检索结果-内蒙古大学图书馆

2nd International Conference on Intelligent manufacturing and Robotics, ICIMR 2024

作者： Mahendren, Aniel P.P. Abdul Majeed, Anwar Ab Nasir, Ahmad Fakhri Luo, Yang Aslam, Saad Behjati, Mehran Jasser, Muhammed Basheer Mohd Khairuddin, Ismail School of Engineering and Technology Sunway University Bandar Sunway Selangor Subang Jaya47500 Malaysia Faculty of Computing University Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China Faculty of Manufacturing and Mechatronics Engineering Technology University Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819639489

White Blood Cells (WBCs) are vital components of the immune system, playing a crucial role in defending the body against infections and diseases. Traditionally, the classification of WBCs was performed manually by hematologists, a method fraught with limitations such as subjectivity and time consumption. To address these challenges, this study explores the efficacy of a transfer learning pipeline for WBC classification, leveraging pre-trained models like VGG16, VGG19, and InceptionV3 for feature extraction. The classification models employed include k-Nearest Neighbour (kNN), Support Vector Machine (SVM), and Logistic Regression (LR). The research utilizes Paul Mooney’s white blood cell dataset, which consists of 640 microscopic images of four different WBC types. The dataset is divided into training, validation, and testing sets in a 70:15:15 ratio. The results demonstrate that the VGG16-LR pipeline achieves superior classification accuracy compared to other model combinations tested. This study highlights the potential of integrating transfer learning with robust classifiers to improve the accuracy and efficiency of WBC classification, offering promising implications for medical diagnostics and treatment planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Transfer Learning as Feature Extractors for Lung Cancer Classification: Insights from VGG19 and ResNet152 Pipelines 2nd

Leveraging Transfer Learning as Feature Extractors for Lung ...

引用

2nd International Conference on Intelligent manufacturing and Robotics, ICIMR 2024

作者： Xuan, Darren Soong Kai P.P. Abdul Majeed, Anwar Musa, Rabiu Muazu Luo, Yang Aslam, Saad Ajibade, Samuel-Soma M. Behjati, Mehran Jasser, Muhammed Basheer Abdullah, Muhammad Amirul School of Engineering and Technology Sunway University Bandar Sunway Selangor Subang Jaya47500 Malaysia Centre for Fundamental and Continuing Education Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China Faculty of Manufacturing and Mechatronics Engineering Technology University Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819639489

Lung cancer remains a significant global health challenge, with early detection being crucial for improving patient outcomes. This study explores the use of transfer learning in enhancing the diagnostic accuracy of lung cancer classification from CT images. Specifically, it compares the effectiveness of two transfer learning pipelines utilizing pre-trained models, VGG19 and ResNet152, as feature extractors combined with Logistic Regression (LR) for classification. The results demonstrate that the VGG16 + LR pipeline outperforms the ResNet152 pipeline, achieving superior classification accuracy for both validation and testing datasets at 97%, respectively. This finding underscores the potential of transfer learning in medical imaging, offering a promising approach to improving early detection and treatment strategies for lung cancer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Segmented rotor: a viable alternative to flux-barrier design for small-diameter synchronous reluctance machines

引用

Electrical engineering 2025年 1-17页

作者： Rasid, M. A. H. Ospina, Alejandro Lanfranchi, Vincent Kuptsov, Vladimir Faculty of Manufacturing & Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia Roberval Laboratory Université de Technology de Compiègne Compiègne France MotorXP LLC US Chino USA

The flux-barrier rotor topology, widely recognized as optimal for synchronous reluctance machines (SynRM), has well-established design parametrization guidelines. However, these conventional design rules face challenges when applied to small-diameter applications, particularly where manufacturing constraints limit design options. This study investigates the applicability of conventional flux-barrier design principles in a SynRM with a 25.24-mm rotor diameter and evaluates alternative rotor configurations. A comparative analysis was conducted between two conventionally parameterized flux-barrier designs, an innovative segmented rotor topology, and a theoretical three-barrier design. Finite element analysis revealed that the segmented rotor achieves superior performance with a saliency ratio of 2.07 and torque density of 2.2 × 10⁻5 N·m/mm3, comparable to larger machines reported in recent literature. At 5000 r/min and 50A, this configuration delivers 0.78 N·m torque with 45.75% efficiency and 0.63 power factor. Mechanical analysis confirmed structural integrity under high-speed operation, with maximum stress and deformation values of 6.2 × 105 N/mm2 and 5.47 × 10⁻5 mm respectively. Experimental validation of a prototype largely corroborated simulation results though revealed higher torque ripple attributed to the modular stator construction necessitated by the small diameter. This study demonstrates that conventional flux-barrier design rules become less effective at small scales and presents a segmented rotor topology as a viable alternative, offering comparable performance while simplifying manufacturing processes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Determining the best hatch distances for selective laser melted SiC/Ti6Al4V(ELI) composites of different volume fractions of SiC

Results in Materials

引用

Results in Materials 2025年 25卷

作者： Thamae, Masenate Maringa, Maina Bouwer du Preez, Willie Department of Mechanical and Mechatronics Engineering Faculty of Engineering Built Environment and Information Technology Central University of Technology Free State Bloemfontein 9301 South Africa Centre for Rapid Prototyping and Manufacturing Faculty of Engineering Built Environment and Information Technology Central University of Technology Free State Bloemfontein 9301 South Africa

The goal of this research was to determine the best hatch distances for different SiC volume fractions of selective laser sintered SiC/Ti6Al4V(ELI) composites. The constituents of this composite have different thermal physical properties, which give rise to different melt characteristics of different SiC volume fractions. Non-overlapped tracks lead to debonding of the layers and the production of pores, which degrade the mechanical qualities of the final additively manufactured parts, whereas high overlap rates lead to inefficient use of material and long build times of parts. As a result, different hatch distances should be explored to determine the best overlap rate for each SiC volume fraction. To print single layers in the present work, different laser powers ranging from 100 W to 350 W and scanning speeds ranging from 0.3 m/s to 2.7 m/s were used. The single layers for each SiC volume fraction were built with different hatch distances of 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, and 110 μm. The parameters of laser power, scanning speed and therefore, linear energy density, as well as layer thickness, for each SiC volume fraction were kept constant. A cross-sectional analysis of the printed layers was performed with a scanning electron microscope to investigate the degree of overlapping of adjacent tracks, internal pores, and variation in the depth of penetration into the substrate in a single layer, while a top-surface analysis was performed to investigate surface roughness, surface interconnection of the adjacent tracks, and the formation of surface irregularities. The data collected and analysis carried out here yielded values of best hatch distances at SiC volume fractions ranging from 5 % to 25 %, while no best hatch distances were achieved at an SiC volume fraction of 30 %. © 2025 The Authors

关键词： Hatch distance Mechanical properties Overlap rate Single layers

来源：评论

学校读者我要写书评

暂无评论

Dynamic performance and wear of ceramic aerodynamic tilting-pad journal bearings: Tested and simulated under excessive vibrations

引用

Wear 2025年

作者： Pokorný, Jan Návrat, Tomáš Vajdák, Michal Cabaj, Gabriel Sliwková, Petra Institute of Solid Mechanics Mechatronics and Biomechanics Faculty of Mechanical Engineering Brno University of Technology Technická 2896/2 Brno616 69 Czech Republic Inpraise systems s.r.o. Veveří 2581/102 Brno616 00 Czech Republic Institute of Manufacturing Technology Faculty of Mechanical Engineering Brno University of Technology Technická 2896/2 Brno616 69 Czech Republic

Ceramic aerodynamic tilting-pad journal bearings are emerging as a crucial component in hydrogen-electric mobility, specifically within electric compressors that deliver compressed air to hydrogen fuel cells. These bearings provide an environmentally friendly solution by eliminating the need for oil lubrication, thus preventing contamination of the fuel cells. This study focuses on the wear performance of these bearings under extreme conditions, operated near critical bending speed with elevated vibrations surpassing assembly clearance levels. To assess performance, a custom high-speed test rig was developed, designed without coupling to ensure precise measurements and to prevent the transfer of vibrations from the electric drive to the bearing system. Detailed vibration measurements were conducted under extreme conditions across various rotational speeds, reaching up to 70000 rpm. Alongside this, a dynamic computational model of the aerodynamic bearing was created, incorporating an analysis of rotor vibrations. Experimental results were systematically compared with simulation data to validate the model's accuracy. Key to this study is the wear assessment of the bearing pads under these demanding conditions. The findings reveal that the proposed bearings exhibit reliable performance even in highly demanding scenarios, demonstrating their robustness and potential applicability in other critical and high-stress environments. © 2025 The Authors

关键词： Compressed air

来源：评论

学校读者我要写书评

暂无评论

Utilizing Transfer Learning Models to Classify Absence Defects on Aluminum Plates Using Feature-Based Approaches 2nd

Utilizing Transfer Learning Models to Classify Absence Defec...

引用

2nd International Conference on Intelligent manufacturing and Robotics, ICIMR 2024

作者： Pandian, Kiran Zhen, Lim Weng Majeed, Anwar P. P. Abdul Teh, Sze-Hong Goon, Koon Yin Razman, Mohd Azraai Mohd Faculty of Manufacturing and Mechatronics Engineering Technology University Malaysia Pahang Al-Sultan Abdullah Pahang26600 Malaysia Department of Computing and Information Systems Sunway University Bandar Sunway Selangor Darul Ehsan47500 Malaysia Xi’an Jiaotong-Liverpool University Suzhou215123 China TT Vision Technologies Sdn. Bhd. Plot 106 Hilir Sungai Keluang 5 Bayan Lepas Phase 4 Penang11900 Malaysia

ISBN: (纸本)9789819639489

Screw – the small but important elements used in various industry. Its presence plays a significant role as it securely holds the product in place, preventing loosening or collision with the case. Such occurrences could lead to the displacement of small components or compartments, resulting in product failure. The advent of Industry 4.0 has contributed to reducing labor costs and human errors. This research aims to develop a robust classification model for machine vision detection, specifically for identifying the absence or presence of a screw. The model can be integrated into relevant robotics applications. To collect the customized dataset, a 6-degree-of-freedom (DOF) robot. The collected dataset was then categorized into two groups: absent and present. For the training process, a pretrained dataset called ImageNet was employed to facilitate the training process. Transfer learning techniques were used to extract the features required for different machine learning models. Each machine learning model underwent hyperparameter tuning to achieve the highest classification accuracy. The data was divided into training, validation, and testing sets using a sampling ratio of 60:20:20, respectively, before being fed into the various machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Screws

来源：评论

学校读者我要写书评

暂无评论

Hybrid deep learning model to predict the ultimate tensile strength of friction stir welded joints

引用

engineering Applications of Artificial Intelligence 2025年 154卷

作者： Mishra, Akshansh Miloudi, Abdelkader Sefene, Eyob Messele Arroussi, Chaaben Chekalil, Ismail Muthanna, Bassam Gamal Nasser School of Industrial and Information Engineering Politecnico Di Milano Milan Italy Sidi Bel Abbes Algeria Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology Bahir Dar University P.O. Box 26 Bahir Dar Ethiopia LPTPM Departement of Mechanical Engineering Faculty of Technology Hassiba BenBouali University of Chlef P.O. Box. 151 Hay Salem Chlef02000 Algeria Interdisciplinary Research Center for Intelligent Manufacturing & Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechatronics Engineering Faculty of Engineering and Computing University of Science and Technology Aden Yemen

Accurate prediction of tensile strength in friction stir welded joints is essential to improve the reliability of welded structures. Traditional Artificial Neural Networks (ANNs) often struggle to capture the complex relationships between welding parameters, leading to limited predictive accuracy. To address this challenge, a hybrid model was developed, combining Long Short-Term Memory (LSTM) and dense layers to leverage the ability of LSTM to learn sequential patterns and dependencies between inputs such as rotational speed, feed rate, and tool tilt angle. This hybrid model was compared to a traditional ANN model using a dataset containing welding parameters and tensile strength results. Performance indicators, including correlation coefficient and Mean Square Error (MSE), showed that the LSTM + Dense hybrid model achieved a higher correlation coefficient (0.9803) and lower MSE on the validation sets, indicating improved predictive ability. Analyses of error distribution and convergence of training and validation losses also showed that the hybrid model had higher consistency and reliability with reduced overfitting, even when dealing with outliers. The LSTM + Dense model was shown to significantly outperform the traditional ANN, making it a powerful tool for predicting tensile strength in welding applications with complex and nonlinear data. Although the accuracy and reliability of the proposed hybrid model have improved, limitations remain, including the need for larger datasets to enhance generalization and potential challenges in real-time implementation. © 2025 Elsevier Ltd

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Intrinsic magnetic field sensitivity in coinage transition metal-based nanostructures (Ag, Au and Cu)

引用

Materials Today Chemistry 2025年 46卷

作者： Nurul Akmal Che Lah Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah 26600 Pekan Pahang Malaysia

The magnetic properties of coinage metal nanostructures (Ag, Au, Cu) are profoundly influenced by magnetic interactions within nanohybrids or nanocomposites. These interactions, particularly magnetic dipolar interactions at interfaces, lead to deviations from diamagnetic to paramagnetic or ferromagnetic behaviour. The key to achieving this condition lies in the relativistic interaction between an electron's orbital motion and its spin. Despite this, the characteristics of orbital angular momentum (OAM) in coinage nanostructures remain underexplored. This review elucidates the mechanism of OAM at the interface of coinage nanostructures, based on sp orbital hybridisation, which ultimately leads to the electric polarisation of surface states even in the absence of spin-orbit coupling. These discoveries have catalysed the rise of a rapidly expanding field of orbitronics, which leverages OAM to manipulate magnetic device-based coinage nanostructures. Additionally, recent developments in fundamental orbitronics and their implications for related behaviours are reviewed. Lastly, the future avenues of research concerning magnetically influenced OAM are outlined.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Estimation of Iron Losses in a SynRM with Segmented Rotor 1

Estimation of Iron Losses in a SynRM with Segmented Rotor

引用

1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Rasid, M.A.H. Universiti Malaysia Pahang Faculty of Manufacturing and Mechatronics Engineering Technology Malaysia

ISBN: (纸本)9798350398069

Besides the copper losses and mechanical losses, iron losses are inevitably reducing the efficiency of an ac machine. Despite being generally agreed to be much smaller in proportion in comparison to copper losses at lower speed, it is nonetheless necessary to be estimated before concluding that it could eventually be neglected for further studies like thermal analysis. In this study, the iron losses in a 180W SynRM machine with segmented rotor was estimated using Bertotti iron losses model across its operating area. The peak flux density across the laminated core was determined using FE analysis where the harmonics are neglected in this preliminary study. The hysteresis and excess losses' coefficient was determined by curve-fitting the Epstein data of the material which is a M330-50A lamination. The results show a maximum iron losses of only 4.9W across the operating area of the machine, occurring at the operating point of (torque;speed) = (0.21N.m;6800rpm). The Eddy current losses makes the biggest part of it at 2.8W. © 2022 IEEE.

关键词： Thermoanalysis

来源：评论

学校读者我要写书评

暂无评论

A Simulation of Insertion and Retention Forces in Cantilever Hook Snap-Fit Joint

A Simulation of Insertion and Retention Forces in Cantilever...

引用

2022 engineering technology International Conference, ETIC 2022

作者： Manan, Siti Sarah Abdul Chien, Law Ping Zahid, Muhammed Nafis Osman Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9781839537820

There are numerous methods for joining plastic parts together, including adhesive bonding, mechanical fastening and snap-fit. Snap-fit is used in this study to assemble plastic parts because it is an effective, cost-effective and fast joining technique. When it comes to snap-fits, there are two options: separable and inseparable. Separable indicates that the pieces may be disassembled effectively without breaking, whereas inseparable means that the plastic parts are permanently *** study focuses on cantilever snap-fit since it is widely utilized in the automobile, aerospace and other industries. There are numerous parameters that influence the functionality of snap-fits, particularly the forces of insertion and retention. The parameters are the feature thickness (Tb), beam length (Lb), beam width (Wb), base radius (Rb), mounting (α) and dismounting angle (β). The increase of insertion and retention angle is believed to increase the forces required to joint and disjoint the cantilever hook snap-fit. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Finite element method

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：