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Bearing Fault Diagnosis Based on Multiple Feature Transfer learning Network 11

Bearing Fault Diagnosis Based on Multiple Feature Transfer L...

引用

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

作者： Qiao, Huanzhang Liu, Lida Tian, Xincheng Liu, Yan Center for Robotics School of Control Science and Engineering Shandong University Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Shandong Raone Technology Co. LTD Jinan250012 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the intelligent fault diagnosis methods have gained great success, they usually depend on large amounts of labeled data. Unfortunately, it is a realistic problem to collected massive labeled data in actual industrial scene. In addition, even labeled data is easy to collect, the methods trained by these labeled data may not be able to classify unlabeled data obtained under different working states. Inspired by transfer learning, in the paper we propose a deep learning-based fault diagnosis method named multiple feature transfer learning network (MFTLN) to resolve the data distribution difference due to changed working conditions. MFTLN employee multiple extractors to get domain-invariant features and align source domain and target domain by maximum mean discrepancy (MMD) metric. We performed six bearing fault diagnosis transfer learning experiments to verify the superiority of the proposed method, the experimental results indicated that the recognition accuracy of the proposed method is obviously higher than those of the other 5 popular domain adaptive fault diagnosis methods when the target domain data is unlabeled. © 2022 IEEE.

关键词： Failure analysis

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An Effective System for Resume Analysis and Personality Prediction Using SVM 11th

An Effective System for Resume Analysis and Personality Pred...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Jaiswal, Updesh Kumar Gupta, Archit Dwivedi, Malay Shamsi, Mohd Shahzeb Agarwal, Anshul Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Department of Information Technology IMS Engineering College Ghaziabad India

ISBN: (纸本)9789819717231

the human persona has performed a vital function in a character's existence and in the growth of an industry. One method to decide about the human character is the use of preferred questionnaires or via studying the resumes. Many algorithms have been utilized up to this point to extract words from unprocessed text input for use in personality prediction systems;some of them make use of composite models and deep learning methods. the following are some drawbacks of machine learning (ML) algorithms like random forest and logistic regression, notwithstanding their usefulness. Due to sequential input, these techniques first require a lot of time to train the model. Second, because these algorithms are unable to accurately express the actual significance of words so, some context of the words is lost. In this paper, we gift a new system that analyzes the resume of the candidate and predicts his or her personality. the system uses the submitted resume to predict the professional personality. the preprocessing is done using BERT and XLNet which are pre-trained language frameworks. then extracted features are given to the trained support vector machine (SVM) model. SVM is selected as it provides the best accuracy. the system will output a predicted personality for the resume having 92% accuracy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Human-AI collaboration in bridge inspection using mixed reality platform 11th

Human-AI collaboration in bridge inspection using mixed real...

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Zakaria, M. Karaaslan, E. Catbas, F.N. Department of Civil Environmental and Construction Engineering University of Central Florida Orlando United States

ISBN: (纸本)9781032345314

Traditional methods for concrete bridge inspection are highly subjective, time-consuming, and in cases, not accurate. However, the advances in deep learning and convolutional neural networks (CNN) have revolutionized bridge inspection. Many studies have used CNNs to localize and quantify defects on the surface of structures which includes detecting delamination and rusting or measuring cracks and spalls. However, in most cases, the AI-based bridge inspection is designed for automated systems such as UAEs and robots. While these methods address many important issues in bridge inspection, such as accessibility and decreased labor, they also have shortcomings. the automated systems solely work based on the AI's performance. In many cases, inspectors have to visit the site more than once to correct the mistakes of the AI. Moreover, the lack of data on concrete defects directly affects the accuracy of the CNN models and increases the post-processing time. this study proposes a methodology that allows the inspector to communicate with the AI on-site and in real-time. the method is designed to work on Mixed-Reality platforms to assist the inspector in localizing and quantifying concrete defects. Two different CNN models were selected and trained out of many different CNN architectures based on their accuracy, memory footprint, and performance speed. A Mixed-Reality user platform and tracking are also designed to allow the inspector to communicate with the AI at each step and also accurately save the location of the analyzed defects. the study also proposes a semi-supervised approach for increasing more labeled data and improving the accuracy of the defect quantification model. Lastly, a methodology for condition assessment of concrete defects using the Mixed Reality system is discussed. the inspection method in this study eliminates the subjectivity of inspection and guarantees human-verified results without the need for post-processing. © 2022 the Author(s).

关键词： Mixed reality

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Application of Machine learning for Sentiment Analysis of Movies Using IMDB Rating 11

Application of Machine Learning for Sentiment Analysis of Mo...

引用

11th IEEE international conference on Communication Systems and Network Technologies, CSNT 2022

作者： Rathor, Sandeep Prakash, Yuvraj Gla University Dept of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665480383

In today's environment, the customer behavior related to the product is important. the rapid growth of social media has made it easier to collect information about our products. Billions of customers share information about their products on social media pages. Manually managing large amounts of social media information is difficult. However, we have proposed a framework for analyzing customer sentiment using data mining and machine learning algorithms. the proposed framework consists of several steps to processes the information and try to find the good accuracy of the classification. the proposed framework is used for sentiment analysis of movie using IMDB rating. In this paper, the data preprocessing and feature extraction are done using data mining techniques. the result analysis shows that the proposed framework can be useful for better decision-making. © 2022 IEEE.

关键词： Sentiment analysis

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A TinyML-based system for gas leakage detection 11

A TinyML-based system for gas leakage detection

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11th international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Gkogkidis, Anargyros Tsoukas, Vasileios Papafotikas, Stefanos Boumpa, Eleni Kakarountas, Athanasios Univ Thessaly Dept Comp Sci & Biomed Informat Intelligent Syst Lab Lamia Greece

ISBN: (数字)9781665467179

ISBN: (纸本)9781665467179

Internet of things devices are commonly utilized in smart homes to provide smart services such as lighting, entertainment, and easy access, but they are also employed to warn occupants in the event of an emergency. Due to the computationally intensive nature of existing Neural Network implementations, data must be transmitted to the cloud for analysis to produce intelligent machines. TinyML is a promising approach that the scientific community has proposed as a method of constructing autonomous and secure devices that can collect, analyze, and output data without requiring it to be transferred to remote entities. this work presents a TinyML-based system for detecting hazardous gas leaks. the system may be trained to detect irregularities and notify occupants using BLE technology via a message sent to their smartphones, as well as through an integrated screen. Two different test cases are presented and evaluated via experiments. For the smoke detection test case the system achieved an F1-Score of 0.77, whereas for the ammonia test case the evaluation metric of F1-Score is 0.70.

关键词： TinyML machine learning gas detection constrained hardware microcontrollers sensors

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset 11

Multi-objective performance prediction based on a self-const...

引用

11th international conference on Information Systems and Computing Technology, ISCTech 2023

作者： Hu, Dan Liu, Yingli Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

the combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction. © 2023 IEEE.

关键词： Feature Selection

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Prediction of Fatigue Life of Materials Using Machine learning Approach: A Review 11th

Prediction of Fatigue Life of Materials Using Machine Learni...

引用

11th EAI international conference on Advancement of Science and Technology, ICAST 2023

作者： Tefera, Getaw Ayay Koricho, Ermias Gebrekidan Department of Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031641503

Predicting the fatigue life of materials accurately and efficiently remains a challenge due to complex loading conditions, multidimensional stress factors, and the nonlinear properties of composite materials. However, advancements in data-driven modeling, specifically machine learning (ML) techniques, have shown promise in mitigating these limitations. Different ML models have been extensively employed to forecast the fatigue life of materials. this review provides a comprehensive analysis of data sources, ML model types, and evaluation techniques utilized in the field of ML-based fatigue life prediction. It is important to note that all ML models comprise two fundamental elements: datasets and algorithms employed during the training process. the ultimate objective of any ML model is to achieve accurate predictions, which necessitates the optimization of hyperparameters. Hyperparameter tuning is performed using various techniques, as there is no universally recognized rule for maximizing ML model accuracy. the present state of research in this field emphasizes expanding fatigue datasets, refining hyperparameter tuning methods, and identifying influential factors to enhance prediction accuracy while optimizing computational time and cost. this paper aims to provide a comprehensive understanding of the advancements made in ML-based fatigue life prediction, shedding light on potential avenues for future research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Investigating White-Box Attacks for On-Device Models 24

Investigating White-Box Attacks for On-Device Models

引用

44th ACM/IEEE international conference on Software engineering, ICSE 2024

作者： Zhou, Mingyi Gao, Xiang Wu, Jing Liu, Kui Sun, Hailong Li, Li Monash University MelbourneVIC Australia Beihang University Beijing China Huawei Software Engineering Application Technology Lab China Beihang University Beijing China Yunnan Key Laboratory of Software Engineering China

ISBN: (纸本)9798400702174

Numerous mobile apps have leveraged deep learning capabilities. However, on-device models are vulnerable to attacks as they can be easily extracted from their corresponding mobile apps. Although the structure and parameters information of these models can be accessed, existing on-device attacking approaches only generate black-box attacks (i.e., indirect white-box attacks), which are less effective and efficient than white-box strategies. this is because mobile deep learning (DL) frameworks like TensorFlow Lite (TFLite) do not support gradient computing (referred to as non-debuggable models), which is necessary for white-box attacking algorithms. thus, we argue that existing findings may underestimate the harm-fulness of on-device attacks. To validate this, we systematically analyze the difficulties of transforming the on-device model to its debuggable version and propose a Reverse engineering framework for On-device Models (REOM), which automatically reverses the compiled on-device TFLite model to its debuggable version, enabling attackers to launch white-box attacks. Our empirical results show that our approach is effective in achieving automated transformation (i.e., 92.6%) among 244 TFLite models. Compared with previous attacks using surrogate models, REOM enables attackers to achieve higher attack success rates (10.23%→89.03%) with a hun-dred times smaller attack perturbations (1.0→0.01). Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models. Our artifacts11https://***/zhoumingyi/REOM are available. © 2024 ACM.

关键词： Deep learning

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the Applicability of Federated learning to Official Statistics 24th

The Applicability of Federated Learning to Official Statis...

引用

24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Stock, Joshua Hauke, Oliver Weißmann, Julius Federrath, Hannes Universität Hamburg Hamburg Germany Wiesbaden Germany

ISBN: (纸本)9783031482311

this work investigates the potential of Federated learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods. FL is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data. By simulating three different use cases, important insights on the applicability of the technology are gained. the use cases are based on a medical insurance data set, a fine dust pollution data set and a mobile radio coverage data set–all of which are from domains close to official statistics. We provide a detailed analysis of the results, including a comparison of centralized and FL algorithm performances for each simulation. In all three use cases, we were able to train models via FL which reach a performance very close to the centralized model benchmarks. Our key observations and their implications for transferring the simulations into practice are summarized. We arrive at the conclusion that FL has the potential to emerge as a pivotal technology in future use cases of official statistics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： learning systems

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intelligent Fault Diagnosis System of Electrical Equipment Based on Neural Network Algorithm 11th

Intelligent Fault Diagnosis System of Electrical Equipment B...

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11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Zhang, Chuanjun Zhang, Chunfang College of Intelligent Manufacturing and Automotive Anhui business and Technology College Anhui Hefei231141 China

With the progress of electrical equipment, various faults often occur during its operation, which adversely affect the efficiency and quality of power supply. In this paper, the neural network algorithm is used to detect the system parameters. this paper first discusses the shortcomings and defects of traditional diagnosis methods, and then proposes a new method combining with existing technologies to build fault state identification criteria and classifiers based on neural network model, and also uses a variety of intelligent algorithms such as fuzzy reasoning and support vector machine to solve the common short-circuit problem of electrical equipment on the power line, so as to provide reliable security for actual operation and help the power sector. the performance of the diagnostic system is tested in this paper. the test results show that in the process of real-time monitoring, analysis and feedback processing of the fault identification system, the fault detection time is 5.2, 5.1, 4.9, 4.7, 4.6, 4.5, 4.4, 4.3, 4.2, 4.1 seconds by applying the neural network algorithm. this data represents a significant improvement in fault detection speed, from 5.2 seconds to 4.1 seconds. © 2024 the Authors. Published by Elsevier B.V.

关键词： Power distribution faults

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