Relevant bibliographies by topics / Linear Sampling Method (LSM)

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Academic literature on the topic 'Linear Sampling Method (LSM)'

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Published: 26 January 2024

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Journal articles on the topic "Linear Sampling Method (LSM)"

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Narumanchi, Venkatalakshmi V., Fatemeh Pourahmadian, Jordan Lum, Andrew Townsend, Joseph W. Tringe, David M. Stobbe, and Todd W. Murray. "Laser ultrasonic imaging of subsurface defects with the linear sampling method." Optics Express 31, no. 5 (February 24, 2023): 9098. http://dx.doi.org/10.1364/oe.485084.

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Laser ultrasonics is a remote nondestructive evaluation technique suitable for real-time monitoring of fabrication processes in semiconductor metrology, advanced manufacturing, and other applications where non-contact, high fidelity measurements are required. Here we investigate laser ultrasonic data processing approaches to reconstruct images of subsurface side drilled holes in aluminum alloy specimens. We demonstrate through simulation that the model-based linear sampling method (LSM) can perform accurate shape reconstruction of single and multiple holes and produce images with well-defined boundaries. We experimentally confirm that LSM produces images that represent the internal geometric features of an object, some of which may be missed by conventional imaging.
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Sun, Jiyu, and Yuhui Han. "Two-Step Extended Sampling Method for the Inverse Acoustic Source Problem." Mathematical Problems in Engineering 2020 (February 7, 2020): 1–8. http://dx.doi.org/10.1155/2020/6434607.

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Recently, a new method, called the extended sampling method (ESM), was proposed for the inverse scattering problems. Similar to the classical linear sampling method (LSM), the ESM is simple to implement and fast. Compared to the LSM which uses full-aperture scattering data, the ESM only uses the scattering data of one incident wave. In this paper, we generalize the ESM for the inverse acoustic source problems. We show that the indicator function of ESM, which is defined using the approximated solutions of some linear ill-posed integral equations, is small when the support of the source is contained in the sampling disc and is large when the source is outside. This behavior is similar to the ESM for the inverse scattering problem. Numerical examples are presented to show the effectiveness of the method.
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Salarkaleji, Mehdi, Mohammadreza Eskandari, Jimmy Chen, and Chung-Tse Wu. "Frequency and Polarization-Diversified Linear Sampling Methods for Microwave Tomography and Remote Sensing Using Electromagnetic Metamaterials." Electronics 6, no. 4 (October 18, 2017): 85. http://dx.doi.org/10.3390/electronics6040085.

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Metamaterial leaky wave antennas (MTM-LWAs), one kind of frequency scanning antennas, exhibit frequency-space mapping characteristics that can be utilized to obtain a sufficient field of view (FOV) and reconstruct shapes in both remote sensing and microwave imaging. In this article, we utilize MTM-LWAs to conduct a spectrally encoded three-dimensional (3D) microwave tomography and remote sensing that can reconstruct conductive targets with various dimensions. In this novel imaging technique, we employ the linear sampling method (LSM) as a powerful and fast reconstruction approach. Unlike the traditional LSM using only one single frequency to illuminate a fixed direction, the proposed method utilizes a frequency scanning MTM antenna array able to accomplish frequency-space mapping over the targeted 3D background that includes unknown objects. In addition, a novel technique based on a frequency and polarization hybrid method is proposed to improve the shape reconstruction resolution and stability in ill-posed inverse problems. Both simulation and experimental results demonstrate the unique advantages of the proposed LSM using MTM-LWAs with frequency and polarization diversity as an efficient 3D remote sensing and tomography scheme.
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Hossain, F., E. N. Anagnostou, and K. H. Lee. "A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model." Nonlinear Processes in Geophysics 11, no. 4 (September 24, 2004): 427–40. http://dx.doi.org/10.5194/npg-11-427-2004.

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Abstract. This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM). The scheme is assessed within a Monte Carlo (MC) simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output) is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input) uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE) and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearest-neighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.
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Di Donato, Loreto, Rosa Scapaticci, Tommaso Isernia, Ilaria Catapano, and Lorenzo Crocco. "An Effective Method for Borehole Imaging of Buried Tunnels." International Journal of Antennas and Propagation 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/246472.

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Detection and imaging of buried tunnels is a challenging problem which is relevant to both geophysical surveys and security monitoring. To comply with the need of exploring large portions of the underground, electromagnetic measurements carried out under a borehole configuration are usually exploited. Since this requires to drill holes in the soil wherein the transmitting and receiving antennas have to be positioned, low complexity of the involved apparatus is important. On the other hand, to effectively image the surveyed area, there is the need for adopting efficient and reliable imaging methods. To address these issues, in this paper we investigate the feasibility of the linear sampling method (LSM), as this inverse scattering method is capable to provide almost real-time results even when 3D images of very large domains are built, while not requiring approximations of the underlying physics. In particular, the results of the reported numerical analysis show that the LSM is capable of performing the required imaging task while using a quite simple measurement configuration consisting of two boreholes and a few number of multiview-multistatic acquisitions.
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Kuo, Yu-Hsin, and Jean-Fu Kiang. "An Iterative Approach to Improve Images of Multiple Targets and Targets with Layered or Continuous Profile." International Journal of Microwave Science and Technology 2015 (September 27, 2015): 1–13. http://dx.doi.org/10.1155/2015/376374.

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An iterative approach, based on the linear sampling method (LSM) and the contrast source inversion (CSI) method, is proposed to improve the recovered images of multiple targets and targets with layered or continuous profile, including shape and distribution of electric properties. The difficulties in dealing with large targets or high contrast are partly overcome with this approach. Typical targets studied in the literatures are chosen for simulations and comparison.
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Guerriero, Vincenzo. "Maximum Likelihood Instead of Least Squares in Fracture Analysis by Means of a Simple Excel Sheet with VBA Macro." Geosciences 13, no. 12 (December 11, 2023): 379. http://dx.doi.org/10.3390/geosciences13120379.

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This technical note illustrates a linear regression algorithm based on the Maximum Likelihood Estimation (MLE), with a related Excel spreadsheet and VBA program, adapted to the case of fracture aperture data sets in which sampling of the smallest values is problematic. The method has been tested by means of Monte Carlo simulations and exhibits significantly better convergence against Least Squares criterion (LSM). As the method is conceptually simple and, following the indications illustrated here, the relative spreadsheet can be easily designed, it may be routinely used, instead of the Least Squares, in fracture analysis. Furthermore, the proposed method, with the appropriate modifications, might be potentially extended to other cases in geology and geophysics, in which significant biases at the lower limits of the sampling scale occur.
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Zeng, Pengyuan, Xuan Song, Huan Yang, Ning Wei, and Liping Du. "Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms." ISPRS International Journal of Geo-Information 11, no. 5 (May 6, 2022): 299. http://dx.doi.org/10.3390/ijgi11050299.

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Digital soil mapping has emerged as a new method to describe the spatial distribution of soils economically and efficiently. In this study, a lightweight soil organic matter (SOM) mapping method based on a deep residual network, which we call LSM-ResNet, is proposed to make accurate predictions with background covariates. ResNet not only integrates spatial background information around the observed environmental covariates, but also reduces problems such as information loss, which undermines the integrity of information and reduces prediction uncertainty. To train the model, rectified linear units, mean squared error, and adaptive momentum estimation were used as the activation function, loss/cost function, and optimizer, respectively. The method was tested with Landsat5, the meteorological data from WorldClim, and the 1602 sampling points set from Xinxiang, China. The performance of the proposed LSM-ResNet was compared to a traditional machine learning algorithm, the random forest (RF) algorithm, and a training set (80%) and a test set (20%) were created to test both models. The results showed that the LSM-ResNet (RMSE = 6.40, R2 = 0.51) model outperformed the RF model in both the roots mean square error (RMSE) and coefficient of determination (R2), and the training accuracy was significantly improved compared to RF (RMSE = 6.81, R2 = 0.46). The trained LSM-ResNet model was used for SOM prediction in Xinxiang, a district of plain terrain in China. The prediction maps can be deemed an accurate reflection of the spatial variability of the SOM distribution.
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Meng, Shuo, Chen Meng, Cheng Wang, and Xiang Yin. "A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter." Journal of Sensors 2023 (March 22, 2023): 1–11. http://dx.doi.org/10.1155/2023/2499336.

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The compressed sampling theory provides a good idea for sampling broadband linear frequency-modulated (LFM) signal, but the signal reconstruction after sampling should be considered when using the compressed sampling system to sample the signal. For the problem of estimating the parameters of the LFM signal under the condition of nonreconstruction, a method combining random demodulation (RD) and waveform matching (WM) dictionary is proposed in this paper. Firstly, the RD system is used to collect the compressed sampling data of LFM signal. In order to ensure the validity of the compressed sampling data, the value of the compressed sampling rate R in the RD system is discussed. Then, the feasibility of using compressed sampling data to estimate LFM signal parameters is theoretically demonstrated. According to the construction principle of waveform matching dictionary, the corresponding relationship between sparse representation coefficients, dictionary atoms, and signal parameters can be determined. Using this theory, the LFM signal parameters can be estimated. Finally, simulation and real signal experiments verify that the proposed method can realize the estimation of initial frequency and chirp rate of LFM signal when some prior information is known.
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Jia, Pengxiang, Jianhua Yang, Chengjin Wu, and Miguel A. F. Sanjuán. "Amplification of the LFM signal by using piecewise vibrational methods." Journal of Vibration and Control 25, no. 1 (April 24, 2018): 141–50. http://dx.doi.org/10.1177/1077546318772257.

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We propose the piecewise re-scaled vibrational resonance (VR) method and the piecewise twice sampling VR method to amplify the weak linear frequency-modulated (LFM) signal. The system used to amplify the weak LFM signal is a typical bistable system with fractional-order deflection nonlinearity. The concrete procedures of both the piecewise re-scaled VR method and the piecewise twice sampling VR method are explained in detail. Through studying the effect of the factional-order exponent on VR, we find that the traditional bistable system is not the optimal model to improve the weak LFM signal. By investigating different parameters on the VR phenomenon, we verify the effectiveness of the two proposed methods.
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Dissertations / Theses on the topic "Linear Sampling Method (LSM)"

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Hörmann, Wolfgang, and Josef Leydold. "Sampling from Linear Multivariate Densities." WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/3192/1/Report111.pdf.

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It is well known that the generation of random vectors with non-independent components is difficult. Nevertheless, we propose a new and very simple generation algorithm for multivariate linear densities over point-symmetric domains. Among other applications it can be used to design a simple decomposition-rejection algorithm for multivariate concave distributions.
Series: Research Report Series / Department of Statistics and Mathematics
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Recoquillay, Arnaud. "Méthodes d'échantillonnage appliquées à l'imagerie de défauts dans un guide d'ondes élastiques." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLY001/document.

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De nombreuses structures utilisées industriellement peuvent être considérées comme des guides d'ondes, comme les plaques, les tuyaux ou encore le rails. La maintenance de ces structures nécessite de pouvoir détecter efficacement des défauts internes par le Contrôle Non Destructif. Nous nous intéressons dans ce manuscrit à l'application d'une méthode d'échantillonnage, la Linear Sampling Method, au CND des guides d'ondes élastiques, qui en particulier impose des sollicitations et des mesures à la surface du guide en régime temporel. La stratégie choisie repose sur une formulation modale et multi-fréquentielle de la LSM, spécifique aux guides d'ondes, qui permet une régularisation efficace et de nature physique du problème inverse, qui est par nature mal posé. Cette stratégie permet par ailleurs une optimisation du nombre et de la position des émetteurs et des récepteurs. Nous nous limitons dans un premier temps au cas scalaire du guide d'ondes acoustiques, pour ensuite s'attaquer au cas vectoriel, et par conséquent plus complexe, du guide d'ondes élastiques.L'efficacité de la méthode inverse est dans un premier temps démontrée sur des données artificielles (obtenues numériquement), puis sur des données réelles obtenues à l'aide d'expériences réalisées sur des plaques métalliques. Ces expériences confirment la faisabilité du CND par méthode d'échantillonnage dans un cadre industriel. Dans le cas où une seule sollicitation est réalisée, l'utilisation de la LSM est exclu. Nous utilisons une approche tout à fait différente et dite "extérieure", couplant une formulation mixte de quasi-réversibilité et une méthode de lignes de niveau, pour reconstruire le défaut
Widely used structures in an industrial context, such as plates, pipes or rails, can be considered as waveguides. Hence efficient Non Destructive Testing techniques are needed in order to detect defects in these structure during their maintenance. This work is about adapting a sampling method, the Linear Sampling Method, to the context of NDT for elastic waveguides. This context implies that the sollicitations and measurements must be on the surface of the waveguide in a time-dependent regime. A modal and multi-frequency formulation of the LSM, specific to waveguides, has been chosen to solve the problem. This formulation allows an efficient and physical regularization of the inverse problem, which is naturally ill-posed. An optimization of the number of sources and measurements and of their positioning is possible thanks to the methodology used to solve the problem. The scalar case of an acoustic waveguide is considered as a first step, while the vectorial case of an elastic waveguide, more complex by nature, is addressed in a second time.The efficiency of the method is at first tested on artificial data (numerically made), and then on real data obtained from experiments on metallic plates. These experiments show the feasibility of using sampling methods for Non Destructive Testing in an industrial context. In the case when only one sollicitation is available, the LSM can not be applied. A completely different approach is then used, which is called the ``exterior'' approach, coupling a mixed formulation of quasi-reversibility and a level-set method in order to recover the shape of the defect
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Oliveira, Hugo Luiz. "Modelos numéricos aplicados à análise viscoelástica linear e à otimização topológica probabilística de estruturas bidimensionais: uma abordagem pelo Método dos Elementos de Contorno." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18134/tde-27042017-093145/.

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O presente trabalho trata da formulação e implementação de modelos numéricos baseados no Método dos Elementos de Contorno (MEC). Inspirando-se em problemas de engenharia, uma abordagem multidisciplinar é proposta como meio de representação numérica mais realista. Há materiais de uso corrente na engenharia que possuem resposta dependente do tempo. Nesta tese os fenômenos dependentes do tempo são abordados por meio da Mecânica Viscoelástica Linear associada a modelos reológicos. Neste trabalho, se apresenta a dedução do modelo constitutivo de Maxwell para ser utilizado via MEC. As equações deduzidas são verificadas em problemas de referência. Os resultados mostram que a formulação deduzida pode ser utilizada para representar estruturas compostas, mesmo em casos envolvendo uma junção entre materiais viscoelásticos e não viscoelásticos. Adicionalmente as formulações apresentadas se mantém estáveis na presença de fissuras de domínio e bordo. Verifica-se que a formulação clássica dual pode ser utilizada para simular o comportamento de fissuras com resposta dependente do tempo. Essa constatação serve de base para maiores investigações no campo da Mecânica da Fratura de materiais viscoelásticos. Na sequência, mostra-se como o MEC pode ser aliado a conceitos probabilísticos para fazer estimativas de comportamentos a longo prazo. Estas estimativas incluem as incertezas inerentes nos processos de engenharia. As incertezas envolvem os parâmetros materiais, de carregamento e de geometria. Por meio do conceito de probabilidade de falha, os resultados mostram que as incertezas relacionadas às estimativas das cargas atuantes apresentam maior impacto no desempenho esperado a longo prazo. Esta constatação serve para realizar estudos que colaborem para a melhoria dos processos de concepção estrutural. Outro aspecto de interesse desta tese é a busca de formas otimizadas, por meio da Otimização Topológica. Neste trabalho, um algoritmo alternativo de otimização topológica é proposto. O algoritmo é baseado no acoplamento entre o Método Level Set (MLS) e o MEC. A diferença entre o algoritmo aqui proposto, e os demais presentes na literatura, é forma de obtenção do campo de velocidades. Nesta tese, os campos normais de velocidades são obtidos por meio da sensibilidade à forma. Esta mudança torna o algoritmo propício a ser tratado pelo MEC, pois as informações necessárias para o cálculo das sensibilidades residem exclusivamente no contorno. Verifica-se que o algoritmo necessita de uma extensão particular de velocidades para o domínio a fim de manter a estabilidade. Limitando-se a casos bidimensionais, o algoritmo é capaz de obter os conhecidos casos de referência reportados pela literatura. O último aspecto tratado nesta tese retrata a maneira pela qual as incertezas geométricas podem influenciar na determinação das estruturas otimizadas. Utilizando o MEC, propõe-se um critério probabilístico que permite embasar escolhas levando em consideração a sensibilidade geométrica. Os resultados mostram que os critérios deterministas, nem sempre, conduzem às escolhas mais adequadas sob o ponto de vista de engenharia. Assim, este trabalho contribui para a expansão e difusão das aplicações do MEC em problemas de engenharia de estruturas.
The present work deals with the formulation and implementation of numerical models based on the Boundary Element Method (BEM). Inspired by engineering problems, a multidisciplinary combination is proposed as a more realistic approach. There are common engineering materials that have time-dependent response. In this thesis, time-dependent phenomena are approached through the Linear Viscoelastic Mechanics associated with rheological models. In this work, the formulation of Maxwell\'s constitutive model is presented to be used via MEC. The resultant equations are checked on reference problems. The results show that the presented formulation can be used to represent composite structures, even in cases involving a junction between viscoelastic and non-viscoelastic materials. Additionally the formulations presented remain stable in the presence of cracks. It is found that the classical DUAL-BEM formulation can be used to simulate cracks with time-dependent behaviour. This result serves as the basis for further investigations in the field of Fracture Mechanics of viscoelastic materials. In the sequence, it is shown how the BEM can be associated with probabilistic concepts to make predictions of long-term behaviour. These predictions include the inherent uncertainties in engineering processes. The uncertainties involve the material, loading and geometry parameters. Using the concept of probability of failure, the results show that the uncertainties related to the estimations of loads have important impact on the long-term expected performance. This finding serves to carry out studies that collaborate for the improvement of structural design processes. Another aspect of interest of this thesis is the search for optimized forms through Topological Optimization. In this work, an alternative topological optimization algorithm is proposed. The algorithm is based on the coupling between the Level Set Method (LSM) and BEM. The difference between the algorithm proposed here, and the others present in the literature, is a way of obtaining the velocity field. In this thesis, the normal fields of velocities are obtained by means of shape sensitivity. This change makes the algorithm adequate to be treated by the BEM, since the information necessary for the calculation of the sensitivities resides exclusively in the contour. It is found that the algorithm requires a particular velocity extension in order to maintain stability. Limiting to two-dimensional cases, the algorithm is able to obtain the known benchmark cases reported in the literature. The last aspect addressed in this thesis involves the way in which geometric uncertainties can influence the determination of optimized structures. Using the BEM, it is proposed a probabilistic criterion that takes into consideration the geometric sensitivity. The results show that deterministic criteria do not always lead to the most appropriate choices from an engineering point of view. In summary, this work contributes to the expansion and diffusion of MEC applications in structural engineering problems.
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Alqadah, Hatim F. "Space-Frequency Regularization for Qualitative Inverse Scattering." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321967202.

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Delbary, Fabrice. "Identification de fissures par ondes acoustiques." Paris 6, 2006. http://www.theses.fr/2006PA066605.

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Cette thèse porte sur le problème de reconstruction de fissures par ondes acoustiques. Le premier objectif consiste à développer une méthode d'inversion rapide utilisant un unique couple de données de Cauchy sur la frontière d'un domaine entourant la fissure sous l'hypothèse que la fissure cherchée est plane. L'utilisation de la fonctionnelle Écart à la Réciprocité nous fournit alors une méthode de reconstruction quasi explicite. Le second objectif consiste à trouver une méthode d'inversion pour le problème de Helmholtz en milieu inhomogène. L'utilisation de la fonctionnelle Écart à la Réciprocité combinée à une méthode de type "Sampling Method" permet alors la reconstruction de fissures de formes quelconques à partir de données en champ proche.
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Assareh, Hassan. "Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53342/1/Hassan_Assareh_Thesis.pdf.

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Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.
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Ahmed, Mohamed Salem. "Contribution à la statistique spatiale et l'analyse de données fonctionnelles." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30047/document.

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Ce mémoire de thèse porte sur la statistique inférentielle des données spatiales et/ou fonctionnelles. En effet, nous nous sommes intéressés à l’estimation de paramètres inconnus de certains modèles à partir d’échantillons obtenus par un processus d’échantillonnage aléatoire ou non (stratifié), composés de variables indépendantes ou spatialement dépendantes.La spécificité des méthodes proposées réside dans le fait qu’elles tiennent compte de la nature de l’échantillon étudié (échantillon stratifié ou composé de données spatiales dépendantes).Tout d’abord, nous étudions des données à valeurs dans un espace de dimension infinie ou dites ”données fonctionnelles”. Dans un premier temps, nous étudions les modèles de choix binaires fonctionnels dans un contexte d’échantillonnage par stratification endogène (échantillonnage Cas-Témoin ou échantillonnage basé sur le choix). La spécificité de cette étude réside sur le fait que la méthode proposée prend en considération le schéma d’échantillonnage. Nous décrivons une fonction de vraisemblance conditionnelle sous l’échantillonnage considérée et une stratégie de réduction de dimension afin d’introduire une estimation du modèle par vraisemblance conditionnelle. Nous étudions les propriétés asymptotiques des estimateurs proposées ainsi que leurs applications à des données simulées et réelles. Nous nous sommes ensuite intéressés à un modèle linéaire fonctionnel spatial auto-régressif. La particularité du modèle réside dans la nature fonctionnelle de la variable explicative et la structure de la dépendance spatiale des variables de l’échantillon considéré. La procédure d’estimation que nous proposons consiste à réduire la dimension infinie de la variable explicative fonctionnelle et à maximiser une quasi-vraisemblance associée au modèle. Nous établissons la consistance, la normalité asymptotique et les performances numériques des estimateurs proposés.Dans la deuxième partie du mémoire, nous abordons des problèmes de régression et prédiction de variables dépendantes à valeurs réelles. Nous commençons par généraliser la méthode de k-plus proches voisins (k-nearest neighbors; k-NN) afin de prédire un processus spatial en des sites non-observés, en présence de co-variables spatiaux. La spécificité du prédicteur proposé est qu’il tient compte d’une hétérogénéité au niveau de la co-variable utilisée. Nous établissons la convergence presque complète avec vitesse du prédicteur et donnons des résultats numériques à l’aide de données simulées et environnementales.Nous généralisons ensuite le modèle probit partiellement linéaire pour données indépendantes à des données spatiales. Nous utilisons un processus spatial linéaire pour modéliser les perturbations du processus considéré, permettant ainsi plus de flexibilité et d’englober plusieurs types de dépendances spatiales. Nous proposons une approche d’estimation semi paramétrique basée sur une vraisemblance pondérée et la méthode des moments généralisées et en étudions les propriétés asymptotiques et performances numériques. Une étude sur la détection des facteurs de risque de cancer VADS (voies aéro-digestives supérieures)dans la région Nord de France à l’aide de modèles spatiaux à choix binaire termine notre contribution
This thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country
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Stampfer, Kilian. "The Generalized Operator Based Prony Method." Doctoral thesis, 2019. http://hdl.handle.net/11858/00-1735-0000-002E-E631-3.

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Erhard, Klaus. "Point Source Approximation Methods in Inverse Obstacle Reconstruction Problems." Doctoral thesis, 2005. http://hdl.handle.net/11858/00-1735-0000-0006-B402-4.

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Books on the topic "Linear Sampling Method (LSM)"

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1943-, Colton David L., and Monk Peter 1956-, eds. The linear sampling method in inverse electromagnetic scattering. Philadelphia: Society for Industrial and Applied Mathematics, 2011.

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Rajeev, S. G. Spectral Methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805021.003.0013.

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Thenumerical solution of ordinary differential equations (ODEs)with boundary conditions is studied here. Functions are approximated by polynomials in a Chebychev basis. Sections then cover spectral discretization, sampling, interpolation, differentiation, integration, and the basic ODE. Following Trefethen et al., differential operators are approximated as rectangular matrices. Boundary conditions add additional rows that turn them into square matrices. These can then be diagonalized using standard linear algebra methods. After studying various simple model problems, this method is applied to the Orr–Sommerfeld equation, deriving results originally due to Orszag. The difficulties of pushing spectral methods to higher dimensions are outlined.
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Book chapters on the topic "Linear Sampling Method (LSM)"

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Colton, David, Andreas Kirsch, and Peter Monk. "The Linear Sampling Method in Inverse Scattering Theory." In Surveys on Solution Methods for Inverse Problems, 107–18. Vienna: Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-6296-5_6.

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Collino, Francis, M’Barek Fares, and Houssem Haddar. "On the Validation of the Linear Sampling Method in Electromagnetic Inverse Scattering Problems." In Mathematical and Numerical Aspects of Wave Propagation WAVES 2003, 649–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55856-6_105.

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Petersson, Andreas. "Rapid Covariance-Based Sampling of Linear SPDE Approximations in the Multilevel Monte Carlo Method." In Springer Proceedings in Mathematics & Statistics, 423–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43465-6_21.

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Goshtasbpour, Shirin, and Fernando Perez-Cruz. "Optimization of Annealed Importance Sampling Hyperparameters." In Machine Learning and Knowledge Discovery in Databases, 174–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26419-1_11.

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AbstractAnnealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between initial and the target distribution which affect the estimation performance when the computation budget is limited. In order to reduce the number of sampling iterations, we present a parameteric AIS process with flexible intermediary distributions defined by a residual density with respect to the geometric mean path. Our method allows parameter sharing between annealing distributions, the use of fix linear schedule for discretization and amortization of hyperparameter selection in latent variable models. We assess the performance of Optimized-Path AIS for marginal likelihood estimation of deep generative models and compare it to compare it to more computationally intensive AIS.
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Faddila, Syifa Pramudita, Citra Savitri, Dedi Mulyadi, and Puji Isyanto. "Flash Sale and Brand Image Models in Improving Purchase Decisions on Fashion Products at the Shopee Marketplace Among Students." In Proceedings of the 19th International Symposium on Management (INSYMA 2022), 841–48. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_105.

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AbstractThis study aims to determine the models of flash sales and brand image in improving purchase decisions on fashion products at the Shopee marketplace among students. The research was conducted using a quantitative descriptive method. Sampling in this study were 225 students from the 2017 class who had shopped for fashion products online at the Shopee marketplace. The sampling technique used was purposive sampling. The data collection method used a questionnaire distributed to respondents. The analytical tool used was multiple linear regressions. The results show that flash sales and brand image partially or simultaneously have an influence on purchase decisions.
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Prillya, Laurasia Trya, Prihatin Lumbanraja, and Meilita Tryana Sembiring. "Analysis of Job Satisfaction, Job Stress, and Job Insecurity on Employee Turnover Intention at a Manufacturing Company in the Industrial and Chemical Sector in North Sumatra." In Proceedings of the 19th International Symposium on Management (INSYMA 2022), 664–71. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_83.

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AbstractThe study aims to analyze the effect of job satisfaction, job stress, and job insecurity on employee turnover intention at a Manufacturing Company in the Industrial and Chemical Sector in North Sumatra. The study was conducted on 96 employees at a Manufacturing Company in the Industrial and Chemical Sector in North Sumatra with a purposive sampling technique. The data collection technique used a survey method by distributing questionnaires and processed by SPSS. The analytical method used in this research was multiple linear regression analysis. The results indicate that job satisfaction has a significant positive effect on turnover intention, job stress has a negative effect on turnover intention, and job insecurity has a significant positive effect on turnover intention.
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Ryou, Wonryong, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, and Martin Vechev. "Scalable Polyhedral Verification of Recurrent Neural Networks." In Computer Aided Verification, 225–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_10.

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AbstractWe present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.
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Sitorus, Ombun Rico, Sukaria Sinulingga, and Beby Karina Fawzeea Sembiring. "Green Marketing Strategy Effect on Consumer Awareness Through Marketing Mix Approach." In Proceedings of the 19th International Symposium on Management (INSYMA 2022), 976–81. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_121.

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AbstractThis study aims to determine whether the use of environmental issues in marketing strategies impacts business in terms of consumer awareness through a marketing mix approach. This research was conducted using a survey through a closed question questionnaire with a symmetrical scale. In this study were, 120 respondents selected using simple random sampling through the analytical method used with multiple linear regression analysis. This study indicates that green marketing has a significant influence on consumer awareness as measured by the product, price, place & promotion components of the marketing mix. The study results show that consumers are aware of green products, so they want to buy the products, or it can be concluded that green marketing strategies have a positive effect on business strategies.
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Tran, Hoang-Dung, Neelanjana Pal, Patrick Musau, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Stanley Bak, and Taylor T. Johnson. "Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability." In Computer Aided Verification, 263–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_12.

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AbstractThis paper introduces robustness verification for semantic segmentation neural networks (in short, semantic segmentation networks [SSNs]), building on and extending recent approaches for robustness verification of image classification neural networks. Despite recent progress in developing verification methods for specifications such as local adversarial robustness in deep neural networks (DNNs) in terms of scalability, precision, and applicability to different network architectures, layers, and activation functions, robustness verification of semantic segmentation has not yet been considered. We address this limitation by developing and applying new robustness analysis methods for several segmentation neural network architectures, specifically by addressing reachability analysis of up-sampling layers, such as transposed convolution and dilated convolution. We consider several definitions of robustness for segmentation, such as the percentage of pixels in the output that can be proven robust under different adversarial perturbations, and a robust variant of intersection-over-union (IoU), the typical performance evaluation measure for segmentation tasks. Our approach is based on a new relaxed reachability method, allowing users to select the percentage of a number of linear programming problems (LPs) to solve when constructing the reachable set, through a relaxation factor percentage. The approach is implemented within NNV, then applied and evaluated on segmentation datasets, such as a multi-digit variant of MNIST known as M2NIST. Thorough experiments show that by using transposed convolution for up-sampling and average-pooling for down-sampling, combined with minimizing the number of ReLU layers in the SSNs, we can obtain SSNs with not only high accuracy (IoU), but also that are more robust to adversarial attacks and amenable to verification. Additionally, using our new relaxed reachability method, we can significantly reduce the verification time for neural networks whose ReLU layers dominate the total analysis time, even in classification tasks.
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Irawati, Indrarini Dyah, Gelar Budiman, Kholidiyah Masykuroh, Zein Hanni Pradana, and Arfianto Fahmi. "High Payload Qr-Based Data Hiding Using Secured Compressed Watermark in Polar Domain." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210285.

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Audio Watermarking is a method to insert a copyright marker on audio. This method inserts a watermark in the information form and in a way that does not damage the audio. This technique is one of the ways to solve the problem of copyright infringement. The embedded watermark has to meet the condition of not damaging the audio and must have robustness, imperceptibility, and good capacity. The data hiding technique use the combined method of Lifting Wavelet Transform (LWT), Fast Fourier Transform (FFT), QR Decomposition and Reconstruction, and Cartesian-Polar Transformation (CPT) based on Quantization Index Modulation (QIM) with the secured and compressed watermark using Compressive Sampling (CS) technique. The proposed scheme is blind Audio Watermarking as it no needs for original audio in the detection process. The combination of methods overcomes multiple attacks with guaranteed quality watermarking and high capacity. Compared to the existing technique, the data hiding technique can withstand LPF attacks, Resampling, Linear speed change (LSC), and MP3 compression. This proposed technique is also secured due to the coded watermark by a particular random key using CS. Combining CS and Audio Watermarking techniques can perform well in capacity, imperceptibility, security, and attack resistance.
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Conference papers on the topic "Linear Sampling Method (LSM)"

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Catapano, I., Francesco Soldovieri, Lorenzo Crocco, Loreto Di Donato, and Raffaele Persico. "Utilities mapping via Linear Sampling Method." In 2012 14th International Conference on Ground Penetrating Radar (GPR). IEEE, 2012. http://dx.doi.org/10.1109/icgpr.2012.6254881.

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Kim, Hyun-Chul, Kyu-Hwan Jung, and Jaewook Lee. "Approximate Sampling Method for Locally Linear Embedding." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371023.

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Burfeindt, Matthew J., and Hatim F. Alqadah. "Receive-beamforming-enhanced Linear Sampling Method imaging." In 2021 IEEE Research and Applications of Photonics in Defense Conference (RAPID). IEEE, 2021. http://dx.doi.org/10.1109/rapid51799.2021.9521380.

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Xue, Junliang, Yongjun Wang, Chao Li, Jingwen Liu, Lu Han, Qi Zhang, and Xiangjun Xin. "Integration Extraction Method Aided Linear Optical Sampling System." In 2022 20th International Conference on Optical Communications and Networks (ICOCN). IEEE, 2022. http://dx.doi.org/10.1109/icocn55511.2022.9901118.

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Ambrosanio, M., M. T. Bevacqua, T. Isernia, and V. Pascazio. "Experimental Multistatic Imaging VIA the Linear Sampling Method." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8897877.

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CHARALAMBOPOULOS, ANTONIOS, DROSSOS GINTIDES, and KIRIAKIE KIRIAKI. "THE LINEAR SAMPLING METHOD FOR N-BODIES IN 2-DIMENSIONAL LINEAR ELASTICITY." In Proceedings of the Sixth International Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702593_0024.

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Haghparast, Maysam, Seyed Abdullah Mirtaheri, and Mohammad Sadegh Abrishamian. "Conductor and dielectric object discrimination by linear sampling method." In 2016 24th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2016. http://dx.doi.org/10.1109/iraniancee.2016.7585661.

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Mallikarjun, E., Abhishek Roy, and Amitabha Bhattacharya. "Effect of multipoles in resolution with Linear Sampling Method." In 2014 Twentieth National Conference on Communications (NCC). IEEE, 2014. http://dx.doi.org/10.1109/ncc.2014.6811352.

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Alqadah, Hatim F., Jason Parker, Matthew Ferrara, and Howard Fan. "Space-frequency sparse regularization for the linear sampling method." In Propagation in Wireless Communications (ICEAA). IEEE, 2011. http://dx.doi.org/10.1109/iceaa.2011.6046376.

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Prunty, Aaron, and Roel Snieder. "Imaging, focusing, and inversion with the linear sampling method." In SEG Technical Program Expanded Abstracts 2019. Society of Exploration Geophysicists, 2019. http://dx.doi.org/10.1190/segam2019-3215188.1.

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Reports on the topic "Linear Sampling Method (LSM)"

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Colton, David, and Peter Monk. Linear Sampling Method. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada368321.

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