A systematic approach to quality management has been accumulated in aerospace industry, and also a large amount of quality data has also been accumulated, making it possible to mine the potential value of aerospace qu...
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The objective of this paper is to develop a machine learning-aided cohesive zone model (CZM) for fatigue delamination in composite structures. The so-called string-based CZM can handle pure and mixed fatigue delaminat...
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ISBN:
(纸本)9780791887141
The objective of this paper is to develop a machine learning-aided cohesive zone model (CZM) for fatigue delamination in composite structures. The so-called string-based CZM can handle pure and mixed fatigue delamination. Its solid thermodynamic foundation enables it to handle spectrum loading sequences well. An implicit integration scheme for this CZM is developed for improved accuracy and to generate needed training data. A conditional recurrent neural network (RNN) model can solve mixed sequential and time-invariant data problems. The time-invariant data are first input into a feed-forward neural network to predict the initial state of an RNN model. The RNN model will take the state and sequential data to recurrently predict the time series outputs. The conditional RNN model is trained to take the place of computationally costly finite element analysis (FEA) and then used for interface parameter calibration. The Dakota toolkit (a general-purpose optimizer), along with the trained conditional RNN model, can parameterize, automate, and accelerate model calibration. The trial-and-error process is then accomplished with Dakota and parameterized and automated with Python scripts and accelerate global optimum search with surrogate models. The present CZM is validated by calibrating its associated interface parameters from a series of constant amplitude double cantilever beam (DCB) tests on unidirectional E-glass fiber/E722 composite beams. It may be modified to accommodate other types of fracture or interfacial debonding.
The aerodynamic parameters of a variable-sweep aircraft undergo significant changes during its morphing process, exerting a substantial impact on the aircraft's dynamic characteristics. Therefore, a critical aspec...
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ISBN:
(纸本)9798350366907;9789887581581
The aerodynamic parameters of a variable-sweep aircraft undergo significant changes during its morphing process, exerting a substantial impact on the aircraft's dynamic characteristics. Therefore, a critical aspect involves investigating the modeling of the variable-sweep aircraft throughout its morphing process and addressing stability in flight control. This paper commences by establishing the variant aircraft model using openVSP software to acquire precise aerodynamic parameters. Capitalizing on its nonlinear longitudinal dynamics model, the construction of a linear switching model for the variant aircraft's short-period switching is successfully accomplished. For the variable-sweep aircraft switching system, encountering multiple disturbances and actuator faults, a composite observer is intricately designed to simultaneously estimate external disturbances described by external systems and actuator faults. Subsequently, based on disturbance observer control and fault accommodation, a composite fault-tolerant controller with disturbance suppression is proposed. The attenuation characteristics are thoroughly analyzed using L-1 optimization performance indicators. Employing the Average Dwell Time method and Lyapunov function, a comprehensive stability analysis of the closed-loop system is conducted. Finally, the efficacy of the proposed approach is substantiated through numerical examples.
Downtime caused by equipment failure is the biggest productivity problem in the 24-hour a day operations of the semiconductor industry. Although some equipment failures are inevitable, increases in productivity can be...
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Downtime caused by equipment failure is the biggest productivity problem in the 24-hour a day operations of the semiconductor industry. Although some equipment failures are inevitable, increases in productivity can be gained if the causes of failures can be detected quickly and repaired, thus reducing downtime. Univariate control charts are commonly used to detect failures. However, because of the complexity of the process and the structural characteristics of the equipment, detection and identification of the causes of failures may be difficult. The purpose of this study is to use correlations of variables to detect failures in semiconductor equipment, to predict the parts to be replaced and to identify the primary causes of failures. The proposed method consists of four steps: (1) conversion of the multivariate time series data of the equipment into signature matrixes, (2) detection of anomalies through a convolutional autoencoder, (3) learning classification models with supervised learning methods that use the residual matrixes of fault sections, and (4) application of an explainable algorithm to interpret the classification model. The effectiveness and applicability of the proposed method are demonstrated by the actual multivariate time series data obtained from 8-inch ashing process equipment that produces semiconductors on 8-inch silicon wafers.
In order to achieve real-time monitoring of employment positions and evaluate industry development potential, a method for building a B/S based employment position data visualization monitoring platform was explored. ...
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In recent years, there is an increase in research into shoe last customization and topic analysis methods. The work aims to systematically review the literature on the customization of shoe lasts. The method used in t...
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In recent years, there is an increase in research into shoe last customization and topic analysis methods. The work aims to systematically review the literature on the customization of shoe lasts. The method used in this work is to perform a five-phase systematic review algorithm. data on the research performed are extracted and synthesized from each study: main research objectives, authors, date of publication, journal, or conference in which the article was published, and the quality of each article. The studies included in the review are published between 2018 and 2022. The results of the review are nineteen papers about the process of customization of the shoe last. The conclusions of the analysis indicate that the quality of research has not changed over time, in 2020 there was a decrease in work. Most often, researchers analyze the impact of anthropometric factors on the correct shoe last modeling and methods of shoe last parameterization.
In response to the issues caused by unclear knowledge expression during assembly process, an in-depth analysis was conducted on the sources and domain characteristics of fuze assembly process knowledge. Based on this ...
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In modern industry, data-driven process monitoring systems (PMS), as the initial defense line of industrial control system security, have been widely used in all walks of life. However, the privacy security of the dat...
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ISBN:
(纸本)9798350321050
In modern industry, data-driven process monitoring systems (PMS), as the initial defense line of industrial control system security, have been widely used in all walks of life. However, the privacy security of the data-driven PMS itself has rarely or never received serious attention. Once the data-driven PMS suffers from intrusion and malicious attacks, it will not only interfere with the normal operation of the industrial control system, but also lead to the disclosure of industrial confidential and privacy information and major economic losses. To handle this issue, this work proposes a novel pioneering study on the inference attack and privacy security problem in the data-driven PMS. Firstly, the potential attack and privacy violation risks of data-driven PMS are investigated. Second, a novel industrial inference attack and privacy security benchmark on data-driven PMS is presented, in which a series of membership inference attack and defense experiments are designed and conducted. Third, we provided a detailed discussion about which member reasoning attacks are the most potential threats to the data-driven PMS and which defense technologies are most suitable for mitigating the attack. The experimental results will provide researchers and practitioners with a new perspective when designing a novel data-driven PMS with more robust and privacy protection performance.
This paper focuses on the task of survival time analysis for lung cancer. Despite significant progress in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep lea...
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This paper focuses on the task of survival time analysis for lung cancer. Despite significant progress in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep learning- based approaches for lung cancer survival time analysis primarily rely on textual clinical information such as staging, age, and histology, etc. Unlike these existing methods that predicting on the single modality, we observe that human clinicians usually consider multimodal data, such as textual clinical parameters and visual scans when estimating survival time. Motivated by this observation, we propose Lite-ProSENet, a smart cross-modality network for survival analysis that simulates human decision-making. Specifically, Lite-ProSENet adopts a two-tower architecture that takes the clinical parameters and the CT scans as inputs to produce survival prediction. The textural tower is responsible for modeling the clinical parameters. We build alight transformer using multi-head self-attention as our textural tower. The visual tower, ProSENet, is designed to extract features from CT scans. The backbone of ProSENet is a 3D ResNet that works together with several repeatable building blocks named 3D-SE Resblocks for compact feature extraction. Our 3D-SE Resblock is composed of a 3D channel "Squeeze-and-Excitation"(SE) block and a temporal SE block. The purpose of 3D-SE Resblock is to adaptively select valuable features from CT scans. Besides, to further filter out the redundant information in the CT scans, we developed a simple yet effective frame difference mechanism, which boost the performance of our model to achieve new state-of-the-art results. Extensive experiments were conducted using data from 422 NSCLC patients from The Cancer Imaging Archive (TCIA). The results show that our Lite-ProSENet outperforms favorably against all comparison methods and achieves anew state-of-the-art concordance score of 89.3%. Our code is available at: https://github.c
Conventional method of reactor core analysis pivots on the two-step methodology requires the users to have expert understanding on the methodology, to provide significant manual input, and to maintain substantial data...
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ISBN:
(纸本)9780791888223
Conventional method of reactor core analysis pivots on the two-step methodology requires the users to have expert understanding on the methodology, to provide significant manual input, and to maintain substantial data consistency. Thereby, the calculation process turns out to be complex and error-prone. In light of this, we developed Bamboo-Frame to automate the modeling procedure in Pressurized Water Reactor (PWR) core analysis. This innovative tool requires users to provide only structural and operational parameters, while effectively managing complex tasks such as lattice recognition, thermal expansion, state matrix setting, core nodes division, and input file generation. This culminates in a user-friendly visualization system, thus significantly increasing accessibility and reducing manual workload. The application of Bamboo-Frame to CNP650, a PWR with a full power output of 1930 MW, showcased its capability to handle the modeling of 52 fuel lattices and numerous reflector lattices. The software's robustness and precision were evident when compared to the case without Bamboo-Frame, with the input file size dramatically reduced from 38.6MB to 168KB, underscoring its efficiency and the potential to reduce human error. Validation through startup physical test simulations and power history following simulations proved its remarkable accuracy within industrial standards, alongside statistical analysis demonstrating compliance with engineering standards.
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