检索结果-内蒙古大学图书馆

7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Zhou, Zhaoxuan Cao, Yan School of Mechanical and Electrical Engineering Xi'an Technological University Xi’an710021 China School of Computer Science and Engineering Xi'an Technological University Xi’an710021 China

The traditional welding seam inspection efficiency is low, the model possesses a significant amount of computational parameters, and it is not suitable for the application of small and medium-sized welding seam recognition machines. In view of the wide application and remarkable effect of deep learning in the domain of machine vision, a welding seam defect detection model designed by G-Efficientnet-CA neural network was proposed and optimized. EfficientNet was employed as the core architecture for extracting features to greatly reduce calculation parameters and model volume. The CA attention mechanism is incorporated to enhance the model's capacity for focusing attention on the weld image, thereby improving its ability to discern and analyze relevant features, and the accuracy is improved. The Generalized Intersection over Union (GIoU) loss function is changed from the original loss function to optimize the calculation of the coincidence degree between the real frame and the predicted frame. The efficient K-Means++ clustering algorithm is utilized to calculate the initial anchor frame which is more suitable for different weld data sets. The experimental outcomes demonstrate that the optimized model, when compared to the YOLOv3 model, exhibits superior effectiveness in detecting the VOC2007 dataset, as evidenced by a notable 12.7% increase in the average precision (mAP) and a significant reduction of 88% in the number of parameters. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Design and Fabrication of Bengali Braille Embosser Using Solenoid 9

Design and Fabrication of Bengali Braille Embosser Using Sol...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Momin, Reshad Ibn Das, Ratan Kumar Raxit, Sourav Muna, Umme Salma Al Miraj, Abdullah University of Southern Denmark-SDU Dept. of Mechanical & Electrical Engineering Sønderborg Denmark Chittagong University of Engineering & Technology Dept. of Mechanical Engineering Chattogram4349 Bangladesh University of New Orleans Department of Computer Science United States American International University Dept. of Computer Science & Engineering Dhaka Bangladesh Chittagong University of Engineering & Technology Dept. of Electrical & Electronic Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798350394474

This paper presents the design and development of a low-cost and user-friendly Bengali braille embosser for the visually impaired population in Bangladesh. To produce braille text, the embosser employs a shifting mechanism and an Embosser head, both controlled by an Arduino microcontroller. A software has also been developed to allow users with basic printer knowledge to operate the embosser. Access to educational resources and reading materials is limited for people with disabilities in Bangladesh, and the availability of Bengali braille writing machines is scarce. The proposed solution aims to address these challenges and improve the learning and communication capabilities of visually impaired individuals. © 2024 IEEE.

关键词： arduino braille embosser portable speed

来源：评论

学校读者我要写书评

暂无评论

CFNet: Cross-Domain Bearing Fault Diagnosis Under Different Operating Conditions 5

CFNet: Cross-Domain Bearing Fault Diagnosis Under Different ...

引用

5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Liu, Zhengyu Sun, Tong Xu, Juan Wu, Tong Wang, Yewei Xu, Rui School of Mechanical engineering Hefei University of Technology Anhui Hefei China School of Computer Science and Information engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798331529192

The methods based on unsupervised domain adaptation are now widely used in the diagnosis of bearing faults. However, the global dependence of features in the feature extraction process is often overlooked. This global dependence is crucial for accurately diagnosing bearing faults, as it can reveal the distribution and variation patterns of fault signals in the overall structure. Therefore, we propose a Centralized Features Network (CFNet) for bearing fault diagnosis. The core of CFNet lies in its Transformer-based feature extractor, which not only captures the local details of fault signals but also preserves the global dependence, thereby achieving comprehensive analysis of fault signals. Furthermore, an Explicit Visual Center Module is proposed to further improve the fusion of long-distance features and local angular regions. Finally, a subdomain adaptation module is also proposed to transform the features of each domain to achieve subclass alignment of the feature space distribution. We have performed experiments on the CWRU and a self-built dataset using the mentioned model. The results demonstrate the effectiveness of our model. © 2024 IEEE.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

Detecting Counterfeit Indian Currency A Comparative Analysis of Machine Learning Algorithms and Image Processing 15

Detecting Counterfeit Indian Currency A Comparative Analysis...

引用

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

作者： Vyshnavi, M.R.V. Varshitha, N.L. Harshith Kumar, Konda V. S. Singh, Tripty Sharma, Mansi Chatterjee, Suman Amrita School of Computing Department of Computer Science Engineering Bengaluru India Dehradun India Kongju National University Department of Mechanical Engineering Cheonan Korea Republic of

ISBN: (纸本)9798350370249

The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. This paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. The following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work;Image capture This is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. The objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake Indian currency notes. © 2024 IEEE.

关键词： Counterfeit Detection Gradient Descent Image Processing Machine Learning Mathematical Formulation Naïve Bayes Neural Networks Support Vector Classifier

来源：评论

学校读者我要写书评

暂无评论

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Po...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Keyi Luo, Yeqi Yang, Weidong Xu, Jingyi Li, Zhijun Chen, Wen-Ming Fei, Ben School of Computer Science Fudan University China School of Mechanical Engineering Tongji University China Academy for Engineering and Technology Fudan University China

ISBN: (纸本)9798350368741

Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face challenges such as limited data diversity and inadequate augmentation for effective feature learning. To address these challenges, we propose GS-PT, which integrates 3D Gaussian Splatting (3DGS) into point cloud self-supervised learning for the first time. Our pipeline utilizes transformers as the backbone for self-supervised pre-training and introduces novel contrastive learning tasks through 3DGS. Specifically, the transformers aim to reconstruct the masked point cloud. 3DGS utilizes multi-view rendered images as input to generate enhanced point cloud distributions and novel view images, facilitating data augmentation and cross-modal contrastive learning. Additionally, we incorporate features from depth maps. By optimizing these tasks collectively, our method enriches the tri-modal self-supervised learning process, enabling the model to leverage the correlation across 3D point clouds and 2D images from various modalities. We freeze the encoder after pre-training and test the model's performance on multiple downstream tasks. Experimental results indicate that GS-PT outperforms the off-the-shelf self-supervised learning methods on various downstream tasks including 3D object classification, real-world classifications, and few-shot learning and segmentation. Project page: https://***/Luoyeqi1/***. © 2025 IEEE.

关键词： 3D Gaussian Splatting 3D understanding point clouds pre-training self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Advanced dairy cow monitoring: enhanced detection with precision 3D tracking

引用

Multimedia Tools and Applications 2025年第18期84卷 19117-19146页

作者： Wang, Ranran Li, Yingxiu Yue, Peng Yuan, Chunhong Tian, Fuyang Lu, Xin College of Mechanical and Electronic Engineering Shandong Agricultural University Shandong Tai’an271018 China Department of Computer Science Faculty of Engineering University of Sheffield SheffieldS1 4DP United Kingdom Faculty of Agriculture Iwate University Iwate Morioka020-8550 Japan Faculty of Science and Engineering Iwate University Iwate Morioka020-8550 Japan

Ensuring the welfare of dairy cows requires precise monitoring of their daily exercise to evaluate their physical health. This necessitates innovative methods beyond traditional motion sensors. We present a novel method that integrates an enhanced YOLOv5s object detection model with the DeepSORT multi-object tracking algorithm to meticulously track dairy cow movements, providing holistic information about their health. Our research started with the establishment of a dedicated dataset tailored for cow detection. We then segmented the detection scope to focus on specific regions of interest. Within the modified YOLOv5s model, we replaced the standard CSPDarknet53 backbone with DenseNet to achieve deep separable convolution and feature reorganization modules, leading to reduced parameters, augmented feature expression, and better information flow. In particular, the SPD-Conv module was incorporated to retain intricate details, essential for detecting smaller and low-resolution targets. The transition from Generalized Intersection over Union (GIoU) Loss to Complete Intersection over Union (CIoU) loss improved detection accuracy and sped up model convergence. Our clustering approach, based on the elbow rule, optimized K-means clustering, enhancing speed and precision. For multi-object tracking, the DeepSORT model was tailored to cater to varying cow sizes, and we chose an algorithm to associate appearance information. We converted pixel data into real-world distances, providing exact 3D cow movement metrics. Experimental validation confirmed the efficacy of our approach. Our enhanced model surpassed the original YOLOv5s in performance by 11.1% for accuracy (97.4%), 9.6% for recall (97.8%), and 11.0% for average accuracy (98.2%). The comprehensive accuracy stood at 92.1% for our model. In conclusion, our innovative methodology offers a non-invasive means to monitor dairy cow exercise, paving the way for advanced health assessment techniques in the dairy sector. © The Aut

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

HCCL: Hierarchical Channels and Contrastive Learning for Drug-Gene Multi-Relation Prediction

HCCL: Hierarchical Channels and Contrastive Learning for Dru...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shang, Yizhe Chen, Jianrui Lei, Xiujuan Wu, Fang-Xiang Shaanxi Normal University School of Computer Science Xi'an China University of Saskatchewan Department of Mechanical Engineering Division of Biomedical Engineering Department of Computer Science SaskatoonS7N 5A9 Canada

ISBN: (纸本)9798350386226

Drug-gene interaction plays a crucial role in drug discovery and personalized medicine. Although existing methods have improved the accuracy of exploring multiple relationships between drugs and genes, there are still some limitations, such as susceptibility to data sparsity and poor generalization, which pose some challenges for practical applications. To address these challenges, we propose a novel Hierarchical Channels and Contrastive Learning (HCCL) framework in which drug feature extractor captures structural information of drug molecules from atom and bond channels. After obtaining the initial features of drugs and genes, we employ high-low-order channels to update them, where the low-order channel adopts graph convolutional networks while the high-order channel leverages hypergraph structures for message propagation. Finally, we adopt contrastive learning and inter-channel attention to fuse high-low-order features, which improves the robustness of the model and prevents feature information loss. Experimental results demonstrate the superior performance of HCCL. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

TOWARDS PHYSICALLY TALENTED AERIAL ROBOTS WITH INTELLIGENT SWARM BEHAVIOR THEREOF: AN EFFICIENT CO-DESIGN APPROACH

TOWARDS PHYSICALLY TALENTED AERIAL ROBOTS WITH INTELLIGENT S...

引用

ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： KrisshnaKumar, Prajit Paul, Steve Manjunatha, Hemanth Corra, Mary Esfahani, Ehsan Chowdhury, Souma Department of Mechanical and Aerospace Engineering University at Buffalo BuffaloNY United States Department of Computer Science Engineering University at Buffalo BuffaloNY United States

ISBN: (纸本)9780791888360

The collective performance or capacity of collaborative autonomous systems such as a swarm of robots is jointly influenced by the morphology and the behavior of individual systems in that collective. In that context, this paper explores how morphology impacts the learned tactical behavior of unmanned aerial/ground robots performing reconnaissance and search & rescue. This is achieved by presenting a computationally efficient framework to solve this otherwise challenging problem of jointly optimizing the morphology and tactical behavior of swarm robots. Key novel developments to this end include the use of physical talent metrics and modification of graph reinforcement learning architectures to allow joint learning of the swarm tactical policy and the talent metrics (search speed, flight range, and cruising speed) that constrain mobility and object/victim search capabilities of the aerial robots executing these tactics. Implementation of this co-design approach is supported by advancements to an open-source Pybullet-based swarm simulator that allows the use of variable aerial asset capabilities. The results of the co-design are observed to outperform those of tactics learning with a fixed Pareto design, when compared in terms of mission performance metrics. Significant differences in morphology and learned behavior are also observed by comparing the baseline design and the co-design outcomes. Copyright © 2024 by ASME.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Robust Particle Swarm Optimization Algorithm for Modeling the Effectof Oxides Thermal Properties on AMIG 304L Stainless Steel Welds

引用

computer Modeling in engineering & sciences 2024年第11期141卷 1809-1825页

作者： Rachid Djoudjou Abdeljlil Chihaoui Hedhibi Kamel Touileb Abousoufiane Ouis Sahbi Boubaker Hani Said Abdo Department of Mechanical Engineering College of Engineering in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 655Al-Kharj16273Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Laboratory of Mechanics of Sousse(LMS) National Engineering School of SousseUniversity of SousseSousse4054Tunisia Center of Excellence for Research in Engineering Materials(CEREM) King Saud UniversityP.O.Box 800Al-Riyadh11421Saudi Arabia Department of Metallurgical and Materials Engineering Faculty of Petroleum and Mining EngineeringSuez UniversitySuez43512Egypt

There are several advantages to the MIG(Metal Inert Gas)process,which explains its increased use in variouswelding sectors,such as automotive,marine,and construction.A variant of the MIG process,where the sameequipment is employed except for the deposition of a thin layer of flux before the welding operation,is the AMIG(Activated Metal Inert Gas)*** study focuses on investigating the impact of physical properties ofindividual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can helpdetermine a relationship among weld depth penetration,the aspect ratio,and the input physical properties ofthe *** types of oxides,TiO_(2),SiO_(2),Fe_(2)O_(3),Cr_(2)O_(3),and Mn_(2)O_(3),are tested on butt joint design withoutpreparation of the edges.A robust algorithm based on the particle swarm optimization(PSO)technique is appliedto optimally tune the models’parameters,such as the quadratic error between the actual outputs(depth and aspectratio),and the error estimated by the models’outputs is *** results showed that the proposed PSOmodel is first and foremost robust against uncertainties in measurement devices and modeling errors,and second,that it is capable of accurately representing and quantifying the weld depth penetration and the weld aspect ratioto the oxides’thermal properties.

关键词： Activated metal inert gas welding stainless steel activating flux oxides’thermal properties particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Accurate Attack Preventing System Implementation in Grids

Accurate Attack Preventing System Implementation in Grids

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sharma, Vishal Batra, Raman Prabhu, A. Vivekananda Global University Department of Mechanical Engineering Jaipur India Noida Institute of Engineering & Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

A comprehensive approach to the ongoing and numerical evaluation of cyber risks in conditions involving Internet of the circumstances (IoT)-based electrical power systems, where attempts and protection may target different components, is lacking, despite the long history of risk assessment in a tool for helping decision-making in security investments. This study presents a comprehensive approach that continually evaluates cyber threats to help decision makers make well-informed security protection choices for smart grid systems. The suggested approach focuses on using attack-defense trees that are modeled after the system in conjunction with risk attribute calculation. This method propagates the risk characteristics across the tree nodes, enabling the evaluation of system hazards. By analyzing the sensitivity of system risk to different attack and defensive scenarios, the technique helps to optimize security measures and reduce risks. In order to facilitate security certifications, it promotes the adoption of common security and privacy protection taxonomies from internationally recognized privacy and security control families, which include NIST SP 800-53. The article describes the use of the approach in a real-world smart construction energy-efficiency scenario, integrating many components distributed in IoT and cloud resources, in order to verify it. The scenario that is being described shows that the approach is feasible not only in initial quantitative risk calculations but also for ongoing risk assessment updates based on system operating circumstances. © 2023 IEEE.

关键词： Attack-Defense Trees (Adts) Cyber Threats Decentralized Industrial lot Defense Optimization Ongoing Updates Quantitative Evaluation Risk Assessment Security Measures Sensitivity Analysis Smart Grid Systems

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：