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International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Xiaojun Bai Yunpeng Sun Gong Meng School of Computer Science and Engineering Xi’an Technological University Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Aiming at the problems of insufficient feature extraction and inaccurate prediction in existing aircraft engines remaining useful life prediction algorithms, a fusion model based on feature attention mechanism, stacked noise reduction autoencoder and bidirectional gated cycle units (BiGRU) was proposed. Firstly, weight is assigned to each sensor data of aircraft engine by the feature attention mechanism, and new weight data is obtained. Next, the stack noise reduction autoencoder is used to extract the weighted data. Then, bidirectional gated recurrent units are employed to predict the time series data and obtain the remaining useful life of the aircraft engine. To accurately select the hyperparameters of BiGRU, the Bayesian optimization algorithm is utilized for hyperparameter optimization. Finally, ablation experiments were conducted on the CMPASS dataset to validate the effectiveness of the proposed method, resulting in a root mean square error and score of 13.51 and 252.61, respectively.

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Hard Disk Failure Prediction Based on Association Analysis and Long Short-Term Memory Network

SSRN

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SSRN 2023年

作者： Xiaojun, Bai Pan, Zhaofeng Meng, Gong Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature;secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Enhancing neural network with LIDAR data for navigation

Enhancing neural network with LIDAR data for navigation

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International Conference on control Systems and computer science (CSCS)

作者： Diana Baicu Mihai CrĂCiunescu Thomas Duvinage Ştefan Mocanu Faculty of Automatic Control and Computer Science University Politehnica of Buchaerst Bucharest Romania University of Belfort Montbéliard Belfort France

In this paper, a method of fusing distance information and camera data is presented, the objective being that of combining LIDAR measurements with the output of a segmentation neural network, applied on the merged image from three frontal cameras, for navigating in an indoor environment. Usually, these types of environments are not structured, being diverse in their composition: for example, some buildings have more glass walls and doors, others might have large corridors and concrete structures. The proposed fusing method is used for reducing the errors generated by both techniques individually, which is then applied to control the trajectory of an electric wheelchair, as well as to gather a dataset of corrected and annotated images for reducing the dependency on the LIDAR data in the future.

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Fault Diagnosis Research of Laser Gyroscope Based on Optimized-Kernel Extreme Learning Machine

SSRN

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SSRN 2023年

作者： Xiaojun, Bai Ma, Zhenxi Chen, Wei Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Traditional diagnostic models for laser gyroscopes, widely utilized as high-precision angular velocity sensors in aerospace applications, often suffer from limited reliability and accuracy due to the difficulty of feature extraction, a high computational cost, and training time. In light of these challenges, the present study puts forth a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method leverages Wavelet Packet Decomposition (WPD) to effectively extract features from the laser gyroscope signal as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis, after which we employ the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for enhanced optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches, while also offering the advantages of a shorter training time and increased precision. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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Reliability Prediction Based on SVR and Improved SCSO

Reliability Prediction Based on SVR and Improved SCSO

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International Conference on computer Network, Electronic and Automation (ICCNEA)

作者： Xiaojun Bai Hongyue Liu Gong Meng School of Computer Science and Engineering Xi’an Technological University Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Reliability prediction can provide basis for the identification of potential improvement area, cost control, and mission reliability assessment, etc. However, for complex equipment, there are many reliability influencing factors and incomplete knowledge of failure causes, which lead to a significant disparity between the predicted outcomes and the real values for traditional reliability prediction methods. To address the above issues, this research paper introduces an approach that utilizes the Support Vector Regression (SVR) model and Sand Cat Swarm Optimization (SCSO). To begin with, the sliding window technique is employed on the historical reliability data to generate time series samples, with the 5 adjacent data as sample data, and the sixth as label of the sample, and train SVR model on these samples; Second, the SVR model parameters are optimized using the ISCSO algorithm to obtain the optimal combination of parameters. In the testing stage, firstly, historical reliability data was used to predicted future data by the model, and the predicted data are then added to the sequence to form new samples, thus old data are discarded and new data are predicted continuously to realize continuous reliability prediction. Finally, the algorithm proposed in this paper is validated on a diesel engine reliability dataset. The algorithm proposed in this paper demonstrates its superiority through the Normalized Root Mean Squared Error (NRMSE) evaluation. The NRMSE of SVR-ISCSO is 8.86E-05, showcasing a remarkable 99.24% year-on-year decrease compared to the standard SVR. Additionally, it exhibits a 5.86% year-on-year decrease compared to SVR-SCSO, further validating the effectiveness of the proposed algorithm.

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Integration of Industrial Automation and IoT Devices on the Example of the Assembly Station with the Astorino Educational Robot 4th

Integration of Industrial Automation and IoT Devices on the ...

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4th International Conference on Intelligent Systems in Production Engineering and Maintenance, ISPEM 2023

作者： Ćwikla, Grzegorz Kalinowski, Krzysztof Kciuk, Marek Gold, Michal Waluśkiewicz, Marek Sladek, Michal Tlolka, Jaroslaw Woźnica, Robert Wlazlo, Aleksander Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Electrical Engineering Silesian University of Technology Akademicka 10A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783031442810

The article presents an automated assembly station designed by students as part of Project Based Learning. The task of the project was to familiarize with the issues of integration of automation and control elements operating in various standards in order to implement the idea of Industry 4.0. The main elements of the station are the Astorino educational robot, gripper, servo drive and other professional elements of industrial automation and IoT systems. Raspberry PI, microcontrollers, etc. are also used. The assembly process of the product in the configuration in accordance with the user’s order is carried out at the station, quality control is carried out during the process using a vision system and various sensors. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data acquisition

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Integration of Generative Artificial Intelligence in Medicine - First Steps Toward an Ethical Risk Management Methodology

Integration of Generative Artificial Intelligence in Medicin...

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E-Health and Bioengineering Conference (EHB)

作者： Miruna – Elena Iliuță Damien Trentesaux Eugen Pop Mihnea – Alexandru Moisescu Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Université Polytechnique Hauts-de-France UPHF LAMIH-UMR CNRS 8201 Valenciennes France

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

The integration of Generative Artificial Intelligence into clinical processes requires addressing ethical aspects and inherent risks, contributing not only to the automation of these processes but also to the ongoing development of personalized medicine. The generation of complex diagnostic and personalized treatment solutions must be carried out in accordance with deontological norms and ethical standards, aimed at ensuring the protection of patient autonomy and safety. The development of an architectural framework for the identification of pathologies and the provision of treatment scenarios must ensure both the efficiency of the solutions offered and their compliance with ethical standards, involving the creation of a set of rules and practices that encourage interdisciplinary collaboration between experts in technology, medicine, and law. The fundamental principles that facilitate the integration of emerging technologies into clinical processes promote patient autonomy, decision-making transparency, and clinical responsibility. (Abstract)

关键词： Ethics Pathology Generative AI Precision medicine Decision making Medical services Solids Medical diagnostic imaging Standards Optimization

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MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media

arXiv

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arXiv 2023年

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Nicolescu, Radu-Cătălin Karras, Panagiotis Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Department of Computer Science University of Copenhagen Copenhagen2100 Denmark

The widespread availability of internet access and handheld devices confers to social media a power similar to the one newspapers used to have. People seek affordable information on social media and can reach it within seconds. Yet this convenience comes with dangers;any user may freely post whatever they please and the content can stay online for a long period, regardless of its truthfulness. A need arises to detect untruthful information, also known as fake news. In this paper, we present an end-to-end solution that accurately detects fake news and immunizes network nodes that spread them in real-time. To detect fake news, we propose two new stack deep learning architectures that utilize convolutional and bidirectional LSTM layers. To mitigate the spread of fake news, we propose a real-time network-aware strategy that (1) constructs a minimum-cost weighted directed spanning tree for a detected node, and (2) immunizes nodes in that tree by scoring their harmfulness using a novel ranking function. We demonstrate the effectiveness of our solution on five real-world datasets. © 2023, CC BY-NC-ND.

关键词： Trees (mathematics)

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Analysis of Semestral Progress in Higher Technical Education with HMM Models 21st

Analysis of Semestral Progress in Higher Technical Education...

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21st International Conference on Computational science, ICCS 2021

作者： Lach, Ewa Grzechca, Damian Polański, Andrzej Rutkowski, Jerzy Staniszewski, Michal Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783030779665

Supporting educational processes with Hidden Markov Models (HMMs) has great potential. In this paper, we explore the possibility of identifying students’ learning progress with HMMs. Students’ grades are used to train the HMMs to find out if the analysis of obtained models lets us detect patterns emerging from student’s results. We also try to predict the final students’ results on the basis of their partial grades. A new, classification approach for this problem, using properties of HMMs is proposed: High and Low State Model (HLSM). © 2021, Springer Nature Switzerland AG.

关键词： Hidden Markov models

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Multiple-image super-resolution reconstruction using deep learning: A Sentinel-2 case study

Multiple-image super-resolution reconstruction using deep le...

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IAF Earth Observation Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Kawulok, Michal Tarasiewicz, Tomasz Ziaja, Maciej Tyrna, Diana Kostrzewa, Daniel Nalepa, Jakub KP Labs Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

ISBN: (纸本)9781713843016

Super-resolution reconstruction embraces a variety of techniques aimed at generating a high-resolution image from a low-resolution input. The goals of super-resolution range from hallucination that consists in producing a visually-attractive high-resolution image to reconstructing the real high-resolution information that is commonly required in Earth observation scenarios. The latter can be achieved by fusing a number of images with the same georeference, captured at a different time. Such multi-image super-resolution solutions underpinned with deep learning have been recently proposed for enhancing images acquired with the Proba-V sensor. This satellite captures images of 100 m and 300 m ground sampling distance, which makes it relatively easy to collect sufficient amounts of real-world data that can be used for training deep convolutional neural networks. As such data are unavailable for most of other satellites, alternative ways for training the networks must be adopted. In this paper, we demonstrate our solution for super-resolving multispectral Sentinel-2 images, and we present its most important components that may help implement multi-image super-resolution for other satellites. Our main focus is on the data that are used for training, as well as on the low-resolution images that are presented for reconstruction. We expect that the reported techniques will allow for increasing the capabilities of using Sentinel-2 images in a variety of practical Earth observation scenarios, but even more importantly, the presented methodology may be helpful in exploiting multiple-image SR for enhancing images captured with other satellites. Copyright © 2021 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Satellites

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