检索结果-内蒙古大学图书馆

3rd FORTEI-International Conference on Electrical Engineering, FORTEI-ICEE 2024

作者： Ihsan, Muhammad Putra, Karisma Trinanda Mustar, Muhamad Yusvin Setyawan, Aufa Faiz Chu, Hsueh-Ting Chen, Hsing-Chung Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Computer Science and Information Engineering Asia University Taichung Taiwan

ISBN: (纸本)9798331542207

In post-disaster search and rescue (SAR) missions, it is crucial for robots to distinguish between actual victims and dummy objects, despite their similar characteristics. Edge video analytics have demonstrated exceptional performance in such missions, however, the trade-off between high-performing deep learning models and computational complexity often results in inadequate computational efficiency for SAR robotics. This study tackles this challenge by deploying a deep learning (DL) algorithm, i.e., MobileNet Single Shot Detector (SSD), specifically tailored for embedded systems, to perform object detection and classification tasks on lab-scale SAR robots. The proposed algorithm is developed in Python and implemented on a low-powered edge computing device. The effectiveness of the proposed algorithm in object localization is highlighted by its mAP score of 0.6247 and an average IoU of 0.9036. Experimental tests using a confusion matrix showed that the model accurately predicted 42 dummy objects and 43 victim dolls. The model achieved an overall accuracy of 89.47%, with a precision of 1.0, a recall of 0.8947, and an F1 Score of 0.9444. Real-time testing confirmed the model’s robust performance, maintaining high accuracy and a stable frame rate of 47 FPS. These findings corroborate the efficacy of the MobileNet SSD algorithm in reliably detecting and differentiating objects within embedded SAR robotic operations. © 2024 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval 15

Interpreting Deep Convolutional Neural Networks for Medical ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasture, Shreyas Maurya, Sudhanshu Verma, Rohan Yadav, Ajay Kumar Shukla, Amit Nagpur Campus Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India United Institute of Management Department of Computer Application Prayagraj India Centre of Artificial Intelligence & Robotics Mandi India

ISBN: (纸本)9798350370249

The robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. This interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people's lives are at risk. The study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. The models for proposed models succeeded in the clinical tasks of medical diagnosis. They reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. These models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. The suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%. © 2024 IEEE.

关键词： Active Learning Deep Neural Networks Interpretation Synthesis Medical Diagnosis Network Traffic Forecasting Saliency Map Aggregation and Saliency Maps

来源：评论

学校读者我要写书评

暂无评论

Application of 3D Technology in Film and Television Animation System

Application of 3D Technology in Film and Television Animatio...

引用

作者： Zhang, Xiaoye Qiu, Sisi Altrjman, Chadi School of Comics Jilin Animation Institute Changchun130000 China Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin10 Turkey

In order to understand the application of 3D technology in film and television animation system, this paper will carry out relevant research. The study first introduces the basic concept and classification of 3D animation technology, then analyzes the key points of 3D technology application in each stage of film and television animation design, and discusses the application performance of 3D technology. Through research, 3D technology plays an important role in modern film and television animation system, and giving full play to the role of this technology can effectively improve the quality of animation and bring visual feast to people. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Animation

来源：评论

学校读者我要写书评

暂无评论

A novel piezoelectric motor design and its controller

引用

Materials Today: Proceedings 2023年 92卷 38-47页

作者： Veeresha R.K. Bhat K. Muralidhara Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Department of Robotics and Artificial Intelligence Udupi 574110 India Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Department of Mechanical Engineering Udupi 574110 India

The paper aims to develop a novel piezoelectric motor and its controller; here the motor shaft needs to be rotated fully by 360° using two sets of Parallelly Prestressed Actuators (PPA) and one set of lock release actuators (LR). The first set of PPA drives the driving element and produces a displacement of 39 μm for an applied input voltage of 150 V & rotates by an angle of 0.145°, thus taking 2483 steps to complete one complete rotation. The second set of PPA is deployed to transfer the rotation caused by the first set of PPA to the output shaft. The lock release actuator will arrest the rotation that occurred and avoids the shaft from moving back to its original position till the next cycle begins. The above set of operations is continued to obtain continuous rotation of the piezoelectric motor shaft. There is a FAP used to hold the shaft during the power-off condition. The mathematical controller is developed to obtain both forward and reverse rotation of the shaft by applying an input voltage, further the developed model is simulated using MATLAB Simulink software to illustrate the working of a piezoelectric motor. © 2023 Elsevier Ltd. All rights reserved.

关键词： Flexuraly Amplified Piezoactuator (FAP) Lock-Release actuator (LR) Parallelly Prestressed Actuator (PPA) Piezo-stack actuator Piezoelectric Motor

来源：评论

学校读者我要写书评

暂无评论

GROUNDING GRAPH NETWORK SIMULATORS USING PHYSICAL SENSOR OBSERVATIONS 11

GROUNDING GRAPH NETWORK SIMULATORS USING PHYSICAL SENSOR OBS...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Linkerhägner, Jonas Freymuth, Niklas Scheikl, Paul Maria Mathis-Ullrich, Franziska Neumann, Gerhard Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-University Erlangen-Nürnberg Erlangen Germany

Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional simulators. Yet, the resulting predictors are confined to learning from data generated by existing mesh-based simulators and thus cannot include real world sensory information such as point cloud data. As these predictors have to simulate complex physical systems from only an initial state, they exhibit a high error accumulation for long-term predictions. In this work, we integrate sensory information to ground Graph Network Simulators on real world observations. In particular, we predict the mesh state of deformable objects by utilizing point cloud data. The resulting model allows for accurate predictions over longer time horizons, even under uncertainties in the simulation, such as unknown material properties. Since point clouds are usually not available for every time step, especially in online settings, we employ an imputation-based model. The model can make use of such additional information only when provided, and resorts to a standard Graph Network Simulator, otherwise. We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Biodegradable and Sustainable Materials:Exploring the Synergy of Polylactic Acid Blends with Sea Shells

引用

Journal of Renewable Materials 2024年第12期12卷 2115-2134页

作者： Prashanth K P Rudresh M Venkatesh N Poornima Gubbi Shivarathri Shwetha Rajappa Department of Mechanical Engineering Acharya Institute of TechnologyBangalore560107India Department of Robotics and Artificial Intelligence Dayananda Sagar College of EngineeringBangalore560111India Department of Mechanical Engineering University of Visvesvaraya College of EngineeringBangalore560001India Department of Chemistry New Horizon College of EngineeringBangalore560103India Department of Sciences and Humanities Christ(Deemed to be University)Bangalore560074India

This study explores the mechanical properties of a novel composite material,blending polylactic acid(PLA)with sea shells,through a comprehensive tensile test *** tensile test results offer valuable insights into the material’s behavior under axial loading,shedding light on its strength,stiffness,and deformation *** results suggest that the incorporation of sea shells decrease the tensile strength of 14.55%and increase the modulus of 27.44%for 15 wt%SSP(sea shell powder)into PLA,emphasizing the reinforcing potential of the mineral-rich sea shell ***,a potential trade-off between decreased strength and reduced ductility is noted,highlighting the need for a delicate balance in material *** study underscores the importance of uniform sea shell particle distribution within the PLA matrix for consistent mechanical *** results offer a basis for additional PLA-sea shell blend optimization,directing future efforts to balance strength,flexibility,and other critical attributes for a range of applications,including biomedical devices and sustainable *** investigation opens the door to more sustainable and mechanically strong materials in the field of additive manufacturing by demonstrating the positive synergy between nature-inspired materials and cutting-edge testing techniques.

关键词： PLA sea shell additive manufacturing tensile flexural biodegradability

来源：评论

学校读者我要写书评

暂无评论

Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid El-Shafai Randa Ali Ahmed Sedik Taha El-Sayed Taha Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart ***-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare *** AI(Artiﬁcial intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to *** are several modalities of imaging performed on the brain for the pur-pose of tumor *** paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)*** proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal *** dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:*** main contribution of this paper is introdu-cing an optimum deep learning structure of CNN *** simulation results are obtained for 50 epochs in the training *** simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

来源：评论

学校读者我要写书评

暂无评论

Advanced Endoscopy Imaging with Automatic Feedback 27th

Advanced Endoscopy Imaging with Automatic Feedback

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Bappy, D.M. Kang, Donghwa Lee, Jinkyu Lee, Youngmoon Koo, Minsuk Baek, Hyeongboo Department of Computer Science and Engineering Incheon National University Incheon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Department of Robotics Hanyang University Ansan Korea Republic of Department of Artificial Intelligence University of Seoul Seoul Korea Republic of

ISBN: (纸本)9783031781940

As we move towards a future where minimally invasive methods become the norm for surgeries and diagnostic procedures, it is increasingly vital to improve our strategies for viewing the organs and complex structures within our bodies. Image stitching presents an enticing solution, expanding our field of view by seamlessly weaving together a sequence of images. While existing stitching techniques do lean on the capabilities of endoscopy imaging, they, unfortunately, overlook the critical need for automated feedback when grappling with the complexities and challenges innate to endoscopy imaging. these methods struggle to stand firm against deformations and regions with low texture. In this paper, we introduce a robust endoscopic image-stitching algorithm designed to thrive in adversity. Its unique resilience to deformations and low-texture regions is reinforced by the inclusion of a radial basis function weighting that is paired harmoniously with location-dependent homography based on the corresponding locations of the strong features extracted by affine shape-adapted Hessian-Laplace detector. Crucially, this algorithm is steered by a sophisticated automatic feedback mechanism. This feedback system makes astute evaluations based on an image quality metric and the structural comparison between the sequences of endoscopy images. We have thoroughly validated the efficacy of our new approach using two public datasets, namely EndoSLAM and EndoAbS, under demanding conditions. The results eloquently illustrate the superior benefits of our technique. Our proposed method surpasses commonly employed techniques, delivering superior performance in quantitative metrics, including precision at 30.07%, recall at 114.89%, F1-score at 84.62%, and TRE at 46.07%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Specular Region Detection and Covariant Feature Extraction 27th

Specular Region Detection and Covariant Feature Extraction

引用

27th International Conference on Pattern Recognition, ICPR 2024

ISBN: (纸本)9783031781971

Endoscopy images pose a distinct set of challenges, such as specularity, uniformity, and deformation, which can obstruct surgeons’ observations and decision-making processes. These hurdles complicate feature extraction and may ultimately lead to the failure of a surgical navigation system. To tackle these obstacles, we introduce a Modified Maximal Stable Extremal Region (MMSER) detector that specifically targets fine specular regions. Subsequently, we ingeniously fuse the capabilities of MMSER and saturation region properties to precisely identify specular regions within endoscopy images. Furthermore, our approach harnesses the shared properties of covariant features and endoscopic imaging to detect features in intricate regions, such as low-textured and deformed areas. Surpassing contemporary methods, our proposed technique demonstrates remarkable performance when evaluated on the available CVC-ClinicSpec datasets. Our method has shown improvements in accuracy, recall, f1-score, and Jaccard index by 0.21%,25.42%,7.77% snd 11.77%, respectively, when compared to recent techniques. Owing to its exceptional ability to accurately pinpoint specular regions and extract features from complex areas, our approach holds the potential to significantly advance surgical navigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Internet of Things (IoT) and Machine Learning (ML) in Cyber-Security 2

Internet of Things (IoT) and Machine Learning (ML) in Cyber-...

引用

2nd IEEE International Conference on artificial intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Abdelrahim, Malik Al-Turjman, Fadi Research Center for Ai and IoT Near East University Artificial Intelligence Engineering Department Nicosia Cyprus Artificial Intelligence Engineering Department Ai and Robotics Institute Near East University Mersin10 Turkey

ISBN: (纸本)9798350334104

With the technological advancement, the world is going through, at such a high pace, new challenges arise that might require new approaches. ML is, without a doubt, on the lead of technology, as it is evolving with minimal human intervention. However, new models of ML are crucial to preventing hackers from manipulating data. It is true that with new models, new gates for attacks are opened, which is why ML security must follow the same pace in evolving. In this paper, a brief introduction to ML and ML security will be discussed to give an insight on the topic, before going through a more specific point;the challenges faced by ML security. By introducing these terms, the reader will come to grasp the importance of ML security, and the huge role it plays when it comes to protecting data against hackers. However, this by no means indicates that it is flawless;on the contrary, there are flaws in the methods used in our present day, which is another topic this paper will give an insight on. Our main focus in this paper will be supervised learning due to its application in security. Algorithms used in ML models is the main gate that hackers use to breach the system, for this precise reason, engineers and experts are challenged to come with new algorithms, as we will conclude this work. © 2022 IEEE.

关键词： Machine learning

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：