","DOIUrl":"https://doi.org/10.1002/aisy.202400467","url":null,"abstract":"This article presents a novel dual-body negative-pressure ground-wall transition robot (DNPTR) aimed at expanding the application scenarios of climbing robots to meet the functional requirements of obstacle traversal and wall transition in high-altitude operations. As a typical representative of highly nonlinear and multivariable strongly coupled systems, the wall transition actions of the DNPTR are analyzed and planned based on the mechanical structure characteristics. Subsequently, unified kinematic and dynamic models of the bipedal negative-pressure climbing robot are established. To improve the automation and ensure safe, efficient operation of climbing robots, effective trajectory tracking control for the DNPTR is crucial. Addressing challenges such as model parameter uncertainties and external disturbances, this study proposes an adaptive trajectory tracking control method based on a radial basis function neural network. The method integrates a boundary layer with an improved exponential reaching law, forming a non-singular terminal sliding mode control strategy. Using Lyapunov theory, the global asymptotic stability of the system is verified. Both simulation and experimental results demonstrate that this approach achieves faster convergence and effectively suppresses oscillations during trajectory tracking. This research is of practical importance, offering valuable guidance for the design of high-accuracy, robust trajectory-tracking controllers for dual-body climbing robots.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hand Gesture Recognition Using Frequency-Modulated Continuous Wave Radar on Tactile Displays for the Visually Impaired 调频连续波雷达在视障触觉显示器上的手势识别

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-15 DOI: 10.1002/aisy.202570009

Ahmed Hamza, Santosh Kumar Prabhulingaiah, Pegah Pezeshkpour, Bastian E. Rapp

引用次数: 0

Magnetic Actuation for Mechanomedicine 机械医学的磁致动

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-15 DOI: 10.1002/aisy.202570007

Daniel Garcia-Gonzalez, Ritu Raman, Simone Schuerle, Andy Tay

引用次数: 0

Ecofriendly Printed Wood-Based Honey-Gated Transistors for Artificial Synapse Emulation 用于人工突触仿真的环保印刷木基蜂蜜门控晶体管

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-15 DOI: 10.1002/aisy.202570006

Douglas Henrique Vieira, Emanuel Carlos, Maíza Silva Ozório, Maria Morais, Elvira Fortunato, Neri Alves, Rodrigo Martins

引用次数: 0

Compensated Current Mirror Neuron Circuits for Linear Charge Integration with Ultralow Static Power in Spiking Neural Networks 脉冲神经网络中超低静功率线性电荷集成的补偿电流镜像神经元电路

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-15 DOI: 10.1002/aisy.202570010

Jonghyuk Park, Sungjoon Kim, Woo Young Choi

引用次数: 0

Solving Data Overlapping Problem Using A Class-Separable Extreme Learning Machine Auto-Encoder 用类可分极限学习机自动编码器解决数据重叠问题

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-11 DOI: 10.1002/aisy.202400255

Ekkarat Boonchieng, Wanchaloem Nadda

引用次数: 0

Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision 基于多任务贝叶斯优化和计算机视觉的熔融沉积建模3D打印机加速多目标标定

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-05 DOI: 10.1002/aisy.202400523

Graig S. Ganitano, Benji Maruyama, Gilbert L. Peterson

{"title":"Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision","authors":"Graig S. Ganitano, Benji Maruyama, Gilbert L. Peterson","doi":"10.1002/aisy.202400523","DOIUrl":"https://doi.org/10.1002/aisy.202400523","url":null,"abstract":"Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three-dimensional (3D) printing, but is time-consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera-based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct 3D printers. The results show that the system significantly outperforms manufacturer calibration across the machine and material configurations, achieving an average improvement of 32.2% in quality and a 31.2% decrease in completion time with respect to a popular benchmark.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D-HAT: A Diatom-Inspired Structure for a Helmet Concept Against Trauma D-HAT：一种硅藻启发结构的头盔概念抗创伤

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-05 DOI: 10.1002/aisy.202400419

Ludovico Musenich, Lorenzo Strozzi, Massimiliano Avalle, Flavia Libonati

引用次数: 0

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis 用于脑肿瘤诊断的混合迁移学习框架

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-05 DOI: 10.1002/aisy.202400495

Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni

{"title":"A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis","authors":"Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni","doi":"10.1002/aisy.202400495","DOIUrl":"https://doi.org/10.1002/aisy.202400495","url":null,"abstract":"Brain tumors are among the most severe health challenges, necessitating early and precise diagnosis for effective treatment planning. This study introduces an optimized hybrid transfer learning (TL) framework for brain tumor classification using magnetic resonance imaging images. The proposed system integrates advanced preprocessing techniques, an ensemble of pretrained deep learning models, and explainable artificial intelligence (XAI) methods to achieve high accuracy and reliability. The methodology enhances image quality through noise reduction and contrast enhancement, facilitating robust feature extraction. The ensemble model combines VGG16 and ResNet152V2 architectures, achieving a classification accuracy of 99.47% on a challenging four-class dataset. Additionally, gradient-weighted class activation mapping and SHapley Additive exPlanations (SHAP)-based XAI techniques provide visual and quantitative insights into model predictions, improving interpretability and clinical trust. This comprehensive framework demonstrates the potential of hybrid TL and XAI in advancing diagnostic accuracy and supporting clinical decision-making for brain tumor detection. The results underscore its applicability in clinical settings, particularly in resource-constrained environments.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network 使用 Critic 神经网络的四旋翼无人飞行器鲁棒强化学习控制框架

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-02-02 DOI: 10.1002/aisy.202400427

Yu Cai, Yefeng Yang, Tao Huang, Boyang Li

引用次数: 0