检索结果-内蒙古大学图书馆

A Deep learning-based Probabilistic Approach for Non-Destructive Testing of Aircraft Components Using Laser Ultrasonic Data

引用

IEEE ACCEss 2025年 13卷 66761-66773页

作者： Liso, Adriano Patruno, Cosimo Cardellicchio, Angelo Ardino, Pierfrancesco Gallo, Nicola del Prete, Giuseppe Dentico, Valerio Vespini, Veronica Coppola, sara Ferraro, Pietro Reno, Vito Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg I-70126 Bari Italy Leonardo SpA Div Aerostrutture I-74023 Taranto Italy Natl Res Council Italy Inst Appl Sci & Intelligent Syst Eduardo Caianiell I-80078 Naples Italy

Composite structures are commonly used in complex applications such as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic events during their useful life that can cause invisible failures that extend and severely compromise their performance over time. Detecting these defects preventively and repairing them could avoid dramatic accidents. Here, we present a deep learning-based method for the non-destructive detection of defects in composite samples based on a laser ultrasonic system (LUT). Laser ultrasonic technology is a promising non-destructive testing (NDT) method for detecting inner defects in a non-contact way, as it does not require liquid coupling media. We investigated a composite laminate specimen containing six programmed defects as a test sample. We show that training deep learning-based models as autoencoders makes it possible to extract features that can be used to discern defective areas from non-defective ones in the Us C-scan maps. The results demonstrate high detection accuracies (above 90% balanced accuracy and 75% F-1-score), indicating a promising and effective approach to NDT on composite materials.

关键词： Feature extraction Acoustics Aircraft Testing Lasers Defect detection Aircraft manufacture Deep learning Anomaly detection Accuracy Autoencoders machine learning distinctive signatures non-destructive defect detection ultrasonic signals aircraft components

来源：评论

学校读者我要写书评

暂无评论

Progressive Growth-based Momentum Contrast for Unsupervised Representative learning in Classification Tasks

IEEE Networking Letters

引用

IEEE Networking Letters 2025年第1期7卷 31-35页

作者： Li, Yantao Ling, shijun Huang, Hongyu Rabevohitra, Feno H. Chongqing University College of Computer Science Chongqing400044 China Studio A Department of Research and Development Antananarivo101 Madagascar

Contrastive unsupervised learning has made significant progress, but there is still potential for improvement by capturing finer details in input data. In this letter, we present PGMoCo, a Progressive Growth-based Momentum Contrast framework for unsupervised representative learning in classification tasks. PGMoCo begins by learning the overall distribution of samples at a coarse scale and progressively refines the representation by incorporating increasingly finer details. PGMoCo consists of data augmentation, progressive growth, an alternative multilayer perceptron (MLP) head, and a loss function. First, PGMoCo applies transformation-based data augmentation to the input samples. Then, it progressively learns features at multiple scales, uses an alternative MLP head to project latent representations into a contrastive loss space, and finally employs a specialized loss function to classify the samples. We evaluate PGMoCo on three datasets: CIFAR-10 and PolyU Palmprint (image classification) and H-MOG (person identification). PGMoCo achieves classification accuracies of 86.76% on CIFAR-10, 95.94% on PolyU Palmprint, and 80.10% on H-MOG, outperforming existing state-of-the-art methods. © 2019 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive AI-based Personalized learning for Accelerated Vocabulary and syntax Mastery in Young English Learners

引用

International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： students

来源：评论

学校读者我要写书评

暂无评论

Predicting Lactobacillus delbrueckii ***-streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method

引用

science China(Life sciences) 2025年第2期68卷 558-574页

作者： shujuan Yang Mei Bai Weichi Liu Weicheng Li Zhi Zhong Lai-Yu Kwok Gaifang Dong Zhihong sun Key Laboratory of Dairy Biotechnology and Engineering Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China Key Laboratory of Dairy Products Processing Ministry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Collaborative Innovative Center for Lactic Acid Bacteria and Fermented Dairy Products Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China College of Computer and Information Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot 010018China

Lactobacillus delbrueckii ***(***)and streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb st I)substantially impact dairy product quality and *** biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is ***,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled ***,this study aimed to develop a semi-supervised learning framework for predicting Lb st I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb st I prediction results,which were verified through milk fermentation *** through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk ***,this finding requires further experimental *** presented model and data are valuable resources for academics an

关键词： Lactobacillus delbrueckii subsp.bulgaricus and streptococcus thermophilus interaction prediction semi-supervised learning dairy starter artificial intelligence milk fermentation

来源：评论

学校读者我要写书评

暂无评论

From Global to Hybrid: A Review of supervised Deep learning for 2D Image Feature Representation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1540-1560页

作者： Dong, Xinyu Wang, Qi Deng, Hongyu Yang, Zhenguo Ruan, Weijian Liu, Wu Lei, Liang Wu, Xue Tian, Youliang Guizhou University State Key Laboratory of Public Big Data College of computer science Guiyang550025 China Guangdong University of Technology Guangzhou510006 China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China AI Research of *** Beijing100101 China

computer vision is the science that aims to enable computers to emulate human visual perception, and it encompasses various techniques and methods for extracting and interpreting information from two-dimensional images. supervised deep 2D image feature representation is a fundamental problem in computer vision that applies deep learning techniques to extract and process information from a given 2D image under supervised settings. The goal is to obtain a feature vector that can be utilized for various downstream computer vision applications. The quality of supervised deep 2D image feature representation algorithms directly affects the performance of downstream applications. However, most of the existing vision research only explores supervised deep 2D image feature representation for specific subtasks. Therefore, a comprehensive discussion on this topic is needed. In this paper, we propose a taxonomy of supervised deep 2D image feature representation methods based on four categories: global representation, region representation, hash representation, and hybrid representation, and we introduce their typical approaches. Furthermore, we perform a comparative analysis of the representative methods on three fundamental tasks: image classification, object detection, and semantic segmentation, as well as other common tasks. We also discuss the limitations of supervised deep 2D image feature representation and investigate future directions in image representation to facilitate the advancement of computer vision through image representation. © 2020 IEEE.

关键词： self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Automation of Human-Machine Interface Design for Highly-Automated substations

引用

Power Technology and Engineering 2025年第5期58卷 872-876页

作者： Dobrokhotov, D.s. Trofimov, A.V. Trofimov, V.A. DEP Company" LLC Moscow Russia National Research University "Moscow Power Engineering Institute " Moscow Russia

The issues of automated generation of mnemonic diagrams for automated workstations used by the operators of electrical installations are considered. The algorithm is implemented based on the substation configuration description file, compiled in accordance with IEC 61850. The description of mnemonic diagrams is generated using the standard computer graphics format (sVG) based on conventional graphical symbols intended for the syTrack sCADA system developed by the DEP Company. The project management is performed within the framework of the computer-Aided Design system for secondary Circuits (CAD sC). © springer science+Business Media, LLC, part of springer Nature 2025.

关键词： project management

来源：评论

学校读者我要写书评

暂无评论

Bidirectional LsTM-based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

引用

computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： sheik Mohideen shah Meganathan selvamani Mahesh Thyluru Ramakrishna surbhi Bhatia Khan shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long short-Term Memory(LsTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LsTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LsTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMsE and an 18.8%improvement in MAE over conventional unidirectional LsTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including sVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMsE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LsTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

来源：评论

学校读者我要写书评

暂无评论

Multi-agent deep reinforcement learning with traffic flow for traffic signal control

引用

Journal of Control and Decision 2025年第1期12卷 81-92页

作者： Liang Hou Dailin Huang Jie Cao Jialin Ma College of Computer and Communication Lanzhou University of TechnologyLanzhouPeople’s Republic of China Engineering Research Center of Manufacturing Information of Gansu Province LanzhouPeople’s Republic of China

Multi-agent Reinforcement learning(MARL)has become one of the best methods in Adaptive Traffic signal Control(ATsC).Traffic flow is a very regular traffic volume,which is highly critical to signal control ***,dynamic control policies will directly affect traffic flow formation,and it is impossible to provide observation through the original traffic flow *** paper proposes a method for estimating traffic flow according to the time window in Reinforcement learning(RL)***,it is verified on both the regular road network and the real road *** method further reduces the intersection delay and queue length compared with the original method.

关键词： Multi-agent reinforcement learning actor-critic adaptive traffic signal control traffic flow

来源：评论

学校读者我要写书评

暂无评论

step-by-step and Tailored Teaching: Dynamic Knowledge Distillation 18th

Step-by-Step and Tailored Teaching: Dynamic Knowledge Disti...

引用

18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WAsA 2024

作者： Zhang, Zhenqiang Geng, Liting Du, Wenqing Li, Feng Wang, Chunxiao Zhao, Zhigang Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714634

Knowledge distillation typically involves transferring knowledge from large-scale teacher models to student models, garnering widespread attention in the fields of model compression and knowledge transfer. However, existing research often encounters several challenges. Firstly, the transmission process usually fixes the teacher model, making it difficult to adjust the learning situation of the student model according to individual needs. secondly, the difficulty of tasks affects the learning ability of the student model. In training tasks, difficulty levels are often coupled with data, posing a challenge in guiding the student model to gradually master knowledge. To address these issues, this study proposes a dynamic learning temperature-based meta-learning knowledge distillation method, namely Temperature Meta-learning Knowledge Distillation (TMKD). Inspired by meta-learning heuristics, this algorithm enables the teacher model to dynamically adjust knowledge transfer strategies based on student feedback, facilitating tailored teaching. Furthermore, a Dynamic Temperature Regulation Module (DTRM) is constructed to flexibly control the difficulty level of tasks, allowing the student model to progressively learn knowledge. Finally, we design a selective Insight Attention mechanism to ensure that the student network focuses more on key information during learning and inference, thereby enhancing overall performance. Extensive experiments on CIFAR-100 and ImageNet demonstrate the effectiveness of our method. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Advancing Lymphoma Diagnosis in Histopathology Image Classification Using Multi Deep learning Models

Iraqi Journal for Computer Science and Mathematics

引用

Iraqi Journal for computer science and Mathematics 2025年第2期6卷 180-187页

作者： Elaraby, Ahmed Nechaevskiy, Andrey saad, Aymen Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt Cairo Egypt Meshcheryakov Laboratory of Information Technologies Joint Institute for Nuclear Research Joliot-Curie 6 Dubna141980 Russia Department of Information Technology Management Technical College Al-Furat Al-Awsat Technical University Kufa54003 Iraq School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 UTM Malaysia

Deep learning’s rapid development is generating significant interest in its potential to improve medical imaging. It has shown promising results in detecting malignant lymphoma in histopathology medical images. Image classification methods are widely used to aid in making diagnoses from medical images. In recent years, deep learning methods have achieved high performance in detecting malignant lymphoma in histopathology images. This study proposes a novel approach to improving lymphoma diagnosis in histopathology images called the Lightweight Convolutional Neural Network (LWCNN). The proposed LWCNN model comprises multiple deep learning architectures, including a convolutional neural network (CNN) that has been trained to classify lymphoma subtypes based on histopathology images using ResNet50 and MobileNetV2. The LWCNN model aggregates the predictions of disparate architectures to arrive at a definitive diagnosis, leveraging the unique capabilities of each. A comprehensive dataset of annotated lymphoma histopathology images was assembled for the purpose of training the multi-deep learning model. To ensure a representative and diverse training set, the dataset was meticulously curated to encompass various subtypes of lymphoma. Performance evaluation of the proposed deep learning model for lymphoma classification using standard metrics revealed the following accuracies: LWCNN (97.34% training, 86.71% testing), ResNet50 (88.76% training, 86.37% testing), and MobileNetV2 (88.47% training, 86.60% testing). These results indicate that the LWCNN model significantly surpasses existing approaches in diagnostic accuracy. © 2025 The Author(s).

关键词： Photointerpretation

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：