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IEEE Statistical Signal Processing Workshop (SSP)

作者： Halimi, Abderrahim Altmann, Yoann Dobigeon, Nicolas Tourneret, Jean-Yves Univ Toulouse IRIT INP ENSEEIHT F-31071 Toulouse 7 France

ISBN: (纸本)9781457705700

This paper studies a generalized bilinear model and a hierarchical bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization of the accepted linear mixing model but also of a bilinear model recently introduced in the literature. Appropriate priors are chosen for its parameters in particular to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the unknown parameter vector is then derived. A Metropolis-within-Gibbs algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

关键词： Hyperspectral imagery bilinear model spectral unmixing bayesian algorithm Gibbs sampler MCMC methods

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Nonlinear regression using smooth bayesian estimation

Nonlinear regression using smooth Bayesian estimation

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： A. Halimi C. Mailhes J. Y. Tourneret Univ. of Toulouse Toulouse France

ISBN: (纸本)9781467369985

This paper proposes a new bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters.

关键词： bayesian algorithm Hamiltonian Monte-Carlo MCMC Parameter estimation Radar altimetry

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THE INTEGRATED DISTRIBUTED HYDROLOGICAL MODEL, ECOFLOW-A TOOL FOR CATCHMENT MANAGEMENT

THE INTEGRATED DISTRIBUTED HYDROLOGICAL MODEL, ECOFLOW-A TOO...

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作者： Nikolay Sokrut KTH Royal Institute of Technology

学位级别：博士

In order to find effective measures that meet the requirements for proper groundwater quality and quantity management, there is a need to develop a Decision Support System (DSS) and a suitable modelling tool. Central components of a DSS for groundwater management are thought to be models for surface- and groundwater flow and solute transport. The most feasible approach seems to be integration of available mathematical models, and development of a strategy for evaluation of the uncertainty propagation through these models. The physically distributed hydrological model ECOMAG has been integrated with the groundwater model MODFLOW to form a new integrated watershed modelling system - ECOFLOW. The modelling system ECOFLOW has been developed and embedded in Arc View. The multiple-scale modelling principle, combines a more detailed representation of the groundwater flow conditions with lumped watershed modelling, characterised by simplicity in model use, and a minimised number of model parameters. A bayesian statistical downscaling procedure has also been developed and implemented in the model. This algorithm implies downscaling of the parameters used in the model, and leads to decreasing of the uncertainty level in the modelling results. The integrated model ECOFLOW has been applied to the Vemmenhog catchment, in Southern Sweden, and the Orsundaan catchment, in central Sweden. The applications demonstrated that the model is capable of simulating, with reasonable accuracy, the hydrological processes within both the agriculturally dominated watershed (Vemmenhog) and the forest dominated catchment area (Orsundaan). The results show that the ECOFLOW model adequately predicts the stream and groundwater flow distribution in these watersheds, and that the model can be used as a possible tool for simulation of surface– and groundwater processes on both local and regional scales. A chemical module ECOMAG-N has been created and tested on the Vemmenhog watershed with a highly dense

关键词： Multiple-scale hydrological modelling bayesian algorithm Parameter estimation Groundwater management Statistical analysis

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Incorporating BERT with Naive Bayes into Neutral Sentiment Analysis 3

Incorporating BERT with Naive Bayes into Neutral Sentiment A...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Shi, Zesheng Cao, Tianhao Zhang, Xiangyue School of Mathematical Sciences Nankai University Tianjin China Software College Heilongjiang University Harbin China School of Geoscience and Info-Physics Central South University Changsha China

ISBN: (纸本)9798350341546

With the rapid increase in online text data, sentiment analysis has become increasingly important in various applications. However, current research on neutral sentiment analysis is relatively limited, and there is still no effective method for accurately identifying neutral emotions. While Bert has shown superior performance in classifying positive and negative emotions compared to traditional approaches like the Naive Bayes algorithm, its accuracy in identifying neutral sentiment remains a challenge. To address this, we propose a method that combines Bert with the bayesian algorithm and employs a sentiment dictionary constructed from the text data in the database to calculate correction weights for the two sentiments. This approach aims to improve Bert's accuracy in identifying neutral sentiment texts and thus enhance the overall performance of sentiment analysis. © 2023 IEEE.

关键词： bayesian algorithm BERT Deep Learning emotion analysis sentiment classification

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Fault Diagnosis Mechanism based on FTA and bayesian for Large-scale CNC Equipments

Fault Diagnosis Mechanism based on FTA and Bayesian for Larg...

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2012 3rd International Conference on Information Technology for Manufacturing Systems(ITMS 2012)

作者： Liu Jinfei Chen Ming Li Yinglei Postdoctoral Scientific Research Flow Stands of Control Science and Engineering of school of Electronics and Information Engineering Tongji University Sino-German College of Applied Sciences Tongji University Shanghai Volkswagen automobile Company

In view of the disadvantage of current FAT-based fault diagnosis method in large-scale complicated system, fault diagnosis method of heavy NC machine based on FTA and bayesian is ***, building fault trees with the help of reachability matrix, and to set the determinate conditions at every node of fault tree combining FTA with rule reasoning, the minimum cut set of fault reasons are determined as a result of step by step screening fault tree from top-down;Secondly, bayesian method is integrated into the fault tree diagnostic method to calculate the posterior probability triggered by each fault tree in order to locate the fault tree where the fault had occurred and ensure high efficiency of fault diagnosis;Finally, B/S based intelligent fault diagnosis system for large-scale CNC equipments is developed, and the feasibility and efficiency of this method are proved in an example of fault diagnosis of Φ 160 NC boring and milling machine.

关键词： FAT bayesian algorithm Reachability Matrix Fault Trees Fault Diagnosis System

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Chisel bits cutting force estimation using XGBoost and different optimization algorithms

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COMPUTERS AND GEOTECHNICS 2024年 172卷

作者： Rouhani, Mohammad Matin Farrokh, Ebrahim Amirkabir Univ Technol Min Engn Dept Hafez Ave Tehran Iran

The design and selection of tools for mechanized excavations is a crucial topic in the field of tunneling. Chisel bits are commonly used tools for soft ground, with the required cutting force being a vital factor in tool design. However, current methods either lack accuracy or fail to consider all necessary parameters. To address this issue, a new cutting force prediction model was developed to improve response quality and consider the most effective parameters. 206 data points were collected from studies on rock cutting with different strength, including information such as Uniaxial compressive strength, Brazilian tensile strength, Depth, Width, Rake angle, and clearance angle. Five different algorithms were then used to optimize the Hyperparameters in the XGBoost method, including Grid Search, Random Search, bayesian algorithm, Differential Evolution, and Optuna. Results showed that while most algorithms provided appropriate responses, the Grid Search and bayesian algorithm methods were the most effective, with R 2 values (in test and train) of 0.876 and 0.93 in GS and 0.872 and 0.926 in BO, respectively. Upon comparison of the two methods, it was discovered that the GS approach yields a superior solution;however, it is also more sensitive to tuning, requires more calculations, and takes longer to provide a solution. When assessing the newly presented approach against existing methods, it was noted that the Evans and Roxborough methods produced R 2 values of 0.48 and 0.53, respectively, which are notably lower than those of the new method. Lastly, the parametric analysis revealed that the cutting force is primarily affected by the depth, width, and rake angle in that order.

关键词： Cutting force Chisel bit XGBoost Grid search bayesian algorithm

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Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第12期24卷 4904-4917页

作者： Halimi, Abderrahim Dobigeon, Nicolas Tourneret, Jean-Yves Univ Toulouse F-31071 Toulouse France

This paper presents an unsupervised bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is also considered in the proposed model, generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated through simulations conducted on both synthetic and real data.

关键词： Hyperspectral imagery endmember variability image classification spectral unmixing bayesian algorithm Hamiltonian Monte-Carlo MCMC methods

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Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

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REMOTE SENSING 2023年第20期15卷 5063-5063页

作者： Liu, Liling Dong, Xiaolong Lin, Wenming Lang, Shuyan China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China Chinese Acad Sci Natl Space Sci Ctr CAS Key Lab Microwave Remote Sensing Beijing 100190 Peoples R China Nanjing Univ Informat Sci & Technol Sch Marine Sci Nanjing 210044 Peoples R China Natl Satellite Ocean Applicat Serv Beijing 100081 Peoples R China

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019-2022) (by setting a 55% threshold on the bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration >= 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 x 106 km2.

关键词： scatterometer CSCAT sea ice detection bayesian algorithm

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Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

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SENSORS 2023年第7期23卷 3457-3457页

作者： Mak, Hugo Wai Leung Han, Runze Yin, Hoover H. F. Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Math Kowloon Clear Water Bay Hong Kong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Shatin Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering has also been minimally discussed in the literature neither. This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications;then applies the established VAE model to generate new game levels based on two well-known game settings;and to validate the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments are also utilized to evaluate the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and graphical results, several potential deficiencies, for example, difficulties in handling high-dimensional and vast datasets, as well as insufficient clarity of outputs are discussed;then measures of future enhancement, such as tokenization and the combination of VAE and GAN models, are also outlined. Hopefully, this can ultimately maximize the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

关键词： game design variational autoencoder (VAE) image and video generation bayesian algorithm loss function data clustering data and image analytics MNIST database generator and discriminator

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Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

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ENERGIES 2021年第15期14卷 4623页

作者： Mariani, Valerio Pulga, Leonardo Bianchi, Gian Marco Falfari, Stefania Forte, Claudio Univ Bologna Dept Ind Engn DIN Alma Mater Studiorum Viale Risorgimento 2 I-40136 Bologna Italy NAIS Srl Via M Callas 4 I-40131 Bologna Italy

Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the 'unicity' of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the 'unicity' of the surrogate comp

关键词： stratified fuel surrogate fuel gasoline surrogate bayesian algorithm machine learning

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