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 documen...
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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 st...
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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
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). Th...
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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 controlprocess. 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
process mining uses data from event logs to understand which activities were undertaken, their timing, and the involved entities, providing a data trail for processanalysis and improvement. However, a significant cha...
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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 conside...
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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 mass...
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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.
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, ...
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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 ...
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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.
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 methodo...
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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 substrat...
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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.
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