Letteratura scientifica selezionata sul tema "Estimations de gradient"
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Articoli di riviste sul tema "Estimations de gradient"
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Liu, Zijian, Chunbo Luo, Peng Ren, Tingwei Wang e Geyong Min. "Population based optimization via differential evolution and adaptive fractional gradient descent". Filomat 34, n. 15 (2020): 5173–85. http://dx.doi.org/10.2298/fil2015173l.
Testo completoAbstract (sommario):
We propose a differential evolution algorithm based on adaptive fractional gradient descent (DE-FGD) to address the defects of existing bio-inspired algorithms, such as slow convergence speed and local optimum. The crossover and selection processes of the differential evolution algorithm are discarded and the adaptive fractional gradients are adopted to enhance the global searching capability. For the benchmark functions, our proposed algorithm Specifically, our method has higher searching accuracy than several state of the art bio-inspired algorithms. Furthermore, we apply our method to specific tasks - parameters estimation of system response functions and approximate value functions. Experiment results validate that our proposed algorithm produces accurate estimations and improves searching efficiency
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Håkegård, Jan Erik, Mohammed Ouassou, Nadezda Sokolova e Aiden Morrison. "Assessment and Validation of Small-Scale Tropospheric Delay Estimations Based on NWP Data". Sensors 24, n. 20 (12 ottobre 2024): 6579. http://dx.doi.org/10.3390/s24206579.
Testo completoAbstract (sommario):
This paper investigates the applicability of the Numerical Weather Prediction (NWP) data for characterizing the gradient of zenith wet delay in horizontal direction observed on short baselines over larger territories. A three-year period of data for an area covering Scandinavia and Finland is analyzed, and maximum gradients during the considered period are identified. To assess the quality of the NWP-based estimates, results for a smaller region are compared with the estimates obtained using Global Navigation Satellite System (GNSS) measurements processed by the GipsyX/RTGx software package (version 2.1) from a cluster of GNSS reference stations. Additionally, the NWP data from 7 to 9 August 2023 covering a period that includes a storm with high rain intensities over Southern Norway leading to sustained flooding are processed and analyzed to assess if the gradient of zenith wet delay in the horizontal direction increases significantly during such events. The results show that maximum gradients in the range of 40–50 mm/km are detected. When comparing NWP-based estimates to GNSS-based estimates, the tropospheric delays show a very strong correlation. The tropospheric gradients, however, show a weak correlation, probably due to the uncertainty in the NWP data exceeding the gradient values. The data captured during the storm show that while the tropospheric delay increases significantly it is difficult to see increases in the gradient of zenith wet delay in the horizontal direction using this data source and resolution.
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Kong, Xiangsong, e Dongbin Zheng. "A Knowledge-Informed Simplex Search Method Based on Historical Quasi-Gradient Estimations and Its Application on Quality Control of Medium Voltage Insulators". Processes 9, n. 5 (28 aprile 2021): 770. http://dx.doi.org/10.3390/pr9050770.
Testo completoAbstract (sommario):
Quality control is of great significance for the economical manufacturing and reliable application of medium voltage insulators. With the increasingly stringent quality control requirement, traditional quality control methods in this field face a growing challenge on their efficiency. Therefore, this study aims to achieve quality specifications by optimizing process conditions with the least costs. Thus, a knowledge-informed simplex search method was proposed based on an idea of knowledge-informed optimization to enhance the optimization efficiency. Firstly, a new mathematical quantity, quasi-gradient estimation, was generated following a reconstruction of the simplex search from the essence and the development history of the method. Based on this quantity, the gradient-free method possessed the same gradient property and unified form as the gradient-based methods. Secondly, an implementation of the knowledge-informed simplex search method based on historical quasi-gradient estimations (short for GK-SS) was constructed. The GK-SS-based quality control method utilized the historical quasi-gradient estimations for each simplex generated during the optimization process to improve the method’s search directions’ accuracy in a statistical sense. Finally, this method was applied to the weight control of a kind of post insulator. The experimental simulation results showed that the method is effective and efficient in the quality control of medium voltage insulators.
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Akgul, Volkan, Gokhan Gurbuz, Senol Hakan Kutoglu e Shuanggen Jin. "Effects of the High-Order Ionospheric Delay on GPS-Based Tropospheric Parameter Estimations in Turkey". Remote Sensing 12, n. 21 (31 ottobre 2020): 3569. http://dx.doi.org/10.3390/rs12213569.
Testo completoAbstract (sommario):
The tropospheric delay and gradients can be estimated using Global Positioning System (GPS) observations after removing the ionospheric delay, which has been widely used for atmospheric studies and forecasting. However, high-order ionospheric (HOI) delays are generally ignored in GPS processing to estimate atmospheric parameters. In this study, HOI effects on GPS-estimated tropospheric delay and gradients are investigated from two weeks of GPS data in June 2011 at selected GPS stations in Turkey. Results show that HOI effects are up to 6 mm on zenith tropospheric delay (ZTD), 4 mm on the North-South (NS) gradient and 12 mm on the East-West (EW) gradient during this period, but can reach over 30 mm in slant tropospheric delays. Furthermore, the HOI effects on tropospheric delay and gradient are larger in the daytime than the nighttime. Furthermore, HOI effects on tropospheric delay are further investigated on low and high solar activity days. The HOI effects on GPS estimated tropospheric delay and gradients in high solar activity days are higher than those in low solar activity days.
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Dai, Yaqiao, Renjiao Yi, Chenyang Zhu, Hongjun He e Kai Xu. "Multi-Resolution Monocular Depth Map Fusion by Self-Supervised Gradient-Based Composition". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 1 (26 giugno 2023): 488–96. http://dx.doi.org/10.1609/aaai.v37i1.25123.
Testo completoAbstract (sommario):
Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually lack fine-grained details due to convolution operations and down-samplings in networks. We find that increasing input resolution is helpful to preserve more local details while the estimation at low resolution is more accurate globally. Therefore, we propose a novel depth map fusion module to combine the advantages of estimations with multi-resolution inputs. Instead of merging the low- and high-resolution estimations equally, we adopt the core idea of Poisson fusion, trying to implant the gradient domain of high-resolution depth into the low-resolution depth. While classic Poisson fusion requires a fusion mask as supervision, we propose a self-supervised framework based on guided image filtering. We demonstrate that this gradient-based composition performs much better at noisy immunity, compared with the state-of-the-art depth map fusion method. Our lightweight depth fusion is one-shot and runs in real-time, making it 80X faster than a state-of-the-art depth fusion method. Quantitative evaluations demonstrate that the proposed method can be integrated into many fully convolutional monocular depth estimation backbones with a significant performance boost, leading to state-of-the-art results of detail enhancement on depth maps. Codes are released at https://github.com/yuinsky/gradient-based-depth-map-fusion.
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Ogunsanwo, F. O., O. T. Olurin, J. D. Ayanda, S. A. Ganiyu e A. O. Mustapha. "Estimation of ground depth of radioelements sources in Ogun State, Southwestern Nigeria using gradient techniques". Ife Journal of Science 25, n. 2 (31 agosto 2023): 319–30. http://dx.doi.org/10.4314/ijs.v25i2.10.
Testo completoAbstract (sommario):
Radioelement exploration has gained economic interest recently due to its usefulness in the detection and delineation of mineral deposits. In this study, the airborne radiometric data were analysed for depth estimation of the radioelement deposit in Ogun state, Nigeria. Three enhancement gradient techniques, namely; Analytical Signal Amplitude (ASA), Horizontal Gradient Magnitude (HGM) and Local Wave Number (LWN) were employed to estimate the possible depth of radioelements for mineralization. Geosoft's (Oasis Montaj) software and Potential Field's (PF) software were used to conduct the estimations. The result obtained revealed shallow sources of 0.584 km (LWN) and 0.387 km (ASA), and deep-seated sources of 5.950 km (ASA) and 5.880 km (ASA) for uranium and thorium, respectively. The shallow source and deep source for potassium are 0.259 km (ASA) and 2.540 km (ASA), respectively. In this study, the position and depth of the source were automatically estimated using linear equations based on derivatives without the use of any a priori knowledge. The three gradient methods are therefore found suitable in estimating the depth to radioelement anomalous source.
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Goldblatt, Jennifer, Andrew Ward, Mohammed Yusuf, Martin Day, Gauri Godbole e Stephen Morris-Jones. "Azithromycin susceptibility testing for Salmonella enterica isolates: discordances in results using MIC gradient strips". Journal of Antimicrobial Chemotherapy 75, n. 7 (28 marzo 2020): 1820–23. http://dx.doi.org/10.1093/jac/dkaa097.
Testo completoAbstract (sommario):
Abstract Background Azithromycin resistance is emerging in typhoidal Salmonella. Confirmation of azithromycin MIC is the most frequent antibiotic susceptibility request made to the Gastrointestinal Bacteria Reference Unit (GBRU) laboratory in England by local diagnostic laboratories. Objectives (i) Determine concordance between local diagnostic and reference laboratory estimations of azithromycin MIC by gradient strip in Salmonella enterica serovars Typhi and Paratyphi. (ii) Consider causes of variation. Methods Isolates from patients with enteric fever attending a central London hospital between May 2011 and April 2019 were tested for azithromycin susceptibility using gradient strips, according to EUCAST methodology. Matched local diagnostic and reference laboratory estimations of azithromycin and ciprofloxacin (as a comparator) MICs were included; concordance in estimations was examined. Results Local diagnostic laboratory readings overestimated azithromycin MIC values compared with the reference laboratory, resulting in poor concordance in susceptibility/resistance attribution (concordant susceptibility interpretation in 8/19, κ = 0). In contrast, ciprofloxacin MIC estimation demonstrated superior concordance (concordant susceptibility interpretation in 16/17, κ = 0.85). None of the isolates was resistant to azithromycin at the reference laboratory and no known genes associated with azithromycin resistance were detected in any isolate using WGS. Conclusions Overestimation of azithromycin resistance is likely to be due to difficulty in interpreting the point of intersection of the ‘trailing edge’ with the gradient strip, used to determine MIC. We advise local diagnostic laboratories to review their experience and consider adopting a ‘second reader’ system to mitigate this.
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Grünewald, Thomas, e Michael Lehning. "Altitudinal dependency of snow amounts in two small alpine catchments: can catchment-wide snow amounts be estimated via single snow or precipitation stations?" Annals of Glaciology 52, n. 58 (2011): 153–58. http://dx.doi.org/10.3189/172756411797252248.
Testo completoAbstract (sommario):
AbstarctThe spatial distribution and the local amount of snow in mountainous regions strongly depend on the spatial characteristics of snowfall, snow deposition and snow redistribution. Uniform altitudinal gradients can only represent a part of these influences but are without alternative for use in larger-scale models. How well altitudinal gradients represent the true snow distribution has not been assessed. We analyse altitudinal characteristics of snow stored in two high-alpine catchments in Switzerland. Peak winter snow depths were monitored using high-resolution airborne laser scanning technology. These snow depths were transferred to snow water equivalent by applying simple density estimations. From these data, altitudinal gradients were calculated for the total catchment areas and for selected subareas characterized by different accumulation patterns. These gradients were then compared with gradients resulting from automated snow depth measurements obtained from several snow stations on different height levels located in the catchments, and with estimations from climatological precipitation gradients. The analysis showed that neither precipitation gradients nor flat-field stations estimate catchment-wide snow amounts accurately. While the climatological gradient showed different trends for different areas and years, the snow stations tended to overestimate mean snow amounts.
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Zonia, Laura, e Dennis Bray. "Swimming patterns and dynamics of simulated Escherichia coli bacteria". Journal of The Royal Society Interface 6, n. 40 (25 febbraio 2009): 1035–46. http://dx.doi.org/10.1098/rsif.2008.0397.
Testo completoAbstract (sommario):
A spatially and temporally realistic simulation of Escherichia coli chemotaxis was used to investigate the swimming patterns of wild-type and mutant bacteria within a rectangular arena in response to chemoattractant gradients. Swimming dynamics were analysed during long time series with phase-space trajectories, power spectra and estimations of fractal dimensions (FDs). Cell movement displayed complex trajectories in the phase space owing to interaction of multiple attractors that captured runs and tumbles. Deletion of enzymes responsible for adaptation (CheR and CheB) restricted the pattern of bacterial swimming in the absence of a gradient. In the presence of a gradient, there was a strong increase in trajectories arising from runs and attenuation of those arising from tumbles. Similar dynamics were observed for mutants lacking CheY, which are unable to tumble. The deletion of CheR, CheB and CheY also caused significant shifts in chemotaxis spectral frequencies. Rescaled range analysis and estimation of FD suggest that wild-type bacteria display characteristics of fractional Brownian motion with positive correlation between past and future events. These results reveal an underlying order in bacterial swimming dynamics, which enables a chemotactic search strategy conforming to a fractal walk.
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Wen, Jiawei, Zhe Ouyang, Donghu Nie e Cong Ren. "Fast Parameter Estimation of Linear Frequency Modulation Signals in Marine Environments Based on Gradient Optimization Strategy". Journal of Marine Science and Engineering 12, n. 12 (1 dicembre 2024): 2195. https://doi.org/10.3390/jmse12122195.
Testo completoAbstract (sommario):
Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization.
Tesi sul tema "Estimations de gradient"
1
Mhanna, Elissa. "Beyond gradients : zero-order approaches to optimization and learning in multi-agent environments". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG123.
Testo completoAbstract (sommario):
L'essor des dispositifs connectés et des données qu'ils génèrent a stimulé le développement d'applications à grande échelle. Ces dispositifs forment des réseaux distribués avec un traitement de données décentralisé. À mesure que leur nombre augmente, des défis comme la surcharge de communication et les coûts computationnels se présentent, nécessitant des méthodes d'optimisation adaptées à des contraintes de ressources strictes, surtout lorsque les dérivées sont coûteuses ou indisponibles. Cette thèse se concentre sur les méthodes d'optimisation sans dérivées, qui sont idéales quand les dérivées des fonctions sont inaccessibles. Ces méthodes estiment les gradients à partir des évaluations de fonction, ce qui les rend adaptées à l'apprentissage distribué et fédéré, où les dispositifs collaborent pour résoudre des tâches d'optimisation avec peu d'informations et des données bruitées. Dans le premier chapitre, nous traitons de l'optimisation distribuée sans dérivées pour des fonctions fortement convexes sur plusieurs agents. Nous proposons un algorithme distribué de descente de gradient projete sans dérivées, qui utilise des estimations de gradient à un point, où la fonction est interrogée une seule fois par réalisation stochastique, et les évaluations sont bruitées. Ce chapitre démontre la convergence presque sûre de l'algorithme et fournit des bornes théoriques sur le taux de convergence. Avec des pas constants, l'algorithme atteint un taux de convergence linéaire. C'est la première fois que ce taux est établi pour des estimations de gradient à un point (voire même pour des estimations de gradient à deux points) pour des fonctions stochastiques. Nous analysons aussi les effets des pas décroissants, établissant un taux de convergence correspondant aux méthodes centralisées sans dérivées. Le deuxième chapitre se penche sur les défis de l'apprentissage fédéré qui est limité par le coût élevé de la transmission de données sur des réseaux à bande passante restreinte. Pour y répondre, nous proposons un nouvel algorithme qui réduit la surcharge de communication en utilisant des estimations de gradient à un point. Les dispositifs transmettent des valeurs scalaires plutôt que de grands vecteurs de gradient, réduisant ainsi la quantité de données envoyées. L'algorithme intègre aussi directement les perturbations des communications sans fil dans l'optimisation, éliminant le besoin de connaître explicitement l'état du canal. C'est la première approche à inclure les propriétés du canal sans fil dans un algorithme d'apprentissage, le rendant résilient aux problèmes de communication réels. Nous prouvons la convergence presque sûre de cette méthode dans des environnements non convexes et validons son efficacité à travers des expériences. Le dernier chapitre étend l'algorithme précédent au cas des estimations de gradient à deux points. Contrairement aux estimations à un point, les estimations à deux points interrogent la fonction deux fois, fournissant une approximation plus précise du gradient et améliorant le taux de convergence. Cette méthode conserve l'efficacité de communication des estimations à un point, avec uniquement des valeurs scalaires transmises, et assouplit l'hypothèse de bornitude de la fonction objective. Nous prouvons des taux de convergence linéaires pour des fonctions fortement convexes et lisses. Pour les problèmes non convexes, nous montrons une amélioration notable du taux de convergence, en particulier pour les fonctions dominées par le gradient K, atteignant également un taux linéaire. Nous fournissons aussi des résultats montrant l'efficacité de communication par rapport à d'autres techniques d'apprentissage fédéréThe rise of connected devices and the data they produce has driven the development of large-scale applications. These devices form distributed networks with decentralized data processing. As the number of devices grows, challenges like communication overhead and computational costs increase, requiring optimization methods that work under strict resource constraints, especially where derivatives are unavailable or costly. This thesis focuses on zero-order (ZO) optimization methods are ideal for scenarios where explicit function derivatives are inaccessible. ZO methods estimate gradients based only on function evaluations, making them highly suitable for distributed and federated learning environments where devices collaborate to solve global optimization tasks with limited information and noisy data. In the first chapter, we address distributed ZO optimization for strongly convex functions across multiple agents in a network. We propose a distributed zero-order projected gradient descent algorithm that uses one-point gradient estimates, where the function is queried only once per stochastic realization, and noisy function evaluations estimate the gradient. The chapter establishes the almost sure convergence of the algorithm and derives theoretical upper bounds on the convergence rate. With constant step sizes, the algorithm achieves a linear convergence rate. This is the first time this rate has been established for one-point (and even two-point) gradient estimates. We also analyze the effects of diminishing step sizes, establishing a convergence rate that matches centralized ZO methods' lower bounds. The second chapter addresses the challenges of federated learning (FL) which is often hindered by the communication bottleneck—the high cost of transmitting large amounts of data over limited-bandwidth networks. To address this, we propose a novel zero-order federated learning (ZOFL) algorithm that reduces communication overhead using one-point gradient estimates. Devices transmit scalar values instead of large gradient vectors, lowering the data sent over the network. Moreover, the algorithm incorporates wireless communication disturbances directly into the optimization process, eliminating the need for explicit knowledge of the channel state. This approach is the first to integrate wireless channel properties into a learning algorithm, making it resilient to real-world communication issues. We prove the almost sure convergence of this method in nonconvex optimization settings, establish its convergence rate, and validate its effectiveness through experiments. In the final chapter, we extend the ZOFL algorithm to include two-point gradient estimates. Unlike one-point estimates, which rely on a single function evaluation, two-point estimates query the function twice, providing a more accurate gradient approximation and enhancing the convergence rate. This method maintains the communication efficiency of one-point estimates, where only scalar values are transmitted, and relaxes the assumption that the objective function must be bounded. The chapter demonstrates that the proposed two-point ZO method achieves linear convergence rates for strongly convex and smooth objective functions. For nonconvex problems, the method shows improved convergence speed, particularly when the objective function is smooth and K-gradient-dominated, where a linear rate is also achieved. We also analyze the impact of constant versus diminishing step sizes and provide numerical results showing the method's communication efficiency compared to other federated learning techniques
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Calvez, Vincent. "Modèles et analyses mathématiques pour les mouvements collectifs de cellules". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2007. http://tel.archives-ouvertes.fr/tel-00255811.
Testo completoAbstract (sommario):
Cette thèse est consacrée à certains modèles mathématiques décrivant le mouvement d'une population de cellules, qui interagissent via un signal chimique. L'accent est mis sur le modèle parabolique de Patlak-Keller-Segel, et dans une moindre mesure, sur le modèle cinétique d'Othmer-Dunbar-Alt.Dans une première partie nous étudions plusieurs variantes du modèle PKS classique, incluant notamment une diffusion non-linéaire des cellules, ou bien une loi de diffusion chimique à noyau de Green logarithmique. Puis nous montrons l'existence globale pour une masse sous-critique du modèle PKS classique dans tout l'espace $\mathbb{R}^2$.
On complexifie ensuite le modèle de base en ajoutant un intermédiaire chimique réactionnel, ce qui modifie l'homogénéité du système. Enfin les conditions d'existence globale pour le modèle cinétique ODA avec effets délocalisants sont affaiblies par rapport aux travaux précédents.
Dans une deuxième partie nous appliquons le modèle phénoménologique de PKS, et son principe de masse critique, à un processus d'auto-organisation remarquable dans le cerveau: la sclérose concentrique de Baló. Un couplage adéquat entre un front de propagation et une instabilité de PKS décrit raisonnablement les motifs en anneaux de la maladie.
La troisième partie adopte le point de vue du transport optimal de masse pour analyser le modèle de PKS unidimensionnel modifié auparavant (afin de partager les caractéristiques de PKS 2D). Bien que la fonctionnelle d'énergie ne soit pas convexe par déplacement, nous démontrons la convergence vers un unique état d'équilibre, lorsqu'il existe. Ces nouvelles idées sont mises en oeuvre numériquement~: un flot gradient discret pour la distance de Wasserstein est analysé, puis simulé en dimension un d'espace.
Plusieurs annexes viennent compléter ce travail, dont une annexe qui regroupe tous les aspects numériques de la thèse.
3
Swapp, David. "Estimation of visual textural gradient using Gabor functions". Thesis, University of Strathclyde, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320238.
Testo completo
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Yang, Junjun. "Seafloor Topography Estimation from Gravity Gradients". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512048462472145.
Testo completo
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Shahnaz, Sabina. "Gas flux estimation from surface gas concentrations". Thesis, Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55073.
Testo completoAbstract (sommario):
A gradient-independent model of gas fluxes was formulated and tested. The
model is built on the relationship between gas flux and the time history of surface gas
concentration, known as half-order derivative (HOD), when the transport of the gas in the
boundary layer is described by a diffusion equation. The eddy-diffusivity of gas is
parameterized based on the similarity theory of boundary layer turbulence combined with
the MEP model of surface heat fluxes. Test of the new model using in-situ data of CO2
concentration and fluxes at several locations with diverse vegetation cover, geographic
and climatic conditions confirms its usefulness and potential for monitoring and
modeling greenhouse gases. The proposed model may also be used for estimating other
GHGS fluxes such as methane (CH4) and Water vapor flux. This proof-of-concept study
justifies the proposed model as a practical solution for monitoring and modeling global
GHGS budget over remote areas and oceans where ground observations of GHGS fluxes
are limited or non-existent. One focus of the on-going research is to investigate its
application to producing regional and global distributions of carbon fluxes for identifying
sinks and sources of carbon and re-evaluating the regional and global carbon budget at
monthly and annual time scales.
6
Lee, Choon. "Interframe image coding with three-dimensional gradient motion estimation". Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-08252008-162144/.
Testo completo
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Miyoshi, Naoto. "Studies on Gradient Estimation for Stationary Single-Server Queues". Kyoto University, 1997. http://hdl.handle.net/2433/202289.
Testo completoAbstract (sommario):
Kyoto University (京都大学)0048
新制・課程博士
博士(工学)
甲第6839号
工博第1590号
新制||工||1063(附属図書館)
15926
UT51-97-H223
京都大学大学院工学研究科応用システム科学専攻
(主査)教授 長谷川 利治, 教授 沖野 教郎, 教授 茨木 俊秀
学位規則第4条第1項該当
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Zotov, Alexander. "Models of disparity gradient estimation in the visual cortex". Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2008r/zotov.pdf.
Testo completo
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Mehrparvar, Arash. "ATTITUDE ESTIMATION FOR A GRAVITY GRADIENT MOMENTUM BIASED NANOSATELLITE". DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1097.
Testo completoAbstract (sommario):
Attitude determination and estimation algorithms are developed and implemented in simulation for the Exocube satellite currently under development by PolySat at Cal Poly. A mission requirement of ±5˚ of attitude knowledge has been flowed down from the NASA Goddard developed payload, and this requirement is to be met with a basic sensor suite and the appropriate algorithms. The algorithms selected in this work are TRIAD and an Extended Kalman Filter, both of which are placed in a simulation structure along with models for orbit propagation, spacecraft kinematics and dynamics, and sensor and reference vector models. Errors inherent from sensors, orbit position knowledge, and reference vector generation are modeled as well. Simulations are then run for anticipated dynamic states of Exocube while varying parameters for the spacecraft, attitude algorithms, and level of error. The nominal case shows steady state convergence to within 1˚ of attitude knowledge, with sensor errors set to 3.5˚ and reference vector errors set to 2˚. The algorithms employed have their functionality confirmed with the use of STK, and the simulations have been structured to be used as tools to help evaluate attitude knowledge capabilities for the Exocube mission and future PolySat missions.
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Castillo, Anthony. "Contribution à l'étude de l'endommagement de matériaux composites par estimation des termes sources et des diffusivités thermiques". Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0592/document.
Testo completoAbstract (sommario):
Ce travail porte sur la détection de l’endommagement de matériaux composites. Une première partie concerne l’élaboration de méthodes permettant d’estimer les termes sources de chaleur d’un matériau sollicité mécaniquement. Lors de ce processus, un ensemble de défauts mécaniques mènent à des productions de chaleur. La détection des sources peut permettre la détection de ces défauts. Deux principales méthodes sont présentées : une méthode dite « directe » basée sur une discrétisation du champ de température mesuré et une méthode « itérative » basée sur la méthode du gradient conjugué. A ces méthodes sont couplées des techniques de filtrages des données comme la SVD. Les équations sont résolues par différences finies sous leur forme linéaire. Des modifications sont apportées à l’algorithme itératif pour améliorer sa convergence ainsi que les résultats. Les problématiques envisagées font partie des problèmes inverses en thermique. L’objectif de la première partie est de trouver un lien entre l’apparition de macro-fissure et la localisation de termes sources de chaleur au sein d’un matériau composite. La seconde partie consiste à élaborer des méthodes d’estimation des diffusivités thermiques directionnelles. Les méthodes reposent sur une modélisation du transfert de chaleur à l’aide des quadripôles thermiques. Les estimations de paramètres sont réalisées sur des zones ciblées à risque sur un matériau déjà endommagé. Le but est de faire le lien entre un endommagement mécanique connu diffus et une dégradation des propriétés thermiques. Ce manuscrit est présenté en deux parties : une partie de validation des méthodes. Une partie expérimentale où sont analysés les compositesThis work deals with the damage detection of composite materials. These materials are used in the aeronautics industry. The first part concerns the development of methods to estimate the heat sources terms of a stressed material. During this process, a set of mechanical defects leads to heat productions. The sources detection can conduct to the detection of these defects. Two main methods are presented: a "direct" method based on a discretization of the measured temperature field and an "iterative" method based on the conjugate gradient method. These methods are coupled with data filtering techniques such as SVD. In order to optimize computation time, equations are solved by finite differences in their linear form. Modifications are also made for the iterative algorithm to improve its convergence as well as the results of the estimation. These problems are considered as thermal inverse problems. The main objective of the first part is to find an experimental link between the appearance of a macro fissure and the localization of a heat source term within a composite material. The second part consists in the elaboration of methods for estimating thermal directional diffusivities. The methods are based on a modeling of heat transfer using thermal quadrupoles. Parameter estimations are made on targeted "risked" areas on a material, which is already damaged but not under stress. The aim is to link a known mechanical damage, which is called "diffuse" to thermal properties degradation in the main directions. This manuscript is presented in two parts: a validation part of the methods, and an experimental part in which composites are analyzed
Libri sul tema "Estimations de gradient"
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Glasserman, Paul. Gradient estimation via perturbation analysis. Boston: Kluwer Academic, 1991.
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Fu, Michael. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston, MA: Springer US, 1997.
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Fu, Michael. Conditional Monte Carlo: Gradient estimation and optimization applications. Boston: Kluwer Academic Publishers, 1997.
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Skifstad, Kurt D. High-Speed Range Estimation Based on Intensity Gradient Analysis. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3112-7.
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Skifstad, Kurt D. High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.
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Skifstad, Kurt D. High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.
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Cuddapah, Prabhakara, e Goddard Space Flight Center, a cura di. Global warming estimation from microwave sounding unit. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.
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Atherton, Gary L. Grading and paving construction: A practical approach to management and estimating. Concord, Calif: Sandcastles, 1986.
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Stevens, James A. Managing the stands of the future based on the lessons of the past: Estimating western timber species product recovery by using historical data. [Portland, Or.]: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2000.
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Gradient estimation via perturbation analysis. Boston: Kluwer Academic Publishers, 1991.
Cerca il testo completoCapitoli di libri sul tema "Estimations de gradient"
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Fu, Michael C. "Stochastic Gradient Estimation". In Handbook of Simulation Optimization, 105–47. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1384-8_5.
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Pfanzagl, Johann. "Families of gradients". In Estimation in Semiparametric Models, 23–34. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4612-3396-1_6.
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Cheng, Ming-Yen, Peter Hall e John A. Hartigan. "Estimating gradient trees". In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 237–49. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2004. http://dx.doi.org/10.1214/lnms/1196285394.
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Pfanzagl, Johann. "Tangent spaces and gradients". In Estimation in Semiparametric Models, 2–3. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4612-3396-1_2.
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Frühwirth, Rudolf, e Are Strandlie. "Statistics and Numerical Methods". In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 33–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_3.
Testo completoAbstract (sommario):
AbstractThe chapter gives an outline of some statistical and numerical methods that will be applied in later chapters. The first section deals with the minimization of functions. Several gradient-based methods and a popular non-gradient method are discussed. The following section discusses statistical models and the estimation of model parameters. The basics of linear and nonlinear regression models and state space models are presented, including least-squares estimation and the (extended) Kalman filter. The final section gives a brief overview of clustering and different types of clustering algorithms.
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Fu, Michael, e Jian-Qiang Hu. "Conditional Monte Carlo Gradient Estimation". In Conditional Monte Carlo, 73–131. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6293-1_3.
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Romashin, Sergey N., Victoria Yu Presnetsova, Larisa Yu Frolenkova, Vladimir S. Shorkin e Svetlana I. Yakushina. "Theoretical Estimation of the Strength of Thin-film Coatings". In Higher Gradient Materials and Related Generalized Continua, 221–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30406-5_11.
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Zhang, Bin, Yubo Tao e Hai Lin. "Robust Color Gradient Estimation for Photographic Volumes". In E-Learning and Games, 392–402. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40259-8_34.
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Zhang, He, François Petitjean e Wray Buntine. "Hierarchical Gradient Smoothing for Probability Estimation Trees". In Advances in Knowledge Discovery and Data Mining, 222–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_18.
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Hornauer, Julia, e Vasileios Belagiannis. "Gradient-Based Uncertainty for Monocular Depth Estimation". In Lecture Notes in Computer Science, 613–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20044-1_35.
Testo completoAtti di convegni sul tema "Estimations de gradient"
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Zelensky, Alexander, Nikolay V. Gapon, Marina M. Zhdanova, Viacheslav Voronin, Yurii Ilukhin e Khamidullin Ilia. "Image inpainting by anisotropic gradient estimation". In Optoelectronic Imaging and Multimedia Technology XI, a cura di Zhenrong Zheng e Jinli Suo, 68. SPIE, 2024. http://dx.doi.org/10.1117/12.3038592.
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Nguyen, Thien Duy, John Craig Wells, Paritosh Mokhasi e Dietmar Rempfer. "POD-Based Estimations of the Flowfield From PIV Wall Gradient Measurements in the Backward-Facing Step Flow". In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30657.
Testo completoAbstract (sommario):
In this paper, particle image velocimetry (PIV) results from a backward-facing step flow, of which Reynolds number is 2800 based on free stream velocity and step height (h = 16.5 mm), are used to demonstrate the capability of proper orthogonal decomposition (POD)-based estimation models. Three-component PIV velocity fields are decomposed into a set of spatial basis functions and a set of temporal coefficients. The estimation models are built to relate the low-order POD coefficients, determined from an ensemble of 1050 PIV fields by the “snapshot” method, and the time-resolved wall gradients, measured by a near-wall measurement technique called stereo interfacial PIV. These models are evaluated in terms of reconstruction and prediction of the low-order temporal POD coefficients of the velocity fields. In order to determine the coefficients of the estimation models, linear stochastic estimation (LSE), quadratic stochastic estimation (QSE), principal component regression (PCR) and kernel ridge regression (KRR) are applied. In addition, we introduce a possibility of multi-time POD-based estimations in which past and future information of the wall gradient events is used separately or combined. The results show that the multi-time estimation approaches can improve the prediction process. Among these approaches, the proposed multi-time KRR-POD estimation with optimized time duration of wall gradient information in the past yields the best prediction.
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Wen, Baihong, Hui Yang e Kang Liu. "Comparative study on the magnetization direction estimations by magnetic gradient correlation and moment analysis methods". In International Workshop and Gravity, Electrical & Magnetic Methods and their Applications, Chenghu, China, 19-22 April 2015. Society of Exploration Geophysicists and and Chinese Geophysical Society, 2015. http://dx.doi.org/10.1190/gem2015-026.
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Al Zeer, S., e M. M. Hossain. "Evaluation of the Impact of Poor Logging Data on Mud Weight Prediction Through Development of Approximate UCS- Based Correlations for Fracture and Stability Pressure Gradients". In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23349-ms.
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Abstract This proposed paper aims to evaluate the impact of poor logging data on the prediction of accurate mud weight and to assess the impact of Uni-Axial Compressive Strength (UCS) for weak to moderate and moderate to strong formations with fracture and minimum mud/stability pressure gradients. Six approximate correlations are developed as a function of UCS to predict the fracture and stability pressure gradients for the stated range of formation. This study employed MATLAB code for the analysis of logging data to identify instabilities in the offset well and estimate the rock's mechanical properties through numerical simulation. By incorporating data from nearby wells, the logging data were rectified, facilitating the recalculation of optimal mud weights using a geomechanical earth model. The case study focused on three UCS correlations suitable for weak to moderate and moderate to strong formations. Scatter plots of fracture and stability pressure gradients against UCS obtained from MATLAB simulation were generated and plotted, which were then combined to derive approximate correlations for each correlation through regression analysis for the prediction of minimum and maximum mud weights (referred to as stability and fracture pressure gradient) as functions of UCS within the considered formation range. Inaccurate logging data, especially caused by hole enlargements or breakout for offset wells can lead to erroneous estimations of recommended mud weights for drilling plans which can pose serious technical, safety and financial risks in drilling operations. The proposed approach of corrections to the offset well's data has been observed to provide more reliable and safer recommended mud weights. About the UCS exanimation, the first correlation is a function of static Young’s modulus, the second is a function of P-velocity, and the third is a function of dynamic Young’s modulus and dynamic Poisson’s ratio. The first and second correlations have shown the representation of moderate to very strong formations which agrees with the formation of the case study while the third correlation has produced values of UCS which are not representative of the well as it demonstrates the behavior of weak formations. Regarding the relationship between UCS and mud weights, it was found that as UCS increases, the fracture pressure gradient increases while the stability pressure gradient decreases for all three correlations. The workflow presented in this paper can improve the estimation of optimal mud weights for new wells before drilling, potentially reducing uncertainties and the associated risks of wellbore instability. The approximate correlations for various formation types, also detailed in this paper can potentially serve as approximate as well as valuable engineering tools that enhance the estimation of optimal mud weights without the need for in-situ stress data and other mechanical properties of the formation.
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Chen, Shuangshuang, Sihao Ding, Yiannis Karayiannidis e Mårten Björkman. "Monte Carlo Filtering Objectives". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/311.
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Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adaptive importance proposals of time series. MCFOs extend the choices of likelihood estimators beyond Sequential Monte Carlo in state-of-the-art objectives, possess important properties revealing the factors for the tightness of objectives, and allow for less biased and variant gradient estimates. We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models well explain data and importance proposals are more sample efficient on various kinds of time series data.
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Usman, Asad A., e Mohammad Usman. "Determination of Geometric Distortions in Automotive Lamps Using Non-Linear Parametric Estimations". In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34071.
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In automotive lamps, an ideal paraboloid is the reflector shape of choice when lens optics is utilized. However, geometric distortions occur among manufactured automotive lamps. This paper discusses the effects of geometric distortions on spread, packing, and gradient of reflected light from automotive lamps. Relevant legal requirements set by Federal Motor Vehicle Safety Standard on the performance of automotive lamps are also discussed. A new parametric mathematical model is developed to represent the geometry of an ideal lamp reflector. A non-linear parametric estimation problem is formulated using the Box-Kanemasu modification of the Gauss method. An application of methodology is also presented in this paper. The results show significant distortions of paraboloidal reflector with respect to the ideal design-intent reflector geometry. The numerically calculated deviations of focal point, focal length and paraboloidal axis from the ideally designed reflector necessitate improvements in the tooling and the manufacturing process for better dimensional control.
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Zhang, Sijun. "Accurate Data Reconstruction Methods for Unstructured Grid". In ASME 2005 Fluids Engineering Division Summer Meeting. ASMEDC, 2005. http://dx.doi.org/10.1115/fedsm2005-77341.
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This paper presents accurate data reconstruction methods for unstructured grid to enhance the accuracy and robustness of CFD simulations for practical problems. The schemes consist of five novel approaches to more accurately reconstruct 1) the cell-centered data to cell faces, 2) gradient estimations, 3) skew terms for diffusion, 4) skew terms for conjugate wall, and 5) skewness correction for pressure-velocity-density coupling equation. These schemes have demonstrated effectiveness on some critical cases and many problems, and strong potential to improve the accuracy and robustness of CFD computations.
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Parisi, S., F. Ursini, E. Vignati e S. Quattrocchi. "Effect of Quick Salinity Variations in Virtual Meter Applied on Water Injection Wells". In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214979-ms.
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Abstract Real Time Virtual Flowmeter (RTVFM) is a key digital technology for real time monitoring of well performances, for both production and injection wells. The main advantage of this tool is to provide estimations of well flow rates, based on wellbore pressure drop, using real-time (RT) pressure and temperature data measured by gauges installed in the well. This paper focuses on the effects of water properties on RTVFM application to water injection by evaluating their impact on the dynamic gradient and its implication in the rate estimation. Injected water can be a mix of different sources: sea water, fresh water, formation water and produced water. As a result of these different contributions, it is common to observe variations of salinity even on an hourly basis. A variation of water salinity impacts on density and viscosity, therefore changing the dynamic gradient. Salinity in injected water is commonly measured by sampling analysis, thus providing data with a much lower frequency than RT gauges. As a result, it is not usually possible to integrate salinity variation into the standard RTVFM workflow, leading to significant errors in the rate estimation. The innovative workflow presented in this paper, named Virtual Salinity, calculates water salinity in real time in wells equipped with reliable flowmeters. It regresses the dynamic gradient equation on salinity values. The results of this workflow improve the quality of RTVFM application to other wells injecting the same water mix. At each timestep, virtual salinity values are used to evaluate the correct pressure gradient for RTVFM calculation. The workflow has been successfully tested on a deepwater offshore asset, to prove its reliability. The Virtual Salinity has been applied on an offshore injection network: three wells injecting a mix of produced and sea water. The workflow, applied to all injectors, generated consistent salinity profiles. A reference virtual salinity profile has been used as an input for RTVFM simulations. For all of the injectors, RTVFM reproduced the independent flowmeter measurement with enough accuracy. The innovative methodology here presented provides a key tool to monitor salinity of injected water and can be used in field where injected salinity is not measured, providing a valuable information at minimal costs. Water salinity is one of the main inputs of production data analysis, that allows to maximize reservoir knowledge and consequently final recovery. Moreover, the greater accuracy of Virtual Meter rates significantly improves the injection monitoring, thus supporting an effective reservoir management.
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Cayeux, Eric. "Order Reduction of a Transient Model of Viscous Pressure Loss Gradient in Laminar Flow for Non-Newtonian Fluid Flowing in Circular Pipes". In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-78633.
Testo completoAbstract (sommario):
Abstract When circulating non-Newtonian fluids in pipes, the flowrate may vary because of inherent fluctuations of the mudpumps, swab and surge due to axial displacement of the drill-string, variations caused by a positive displacement motor (PDM) while drilling. Steady state estimations of viscous pressure loss gradients are not anymore valid under accelerated flowrate conditions. It is possible to precisely calculate the dynamic response in transient conditions, but such numerical calculations tend to be slow and incompatible with real-time constraints when used during a drilling operation. It is therefore desirable to derive a reduced order model that is fast, yet accurate. Numerical schemes utilized to estimate viscous pressure gradients under fluctuating flowrate conditions need to solve a larger problem than just the evolution of the pressure drop. Indeed, they need to calculate the whole fluid velocity field in a cross-section. However, when only the pressure gradient is of interest, a reduced order model would be sufficient. To find a possible reduced order dynamic model that mimics the dynamic response of the advanced numerical model, the method of sparse identification of non-linear dynamic (SINDy) is used on synthetic datasets generated using the advanced model. The SINDy method allows to discover the non-linear ordinary differential equation (ODE) that models precisely the dynamic that is observable from the synthetic dataset. Nevertheless, the calibrated parameters of the discovered non-linear ODE are only valid for specific a fluid density and rheological behavior. Therefore, the method is applied multiple times on various combinations of fluid densities and fluid rheological behavior parameters, and an interpolator function is generated. The interpolator is built using radial basis functions. The interpolator can then be used to find the correct parameters of the non-linear ODE as a function of the current rheological behavior of the fluid that flows in a pipe section and the dynamic pressure gradients can be estimated as a function of the fluctuating flowrate by solving a simple ODE, for example by using the Runge-Kutta method. The reduced order model is very efficient, yet accurate, and can therefore be used in a real-time context. This is possible because many high-quality simulations are made upfront and used by machine learning methods (SINDy and radial basis functions) to discover a non-linear ODE and its associated parameter interpolator. The application of these two machine learning methods results in a solution that has good generalization capabilities. The reason for providing good estimation of the dynamic response outside the domain of the training examples is due to the ability of the SINDy method to extract ODEs that represent a good approximation of the real physical behavior of the system.
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Salazar, J. P., e J. Borri. "Pore Size Distribution from Nuclear Magnetic Resonance: A Comparison Between LWD and Wireline Estimations". In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32688-ms.
Testo completoAbstract (sommario):
Abstract Nuclear Magnetic Resonance (NMR) experiments measure the response of the hydrogen nuclei after perturbed by an external magnetic field. This response consists in the relaxation of the total hydrogen magnetization, which is related with the exponential decay occurring in each pore size. Usually, the Laplace Inverse Transform is used to transform this time decay to a T2 distribution, which represents how is the relaxation inside the pore space and is commonly used in the oil and gas industry to get important petrophysical rock properties, such as total and effective porosity, fluid distribution and rock permeability. The estimation of these rock properties is independent of lithology. The mentioned relaxation of the magnetization occurs for three different mechanisms: interaction of the hydrogen nucleus with the pore surface, interaction between the hydrogen nucleus between them and diffusion in a magnetic field gradient. The biggest contribution to the relaxation in porous media is due to interaction with the pore surface. For this reason, the T2 distribution can be related with the pore size distribution inside the rock. This paper presents the results of calculating the pore size distribution from NMR data obtained through Logging While Drilling (LWD) and wireline technologies. It is shown the complete workflow, including the saturation calculation used to extract the hydrocarbon and/or filtrate signals from the T2 distribution. A comparison between both results is performed, showing similarities and differences of both procedures.
Rapporti di organizzazioni sul tema "Estimations de gradient"
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Glynn, Peter W., Pierre L'Ecuyer e Michel Ades. Gradient Estimation for Ratios. Fort Belvoir, VA: Defense Technical Information Center, agosto 1991. http://dx.doi.org/10.21236/ada248453.
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Glasserman, Paul, e Peter W. Glynn. Gradient Estimation for Regenerative Processes. Fort Belvoir, VA: Defense Technical Information Center, agosto 1992. http://dx.doi.org/10.21236/ada263766.
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Glynn, Peter W. Likelihood Ratio Gradient Estimation: An Overview. Fort Belvoir, VA: Defense Technical Information Center, ottobre 1987. http://dx.doi.org/10.21236/ada197085.
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Liu, X., Z. Chen e S. E. Grasby. Using shallow temperature measurements to evaluate thermal flux anomalies in the southern Mount Meager volcanic area, British Columbia, Canada. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330009.
Testo completoAbstract (sommario):
Geothermal is a clean and renewable energy resource. However, locating where elevated thermal gradient anomalies exist is a significant challenge when trying to assess potential resource volumes during early exploration of a prospective geothermal area. In this study, we deployed 22 temperature probes in the shallow subsurface along the south flank of the Mount Meager volcanic complex, to measure the transient temperature variation from September 2020 to August 2021. In our data analysis, a novel approach was developed to estimate the near-surface thermal distribution, and a workflow and code with python language have been completed for the thermal data pre-processing and analysis. The long-term temperature variation at different depths can be estimated by modelling, so that the relative difference of deducing deeper geothermal gradient anomalies can be assessed. Our proposed inversion and simulation methods were applied to calculating the temperature variation at 2.0 meters depth. The results identified a preferred high thermal flux anomalous zone in the south Mount Meager area. By combining with previous studies, the direct analysis and estimation of anomalous thermal fields based on the collected temperature data can provide a significant reference for interpretation of the regional thermal gradient variation.
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Montalvo-Bartolomei, Axel, Bryant Robbins e John Murphy. Experimental Evaluation of Kovács’ Equations for Estimating Critical Gradients. Geotechnical and Structures Laboratory (U.S.), agosto 2018. http://dx.doi.org/10.21079/11681/28066.
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Turner, Daniel Z. An overview of the gradient-based local DIC formulation for motion estimation in DICe. Office of Scientific and Technical Information (OSTI), luglio 2016. http://dx.doi.org/10.2172/1561808.
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Agüero, Jorge M., e Verónica Frisancho. Systematic Bias in Sensitive Health Behaviors and Its Impact on Treatment Effects: An Application to Violence against Women. Inter-American Development Bank, aprile 2017. http://dx.doi.org/10.18235/0007031.
Testo completoAbstract (sommario):
Violence against women takes place mainly in the private sphere and isperpetrated by people close to the victim. These features can introduce large biases into its reporting in specialized surveys as well as to the authorities. We test for the existence of measurement error in the reporting of such violence using experimental methods in Peru, a country with several specialized surveys but one lacking reliable administrative data. We ask women to report past experiences of violent acts by randomly assigning them one of two questionnaires, one that replicates current surveys and another that relies on list experiments to provide a more private setting. We find no significant reporting bias on average. However, we uncover strong evidence of non-random measurement error by education level. For highly educated women, an increase in privacy leads to higher reporting of violence, while no change is observed for the less educated. The increase is large enough to reverse the education gradient in violence. We discuss how nonclassical error in the outcome variable affects the estimation of the role of risk factors on violence. In particular, randomized controlled trials underperform instrumental variables estimates and, under certain conditions, the former could lead to even larger biases compared to cross-sectional studies.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak e Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, luglio 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Testo completoAbstract (sommario):
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs e Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, ottobre 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
Testo completoAbstract (sommario):
The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,