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A Smart Manufacturing Conceptual Framework for Digitalising data Collection of the Quality Management System for Regulatory Compliance in the Medical Device Industry 6

A Smart Manufacturing Conceptual Framework for Digitalising ...

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6th International conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Kannaraya, Puneeth S Arora, Manish Chakrabarti, Amaresh Department of Design and Manufacturing Indian Institute of Science Bengaluru India

The medical device manufacturing industry is driven by stringent regulatory compliances and faces challenges in implementing quality management systems. One such challenge is the preparation of the paper-based documentation of all the documents as evidence for certification audits. With the advent of Industry 4.0, the increasing amount of data from the shop floor, with variety and veracity, poses additional challenges to the organization. Various technological interventions have made quality systems suffer from categorizing the incoming data and presenting it in suitable formats. A smart manufacturing conceptual framework with elements of intelligent manufacturing, quality control and smart manufacturing has been developed and proposed. The activities and outcomes of the framework enable the organization to capture data from the shop floor according to the various entities. The TOPSIS decision modeling technique is used to perform the structural validation of the framework. The TOPSIS scores reveal the influence of the framework compared to a conventional process. A statistical test was also performed to emphasize the claim. © 2024 The Authors.

关键词： Smart manufacturing

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Design of University Innovation and Entrepreneurship System Based on Cloud Model data Mining Algorithm

Design of University Innovation and Entrepreneurship System ...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Ma, Yingying Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

This article delves into the design and implementation of innovation and entrepreneurship systems based on cloud model data mining algorithms in university environments. Firstly, through in-depth communication with stakeholders such as university management, teachers, and students, a comprehensive analysis and planning of the system's requirements were conducted. Next, the article provides a detailed description of the system architecture design, including key components such as data collection and management, data preprocessing, cloud model data mining, user interface, reporting and visualization, security and privacy protection, as well as user feedback and support. During the system development process, emphasis was placed on the design principles and construction of cloud model data mining algorithms, as well as the optimization measures taken to improve system efficiency and accuracy. The system implementation process follows a series of strategies, including preliminary planning, system development, data preparation and migration, system integration, user training, as well as system deployment and continuous maintenance. Each stage is carefully designed and executed to ensure the effectiveness and reliability of the system. After implementation, a comprehensive performance evaluation of the system was conducted, including accuracy, efficiency, and user satisfaction. The results showed that the system performed well in multiple aspects, but also pointed out some areas that needed improvement. Finally, the article summarizes the main advantages and limitations of the system, and puts forward suggestions for future work directions, including further optimizing user experience, algorithm performance, and expanding application scope. This study provides an efficient and reliable data support and decision-making assistance tool for innovation and entrepreneurship activities in universities, which is of great significance for promoting the development of the innovati

关键词： Decision making

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Finite-time adaptive command filtering control for manipulators based on disturbance observer 2

Finite-time adaptive command filtering control for manipulat...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Liu, Yifan Zhao, Ziyang Zhu, Wenjun Hao, Chongjun College of Mechanical Engineering Taiyuan University of Technology Shanxi Taiyuan China

ISBN: (纸本)9781510689053

In this research, we propose an innovative adaptive command filter control strategy tailored for manipulator systems, incorporating a disturbance observer enhanced by a Radial Basis Function Neural Network (RBFNN). The primary objective is to address the challenges posed by lumped disturbances within the system, which can significantly affect the precision and stability of the manipulator's operation. Initially, the adaptive disturbance observer, augmented with RBFNN, is designed to accurately estimate and compensate for the combined effects of external disturbances and model uncertainties. This integration leverages the non-linear approximation capabilities of RBFNN to capture the complex dynamics of the disturbances, thereby enhancing the robustness of the control system. Subsequently, to further refine the control performance, we introduce a command filter alongside a filter error compensation signal. The command filter is employed to smooth the virtual control signal, mitigating the impact of high-frequency noise and ensuring a more stable control process. Meanwhile, the filter error compensation signal is designed to correct any discrepancies introduced by the filtering process, allowing for the precise computation of the differential signal of the virtual control input. This dual approach not only improves the accuracy of the control signals but also enhances the overall dynamic response of the manipulator system. The theoretical foundation of the proposed control scheme is grounded in Lyapunov stability theory. Through rigorous mathematical analysis, it is demonstrated that the tracking error of the manipulator system converges to zero within a finite time frame. This ensures that the system maintains stable and accurate performance even under varying operating conditions and in the presence of disturbances. To validate the effectiveness of the proposed adaptive command filter control scheme, extensive simulations were conducted. The results clearly indicate

关键词： Adaptive filters

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Enriching Business process Event Logs with Multimodal Evidence 17th

Enriching Business Process Event Logs with Multimodal Evide...

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17th IFIP 8.1 Working conference on the Practice of Enterprise modeling, PoEM 2024

作者： Gavric, Aleksandar Bork, Dominik Proper, Henderik A. Business Informatics Group TU Wien Vienna Austria

ISBN: (纸本)9783031779077

process mining uses data from event logs to understand which activities were undertaken, their timing, and the involved entities, providing a data trail for process analysis and improvement. However, a significant challenge involves ensuring that these logs accurately reflect the actual processes. Some processes leave few digital traces, and their event logs often lack details about manual and physical work that does not involve computers or simple sensors. We introduce the Business-knowledge Integration Cycles (BICycle) method and mm_proc_miner tool to convert raw and unstructured data from various modalities, such as video, audio, and sensor data, into a structured and unified event log, while keeping human-in-the-loop. Our method analyzes the semantic distance between visible, audible, and textual evidence within a self-hosted joint embedding space. Our approach is designed to consider (1) preserving the privacy of evidence data, (2) achieving real-time performance and scalability, and (3) preventing AI hallucinations. We also publish a dataset consisting of over 2K processes with 16K steps to facilitate domain inference-related tasks. For the evaluation, we created a novel test dataset in the domain of DNA home kit testing, for which we can guarantee that it was not encountered during the training of the employed AI foundational models. We show positive insights in both event log enrichment with multimodal evidence and human-in-the-loop contribution. © IFIP International Federation for Information processing 2025.

关键词： data privacy

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Model-Free Quantitative Diagnosis of Internal Short Circuit for Sodium-Ion Batteries With Charging Capacity Difference analysis

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IEEE Transactions on Power Electronics 2025年第8期40卷 11394-11406页

作者： Liu, Gang Gao, Zhengpeng Lin, Mingqiang Liu, Xingtao Wu, Ji Hefei University of Technology School of Automotive and Transportation Engineering Hefei230009 China Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province Hefei230009 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of Matter Jinjiang362200 China

Thermal runaway caused by the internal short circuit (ISC) poses a significant safety risk for sodium-ion batteries (SIBs) in electric vehicles and energy storage applications. Early detection of ISC faults is considered a potential way to reduce the risk associated with fire or explosion and can be identified via minor power leakage. However, diagnosing these leaks in the initial stages of ISC is quite challenging without battery modeling or preliminary experiments. Here, a model-free method based on charging capacity difference analysis is proposed for quantitatively diagnosing ISC fault. Firstly, a wavelet denoising algorithm with improved thresholding is presented to extract the real signals from the noise-containing voltage and current data. Subsequently, the ISC resistance is estimated by calculating the charging capacity difference variation between the ISC cell and the normal cell. Furthermore, the amount of power leakage during the charging process of ISC batteries is considered to improve the accuracy of the ISC diagnosis. Finally, experiments with elaborate ISC batteries are conducted to validate the method, achieving a resistance estimation error below 5% across different ISC severities while maintaining robustness to capacity inconsistencies. Compared to existing model-free methods, the proposed method significantly enhances diagnostic accuracy and broadens its applicability. © 2025 IEEE.

关键词： State of charge

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R-matrix analysis of ²²Ne(α, n)²⁵Mg and ²²Ne(α, γ)²⁶Mg reactions

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JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS 2025年第2期52卷 025201-025201页

作者： Santra, Rajkumar Roy, Subinit Ali, S. K. Mustak Univ Calcutta Dept Phys Kolkata 700009 India Michigan State Univ Facil Rare Isotope Beams E Lansing MI 48824 USA

The reaction rates of 22Ne(alpha, n)25Mg and its competing channel 22Ne(alpha, gamma)26Mg control the production of neutron flux for weak s-process nucleosynthesis in low mass asymptotic giant branch stars and in massive stars with M >= 10M circle dot. The temperature range of interest for these reactions lies between 0.2 and 0.4 GK. However, the rates of these reactions are poorly constrained at these temperatures due to uncertainties in the nuclear properties of several resonance states in the compound nucleus 26Mg, lying within the Gamow window. The present work reports a full R-matrix evaluation of the 22Ne(alpha, n)25Mg and 22Ne(alpha, gamma)26Mg reaction rates using updated nuclear data of 26Mg states. Previous rate evaluation by Adsley et al and R-matrix calculations of Wiescher et al were limited by using narrow resonance approximations and omission of the resonances below Er = 705 keV, respectively. In this work, the R-matrix fit to the available 22Ne(alpha, n)25Mg reaction data is performed by including the contributions of previously neglected resonances below Er = 705 keV and considering the interference effects. The (alpha, n) reaction rate from the present R-matrix evaluations is noticeably higher than the narrow resonance approximation calculations in the temperature range 0.1-0.3 GK. In particular, the present (alpha, n) reaction rate is significantly higher (7.5 - 4.5 times) compared to Adsley et al at 0.2-0.3 GK and approximate to 2 times greater than Wiescher et al at 0.3 GK. The estimated reaction rate ratio of (alpha, n) to (alpha, gamma) in the relevant temperature window 0.2-0.8 GK indicates that the production of neutrons for the s-process is more likely than the radiative alpha capture reaction, compared to the previous estimate by Adsley et al.

关键词： R-matrix modeling resonance and direct capture reaction He induced nuclear reactions reaction rate

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Comparison of Low and High Fidelity Dynamic modeling Software for Small UAS Based on Flight data

Comparison of Low and High Fidelity Dynamic Modeling Softwar...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Powers, Matthew Baruth, Adam Hantla, Avery University of Kansas Aerospace Engineering LawrenceKS66045 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper is a comparison of aircraft analysis techniques of the H-King Bixler 1.1, a small fixed-wing UAS. Geometric characteristics including wing, fuselage, control surfaces, and moments of inertia were measured, estimated, and used to develop physics-based dynamic models for the H-King Bixler. The dynamic analysis was performed using Advanced Aircraft analysis, as well as Athena Vortex Lattice. Limited, selected high-impact stability derivatives were also estimated using the hpMusic computational fluid dynamics (CFD) solver. A linear time-invariant state-space model of the aircraft was created for each physics-based model. A validation flight test was conducted to assess the quality of engineering level and conceptual methods. Overall, the physics-based modeling provided a relatively acceptable prediction for longitudinal and lateral aircraft states. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Fixed wings

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Implementation of Artificial-Intelligence-Powered Tools in data processing for Well Time Estimation and Risk analysis in Mexico and Ecuador Projects

Implementation of Artificial-Intelligence-Powered Tools in D...

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2025 International Petroleum Technology conference, IPTC 2025

作者： Szlapa, S. Douy, C. Sesboue, P. Erives, J. Telles, J. Benavides Delgado, A.A. Paccha, J. Gori, F. Mani, A. Essmat, N. Azuara, O. SLB London United Kingdom SLB Montpellier France SLB Mexico City Mexico SLB Quito Ecuador SLB Dhahran Saudi Arabia

ISBN: (纸本)9781959025436

The introduction of artificial intelligence (AI)-driven tools has revolutionized offset well analysis, particularly in estimating well time for conceptualization and planning phases. Previously labor-intensive manual processes for analyzing legacy data have been transformed into digital predictions of activities, operational sequences, key performance indicators (KPIs), and risk estimations through a web application. This system comprises AI engines that model specific aspects of well construction, working with a user interface that enables control over data and domain parameters. Engineers can develop well designs and, using existing designs as references, the application computes KPIs, forecasts operational sequences, assesses risks, and estimates well durations. By employing probability distributions for each input and iterating the process multiple times, the application generates histograms for each output upon simulation completion. Before AI tools, engineers faced the daunting task of manually analyzing extensive drilling reports, a process that could take weeks. The new web application streamlines this into a swift, efficient process, drastically reducing time and enhancing accuracy. Tested successfully in projects across Mexico and Ecuador, the application has shown significant improvements in efficiency and competitiveness. These AI-driven tools not only save time but also reduce costs and carbon emissions, exemplifying their transformative impact on the oil and gas industry. Copyright 2025, International Petroleum Technology conference.

关键词： Gas industry

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A Combined GAN and DF Based Expansion and Prediction for Small Sample data 16th

A Combined GAN and DF Based Expansion and Prediction for Sma...

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16th International conference on Modelling, Identification and control, ICMIC 2024

作者： Wang, Li Lu, Junfeng Feng, Shanfang Wang, Xiaoyi Jin, Xuebo Yu, Jiabin Bai, Yuting Beijing Laboratory for Intelligent Environmental Protection School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789819617760

The high cost and difficulty of data collection result in discrete intermittency and limited available data for most datasets, posing challenges to modeling and prediction needs. Thus, it is crucial to explore methodologies suitable for discrete small sample data. Concurrently, unsupervised learning methods are employed for small sample prediction to circumvent the issue of low prediction accuracy resulting from inadequate a priori knowledge. Current data modeling and prediction methods rely heavily on neural network deep learning models, which are mainly designed for continuous processes and show low accuracy for intermittent processes. This paper proposes a data expansion method based on Time Generative Adversarial Networks (TimeGAN) and a prediction method based on the combination of Generative Adversarial Networks (GAN) and Deep Forest (DF). Firstly, the TimeGAN model is proposed to learn small sample data for data expansion. Then, the GAN-DF model is constructed so that the model is used for data prediction of unsupervised discrete processes. On this basis, we introduce the DFGAN and LSTM-DFGAN models, which embed DF and LSTM-DF models as generators within the GAN framework to enhance prediction accuracy for small sample data. Simulation validation using contaminant data from a food processing process demonstrates the efficacy of the TimeGAN method in data expansion. The results demonstrate that the TimeGAN method is capable of effectively expanding the data, while the LSTM-DFGAN model exhibits the most optimal combined prediction effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Assessing environmental effects on yardang development in the Qaidam Basin: Findings based on large-scale mapping of yardangs

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GEOMORPHOLOGY 2025年 471卷

作者： Gao, Bowen Chen, Ninghua Zhang, Deguo Xu, Yaochen Weng, Xuhua Gao, Wenqi Jiang, Xinhao Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang P Hangzhou 310000 Peoples R China Zhejiang Univ Sch Earth Sci Hangzhou 310000 Peoples R China

Yardangs are streamlined ridges that form in arid environments mainly driven by the aeolian process. However, the development and evolution of yardangs could also involve feedback among other factors like the substrate, underlying topography, and rainfall. Currently, there is limited consensus on how these factors affect yardang development at the landscape scale. In this work, based on very high spatial resolution remotely sensed imagery and deep learning techniques, we conducted a comprehensive mapping of yardangs across the immense yardang fields in the Qaidam Basin, NW China. Then we used the yardang coverage that is the percentage of yardang areas per gridded zone, to estimate the yardang abundance and its spatial distribution across the study site. Then the partial least squares-structural equation modeling (PLS-SEM) was employed to quantitatively analyze the relationships among yardang coverage and wind force (Wind), substrate weakness (Substrate), underlying topography complexity (Topography), and rainfall force (Rainfall). The results indicated that Rainfall had a significant constraining effect on yardang development, accounting for 34 % of the total effect in the model. In contrast, Substrate, Wind, and Topography exhibited positive effects on yardang development, contributing 31 %, 20 %, and 15 % of the total effect, respectively. A spatial clustering of yardang fields was then carried out using the response-based unit segmentation (REBUS) algorithm, demonstrating the spatial heterogeneity of how these factors contribute to yardang development in different regions. This study improved our understanding of the mechanisms of yardang formation and evolution under the control of multiple factors.

关键词： Yardang Qaidam Basin Deep convolutional neural network Multivariate analysis Structural equation modeling (SEM)

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