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Conference on the Future Computer and Control Systems

作者： Wu, ShuFang Wang, Jing Jiang, QingChao HeBei Software Inst Baoding Dept Informat Engn Baoding Peoples R China Ctr Inst Correctional Police Baoding Dept Informat Management China Lake CA USA

ISBN: (纸本)9783642293863

The cell allocation problem has long been a obstacle in the modern prison management, the way only depend on the experience of manager is not compatible to the development of modern science. In this paper, two intelligent algorithms are proposed to solve this problem. bayesian algorithm and the algorithm based on optimal distance both can solve it effectively.

关键词： Prison management Cell allocation problem bayesian algorithm optimal distance

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bayesian multi-QTL mapping for growth curve parameters

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BMC proceedings 2010年第1期4 Suppl 1卷 S12页

作者： Henri C M Heuven Luc L G Janss Clinical Sciences of Companion Animals Faculty of Veterinary Medicine Utrecht University PO box 80163 3508 TD Utrecht The Netherlands . h.c.m.heuven@uu.nl.

BACKGROUND:Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A two- step approach was used to analyze a simulated data set containing 1000 individuals with 5 measurements each. First the measurements were summarized in latent variables and subsequently a genome wide analysis was performed of these latent variables to identify segregating QTL using a bayesian algorithm. RESULTS:For each individual a logistic growth curve was fitted and three latent variables: asymptote (ASYM), inflection point (XMID) and scaling factor (SCAL) were estimated per individual. Applying an 'animal' model showed heritabilities of approximately 48% for ASYM and SCAL while the heritability for XMID was approximately 24%. The genome wide scan revealed four QTLs affecting ASYM, one QTL affecting XMID and four QTLs affecting SCAL. The size of the QTL differed. QTL with a larger effect could be more precisely located compared to QTL with small effect. The locations of the QTLs for separate parameters were very close in some cases and probably caused the genetic correlation observed between ASYM and XMID and SCAL respectively. None of the QTL appeared on chromosome five. CONCLUSIONS:Repeated observations on individuals were affected by at least nine QTLs. For most QTL a precise location could be determined. The QTL for the inflection point (XMID) was difficult to pinpoint and might actually exist of two closely linked QTL on chromosome one.

关键词： Latent Variable Genetic Correlation bayesian algorithm Single SNPs bayesian Variable Selection

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Vehicle Localization in Urban Environments using Feature Maps and Aerial Images

Vehicle Localization in Urban Environments using Feature Map...

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Mattern, Norman Wanielik, Gerd Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9781457721977

This paper presents two variants of a bayesian algorithm for vehicle localization which use vehicle motion data, a low-cost GNSS receiver, a gray scale camera, and different digital map data. The key idea of the algorithm is not to extract features like points or lines from the camera image for the Bayes update, but to predict entire images. While the first variant performs this image prediction based on explicit landmark information of a digital map, the second variant predicts camera images directly based on aerial images. In doing so, no conversion step from aerial images to feature maps is necessary. Finally, the paper presents results for both approaches based on extensive test drive data with highly accurate reference data.

关键词： Accuracy Atmospheric measurements Bayes methods bayesian algorithm Cameras Feature extraction Particle measurements Tensile stress Vehicles aerial images camera image prediction cameras digital map explicit landmark information feature maps geophysical im

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Design of New Media Data Processing System

Design of New Media Data Processing System

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International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI)

作者： Shan, Hui Zhang, Hongshen Mi, Xueming Dalian Univ Sci & Technol Party Comm Off Dalian Peoples R China

ISBN: (纸本)9781665423168

This paper is mainly for the design of new media data processing system. The research of new media data processing shows that the traditional database processing new media data is more and more huge, which causes the operating system to do a lot of output and input operations (I / O) when querying, and the processing speed is slow. Through the analysis, the main reason that affects the new media data processing is the large amount of data. In this paper, bayesian algorithm classifier model is used, and Map/Reduce function is used in the parallel computing framework. Multi computing resource nodes are used for parallel processing to effectively improve the speed of new media data processing.

关键词： new media data Map/Reduce function bayesian algorithm parallel computing framework

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A Visible Light Indoor Positioning algorithm Based on Fingerprint 4

A Visible Light Indoor Positioning Algorithm Based on Finger...

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4th Annual International Conference on Network and Information Systems for Computers (ICNISC)

作者： Guo, Chen Shao, Jian-Hua Ke, Wei Zhang, Chun-Yan Nanjing Normal Univ Sch Phys & Technol Nanjing Peoples R China Nanjing Normal Univ Jiangsu Key Lab Optoelect Technol Nanjing Peoples R China

ISBN: (纸本)9781538669563

Errors are caused by wall reflection and noise in the visible light indoor positioning system. Some indoor positioning algorithms based on fingerprint are compared through simulation. An improved indoor positioning algorithm that combines K-Nearest Neighbor and bayesian theory algorithm is proposed. Firstly, several grid points which are close to the receiver's position are selected by KNN algorithm, then the posterior probability is calculated by bayesian algorithm. The point with the largest posterior probability is the estimated position. The improved algorithm simplifies the bayesian algorithm and improves the positioning accuracy of KNN algorithm, with an average error of 0.27m.

关键词： indoor positioning k-nearest neighbor bayesian algorithm

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Physics-informed machine learning to analyze oxide defect-induced RTN in gate leakage current

Physics-informed machine learning to analyze oxide defect-in...

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International Reliability Physics Symposium (IRPS)

作者： Varanasi, Anirudh Degraeve, Robin Roussel, Philippe J. Vici, Andrea Merckling, Clement Katholieke Univ Leuven Dept Mat Engn B-3001 Leuven Belgium IMEC B-3001 Leuven Belgium

ISBN: (纸本)9798350369762;9798350369779

In this work, we introduce a physics-informed machine learning framework that models stochastically-behaving defects in the gate oxide of an individual device. We demonstrate the extraction of the total number of active defects and their corresponding behavior from oxide defect-induced random telegraph noise signals existent in time-resolved constant stress-induced leakage current. Physics-informed machine learning framework conducts an automated data-driven analysis of this stress-induced leakage current data with decision-making guided by the incorporated defect physics. Finally, this work includes the generated statistics from a successful implementation of the physics-informed machine learning framework on stress-induced leakage current data of multiple deeply-scaled high-kappa metal gate devices.

关键词： bayesian algorithm oxide defects physics-informed machine learning random telegraph noise stress-induced leakage current

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UNMIXING MULTITEMPORAL HYPERSPECTRAL IMAGES ACCOUNTING FOR ENDMEMBER VARIABILITY 23

UNMIXING MULTITEMPORAL HYPERSPECTRAL IMAGES ACCOUNTING FOR E...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Halimi, Abderrahim Dobigeon, Nicolas Tourneret, Jean-Yves McLaughlin, Steve Honeine, Paul Univ Technol Troyes Inst Charles Delaunay CNRS Troyes France Univ Toulouse IRIT INP ENSEEIHT TeSA Toulouse France Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9780992862633

This paper proposes an unsupervised bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the end members weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable endmemhers for each image pixel. A prior enforcing a smooth temporal variation of both endmembers and abundances is considered. The proposed algorithm estimates the mean vectors and covariance matrices of the endmembers and the abundances associated with each image. Since the estimators are difficult to express in closed form, we propose to sample according to the posterior distribution of interest and use the generated samples to build estimators. The performance of the proposed bayesian model and the corresponding estimation algorithm is evaluated by comparison with other unmixing algorithms on synthetic images.

关键词： Hyperspectral unmixing spectral variability temporal and spatial variability bayesian algorithm Hamiltonian Monte-Carlo MCMC methods

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Utilization of K-NN algorithm for Expectation Maximization Based Classification Method

Utilization of K-NN Algorithm for Expectation Maximization B...

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4th International IEEE Conference Intelligent Systems

作者： Aci, M. Inan, C. Avci, M. Cukurova Univ Balcali Turkey

ISBN: (纸本)9781424417391

K Nearest Neighbor and bayesian algorithms are effective methods of machine learning. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of K Nearest Neighbor and bayesian learning algorithms. The suggested method is tested on well-known machine learning data sets such as Iris, Wine and Breast Cancer and the results are concluded.

关键词： bayesian algorithm classification hybrid method k nearest neighbor

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Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: bayesian optimization machine learning methods

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BIORESOURCE TECHNOLOGY 2024年 395卷 130361页

作者： Ye, Gang Wan, Jinquan Deng, Zhicheng Wang, Yan Chen, Jian Zhu, Bin Ji, Shiming South China Univ Technol Coll Environm & Energy Guangzhou 510006 Peoples R China Guangdong Shunkong Zihua Technol Co Ltd Foshan 528300 Peoples R China

The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively.

关键词： Model prediction WWTPs bayesian algorithm Random seed

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NONLINEAR UNMIXING OF HYPERSPECTRAL IMAGES USING A GENERALIZED BILINEAR MODEL

NONLINEAR UNMIXING OF HYPERSPECTRAL IMAGES USING A GENERALIZ...

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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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