53 research outputs found
Inverting multivariate analytic characteristic functions with financial applications
No full textThis dissertation is devoted to multivariate analytic characteristic functions inversion and applications in option pricing, option sensitivities estimation, and some electronic engineering problems. We will show that under certain analytic conditions for characteristic functions, the underlying pdfs and cdfs have exponential tails. The inversion from multivariate characteristic functions to the corresponding pdfs and cdfs can be approximated by the trapezoidal rule conveniently with great accuracy. Monte Carlo methods can be applied for option sensitivity analysis. Under multi-dimensional models, acceptance-rejection method is desirable. Simulating from a distribution without explicit pdf or CDF is then transformed to sampling from an easy-to-simulate distribution. Detailed algorithms are provided and comparisons against classical methods in terms of accuracy and efficiency are included.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01The student, Runqi Hu, accepted the attached license on 2019-04-19 at 10:23.The student, Runqi Hu, submitted this Dissertation for approval on 2019-04-19 at 10:32.This Dissertation was approved for publication on 2019-04-19 at 14:25.DSpace SAF Submission Ingestion Package generated from Vireo submission #13740 on 2019-08-22 at 15:07:23Made available in DSpace on 2019-08-23T20:36:02Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-04-19Embargo set by: Seth Robbins for item 112175
Lift date: 2021-08-23T20:36:18Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 112175 on 2021-08-24T09:15:20Z
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Automated computer vision system for real-time drilling cuttings monitoring
No full textIn rotary drilling operations, cuttings are continuously transported to the surface by drilling fluid. Real-time monitoring of cuttings and cavings is crucial for early detection and remediation of drilling problems such as stuck pipe, lost circulation, high torque and drag, reduction in rate of penetration, and other wellbore instability issues. These incidents are large contributors to drilling-related Non-Productive Time (NPT). At the current stage, a mud logger performs monitoring manually. This work proposes to use computer vision techniques to automate this procedure. To achieve this application, specific requirements should be established to design an automated machine vision system to maintain drilling safety and speed. Cuttings ramp has been identified as an ideal location to perform the measurement, where cuttings and caving are sliding down a slope at a steady speed. To accomplish this task, an intelligent image processing system must be able to track cuttings speed, measure volume, analyze size, and generate a surface model. Through a detailed review and testing of available 3D sensing techniques, a system consisting of a 2D high-resolution camera and 3D laser profile scanner was designed. By implementing image processing techniques, the cuttings speed on the ramp was estimated which was then synchronized to the 3D depth data from a laser scanner. Finally, the volume of moving cuttings was estimated and a 3D surface profile was reconstructed using point cloud data. Experimental results in the lab environment validated that such a system can be applied to quantify cuttings volume, size distribution, and reconstruct a 3D profile of cuttings and cavings. This measured result can be stored for further analysis. Overall, this work established a foundation for the design of a sophisticated real-time monitoring system for hole cleaning and wellbore risk reduction.Petroleum and Geosystems Engineerin
Big Data Analysis on Economical Urban Traffic in Beijing : Organize overlapping transportation though the underground diameter line of Beijing railway hub
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Research trends on alphavirus receptors: a bibliometric analysis
No full textBackgroundAlphaviruses are a diverse group of pathogens that have garnered considerable attention due to their impact on human health. By investigating alphavirus receptors, researchers can elucidate viral entry mechanisms and gain important clues for the prevention and treatment of viral diseases. This study presents an in-depth analysis of the research progress made in the field of alphavirus receptors through bibliometric analysis.MethodsThis study encompasses various aspects, including historical development, annual publication trends, author and cited-author analysis, institutional affiliations, global distribution of research contributions, reference analysis with strongest citation bursts, keyword analysis, and a detailed exploration of recent discoveries in alphavirus receptor research.ResultsThe results of this bibliometric analysis highlight key milestones in alphavirus receptor research, demonstrating the progression of knowledge in this field over time. Additionally, the analysis reveals current research hotspots and identifies emerging frontiers, which can guide future investigations and inspire novel therapeutic strategies.ConclusionThis study provides an overview of the state of the art in alphavirus receptor research, consolidating the existing knowledge and paving the way for further advancements. By shedding light on the significant developments and emerging areas of interest, this study serves as a valuable resource for researchers, clinicians, and policymakers engaged in combating alphavirus infections and improving public health
Small-Signal Stability Analysis and Optimization of Grid-Forming Permanent-Magnet Synchronous-Generator Wind Turbines
No full textDue to the ability to improve the low-inertia characteristics of power systems and offer reliable voltage and frequency support, grid-forming permanent-magnet synchronous-generator wind turbines (PMSG-WTs) based on virtual synchronous-generator (VSG) technology are emerging se the direction for future developments. Previous studies on the small-signal stability of grid-forming PMSG-WTs that connect to the grid usually simplify them into grid-connected grid-side converters (GSC), potentially leading to errors in stability analyses. Therefore, this paper considers the machine-side converter (MSC) control and establishes impedance models for grid-forming PMSG-WTs. Based on the sensitivity calculation of controller parameters using symmetric difference computation based on zero-order optimization, the impact of the internal controller on outside impedance characteristics is quantitatively analyzed. Additionally, an optimization method to enhance the stability of a hybrid wind farm by adjusting the ratio of grid-forming and grid-following wind turbines is proposed
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
No full textCatastrophic overfitting (CO) presents a significant challenge in single-step adversarial training (AT), manifesting as highly distorted deep neural networks (DNNs) that are vulnerable to multi-step adversarial attacks. However, the underlying factors that lead to the distortion of decision boundaries remain unclear. In this work, we delve into the specific changes within different DNN layers and discover that during CO, the former layers are more susceptible, experiencing earlier and greater distortion, while the latter layers show relative insensitivity. Our analysis further reveals that this increased sensitivity in former layers stems from the formation of pseudo-robust shortcuts, which alone can impeccably defend against single-step adversarial attacks but bypass genuine-robust learning, resulting in distorted decision boundaries. Eliminating these shortcuts can partially restore robustness in DNNs from the CO state, thereby verifying that dependence on them triggers the occurrence of CO. This understanding motivates us to implement adaptive weight perturbations across different layers to hinder the generation of pseudo-robust shortcuts, consequently mitigating CO. Extensive experiments demonstrate that our proposed method, Layer-Aware Adversarial Weight Perturbation (LAP), can effectively prevent CO and further enhance robustness.Accepted by ICML 202
Modulated Convolutional Networks
No full textWhile the deep convolutional neural network (DCNN) has achieved overwhelming success in various vision tasks, its heavy computational and storage overhead hinders the practical use of resource-constrained devices. Recently, compressing DCNN models has attracted increasing attention, where binarization-based schemes have generated great research popularity due to their high compression rate. In this article, we propose modulated convolutional networks (MCNs) to obtain binarized DCNNs with high performance. We lead a new architecture in MCNs to efficiently fuse the multiple features and achieve a similar performance as the full-precision model. The calculation of MCNs is theoretically reformulated as a discrete optimization problem to build binarized DCNNs, for the first time, which jointly consider the filter loss, center loss, and softmax loss in a unified framework. Our MCNs are generic and can decompose full-precision filters in DCNNs, e.g., conventional DCNNs, VGG, AlexNet, ResNets, or Wide-ResNets, into a compact set of binarized filters which are optimized based on a projection function and a new updated rule during the backpropagation. Moreover, we propose modulation filters (M-Filters) to recover filters from binarized ones, which lead to a specific architecture to calculate the network model. Our proposed MCNs substantially reduce the storage cost of convolutional filters by a factor of 32 with a comparable performance to the full-precision counterparts, achieving much better performance than other state-of-the-art binarized models.</p
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