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Active learning-Based Complex Pipeline Weld Defect Detection with Lightweight Neural Network 11

Active Learning-Based Complex Pipeline Weld Defect Detection...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Zuo, Fengyuan Liu, Jinhai Wang, Lei Qu, Fuming Fu, Mingrui Northeastern University School of Information Science and Engineering Shen yang110004 China University of Science and Technology Beijing School of Civil and Resource Engineering Beijing100083 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Weld defect detection plays an important role in pipeline safety maintenance. Due to the complexity of weld defect, it is difficult for existing intelligent detection methods based on deep learning to achieve high accuracy and efficiency. We propose a novel defect detection method that can better alleviate the current dilemma by constructing an active learning framework with well-designed value sample sampling strategy. First, primary defect detector is trained based on data driven by building a lightweight fully convolutional network. then efficient value sample selection strategy is devised by computing the uncertainty of unlabeled images. Finally, Fine-tuning network parameters based on value samples is proposed to obtain large performance gains with minimal resources. the experimental results on the pipeline weld defect dataset in northern China show that the proposed method has higher detection accuracy than traditional deep learning methods. © 2022 IEEE.

关键词： Complex networks

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A IoT Edge Advanced VANET Technique for Vehicle Communication and Improve Safety in Hill Station Critical Scenario 8

A IoT Edge Advanced VANET Technique for Vehicle Communicatio...

引用

8th IEEE international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Institute of Information Technology Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

the safety of the vehicles is given great importance by advanced intelligent transportation systems. We are here to make sure that swift transit vehicles and infrastructure communicate with one other quickly. the majority of the vehicles need regular monitoring for appropriate warnings and road maps to achieve their ultimate objective in a timely manner in the hill station bad environment, and travel in hairpin bends and risky turning in particular is a duty that is vital for ITS. Here, my study will provide a strategy for preventing accidents by applying a sophisticated early warning system before notifying the car in a certain circumstance. In order to prevent accidents, machine learning is a technique where the system automatically learns and enhances the rapid object detection stage in bobby pin road turning scenarios. Here, my study offers effective unsupervised learning of these characteristics based on the acquired data, which in turn serves as the foundation for the clustering I performed, which successfully builds a mobile ad hoc network. In this work, we suggested an innovative automated discovering vehicle prier that uses the UNetXST technique in real-time to notify warring for vehicle in three-way shining LED light with blinking mode, slowing warring message in display unit, and beep alarm sound. By combining V2I and V2V technologies and sending messages to the central traffic light management system, we highlighted safety and communication. In worst-case scenarios, we employ a v2v technology sensor to detect the vehicle that will cross the junction road, and we investigate two-way merging technology's potential to implement and resolve these problems in transparent object tracking in the turning system. We looked at future direction and hurdles at this time in addition to a case study illustrating a VANET-based scenario of a critical urban road turning, receiving the earliest notice to flee from a traffic collision, and experiencing the perfect EV momen

关键词： Vehicles

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Application of YOLO Object Detection Network in Weld Surface Defect Detection 11

Application of YOLO Object Detection Network in Weld Surface...

引用

11th IEEE Annual international conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2021

作者： Zuo, Yinlong. Wang, Jintao. Song, Jilai. Northeastern University Faculty of Robot Science and Engineering ShenYang110000 China Shenyang Institute of Automation Chinese Academy of Sciences ShenYang110000 China

ISBN: (纸本)9781665425278

As industrial production becomes more modern and intelligent today, the inspection of product quality of the workshop is becoming more and more accustomed to replacing the old manual visual inspection methods with automated inspection systems. In the welding field, automated welding robots are not only used in traditional large-scale automobile assembly lines. In more general welding work, welding robots also plays an important role. the inspection of the welding quality of the welding robot is mainly to detect the four main types of weld defects. Compared to traditional defect classification based on support vector machines and defect detection based on template matching, this paper uses a welding surface defect detection system designed based on deep learning methods. By working with workshop welding experts, a large-scale image of nearly 5000 pictures is built. Large-scale weld defect datasets, while using the real-time and accuracy of the YOLO series of deep learning object detection frameworks, the weld defects detection model reaches 75.5% mean average precision(mAP) in constructed weld defect data set. In addition, the construction cost of the detection model and the deployment time of the detection system are greatly reduced. During the field test of the system in the workshop, among a batch of welding workpieces provided by the factory, the detection accuracy of weld defects reached 71%, which initially met the requirements of the workshop for an automated defect detection system. © 2021 IEEE.

关键词： Object detection

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A Brief Survey on Semantic-preserving data Augmentation 11

A Brief Survey on Semantic-preserving Data Augmentation

引用

11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Song, Shaoyue Zhu, Qing Miao, Zhenjiang Department of Software Engineering Beijing University of Technology Beijing100124 China Institute of Information Science Beijing Jiaotong University Beijing100044 China

In the era of deep learning, it's important to obtain enough qualified data for model training. For the labor-intensive and time-consuming characteristics of data collection and annotation, date augmentation has been widely employed in deep learning methods to mitigate over-fitting. It has been proven to be one of the most effective ways to ensure the performance of deep models. In the field of computer vision, the principal goal of data augmentation usually is enlarging both the scale and diversity of the dataset, and maintaining the quality of both the image and the annotation data simultaneously. data augmentation methods at the raw input data level are intuitive to obtain semantic meaningful images. In this paper, we provide a brief survey on some data augmentation studies conducted at image level and briefly categorize the studies into two main classes: those based on image editing and those based on image generation. We also make an analysis of the semantic-preserving considerations within data augmentation techniques. © 2024 the Authors.

关键词： Computer Vision data Augmentation Semantic-preserving

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Employing Adbdconvolutional KNN, Diagnosis of Irregular Driver Behavior 11

Employing Adbdconvolutional KNN, Diagnosis of Irregular Driv...

引用

11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chandramma Manjunath, C.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665487320

Actual observation of unusual aggressive driving is essential for enhancing car safety. to enhance riding habits and behaviour in order to keep fatal crashes. the utilization of perception abnormal driving things that tend is becoming more and more common since it is essential to both the current level of driverless vehicles and the health of both passengers and motorists in vehicles. Recent developments in deep learning techniques, such as the impressive extension capacity of modern deep neural networks and the vast quantities of film clips necessary for fully retraining the data-driven models, may substantially help with this challenging problem. To conclude the study, new massive continuing to learn models are provided. these methods are motivated by the recently created and extensively used networked cnn model known as the Excessive Drive Control System in place (ADBD Net). © 2022 IEEE.

关键词： Deep neural networks

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Hybrid Machine learning Model for Anticipating Cyber Crime Malware in Android: Work on Progress 11

Hybrid Machine Learning Model for Anticipating Cyber Crime M...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Kurniawan, Fandi Stiawan, Deris Antoni, Darius Heriyanto, Ahmad Idris, Mohd. Yazid Budiarto, Rahmad Universitas Sriwijaya Faculty of Engineering Indonesia Universitas Sriwijaya Faculty of Computer Science Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Indonesia Universitas Teknologi Malaysia Faculty of Computer Science & Information Ssystem Malaysia College of Computer Science and IT Universitas Albaha Saudi Arabia

ISBN: (纸本)9798350355314

Improvements in information technology bring new challenges in cyber security, especially on the Android platform which is the main target of malware attacks. the National Cyber and Crypto Agency (BSSN) as the national cybersecurity institute recorded millions of attacks involving the Android Package Kit (.apk) application for electronic wedding invitations in Indonesia. this research aims to develop a hybrid machine learning model to detect and anticipate malware on Android devices, using algorithms such as Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbors (KNN). the main challenges are the limited number of representative datasets and the lack of effective detection techniquesthis research utilizes a primary server with a virtual machine (VM) to analyze the security of Android applications using mobSF and Frida, involving decompilation, TLS testing, and sensitive feature extraction. the system also depends on an Android emulator and Android Debug Bridge (ADB) for simulation, along with PostgreSQL, Job Scheduler, and Remote Desktop Protocol (RDP) for data management and task scheduling. the dataset comprises 1314 malware samples and 770 benign samples, totaling 2048 Android applications, with an average analysis time of 35 minutes per application. through data collection and pre-processing, as well as model training and evaluation, it is hoped that the proposed framework can improve the accuracy of malware detection, making a significant contribution in protecting Android users from ever-evolving cyber threats. After performing a comparison using the algorithms mentioned above, it was found that the Random Forest algorithm showed the best performance with an accuracy of 97.11%, a precision of 97.22%, and a recall of 97.11%. © 2024 IEEE.

关键词： Android malware

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Applications in Software engineering - Proceedings of the 11th international conference on Software Process Improvement, CIMPS 2022

Applications in Software Engineering - Proceedings of the 11...

引用

Applications in Software engineering - 11th international conference on Software Process Improvement, CIMPS 2022

ISBN: (纸本)9798350398960

the proceedings contain 37 papers. the topics discussed include: applying the first phases of software engineering for designing a brain-computer interface-based system;challenges toward teaching international standards in higher education institutions;Scrumban-Ex methodology applied to the development of artificial vision tools for repository generation: human pose case;automatic classification of audio through fruit percussion: toward no destructive estimation of quality;proposal for an adaptation of a strategic communication model applicable to the scrum framework in a GSD environment;database modeling for the cloud: a systematic review of the literature;model for the classification of damaged and vandalized Mexican traffic signs;data lake strategy for data science workflows;smart contract application on blockchain technology in the software industry;probabilistic or convolutional-LSTM neuronal networks: a comparative study of discrimination capacity on frozen - thawed fish fillets;deep learning for nanoparticle classification in SEM micrographs: systematic mapping;and systematic mapping: use of neural networks for analysis in transmission electron microscopy micrographs.

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Feedback Analysis of Online Teaching Using SVM 1

引用

11th international conference on Recent Trends in Computing, ICRTC 2022

作者： Mittal, Punit Tiwari, Kartikey Malik, Kanupriya Tyagi, Meghna Meerut Institute of Engineering and Technology Meerut250001 India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

Study examined how we can upgrade the quality of online teaching (Feng and Bienkowski in Enhancing teaching and learning through educational data mining and learning analytics. Department of Education, Office of Educational Technology [Feng M, Bienkowski M (2012) Enhancing teaching and learning through educational data mining and learning analytics. Department of Education, Office of Educational Technology]) in the upcoming years. data were collected through the Google form which were filled up by the students. Feedback is the most important attribute of assessment as it provides students with a statement of their learning and advises how to improve. Result was helping to enhance the quality of online teaching in educational system and also provide the constituent which helps in improving the online teaching. We will use sentiment analysis (Kumar and Nezhurina in Sentiment analysis on tweets for trains using machine learning. Research Gate, p 10 [Kumar S, Nezhurina MI (2020) Sentiment analysis on tweets for trains using machine learning. Research Gate, p 10]) also. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Scenarios to Handle Shifts data Pattern Using Neural Network for Forecasting Indonesian GDP Based on Financial Growth at Risk 11

Scenarios to Handle Shifts Data Pattern Using Neural Network...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Saputra, Wisnowan Hendy Prastyo, Dedy Dwi Fithriasari, Kartika Institut Teknologi Sepuluh Nopember Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798350355314

Modeling time series data through statistical techniques is a common practice in economics, primarily aimed at forecasting economic indicators such as GDP to prepare for future trends. Over time, statistical modeling has increasingly incorporated machine learning (ML) methods. Although ML offers flexibility in model equations, accurate projections still require that future data patterns align with historical ones, which is often not the case. this research examines the impact of changes or shifts in time series data patterns by exploring various scenarios for determining the length of training data used in modeling. As an empirical application, this study also provides a framework for projecting Indonesia's GDP by utilizing financial indicators such as the Financial Stress Index (FSI) as predictors. the model employed is QRNN-MIDAS, which accommodates differences in the frequency of responses variable (quarterly) and predictors (monthly) to estimate high-frequency financial Growth-at-Risk (GaR)-based on GDP growth, ultimately applied to forecast the actual GDP value. the analysis identifies the optimal scenario as one using a year-on-year GDP growth calculation with data limited to the period following a shift in the data pattern (from 2011 to 2023) and including a dummy predictor variable for the COVID-19 pandemic. According to this scenario, Indonesia's economic growth in 2024 is projected to be between 3.64% and 6%, with the GDP at constant 2010 prices ranging from 2.96 to 3.13 trillion rupiah. Assuming no unforeseen adverse events, Indonesia's economy in 2024 is expected to be stable and show a tendency to grow. © 2024 IEEE.

关键词： Inflation

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Student modeling considering learning behavior history with deep factorization machines 27

Student modeling considering learning behavior history with ...

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27th international conference on Knowledge Based and intelligent Information and engineering Sytems, KES 2023

作者： Kakizaki, Toma Oeda, Shinichi Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

To use an intelligent tutoring system (ITS) in an educational setting effectively, it is necessary to understand the skill status of students and recommend appropriate questions. Existing studies focused on improving the performance of ITS using student modeling to estimate the skill status of students. Knowledge tracing (KT) is the mainstream student modeling method, and deep learning approaches such as deep knowledge tracing and self-attentive knowledge tracing have been studied extensively in recent years. these models take only the questions solved by the student and the correct or incorrect answers to those questions as input;they do not assume the use of other features. In this study, we perform student modeling using DeepFM and FiBiNET that combines factorization machines and deep learning. Our results indicate that these models are more accurate than KT because of their ability to cope with sparse data and consider pairwise feature interactions, and more suited for real-world applications than KT. © 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Deeplearning Educational data mining Factorization machines intelligent tutoring system Knowledge tracing Student modeling

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