Готові списки джерел за темами / Synergistic regularization

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Добірка наукової літератури з теми "Synergistic regularization"

Автор: Grafiati

Опубліковано: 29 березня 2025

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Статті в журналах з теми "Synergistic regularization"

Cueva, Evelyn, Alexander Meaney, Samuli Siltanen, and Matthias J. Ehrhardt. "Synergistic multi-spectral CT reconstruction with directional total variation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (July 5, 2021): 20200198. http://dx.doi.org/10.1098/rsta.2020.0198.

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This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

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Mehranian, Abolfazl, Martin A. Belzunce, Claudia Prieto, Alexander Hammers, and Andrew J. Reader. "Synergistic PET and SENSE MR Image Reconstruction Using Joint Sparsity Regularization." IEEE Transactions on Medical Imaging 37, no. 1 (January 2018): 20–34. http://dx.doi.org/10.1109/tmi.2017.2691044.

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Perelli, Alessandro, and Martin S. Andersen. "Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2200 (May 10, 2021): 20200191. http://dx.doi.org/10.1098/rsta.2020.0191.

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Spectral Computed Tomography (CT) is an emerging technology that enables us to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function, it is possible to reduce the complexity while retaining the complex prior structure given by the data-driven regularizer. We exploit a non-uniform block sub-sampling of the Hessian with inexact but efficient conjugate gradient updates that require only Jacobian-vector products for denoising term. Finally, we show numerical and experimental results for spectral CT materials decomposition. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

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Jørgensen, J. S., E. Ametova, G. Burca, G. Fardell, E. Papoutsellis, E. Pasca, K. Thielemans, et al. "Core Imaging Library - Part I: a versatile Python framework for tomographic imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (July 5, 2021): 20200192. http://dx.doi.org/10.1098/rsta.2020.0192.

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We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

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Bahadur, Rabya, Saeed ur Rehman, Ghulam Rasool, and Muhammad AU Khan. "Synergy Estimation Method for Simultaneous Activation of Multiple DOFs Using Surface EMG Signals." NUST Journal of Engineering Sciences 14, no. 2 (January 31, 2022): 66–73. http://dx.doi.org/10.24949/njes.v14i2.661.

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Surface electromyography signals are routinely used for designing prosthetic control systems. The concept of synergy estimation for muscle control interpretation is being explored extensively. Synergies estimated for a single active degree of freedom (DoF) are found to be uncorrelated and provide better results when used for single movement classification; however, an increase of simultaneously active DoFs leads to complex limb movements and multiple DoF detection becomes a challenge. Synergy estimation is a non-convex optimization technique, to provide better estimation this paper proposes the use of regularized non-negative matrix factorization for the evaluation of synergistic weights in complex movements. The use of regularization constraint makes the overall problem bounded and provide smoothness. The proposed technique showed better accuracy when tested for activation of multiple DoF simultaneously at a significantly lower computational time, i.e., by 34%.

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Zhong, Lihua, Tong Ye, Yuyao Yang, Feng Pan, Lei Feng, Shuzhe Qi, and Yuping Huang. "Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks." Processes 12, no. 9 (August 23, 2024): 1791. http://dx.doi.org/10.3390/pr12091791.

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As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems.

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Du, Lehui, Baolin Qu, Fang Liu, Na Ma, Shouping Xu, Wei Yu, Xiangkun Dai, and Xiang Huang. "Precise prediction of the radiation pneumonitis with RPI: An explorative preliminary mathematical model using genotype information." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e14569-e14569. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14569.

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e14569 Background: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for the radiotherapy of lung cancer. Reliable predictive factors or methods are strongly demanded by radiation oncologists. The purpose of this study is by determining the effectiveness of both genetic and non-genetic factors on their impact on the development of RP, to develop a clinically practicable approach for the risk assessment of RP. Methods: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. RP events were prospectively scored using the National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0 (CTCAE4.0). Seven hundred thousand single-nucleotide polymorphism (SNP) sites of each patient were tested via Generalized Linear Models via Lasso and Elastic-Net Regularization (GLMNET) to determine their synergistic effects on the risk of grade≥2 RP prediction. Non-genetic factors including patient characteristics and dosimetric parameters were separately assessed by statistic test for their association with the risk of grade ≥2 RP. Based on the results of the aforementioned analysis, a multiple linear regression model named Radiation Pneumonitis Index (RPI) was built, for the assessment of grade ≥2 RP risk. Results: No statistically significant association were found between the RP risk (grade ≥2) and any of the non-genetic factors. Twenty five effective SNPs for predicting the grade≥2 RP risk were discovered and their coefficients of the synergistic effect were determined. An RPI score defined only by the information about these 25 SNPs can successfully distinguish the grade ≥2 RP population with 100% specificity and 97.8% accuracy. Conclusions: Non-genetic factors including important dosimetric parameters may not play dominant roles in the development of RP. Genotype information alone can effectively predict the risk of grade ≥2 RP. The combination of genetics and mathematical algorithms can be a new direction for radiotherapy in the field of precision medicine.

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Di Sciacca, G., L. Di Sieno, A. Farina, P. Lanka, E. Venturini, P. Panizza, A. Dalla Mora, A. Pifferi, P. Taroni, and S. R. Arridge. "Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (July 5, 2021): 20200195. http://dx.doi.org/10.1098/rsta.2020.0195.

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Multimodal imaging is an active branch of research as it has the potential to improve common medical imaging techniques. Diffuse optical tomography (DOT) is an example of a low resolution, functional imaging modality that typically has very low resolution due to the ill-posedness of its underlying inverse problem. Combining the functional information of DOT with a high resolution structural imaging modality has been studied widely. In particular, the combination of DOT with ultrasound (US) could serve as a useful tool for clinicians for the formulation of accurate diagnosis of breast lesions. In this paper, we propose a novel method for US-guided DOT reconstruction using a portable time-domain measurement system. B-mode US imaging is used to retrieve morphological information on the probed tissues by means of a semi-automatical segmentation procedure based on active contour fitting. A two-dimensional to three-dimensional extrapolation procedure, based on the concept of distance transform, is then applied to generate a three-dimensional edge-weighting prior for the regularization of DOT. The reconstruction procedure has been tested on experimental data obtained on specifically designed dual-modality silicon phantoms. Results show a substantial quantification improvement upon the application of the implemented technique. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

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Lu, Yifan, Ziqi Zhang, Chunfeng Yuan, Peng Li, Yan Wang, Bing Li, and Weiming Hu. "Set Prediction Guided by Semantic Concepts for Diverse Video Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 2024): 3909–17. http://dx.doi.org/10.1609/aaai.v38i4.28183.

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Diverse video captioning aims to generate a set of sentences to describe the given video in various aspects. Mainstream methods are trained with independent pairs of a video and a caption from its ground-truth set without exploiting the intra-set relationship, resulting in low diversity of generated captions. Different from them, we formulate diverse captioning into a semantic-concept-guided set prediction (SCG-SP) problem by fitting the predicted caption set to the ground-truth set, where the set-level relationship is fully captured. Specifically, our set prediction consists of two synergistic tasks, i.e., caption generation and an auxiliary task of concept combination prediction providing extra semantic supervision. Each caption in the set is attached to a concept combination indicating the primary semantic content of the caption and facilitating element alignment in set prediction. Furthermore, we apply a diversity regularization term on concepts to encourage the model to generate semantically diverse captions with various concept combinations. These two tasks share multiple semantics-specific encodings as input, which are obtained by iterative interaction between visual features and conceptual queries. The correspondence between the generated captions and specific concept combinations further guarantees the interpretability of our model. Extensive experiments on benchmark datasets show that the proposed SCG-SP achieves state-of-the-art (SOTA) performance under both relevance and diversity metrics.

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Anacleto, Adilson, Karina Beatriz dos Santos Ferreira da Rocha, Raíssa Leal Calliari, Maike dos Santos, and Sandro Deretti. "Production Arrangement of Cachaça: Comparative Study Between Morretes in the Paraná Coast and Luiz Alves in Itajaí Valley - Santa Catarina." Revista de Gestão Social e Ambiental 18, no. 2 (June 26, 2024): e07510. http://dx.doi.org/10.24857/rgsa.v18n2-158.

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Objective: The research sought to promote a characterization of the cachaça LPA in Morretes, Paraná in comparison to an LPA in another Brazilian region, whose production is similar and is classified as one of the most developed in Brazil. Theoretical Framework: Studies about Local Productive Arrangements have been relevant to proposals and regional development, so the basis of the research was inserted in the introductory phase. Method: Between May 2022 and February 2023, an exploratory descriptive study was carried out with leaders of two cachaça production arrangements located in the south of Brazil, one consolidated in Santa Catarina and the other one under development in Paraná. Results and Discussion: The results obtained revealed that despite the similar number of producers, the Morretes LPA has a production capacity equivalent to 35% of what is produced in the Luiz Alves LPA. The difference in productivity is not linked to factors such as proximity to the consumer market, distribution logistics, labor or even geoclimatic characteristics. The main factors limiting the easier development of LPAs were the degree of difficulty in regularizing production, the lack of technical assistance and the almost non-existence of public policies, with the Morretes LPA on the coast of Paraná being most impacted. It is concluded that the insertion of tourism associated with beverage consumption, investment in marketing actions and public policies that facilitate the regularization of informal producers can strengthen the Morretes LPA and promote its consolidation as an economic activity that generates regional development. Research Implications: The Morretes LPA has all the favorable and synergistic conditions for the development of the cachaça LPA, and in this context the offer of new services and products arising from production and trade activities can generate a set of entertainment offered as a new experience for tourists, in addition from visits to stills and the creation of parties and fairs with the drink theme. Originality/Value: Comparative studies between local productive arrangements are still little explored in scientific literature and may guide regional development in a more organized way.

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Більше джерел

Дисертації з теми "Synergistic regularization"

Wang, Zhihan. "Reconstruction des images médicales de tomodensitométrie spectrale par apprentissage profond." Electronic Thesis or Diss., Brest, 2024. http://www.theses.fr/2024BRES0124.

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La tomodensitométrie se concentre sur deux sujets clés : la réduction de la dose de radiation et l’imagerie multi-énergétique, qui sont interconnectés. La tomodensitométrie spectrale, une avancée émergente, capture des données sur plusieurs énergies de rayons X pour mieux distinguer les matériaux, minimisant le besoin de scans répétés et ainsi réduisant l’exposition globale aux radiations.Cependant, la réduction du nombre de photons dans chaque bin d’énergie rend les méthodes de reconstruction traditionnelles sensibles au bruit. Ainsi, l’apprentissage profond, qui a montré un potentiel considérable dans l’imagerie médicale, est envisagé.Cette thèse introduit un nouveau terme de régularisation intégrant des réseaux de neurones convolutifs pour relier les bins d’énergie à une variable latente, exploitant l’ensemble des données des bins pour une reconstruction synergique. À titre de preuve de concept, nous proposons Uconnect et sa variante MHUconnect, utilisant respectivement U-Nets et un UNet à multi-têtes en tant que réseaux de neurones convolutifs, où l’image d’un bin d’énergie spécifique sert de variable latente pour l’apprentissage supervisé. Ces méthodes ont été validées comme étant plus performantes que plusieurs méthodes existantes dans les tâches de reconstruction et de débruitage
Computed tomography (CT), a cornerstone of diagnostic imaging, focuses on two contemporary topics: radiation dose reduction and multi-energy imaging, which are inherently interconnected. As an emerging advancement, spectral CT can capture data across a range of X-ray energies for bettermaterial differentiation, reducing the need for repeat scans and thereby lowering overall radiationexposure. However, the reduced photon count in each energy bin makes traditional reconstruction methods susceptible to noise. Therefore, deep learning (DL) techniques, which have shown great promise in medical imaging, are being considered. This thesis introduces a novel regularizationterm that incorporates convolutional neural networks (CNNs) to connect energy bins to a latent variable, leveraging all binned data for synergistic reconstruction. As a proof-of concept, we propose Uconnect and its variant MHUconnect, employing U-Nets and the multi-head U-Net, respectively, as the CNNs, with images at a specific energy bin serving as the latent variable for supervised learning.The two methods are validated to outperform several existing approaches in reconstruction and denoising tasks

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