Relevant bibliographies by topics / Multi-modal statistics

Academic literature on the topic 'Multi-modal statistics'

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Published: 14 March 2023

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Journal articles on the topic "Multi-modal statistics"

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Tai, J. J., and A. J. Gross. "Complex Segregation Analysis on Multi-Modal Distributions." Biometrical Journal 31, no. 1 (1989): 123–29. http://dx.doi.org/10.1002/bimj.4710310116.

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Sharma, Pulkit, Achut Manandhar, Patrick Thomson, Jacob Katuva, Robert Hope, and David A. Clifton. "Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning." Sustainability 11, no. 22 (November 11, 2019): 6312. http://dx.doi.org/10.3390/su11226312.

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In the context of developing countries, effective groundwater resource management is often hindered by a lack of data integration between resource availability, water demand, and the welfare of water users. As a consequence, drinking water-related policies and investments, while broadly beneficial, are unlikely to be able to target the most in need. To find the households in need, we need to estimate their welfare status first. However, the current practices for estimating welfare need a detailed questionnaire in the form of a survey which is time-consuming and resource-intensive. In this work, we propose an alternate solution to this problem by performing a small set of cost-effective household surveys, which can be collected over a short amount of time. We try to compensate for the loss of information by using other modalities of data. By combining different modalities of data, this work aims to characterize the welfare status of people with respect to their local drinking water resource. This work employs deep learning-based methods to model welfare using multi-modal data from household surveys, community handpump abstraction, and groundwater levels. We employ a multi-input multi-output deep learning framework, where different types of deep learning models are used for different modalities of data. Experimental results in this work have demonstrated that the multi-modal data in the form of a small set of survey questions, handpump abstraction data, and groundwater level can be used to estimate the welfare status of households. In addition, the results show that different modalities of data have complementary information, which, when combined, improves the overall performance of our ability to predict welfare.
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Arefi, Ahmad, and Reza Pourtaheri. "Multi-modal tempered stable distributions and prosses with applications to finance." Communications in Statistics - Theory and Methods 49, no. 17 (February 3, 2020): 4133–49. http://dx.doi.org/10.1080/03610926.2019.1594304.

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Sun, Yan, Maoxiang Lang, and Danzhu Wang. "Optimization Models and Solution Algorithms for Freight Routing Planning Problem in the Multi-Modal Transportation Networks: A Review of the State-of-the-Art." Open Civil Engineering Journal 9, no. 1 (September 17, 2015): 714–23. http://dx.doi.org/10.2174/1874149501509010714.

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With the remarkable development of international trade, global commodity circulation has grown significantly. To accomplish commodity circulation among various regions and countries, multi-modal transportation scheme has been widely adopted by a large number of companies. Meanwhile, according to the relevant statistics, the international logistics costs reach up to approximate 30-50% of the total production cost of the companies. Lowering the transportation costs has become one of the most important sources for a company to raise profits and maintain competitiveness in the global market. Thus, how to optimize freight routes selection to move commodities through the multi-modal transportation network has gained great concern of both the decision makers of the companies and the multi-modal transport operators. In this study, we present a systematical review on the multi-modal transportation freight routing planning problem from the aspects of model formulation and algorithm design. Following contents are covered in this review: (1) distinguishing the formulation characteristics of various optimization models; (2) identifying the optimization models in recent studies according to the formulation characteristics; and (3) discussing the solution approaches that are developed to solve the optimization models, especially the heuristic algorithms.
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Whittle, P. "A construction for multi-modal processes, and a potential memory device." Journal of Applied Probability 27, no. 1 (March 1990): 146–55. http://dx.doi.org/10.2307/3214602.

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It is shown that if parameters occurring linearly in the transition intensity of a Markov process are replaced by their respective ‘Bayesian estimates' then the new process thus generated has an equilibrium distribution which is a mixture (over parameter values) of the original parametrised equilibrium distribution.One effectively then has an extra state dependence in that one selects from a given class of transition rules those rules which are most consistent with the value of current state. The effect of this is thus to preserve the status quo, in that unlikely transitions are made even less likely. By this means one can construct processes which show several distinct and metastable modes of behaviour, and which can serve as models for memory devices.
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Whittle, P. "A construction for multi-modal processes, and a potential memory device." Journal of Applied Probability 27, no. 01 (March 1990): 146–55. http://dx.doi.org/10.1017/s0021900200038493.

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It is shown that if parameters occurring linearly in the transition intensity of a Markov process are replaced by their respective ‘Bayesian estimates' then the new process thus generated has an equilibrium distribution which is a mixture (over parameter values) of the original parametrised equilibrium distribution. One effectively then has an extra state dependence in that one selects from a given class of transition rules those rules which are most consistent with the value of current state. The effect of this is thus to preserve the status quo, in that unlikely transitions are made even less likely. By this means one can construct processes which show several distinct and metastable modes of behaviour, and which can serve as models for memory devices.
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Sun, Hong Tao, Yong Shou Dai, Fang Wang, and Xing Peng. "Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 4241–47. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4241.

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Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.
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Rivas, Alberto, Alfonso González-Briones, Juan J. Cea-Morán, Arnau Prat-Pérez, and Juan M. Corchado. "My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles." Electronics 10, no. 11 (May 25, 2021): 1263. http://dx.doi.org/10.3390/electronics10111263.

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New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.
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Park, Chanseok, and W. J. Padgett. "Analysis of strength distributions of multi-modal failures using the EM algorithm." Journal of Statistical Computation and Simulation 76, no. 7 (July 2006): 619–36. http://dx.doi.org/10.1080/10629360500108970.

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Yu, Yuan Chih, Shing Chern D. You, and Dwen Ren Tsai. "Hill Climbing Algorithm for License Plate Recognition." Advanced Materials Research 267 (June 2011): 995–1000. http://dx.doi.org/10.4028/www.scientific.net/amr.267.995.

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Histogram thresholding has been widely used for image processing—it is simple, fast, and computationally inexpensive. In this paper, we develop a creative approach based on histogram’s distributions to segment interest regions from background. Unlike the existing threshold detection methods which measure the statistics of histogram in the multi-modal images, our approach analyses the shape representation of multi-modal which has several hill-climbing curves. The behavior of algorithm works like human vision which focuses on the high contrast areas and scans the shape variation first. Moreover, such an algorithm presents a new type of histogram analysis that depends on the particular shape of certain distribution in histogram. Experimental results reveal that the proposed algorithm performs distinct effects especially on the recognition of artificial signs such as road sign, vehicle plate, and signboard.
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Dissertations / Theses on the topic "Multi-modal statistics"

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McCalman, Lachlan Robert. "Function Embeddings for Multi-modal Bayesian Inference." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/12031.

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Tractable Bayesian inference is a fundamental challenge in robotics and machine learning. Standard approaches such as Gaussian process regression and Kalman filtering make strong Gaussianity assumptions about the underlying distributions. Such assumptions, however, can quickly break down when dealing with complex systems such as the dynamics of a robot or multi-variate spatial models. In this thesis we aim to solve Bayesian regression and filtering problems without making assumptions about the underlying distributions. We develop techniques to produce rich posterior representations for complex, multi-modal phenomena. Our work extends kernel Bayes' rule (KBR), which uses empirical estimates of distributions derived from a set of training samples and embeds them into a high-dimensional reproducing kernel Hilbert space (RKHS). Bayes' rule itself occurs on elements of this space. Our first contribution is the development of an efficient method for estimating posterior density functions from kernel Bayes' rule, applied to both filtering and regression. By embedding fixed-mean mixtures of component distributions, we can efficiently find an approximate pre-image by optimising the mixture weights using a convex quadratic program. The result is a complex, multi-modal posterior representation. Our next contributions are methods for estimating cumulative distributions and quantile estimates from the posterior embedding of kernel Bayes' rule. We examine a number of novel methods, including those based on our density estimation techniques, as well as directly estimating the cumulative through use of the reproducing property of RKHSs. Finally, we develop a novel method for scaling kernel Bayes' rule inference to large datasets, using a reduced-set construction optimised using the posterior likelihood. This method retains the ability to perform multi-output inference, as well as our earlier contributions to represent explicitly non-Gaussian posteriors and quantile estimates.
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Li, Mengbo. "Integration of Multi-Modal Data to Guide Classification in Studies of Complex Diseases." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22693.

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Having entered the big data era, the unprecedentedly fast-growing volume and variety of biological data have swiftly transformed the landscape of biomedical research. Meanwhile, classification methods as a powerful bioinformatics tool have greatly empowered researchers to uncover new aspects of the complex biological systems. This thesis addresses the statistical and methodological challenges that often exist in different stages of biomedical multi-modal data integration with a focus into the application of classification methods in studies of complex diseases. Data generated from mass spectrometry (MS) platforms are inherently susceptible to systematic biases. Widespread missing values, where certain compounds cannot be identified or quantified, pose a prominent challenge to MS data normalisation. We propose a novel normalisation approach for high-dimensional MS data, called ruvms. This novel method is a one-step procedure that is able to handle missing values in input data and does not require imputation. We also explore a challenging situation in multi-modal data integration where not all types of data of interest are available within the same cohort. In brain studies, brain tissue samples are generally inaccessible from the same brain for which fMRI data can be obtained. We propose a gene-expression-guided fMRI network classification method that distinguishes patients of neurological diseases from the healthy control, called brainClass. brainClass links functional connectivity features to potentially involved biological pathways, to bridge the gap between functional biomarkers of neurological disorders and their underpinning molecular mechanisms. We also introduce a post-hoc interpretation framework to provide gene-expression-guided biological interpretations for predictive functional connectivity features identified by existing generic network classifiers applied to fMRI data.
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Sabangan, Rainier Monteclaro. "Identification and Estimation of Location and Dispersion Effects in Unreplicated 2k-p Designs Using Generalized Linear Models." Bowling Green State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1269014397.

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BORLA, OSCAR. "The Energy Emissions as Fracture and Seismic Precursors." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2651169.

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The main purpose of this Doctorate Thesis is to set-up a multi-parameter monitoring system that takes into account the observation of several seismic precursors. A seismic precursor is a phenomenon which can take place largely in advance to quake occurrence, and also at large distance from the epicentre. It is well known that the dimension of the earthquake preparation area is a function of the magnitude of the incoming quake and it can consist in a geographical zone up to thousands of kilometres. Furthermore, these precursory phenomena are of various nature but, despite their obvious diversity, many of them reflect a common physical origin. In the last decades a great number of laboratory tests and experimental observations evidenced that mechanical, electromagnetic and neutron emissions, together with radon levels, carbon dioxide emanations and temperature variation, are the most reliable natural phenomena that can be linked to earthquake preparation. In the finalization of this research many experimental tests were conducted both on the laboratory rock samples, and in a suitable monitoring site. First of all, specific tests carried out in the Fracture Mechanics Laboratory of the Politecnico di Torino are presented. Through these tests it was possible to demonstrate that the failure phenomena, in particular when they occur in a brittle way, i.e. with a mechanical energy release, emit additional forms of energy related to the fundamental natural forces. By subjecting brittle or quasi-brittle materials, such as rock specimens, to mechanical stress tests, bursts of neutron emission (NE) during the failure process were produced, necessarily involving nuclear reactions, besides the well-known acoustic emission (AE), and the phenomenon of electromagnetic radiation (EME). The main idea is that, if all these phenomena are simultaneously analysed in suitable monitoring sites, they could provide the basis for prediction of the three main parameters of an earthquake: place and time of occurrence, and magnitude of the seismic event. The place where it occurs is to be understood around the monitoring site, and in an area where its effects are always instrumentally perceptible. Nevertheless, the most important problem with all these precursors is to distinguish signals from noise. A single precursor may not be helpful, the prediction program strategy must involve an integral approach including different precursors. For the in-site monitoring the "San Pietro - Prato Nuovo" gypsum mine located in Murisengo (Alessandria, Italy) was chosen. In this mine, to avoid interference with human activities, the instrumental control units have been located at one hundred meters underground. Finally, the experimental results obtained from July, 1st 2013 to December, 31 2015 (five semesters) are reported. The experimental observations reveal a strong correlation between acoustic, electromagnetic, and neutron emission peaks and the major earthquakes occurred in the closest areas.
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Ahmad, Ashar [Verfasser]. "Dissecting patient heterogeneity via statistical modeling based on multi-modal omics data / Ashar Ahmad." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/119183199X/34.

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Sanderson, Conrad. "Automatic Person Verification Using Speech and Face Information." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/367191.

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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person’s speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
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Warnes, Gregory R. "The normal kernel coupler : an adaptive Markov Chain Monte Carlo method for efficiently sampling from multi-modal distributions /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/9541.

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Porras, Pérez Antonio Reyes. "Multi-cue image integration for cardiac tissue characterization." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/296796.

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Una caracterización precisa y completa del tejido cardíaco es esencial para el diagnóstico y tratamiento de problemas cardíacos. Para caracterizar la función cardíaca desde diferentes perspectivas, en la práctica clínica, se adquieren grandes cantidades de datos de distinta naturaleza sobre un mismo paciente, proporcionando información útil para la evaluación del corazón. Aunque los distintos datos obtenidos de cada paciente se suelen analizar por separado para evaluar la función cardíaca desde diferentes perspectivas, la combinación efectiva de esta información heterogénea podría ser útil para tener una mejor visión de la anatomía y la función cardíaca. El objetivo principal de esta tesis es el desarrollo de métodos para integrar imágenes e información de distinta naturaleza para una caracterización del tejido cardíaco más completa y precisa.
An accurate and complete cardiac tissue characterization is essential to diagnose and treat heart problems. To characterize cardiac function from different perspectives, large amounts of data of different nature from the same patient are acquired in clinical practice, providing information that is useful for heart assessment. Although the different data obtained from each patient are often analyzed separately to assess cardiac function from different perspectives, the effective combination of this heterogeneous information may be useful for a better insight into heart anatomy and function. The main objective of this thesis is to develop methods to integrate images and information of different nature for a more complete and accurate cardiac tissue characterization.
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Kalkan, Sinan. "Multi-modal Statistics of Local Image Structures and its Applications for Depth Prediction." Doctoral thesis, 2008. http://hdl.handle.net/11858/00-1735-0000-000D-F113-8.

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Kalkan, Sinan [Verfasser]. "Multi-modal statistics of local image structures and its applications for depth prediction / vorgelegt von Sinan Kalkan." 2008. http://d-nb.info/99101300X/34.

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Books on the topic "Multi-modal statistics"

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Novak-Leonard, Jennifer L. Beyond attendance: A multi-modal understanding of arts participation. Washington, DC: National Endowment for the Arts, 2011.

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Book chapters on the topic "Multi-modal statistics"

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Li, Qiuying. "Computer Technology in the Course of Probability and Mathematical Statistics." In Application of Intelligent Systems in Multi-modal Information Analytics, 531–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74814-2_75.

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Bigün, E. S., J. Bigün, B. Duc, and S. Fischer. "Expert conciliation for multi modal person authentication systems by Bayesian statistics." In Audio- and Video-based Biometric Person Authentication, 291–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0016008.

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Kropat, Erik, Gerhard-Wilhelm Weber, Sırma Zeynep Alparslan-Gök, and Ayşe Özmen. "Inverse Problems in Complex Multi-Modal Regulatory Networks Based on Uncertain Clustered Data." In Springer Proceedings in Mathematics & Statistics, 437–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04849-9_25.

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Benabid, Alim-Louis, Dominque Hoffmann, Luc Court, Vincent Robert, Sébastien Burtin, Patrick Pittet, and Jörg Fischer. "The Clinical Use of Multi-modal Resources (2D/3D/Statistics) for Robot Assisted Functional Neurosurgery." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, 1421–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_253.

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Krüger, Norbert, and Florentin Wörgötter. "Statistics of Second Order Multi-modal Feature Events and Their Exploitation in Biological and Artificial Visual Systems." In Biologically Motivated Computer Vision, 239–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_24.

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Majumder, Prasenjit, Mandar Mitra, and Kalyankumar Datta. "Statistical vs. Rule-Based Stemming for Monolingual French Retrieval." In Evaluation of Multilingual and Multi-modal Information Retrieval, 107–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_14.

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Terol, Rafael M., Patricio Martinez-Barco, and Manuel Palomar. "Applying Logic Forms and Statistical Methods to CL-SR Performance." In Evaluation of Multilingual and Multi-modal Information Retrieval, 766–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_96.

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Li, Na, and Na Tao. "National Language Statistical System Based on Fuzzy Cluster Analysis Algorithm." In Application of Intelligent Systems in Multi-modal Information Analytics, 911–18. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05237-8_112.

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Lu, Nan, and Yan He. "A Statistical Recognition System Devise of Negative Transfer of Native Language of Foreign Students Based on Genetic Algorithm." In Application of Intelligent Systems in Multi-modal Information Analytics, 861–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05484-6_113.

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Zöllei, Lilla, John W. Fisher, and William M. Wells. "A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration." In Lecture Notes in Computer Science, 366–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45087-0_31.

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Conference papers on the topic "Multi-modal statistics"

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Olivares, Rolando J., Arvind Rao, Ganesh Rao, Jeffrey S. Morris, and Veerabhadran Baladandayuthapani. "Integrative analysis of multi-modal correlated imaging-genomics data in glioblastoma." In 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2013. http://dx.doi.org/10.1109/gensips.2013.6735914.

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Calanni, Giorgio, Vitali Volovoi, Massimo Ruzzene, Charles Vining, and Peter Cento. "Application of Bayesian Belief Nets for Modeling Uncertainty in Structural Dynamics." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27684.

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The quality of modeling and performance prediction for any structural system is affected and limited by an inherent presence of various sources of uncertainty. To date, uncertainty has been usually quantified by means of uncertainty propagation techniques (e.g. Monte Carlo simulations), where a statistical realization of the system’s input parameters is propagated through a (usually) numerical model to construct the statistics of the system’s outputs. This approach works well for sensitivity studies, but some limitations arise when data is available at the output level (as in the case of experiments) or at some intermediate stage within the analysis. The primary objective of this paper is to investigate the feasibility of using Bayesian Belief Networks (BBN) to model multi-directional uncertainty propagation in a process where experimental data can be introduced as evidence. The problem under consideration has the objective of estimating the modal parameters of a structural system with uncertain parameters. The estimation is based on a model of the system, but it is assumed that a limited set of experimental data may be available on input or output parameters. The procedure is first applied to the simple case of a beam structure, for which a number of natural frequencies are evaluated in the presence of uncertainty. Next, it is extended to the estimation of modal quantities of a turbine engine bladed disk sector, which provides the motivation for these investigations.
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Portilla-Yandún, Jesús. "Open Access Atlas of Global Spectral Wave Conditions Based on Partitioning." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77230.

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An open access atlas of wave spectral characteristics at global scale is presented (GLOSWAC). This atlas is based on a recently developed technique for deriving spectral statistics, following the concept of partitioning. This development has been possible due to the parallel release of the wave spectra variable from the ERA-Interim archive of the European Centre for Medium-Range Weather Forecast (ECMWF). Although wave spectra are commonly available nowadays for wave analysis and forecasting, standard integral wave parameters are still in dominant use, both in practical and scientific applications. Although integrated parameters can give a good account of the wave spectral distribution in unimodal cases, they are subject to serious shortcomings when the sea state is bimodal or multi-modal. This issue can be easily tackled with the use of partitioning approaches. Spectral partitioning allows identifying the different wave components with different meteorological origin present in the spectrum. These components can be represented by their integrated parameters, which are much more meaningful than the averaged ones for the whole spectrum. Apart from the increased consistency, this method allows to summarize spectral information, offering the possibility to develop wave spectral statistics. Based on a statistical descriptor, the Probability Distribution of Spectral Partitions (PDS), which is the main outcome from GLOSWAC, the local long-term wave systems can be identified and characterized. In addition, several other spectral parameters are computed and distributed in a web format. For illustration, an arbitrary reference location is used here to guide the interpretation and the use of the information derived.
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Martin, Jay D. "Using Maximum Likelihood Estimation to Estimate Kriging Model Parameters." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34662.

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A kriging model can be used as a surrogate to a more computationally expensive model or simulation. It is capable of providing a continuous mathematical relationship that can interpolate a set of observations. One of the major issues with using kriging models is the potentially computationally expensive process of estimating the best model parameters. One of the most common methods used to estimate model parameters is Maximum Likelihood Estimation (MLE). MLE of kriging model parameters requires the use of numerical optimization of a continuous but possibly multi-modal log-likelihood function. This paper presents some enhancements to gradient-based methods to make them more computationally efficient and compares the potential reduction in computational burden. These enhancements include the development of the analytic gradient and Hessian for the log-likelihood equation of a kriging model that uses a Gaussian spatial correlation function. The suggested algorithm is very similar to the Scoring algorithm traditionally used in statistics, a Newton-Raphson gradient-based optimization method.
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Shih, H. H., C. Long, M. Bushnell, and K. Hathaway. "Intercomparison of Wave Data Between Triaxys Directional Wave Buoy, ADCP, and Other Reference Wave Instruments." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67235.

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The use of Triaxys directional wave buoy and acoustic Doppler current profiler (ADCP) for wave measurements are relatively recent. The US National Oceanic and Atmospheric Administration’s (NOAA) National Ocean Service (NOS) acquired these instruments in 2001 and systematic laboratory and field tests were conducted during 2001–2002. This paper describes the field tests conducted near the US Army Corps of Engineers’ Field Research Facility (FRF) ocean pier and near the Barren Islands in the Chesapeake Bay. At the FRF site, Triaxys buoy wave measurements were compared with FRF’s field standards of pressure sensor arrays and Datawell Waverider buoy. For the Bay test, ADCP was compared with the Triaxys buoy. There are significant numbers of outlier in the Triaxys peak periods at both test sites. In the Chesapeake Bay, which is dominated by high frequency and low energy waves, there is much scatter in the Triaxys data for significant wave heights below 0.2 m. Detailed analyses were performed after these outliers and noisy data were removed. Statistics of differences in significant wave heights, peak periods and directions between each comparative pair were computed and characteristics of frequency and frequency-direction spectra were examined. Overall, correlations between each instrument pair are very good in significant wave heights, fair in wave peak periods (except the ADCP/Triaxys pair), and marginal in wave directions. Triaxys buoy compared better with Waverider buoy than with others. Both ADCP and FRF pressure sensor array exhibit higher resolution in detecting multi-modal and multi-frequency waves. In most cases, energy distribution of spectral peaks in Triaxys buoy data differs significantly from those obtained from FRF pressure sensor array and ADCP.
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Jingyan Wang, Yongping Li, Xinyu Ao, Chao Wang, and Juan Zhou. "Multi-modal biometric authentication fusing iris and palmprint based on GMM." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278568.

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Shi, Jiangli, Yunmei Chen, Murali Rao, and Jinseop Lee. "A statistical similarity measure for non-rigid multi-modal image registration." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844553.

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Banerjee, Taposh, Gene Whipps, Prudhvi Gurram, and Vahid Tarokh. "CYCLOSTATIONARY STATISTICAL MODELS AND ALGORITHMS FOR ANOMALY DETECTION USING MULTI-MODAL DATA." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646417.

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Thompson, Stephen, Graeme Penney, Damien Buie, Prokar Dasgupta, and Dave Hawkes. "Use of a CT statistical deformation model for multi-modal pelvic bone segmentation." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2008. http://dx.doi.org/10.1117/12.770254.

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Christodoulou, K., and C. Papadimitriou. "A Bayesian Identification Methodology for Selection Among Pareto Optimal Structural Models Using Modal Residuals." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85300.

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The structural parameter estimation problem based on measured modal data is formulated as a multi-objective optimization problem in which modal metrics measuring the fit between measured and model predicted groups of modal properties are simultaneously minimized to obtain all Pareto optimal structural models consistent with the measured data. Equivalently, the multiple Pareto optimal models can be obtained by minimizing a single metric formed as a weighted average of the multiple metrics. The Pareto optimal models are obtained by varying the values of the weights. The optimal values of the parameters are sensitive to the values of the weighting factors. A Bayesian statistical framework is used to provide a rational choice of the optimal values of the weight factors based on the available data. It is shown that the optimal weight values for each group of modal properties are asymptotically, for large number of data, inversely proportional to the optimal prediction errors of the corresponding modal group. Two algorithms are proposed for obtaining simultaneously the optimal weight values and the corresponding optimal values of the structural parameters. The proposed framework is illustrated using simulated data from multi-DOF spring-mass chain structure. In particular, compared to conventional parameter estimation techniques that are based on pre-selected values of the weights, it is demonstrated that the optimal structural models proposed by the methodology are significantly less sensitive to large model errors or bad measured modal data, known to affect optimal selection.
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