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Literatura académica sobre el tema "Schemas augmentation"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Schemas augmentation"

Lee, Harrison, Raghav Gupta, Abhinav Rastogi, Yuan Cao, Bin Zhang y Yonghui Wu. "SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 10 (28 de junio de 2022): 10938–46. http://dx.doi.org/10.1609/aaai.v36i10.21341.

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Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. We explore the robustness of dialogue systems to linguistic variations in schemas by designing SGD-X - a benchmark extending SGD with semantically similar yet stylistically diverse variants for every schema. We observe that two top state tracking models fail to generalize well across schema variants, measured by joint goal accuracy and a novel metric for measuring schema sensitivity. Additionally, we present a simple model-agnostic data augmentation method to improve schema robustness.

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Fearnhead, Paul y Loukia Meligkotsidou. "Augmentation schemes for particle MCMC". Statistics and Computing 26, n.º 6 (12 de octubre de 2015): 1293–306. http://dx.doi.org/10.1007/s11222-015-9603-4.

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Meng, X.-L. "Seeking efficient data augmentation schemes via conditional and marginal augmentation". Biometrika 86, n.º 2 (1 de junio de 1999): 301–20. http://dx.doi.org/10.1093/biomet/86.2.301.

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Fraigniaud, Pierre, Cyril Gavoille, Adrian Kosowski, Emmanuelle Lebhar y Zvi Lotker. "Universal augmentation schemes for network navigability". Theoretical Computer Science 410, n.º 21-23 (mayo de 2009): 1970–81. http://dx.doi.org/10.1016/j.tcs.2008.12.061.

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Tufail, Ahsan Bin, Kalim Ullah, Rehan Ali Khan, Mustafa Shakir, Muhammad Abbas Khan, Inam Ullah, Yong-Kui Ma y Md Sadek Ali. "On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer’s Disease Using Neuroimaging Modalities and Data Augmentation Methods". Journal of Healthcare Engineering 2022 (11 de febrero de 2022): 1–14. http://dx.doi.org/10.1155/2022/1302170.

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Alzheimer’s disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.

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Sinha, Rashmi Sharan, Sang-Moon Lee, Minjoong Rim y Seung-Hoon Hwang. "Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application". Electronics 8, n.º 5 (17 de mayo de 2019): 554. http://dx.doi.org/10.3390/electronics8050554.

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In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices.

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Chen, H. Z. B., R. Schober y L. Lampe. "Two Novel Channel-Augmentation Schemes for MIMO Systems". IEEE Signal Processing Letters 14, n.º 9 (septiembre de 2007): 601–4. http://dx.doi.org/10.1109/lsp.2007.896162.

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Golfarelli, Matteo, Jens Lechtenbörger, Stefano Rizzi y Gottfried Vossen. "Schema versioning in data warehouses: Enabling cross-version querying via schema augmentation". Data & Knowledge Engineering 59, n.º 2 (noviembre de 2006): 435–59. http://dx.doi.org/10.1016/j.datak.2005.09.004.

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Chindanonda, Peeranut, Vladimir Podolskiy y Michael Gerndt. "Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads". Computers 9, n.º 1 (14 de febrero de 2020): 12. http://dx.doi.org/10.3390/computers9010012.

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Internet of Things (IoT) covers scenarios of cyber–physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of user experience matters, e.g., interactivity and media streaming as well as the predictive maintenance for multiple moving vehicles, centralized analytics for wearable devices and smartphones. The self-adaptation mechanism for data processing IoT infrastructure deployed in the cloud is horizontal autoscaling. In this paper we propose augmentations to the computation schemes of data processing component’s desired replicas count from the previous work; these augmentations aim to repurpose original sets of metrics to tackle the task of SLO violations minimization for dynamic workloads instead of minimizing the cost of deployment in terms of instance seconds. The cornerstone proposed augmentation that underpins all the other ones is the adaptation of the desired replicas computation scheme to each scaling direction (scale-in and scale-out) separately. All the proposed augmentations were implemented in the standalone self-adaptive agent acting alongside Kubernetes’ HPA such that limitations of timely acquisition of the monitoring data for scaling are mitigated. Evaluation and comparison with the previous work show improvement in service level achieved, e.g., latency SLO violations were reduced from 2.87% to 1.70% in case of the forecasted message queue length-based replicas count computation used both for scale-in and scale-out, but at the same time higher cost of the scaled data processor deployment is observed.

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Chen, Zhiyu. "Dataset Search and Augmentation". ACM SIGIR Forum 56, n.º 1 (junio de 2022): 1–2. http://dx.doi.org/10.1145/3582524.3582544.

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Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to effectively perform dataset search and augmentation. We start by providing an end-to-end description of a dataset search engine following the lifecycle of datasets. Our review includes web dataset acquisition techniques, dataset profiling and augmentation methods, and dataset search tasks and corresponding methods. In order to extract datasets from research articles, we present an information extraction framework to determine triples of interest which can be used for academic dataset search. We propose a feature-based method to augment tabular datasets with additional schema labels to help users and systems to better understand the datasets. We develop three methods for tabular dataset search: the first utilizes generated schema labels to enhance the search results; the second adopts pretrained language models to learn matching features; the third models the complex relations in the datasets as one or more graphs and uses graph neural networks to learn representations of queries and tables. To support dataset search in which a query is also a dataset, we propose universal dataset encoders which regard a dataset as a point set so that the encoded dataset representations can be used to search for similar datasets. Extensive experiments across multiple tasks demonstrate the superiority of our proposed methods over the state of the art. Awarded by: Lehigh University, Bethlehem, USA on 10 May 2022. Supervised by: Brian D. Davison. Available at: https://github.com/Zhiyu-Chen/Dissertation/blob/main/Dissertation_Dataset_Search.pdf.

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Tesis sobre el tema "Schemas augmentation"

Liu, Rutian. "Semantic services for assisting users to augment data in the context of analytic data sources". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS208.

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La production de collections de données analytiques est une tendance importante et a dépassé le cadre des technologies traditionnelles de production d'information et de données. Les collections de données analytiques sont maintenant directement créées par les utilisateurs (experts, data scientists). Malgré l'apparition des nouvelles technologies "big data" et d'outils de préparation de données agiles, l'intégration et l'enrichissement de schémas analytiques avec des attributs provenant d'autres sources de données reste une tâche difficile qui nécessite une bonne connaissance des schémas de données manipulées. Cette thèse présente une nouvelle solution pour compléter des schémas de données analytiques avec des attributs provenant d'autres sources de données sémantiquement liées : -Nous introduisons les graphes d'attributs comme une nouvelle façon concise et naturelle pour représenter les dépendances fonctionnelles littérales sur des attributs de dimensions hiérarchiques et pour déduire des identificateurs uniques de dimensions et de tables de faits. -Nous donnons des définitions formelles d'augmentation de schémas, de complément de schémas et de requête de fusion dans le contexte des données analytiques. Nous introduisons ensuite plusieurs opérations de réduction pour éviter la multiplication de lignes dans la table de données augmentée. -Nous définissons des critères formels de qualité et des algorithmes pour contrôler l'exactitude, la non-ambiguïté et l'exhaustivité des augmentations et des compléments de schéma générés. -Nous décrivons l'implémentation de notre solution au sein de la plate-forme SAP HANA et fournissons une description détaillée de nos algorithmes. -Nous évaluons la performance de nos algorithmes et analysons l'efficacité de notre approche avec deux scénarios d'application
The production of analytic datasets is a significant big data trend and has gone well beyond the scope of traditional IT-governed dataset development. Analytic datasets are now created by data scientists and data analysts using bigdata frameworks and agile data preparation tools. However, it still remains difficult for a data analyst to start from a dataset at hand and customize it with additional attributes coming from other existing datasets. This thesis presents a new solution for business users and data scientists who want to augment the schema of analytic datasets with attributes coming from other semantically related datasets : We introduce attribute graphs as a novel concise and natural way to represent literal functional dependencies over hierarchical dimension level types to infer unique dimension and fact table identifiers We give formal definitions for schema augmentation, schema complement, and merge query in the context of analytic tables. We then introduce several reduction operations to enforce schema complements when schema augmentation yields a row multiplication in the augmented dataset. We define formal quality criteria and algorithms to control the correctness, non-ambiguity, and completeness of generated schema augmentations and schema complements. We describe the implementation of our solution as a REST service within the SAP HANA platform and provide a detailed description of our algorithms. We evaluate the performance of our algorithms to compute unique identifiers in dimension and fact tables and analyze the effectiveness of our REST service using two application scenarios

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"Heteroscedastic Approaches for Deciphering Multiethnic Genomic Sequences and Microarrays: Harmonious Signal Augmentation Schemes in Genomic Sequences and Microarrays". Tulane University, 2017.

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Capítulos de libros sobre el tema "Schemas augmentation"

Jindal, Nipun y Pranay Kumar. "Devising Auxiliary Glyph Schemas Combined with XOR Filters for Improvised Font Delivery and Reliable Dynamic Font Augmentation". En Lecture Notes in Networks and Systems, 75–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4016-2_8.

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Johnson, Valen E., Wing H. Wong, Xiaoping Hu y Chin-Tu Chen. "Data Augmentation Schemes Applied to Image Restoration". En Medical Images: Formation, Handling and Evaluation, 345–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77888-9_14.

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Hamayun, Mirza Tariq, Christopher Edwards y Halim Alwi. "An Augmentation Scheme for Fault Tolerant Control Using Integral Sliding Modes". En Fault Tolerant Control Schemes Using Integral Sliding Modes, 103–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32238-4_6.

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Seipel, Dietmar y Detlev Ruland. "Designing gamma-acyclic database schemes using decomposition and augmentation techniques". En Graph-Theoretic Concepts in Computer Science, 171–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-19422-3_14.

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Vanderheyden, Peter B. y Christopher A. Pennington. "An augmentative communication interface based on conversational schemata". En Assistive Technology and Artificial Intelligence, 109–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0055974.

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Chagas, Vítor Gomes, Elisa Dell’Arriva y Flávio Keidi Miyazawa. "Approximation Schemes Under Resource Augmentation for Knapsack and Packing Problems of Hyperspheres and Other Shapes". En Approximation and Online Algorithms, 145–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49815-2_11.

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Zaccarian, Luca y Andrew R. Teel. "Static Linear Anti-windup Augmentation". En Modern Anti-windup Synthesis. Princeton University Press, 2011. http://dx.doi.org/10.23943/princeton/9780691147321.003.0004.

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This chapter describes the first constructive tools for anti-windup augmentation. It considers the simplest possible augmentation scheme that may induce on the closed loop the qualitative objectives of anti-windup augmentation, possibly in addition to some of the quantitative performance objectives. The focus is on the “static linear anti-windup” augmentation architecture, wherein the difference between the input and the output of the saturation block drives a static linear system that injects modification signals into the unconstrained controller dynamics. Before introducing the anti-windup design algorithms, the chapter discusses suitable state–space representations of the control systems, along with algorithms providing global and regional guarantees. The class of algorithms examined here is based on linear matrix inequalities that, when appropriately solved, provide gain selections that correspond to optimal values of certain performance measures.

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Yang, Hao-Wei, Ming-Yi Chang y Chih-Ya Shen. "Enhancing Link Prediction with Self-Discriminating Augmentation for Structure-Aware Contrastive Learning". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230596.

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Link prediction is a crucial research area for both data mining and machine learning. Despite the success of contrastive learning in node classification tasks, applying it directly to link prediction tasks has revealed two major weaknesses, i.e., single positive sample contrasting and random augmentation, resulting in inferior performance. To overcome these issues, we propose a new contrastive learning approach for link prediction, called Structure-aware Contrastive Representation Learning with Self-discriminating Augmentation (SECRET). Our approach includes a novel data augmentation scheme based on the prediction model itself and takes into account both the contrastive objective and the reconstruction loss, which jointly improve the performance of link prediction. Our experiments on 11 benchmark datasets demonstrate that SECRET significantly outperforms the other state-of-the-art baselines.

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"System Reliability". En EHT Transmission Performance Evaluation, 23–43. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4941-3.ch002.

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Power system adequacy and security with reference to probabilistic and deterministic approaches are dealt in the second chapter. Increased investment leads to improved power availability and underinvestment leads to reduction in quality of power supply. The related optimal reliability with respect to cost is explained here. Extensive computerization of data is required for assessing the benefit of expansion and reinforcement/augmentation schemes for obtaining improved availability in the context of deregulated utility environment.

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Zaccarian, Luca y Andrew R. Teel. "Dynamic Linear Anti-windup Augmentation". En Modern Anti-windup Synthesis. Princeton University Press, 2011. http://dx.doi.org/10.23943/princeton/9780691147321.003.0005.

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This chapter introduces several algorithms for dynamic direct linear anti-windup (DLAW) compensation, with particular emphasis on algorithms with global performance and stability guarantees. It considers a generalization of the static linear anti-windup scheme wherein the matrix gains are replaced by a linear dynamic anti-windup compensator F with internal states. The static case is recovered by this dynamic generalization when the size of the anti-windup filter state is zero. The interconnection of the dynamic anti-windup filter resembles that of the static case. The chapter first describes key state–space representations of the control systems before discussing the factorization of rank-deficient matrices. It then examines anti-windup algorithms providing global and regional gurantees, including those that lead to compensators of the same order as the plant and algorithms for the design of reduced-order compensators.

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Actas de conferencias sobre el tema "Schemas augmentation"

Cannaviccio, Matteo, Lorenzo Ariemma, Denilson Barbosa y Paolo Merialdo. "Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation". En SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3201463.3201468.

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Fraigniaud, Pierre, Cyril Gavoille, Adrian Kosowski, Emmanuelle Lebhar y Zvi Lotker. "Universal augmentation schemes for network navigability". En the nineteenth annual ACM symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1248377.1248379.

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Hasan, Mohammad H., Fadi Alsaleem, Amin Abbasalipour, Siavash Pourkamali Anaraki, Muhammad Emad-Un-Din y Roozbeh Jafari. "Machine Learning Augmentation in Micro-Sensor Assemblies". En ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22665.

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Abstract The size and power limitations in small electronic systems such as wearable devices limit their potential. Significant energy is lost utilizing current computational schemes in processes such as analog-to-digital conversion and wireless communication for cloud computing. Edge computing, where information is processed near the data sources, was shown to significantly enhance the performance of computational systems and reduce their power consumption. In this work, we push computation directly into the sensory node by presenting the use of an array of electrostatic Microelectromechanical systems (MEMS) sensors to perform colocalized sensing-and-computing. The MEMS network is operated around the pull-in regime to access the instability jump and the hysteresis available in this regime. Within this regime, the MEMS network is capable of emulating the response of the continuous-time recurrent neural network (CTRNN) computational scheme. The network is shown to be successful at classifying a quasi-static input acceleration waveform into square or triangle signals in the absence of digital processors. Our results show that the MEMS may be a viable solution for edge computing implementation without the need for digital electronics or micro-processors. Moreover, our results can be used as a basis for the development of new types of specialized MEMS sensors (ex: gesture recognition sensors).

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Chen, Harry Z. B., Robert Schober y Lutz Lampe. "Two Novel Channel Augmentation Schemes for MIMO Systems". En 2007 IEEE Wireless Communications and Networking Conference. IEEE, 2007. http://dx.doi.org/10.1109/wcnc.2007.213.

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Lai, Wenjie, Xiao Hu, Ziji Liu y Yadong Jiang. "Cross-modal Augmentation: A Data Augmentation Scheme for RGB-Thermal Semantic Segmentation". En 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2023. http://dx.doi.org/10.1109/iccasit58768.2023.10351626.

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ANWAR, SHOAIB, AUSTIN YUNKER, RAJ KETTIMUTHU, MARK A. ANASTASIO, UMBERTO VILLA y JIAZE HE. "EVALUATION OF U-NET FOR TIME-DOMAIN FULL WAVEFORM INVERSION IMPROVEMENT". En Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36986.

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Ultrasound computed tomography (USCT) is one of the advanced imaging techniques used in structural health monitoring (SHM) and medical imaging due to its relatively low-cost, rapid data acquisition process. The time-domain full waveform inversion (TDFWI) method, an iterative inversion approach, has shown great promise in USCT. However, such an iterative process can be very time-consuming and computationally expensive but can be greatly accelerated by integrating an AI-based approach (e.g., convolution neural network (CNN)). Once trained, the CNN model takes low-iteration TDFWI images as input and instantaneously predicts material property distribution within the scanned region. Nevertheless, the quality of the reconstruction with the current CNN degrades with the increased complexity of material distributions. Another challenge is the availability of enough experimental data and, in some cases, even synthetic surrogate data. To alleviate these issues, this paper details a systematic study of the enhancement effect of a 2D CNN (U-Net) by improving the quality with limited training data. To achieve this, different augmentation schemes (flipping and mixing existing data) were implemented to increase the amount and complexity of the training datasets without generating a substantial number of samples. The objective was to evaluate the enhancement effect of these augmentation techniques on the performance of the U-Net model at FWI iterations. A thousand numerically built samples with acoustic material properties are used to construct multiple datasets from different FWI iterations. A parallelized, high-performance computing (HPC) based framework has been created to rapidly generate the training data. The prediction results were compared against the ground truth images using standard matrices, such as the structural similarity index measure (SSIM) and average mean square error (MSE). The results show that the increased number of samples from augmentations improves shape imaging of the complex regions even with a low iteration FWI training data.

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Nuthi, Pavan y Kamesh Subbarao. "Implementation and Testing of Adaptive Augmentation Techniques on a 2-DOF Helicopter". En ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-65237.

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This paper presents the design procedure and experimental results of a high performance adaptive augmentation technique applied to a controller derived based on linear quadratic methods. The Quanser 2-DOF helicopter was chosen as the experimental platform on which these controllers were implemented. The paper studies the implementation of each of these controllers stand-alone as well as in the augmented scheme, and discusses its performance and robustness for cases with parametric uncertainties, and unmodeled dynamics. An attempt is made to combine linear quadratic tracker’s reliability with the adaptive augmentation’s robustness towards modeling uncertainties. It is found that appropriate tuning of parameters in the adaptive framework is key to its performance and thus the process of choosing the parameters is elaborated along with guidelines for choosing a reference model. Tuning considerations for controller implementation on the experimental setup as compared to the same on the numerical model are also addressed. The experiments performed on this nonlinear MIMO system serve as a suitable research test and evaluation basis for robotics and flight control applications.

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Sonowal, Sukanya y Anish Tamse. "Novel Augmentation Schemes for Device Robust Acoustic Scene Classification". En Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-10468.

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Moreno-Barea, Francisco J., Fiammetta Strazzera, Jose M. Jerez, Daniel Urda y Leonardo Franco. "Forward Noise Adjustment Scheme for Data Augmentation". En 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628917.

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Chen, Zhiyong y Li Chen. "Robust Control for Coordinated Motion of Flexible Space Manipulator Based on the Augmentation Approach". En ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28078.

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In this paper, the coordinated control of a flexible space manipulator system with a front flexible link is discussed. With the assumed mode method and linear momentum conservation of the system, the dynamics of the manpulator is derived in Lagrangian formulation. By using the augmentation approach, a robust control scheme for the coordinated motion between the spacecraft’s attitude and arm’s joints of the flexible space manipulator with bounded external disturbances and uncertain parameters to track the desired trajectories in joint space is proposed. It is designed based on a priori knowledge about the uncertainty-bound and possesses the advantage that it can greatly reduce the calculation time needed by the adaptive or neural network control schemes. Simulation results show that the presented controller can stabilize the system to track the desired trajectories and keep the vibration amplitude of the flexible arm to be relatively low-level.

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