检索结果-内蒙古大学图书馆

CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Experimental investigation of mechanical properties and multi-objective optimization of electronic, glass, and ceramic waste–mixed concrete

引用

Environmental science and Pollution Research 2024年第46期31卷 57158-57176页

作者： Mageswari, Deivasigamani Uma Kareemullah, Hakim Jithesh, Korothan Boopathi, Sampath Rachel, Paul Manoharan Premkumar Priya Ramkumar, Mathiyalagan Siva Department of Science and Humanities RMK Engineering College Thiruvallur district Nadu Tamil601206 India Department of Electronics and Instrumentation Engineering B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Mahatma Gandhi College Kerala Iritty Kannur670703 India Department of Mechanical Engineering Muthayammal Engineering College Tamilnadu Namakkal637 408 India Department of Civil Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai602107 India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore641021 India

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete. Taguchi analysis has been applied to predict the optimum composition of waste mixing percentages. The Multi-Objective Optimization Ratio Analysis (MOORA) techniques are applied to estimate the optimum composition of mixing wastes for maximizing the CS, STS, and FS of concrete. It was observed that 10 wt.% of EW, 15 wt.% of GW, and 30 wt.% of CW are predicted as the optimal mixing combinations to obtain a maximum compressive strength of 48.763 MPa, a split tensile strength of 4.178 MPa, and a flexural strength of 7.737 MPa, respectively. Finally, the predicted optimum waste-mixed weight percentages were used to examine the microstructure and various elements in the concrete using SEM and XRD analysis. When compared to conventional concrete, the optimum waste-mixed concrete has improved its compressive strength (38.453%), split tensile strength (41.149%), and flexural strength (36.215%). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Compressive strength

来源：评论

学校读者我要写书评

暂无评论

State-of-Charge (SOC) Balancing and Temperature Assessment of Lithium-Ion Battery with Internet of Things (IoT) Technology 1

State-of-Charge (SOC) Balancing and Temperature Assessment o...

引用

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama Arjun MKSSS's Cummins College of Engineering for Women Department of Mechanical Engineering Pune52 India Girinagar Department of Computer Science and Enginering Pune25 India

ISBN: (纸本)9798350355611

Electric Vehicles (EV's) are now playing crucial role in economical development of any country and adapted as a novel mode of transportation over the conventional Internal Combustion (IC) engines. With advancement of technology distinct types of batteries and their applications for energy storage and main source of energy for EV are discussed by many researchers. State-of-the-art technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Big data analytics technology are reviewed for the possible application to improve the performance of Lithiumion batteries. This paper presents application of Adaptive weighted multi-sensor data fusion algorithm for recording the voltage levels of individual cells of lithium-ion batteries along with interior temperature measurement of lithium-ion power pack, integrated with IoT technology. The lithium-ion battery power pack of 48V is developed in the laboratory and Wireless communication based on 4G IoT is interface with android phone that displays the current State of Charge (SoC), State of Health (SoH) energy balance of battery pack. The voltage of individual cells connected in series is recorded on BMS using CAN protocol along with the interior temperature of Lithiumion power pack. © 2025 IEEE.

关键词： State of charge

来源：评论

学校读者我要写书评

暂无评论

Eyecheck Diabscan: A Productive Approach for Few-Shot Excellence in Retinopathy Diagnosis 2

Eyecheck Diabscan: A Productive Approach for Few-Shot Excell...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Krishnaveni, E. Rao, Sinde Jayavanth Reddy, Kumbala Pradeep Reddy, G Vinoda Reddy, Avala Raji CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India Hyderabad India CMR Technical Campus Department of Mechanical Engineering Hyderabad India

ISBN: (纸本)9798331519056

The design and implementation of a few-shot learning-based diabetic retinopathy diagnostic system is the focus of this research project. According to the NEI (National Eye Institute), The United States report 2021, diabetic retinal degeneration is one of the most common causes of vision loss in people aged 20 to 74. Early recognition and treatment are therefore deemed essential. The National Eye Institute is a resource-based informational center about diseases and eye health. The primary objective of this endeavor is to improve the few-shot learning technique's capacity for early-stage diabetic eye disease detection. This suggested model may operate using a restricted amount of data, i.e., less data processing by enhancing data optimizing, and localize the necessary discriminative regions. Additionally, it enables the model to recognize and follow unique, novel patterns among the inputs. This approach is going to operate along with an assessment of proposals pertaining to patients' geographic and financial obstacles. Diabetic Retinopathy Identification is applied in the medical and healthcare sectors. This enables us to manage progression risk, forecast therapy results, and lessen adverse consequences and acute implications. © 2024 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Rational Design of Atomically Immobilized Fe-N4 Sites via Acid-Amine Chemistry:Leveraging Metal Ionic Assistance for Precise Morphology Control

引用

SusMat 2025年第1期5卷 101-110页

作者： Arunprasath Sathyaseelan Vijayakumar Elumalai Muthukumar Perumalsamy Swapnil Shital Nardekar Arul Saravanan Raaju Sundhar S.C.Karthikeyan Dong Jin Yoo Sang-Jae Kim Nanomaterials&System Laboratory Major of Mechatronics EngineeringFaculty of Applied Energy SystemJeju National UniversityJejuSouth Korea Department of Energy Storage/Conversion Engineering(BK21 FOUR)for Graduate School Hydrogen and Fuel Cell Research CenterJeonbuk National UniversityJeonjuJeollabuk-doSouth Korea Nanomaterials&System Lab Major of Mechanical System EngineeringCollege of EngineeringJeju National UniversityJejuSouth Korea Green Hydrogen Glocal Leading Research Center(gH2-RC) Jeju National UniversityJejuSouth Korea Research Institute of New Energy Industry(RINEI) Jeju National UniversityJejuSouth Korea

Tailoring atomically dispersed single-atom catalyst(Fe-SAC)holding well-defined coordination structure(Fe-N_(4))along with precise control over morphology is a critical ***,we propose a novel acid-amine coupling reaction between metalchelated ionic liquid([1-(3-aminopropyl)3-methylimidazolium tetrachloroferrate(III)][APIM]+[FeCl_(4)]−)and carboxylic groups of carbon allotropes(C=GO,CNT,CNF,and vX-72)to precisely immobilize Fe-N_(x) *** of designed single-atom catalyst(IL-Fe-SAC-C),Fe-N_(4) on graphene(IL-Fe-SAC-Gr)delivered superior oxygen reduction reaction(ORR)activity by holding higher halfwave potential of 0.882 V versus RHE in 1.0 M KOH akin to Pt/C(0.878 V ***)and surpassing recently reported M–N–C catalysts with superior ethanol *** to higher graphitization degree,enhanced surface characteristics,and richness in high-density Fe-N_(4) sites of IL-Fe-SAC-Gr confirmed by XPS,X-ray absorption spectroscopy(XAS),and HAADF *** IL-Fe-SAC-Gr catalyst-coated cathode on testing in flexible direct ethanol fuel cells(f-DEFC)delivered higher peak power density of 18mWcm^(−2) by outperforming Pt/C-based cathode by 3.5 times as a result of excellent ethanol ***,the developed f-DEFCsuccessfully powered the Internet of Things(IoT)-based health monitoring *** method demonstrates novel strategy to tailor high-performance single-atom(Fe-SAC-C)sites on desired morphologies to meet specific application requirements with feasibility and versatility.

关键词： carbon allotropes flexible direct ethanol fuel cells IoT-based health monitoring systems oxygen reduction reaction(ORR) single-atom catalyst

来源：评论

学校读者我要写书评

暂无评论

Obstacle Avoidance for Omnidirectional Mobile Robot Using SLAM

引用

Journal of engineering and science in Medical Diagnostics and Therapy 2023年第1期6卷 011003页

作者： Nandikolla, Vidya K. Ghoslin, Bryan Mechanical Engineering Department College of Engineering and Computer Science California State University Northridge 18111 Nordhoff Street NorthridgeCA91330 United States

In the field of mobile robotics, simultaneous localization and mapping (SLAM) is an algorithmic approach to the computational problem of creating and updating a map of an environment while simultaneously keeping track of where the robot is within the environment. Applications of a SLAM algorithm are important for autonomous mobile systems to traverse an environment while avoiding obstacles and accurately achieving designated goal destinations. This paper presents the design of a SLAM-driven controller for a semi-autonomous omnidirectional mobile robot. Input to the system comes from a brain computer interface in the form of simple driving commands or a goal destination as decided by the user. Due to latency issues of reacting and responding in real-time, the system must safely navigate following the last given commands until it runs out of free space, reaches a goal designation, or receives a new command. The robotic system utilizes a three-wheeled robot kit with an upgraded sensor system. The Intel realsense depth camera D435 and two lidar sensors are utilized to construct a full 360 deg field of view. The SLAM algorithm and system controllers are developed using the robot operating system (ROS). The controllers are developed and tested within Gazebo, which is a physics simulation engine utilized for rapid prototyping. Testing was performed to validate controller performance when given varying commands as well as performing long distance path planning and obstacle avoidance. The system was often capable of achieving its goal destinations with a small error of around 3% or less though the error was found to increase with the more commands the system processed. Copyright © 2023 by ASME.

关键词： Robot Operating system

来源：评论

学校读者我要写书评

暂无评论

IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Precision Smoke Detection system with Counter Propagation Neural Network and Electronic Olfactory 5th

Precision Smoke Detection System with Counter Propagation Ne...

引用

5th International Conference on Recent Advancements in mechanical engineering, ICRAME 2024

作者： Vivekanandan, N. Rajeswari, K. Salve, Sanjay P. Kanna, N. V. Yuvraj Department of Mechanical Engineering Pimpri Chinchwad College of Engineering Pune India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Data Science for Business University of Stirling Stirling United Kingdom

ISBN: (纸本)9789819775347

The Electronic Nose, a compact electro-mechanical device, exhibits the capability to discern an extensive array of odors, encompassing substances such as food, human breath, and hazardous toxins. In this study, the Electronic Nose is employed to classify aromas emanating from three distinct burning materials: joss stick, mosquito coil, and instant benzoin. Utilizing the counter propagation neural network, the Electronic Nose is trained and deployed for the classification of these burning odors. The primary objective of this project is the development of an intelligent smoke detector, ensuring the precise identification of harmful burning materials. This proactive approach aims to detect potential fire hazards promptly, preventing untoward incidents effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic nose

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas

引用

International Journal of Hydrogen Energy 2025年 141卷 585-597页

作者： S, Priya Feenita, C. Goel, Uday T, Manoranjitham Duraisamy, Boopathi Subramanian, Balaji Ganeshan, Kavitha Josephin JS, Femilda Albeshr, Mohammed F. Pugazhendhi, Arivalagan Varuvel, Edwin Geo Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Automobile Engineering College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Mechanical Engineering College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Science and Humanities Agni College of Technology Thazhambur Chennai600130 India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Zoology College of Sciences King Saud University P.O. Box. 2455 Riyadh11451 Saudi Arabia Research and Development Office Asia University Taichung City Taiwan Department of Mechanical Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Autoronics Institute of Automobile Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India

Internal combustion engines (ICEs) have long been essential in both the transportation and industrial sectors, providing primary power for vehicles, ships, and machines globally. Optimising the efficiency of ICEs is vital for decreasing their environmental impart, as increased fuel efficiency and lower emissions play a significant role in mitigating the effects of climate change as well as improving air quality. This study employed 15 regression algorithms and machine learning approaches to analyse and anticipate the performance parameters of ICEs that run on hydrogen-diesel in dual fuel mode. The input parameters include engine torque, speed, hydrogen flow rate, brake power and diesel energy share to hydrogen supply and the output parameters are brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and actual air intake. The model's performance is evaluated using five different performance metrics. Among the studied algorithms, the RANSAC Regressor demonstrated exceptional predictive capability, reaching an R-squared value of 0.999, a mean squared error (MSE) of 0.0064, a root mean square error (RMSE) of 0.08, and a mean absolute error (MAE) of 0.057. These outcomes show the algorithm's accuracy and precision in capturing the complicated data of engine system. The equivalency ratio, volumetric efficiency, brake thermal efficiency, brake specific fuel consumption, and actual air intake are among the critical performance outputs that are optimised by utilizing key input parameters like engine load, rotational speed, hydrogen flow rate, brake power, and the diesel fuel energy share. This study highlights the significant potential of machine learning in optimising ICE performance, offering a reliable alternative to traditional experimental analysis by reducing both risk and economic costs. The research findings also support the paradigm shift towards intelligent and sustainable energy systems by compellingly advocating for the inclusion of da

关键词： Air intakes

来源：评论

学校读者我要写书评

暂无评论

The influence of temperature on ultrasonic signals in online measurement of oil film thickness

引用

Friction 2025年第5期13卷 171-185页

作者： Yaping Jia Pan Dou Peiping Yang Tonghai Wu Shuo Wang Yaguo Lei Min Yu Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK Dongfang Electric Machinery Co. Ltd.Deyang 618000China

Ultrasonic reflection provides a real-time way to monitor oil film thickness in a running machine with a nondestructive ***,the influence mechanism of temperature on reference signals has not been clarified thus far,which hinders the precise measurement of oil film *** on a common three-layer structure of sensor-adhesive-steel,a global propagation model is constructed to investigate variations in the reference signal with *** finite element simulations,distinct influence mechanisms are revealed for different *** piezoelectric sensors and the adhesive layer,temperature may induce amplitude attenuation and wave extensions in the reference *** the steel component,only an overall time shift is observed in the reference ***,a compensation model is established and validated through temperature-controlled *** the effective bandwidth,the compensation model achieves a relative error of±2%and an absolute error of±0.02 radians for the amplitude and phase of the reference waves.

关键词： oil film thickness reference signal finite element simulation temperature compensation

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：