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Probabilistic typology of management decision making in private forest properties

FOREST POLICY AND ECONOMICS 2013年 27卷 34-43页

作者： Ficko, Andrej Boncina, Andrej Univ Ljubljana Biotech Fac Dept Forestry & Renewable Forest Resources Ljubljana 1000 Slovenia

We conducted a quantitative study of private forest owner management behavior based on face-to-face interviews with 380 randomly selected private forest owners in Slovenia. Forest owners were asked to rate the relevance of nineteen factors representing information related to the social, ecological, and economic aspects of decision making based on a five-point Likert scale. This information was consolidated into major categories with Principal Component Analysis. expectation maximization (EM) clustering was used to build a probabilistic private forest owner derision making typology. Six major categories of information determined 64% of the variability in decision making: non-wood goods and services, forest economics, property administration, optimization of wood production, forest protection, and minimum cutting restrictions. EM clustering revealed two decision making types differing in their attitude towards the total economic value of forests: Materialists, whose decisions are mainly related to the extractive value of forests and Non-materialists, who manage for non-extractive vale. Full-time farmers, owners living within 2 km of their holdings, and owners who permanently cooperated with the public forest service were much more likely to be Materialists. The uncertainty in private forest owner typology building and the applicability of probabilistic models of private forest owners to end-users is discussed. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Private forest owners Value of information Management decision Management behavior expectation maximization algorithm Slovenia

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Estimation of vector error correction models with mixed-frequency data

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JOURNAL OF TIME SERIES ANALYSIS 2013年第2期34卷 194-205页

作者： Seong, Byeongchan Ahn, Sung K. Zadrozny, Peter A. Chung Ang Univ Seoul 156756 South Korea Washington State Univ Pullman WA 99164 USA US Bur Labor Stat Washington DC 20212 USA

Vector autoregressive (VAR) models with error-correction structures (VECMs) that account for cointegrated variables have been studied extensively and used for further analyses such as forecasting, but only with single-frequency data. Both unstructured and structured VAR models have been estimated and used with mixed-frequency data. However, VECMs have not been studied or used with mixed-frequency data. The article aims partly to fill this gap by estimating a VECM using the expectation-maximization (EM) algorithm and US data on four monthly coincident indicators and quarterly real GDP and, then, using the estimated model to compute in-sample monthly smoothed estimates and out-of-sample monthly forecasts of GDP. Because the model is treated as operating at the highest monthly frequency and the monthly-quarterly data are used as given (neither interpolated to all-monthly data, nor aggregated to all-quarterly data), the application is expected to be unbiased and efficient. A Monte Carlo analysis compares the accuracy of VECMs estimated with the given mixed-frequency data vs. with their single-frequency temporal aggregate.

关键词： Missing data cointegration state-space model Kalman filter expectation maximization algorithm smoothing C13 C22 C32

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Embryo zebrafish segmentation using an improved hybrid method

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JOURNAL OF MICROSCOPY 2013年第1期250卷 68-75页

作者： Wu, Tao Lu, Jianfeng Lu, Yanting Liu, Tianming Yang, Jingyu Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA

Zebrafish is an invaluable vertebrate model in life science research and has been widely used in biological pathway analysis, molecular screening and disease modelling, among others. As a result, microscopic imaging has become an essential step in zebrafish phenotype analysis, and image segmentation thus plays an important role in the zebrafish microscopy analysis. Due to the nonuniform distribution of intensity and weak boundary in zebrafish microscope images, the traditionally used segmentation methods may lead to unsatisfactory result. Here, a novel hybrid method that integrates region and boundary information into active contour model is proposed to segment zebrafish embryos from the background, which performs better than traditional segmentation models. Meanwhile, how to utilize the gradient information effectively in image segmentation is still an open problem. In this paper, we propose to improve the aforementioned hybrid method in two aspects. Firstly, the mean grey value of background is estimated by the expectation maximization (EM) algorithm to constrain the active curve evolution. Secondly, an edge stopping function sensitive to gradient information is designed to stop curve evolution when the active curve reaches the embryo boundary. Experimental results show that the proposed methods can provide superior segmentation results compared to existing algorithms.

关键词： Active contour model edge stopping function expectation maximization algorithm image segmentation zebrafish embryo image

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Dynamics in cryo EM reconstructions visualized with maximum-likelihood derived variance maps

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JOURNAL OF STRUCTURAL BIOLOGY 2013年第3期181卷 195-206页

作者： Wang, Qiu Matsui, Tsutomu Domitrovic, Tatiana Zheng, Yili Doerschuk, Peter C. Johnson, John E. Cornell Univ Ithaca NY 14853 USA Scripps Res Inst Dept Mol Biol Menlo Pk CA 94025 USA SLAC Natl Accelerator Lab Stanford Synchrotron Radiat Lightsource Menlo Pk CA 94025 USA Scripps Res Inst Dept Mol Biol La Jolla CA USA Purdue Univ W Lafayette IN 47907 USA

CryoEM data capture the dynamic character associated with biological macromolecular assemblies by preserving the various conformations of the individual specimens at the moment of flash freezing. Regions of high variation in the data set are apparent in the image reconstruction due to the poor density that results from the lack of superposition of these regions. These observations are qualitative and, to date, only preliminary efforts have been made to quantitate the heterogeneity in the ensemble of particles that are individually imaged. We developed and tested a quantitative method for simultaneously computing a reconstruction of the particle and a map of the space-varying heterogeneity of the particle based on an entire data set. The method uses a maximum likelihood algorithm that explicitly takes into account the continuous variability from one instance to another instance of the particle. The result describes the heterogeneity of the particle as a variance to be plotted at every voxel of the reconstructed density. The test, employing time resolved data sets of virus maturation, not only recapitulated local variations obtained with difference map analysis, but revealed a remarkable time dependent reduction in the overall particle dynamics that was unobservable with classical methods of analysis. (c) 2012 Elsevier Inc. All rights reserved.

关键词： Time-resolved Single particle reconstruction Virus maturation Protein folding Quasi equivalence Cryo electron microscopy Nudaurelia Capensis Omega Virus Maximum likelihood estimation expectation maximization algorithm Heterogeneous particles Variance map

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Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain

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JOURNAL OF NEUROSCIENCE METHODS 2013年第1期212卷 43-55页

作者： Kim, Sun Hyung Fonov, Vladimir S. Dietrich, Cheryl Vachet, Clement Hazlett, Heather C. Smith, Rachel G. Graves, Michael M. Piven, Joseph Gilmore, John H. Dager, Stephen R. McKinstry, Robert C. Paterson, Sarah Evans, Alan C. Collins, D. Louis Gerig, Guido Styner, Martin Andreas Univ N Carolina Dept Psychiat Chapel Hill NC 27515 USA McGill Univ Montreal Neurol Inst McConnell Brain Imaging Ctr Montreal PQ Canada Univ Utah Sci Comp & Imaging Inst Sch Comp Salt Lake City UT USA Univ N Carolina Dept Comp Sci Chapel Hill NC USA Univ Washington Dept Radiol Seattle WA 98195 USA Washington Univ Dept Radiol St Louis MO USA Childrens Hosp Philadelphia Dept Pediat Philadelphia PA 19104 USA

The degree of white matter (WM) myelination is rather inhomogeneous across the brain. White matter appears differently across the cortical lobes in MR images acquired during early postnatal development. Specifically at 1-year of age, the gray/white matter contrast of MRT1 and T2 weighted images in prefrontal and temporal lobes is reduced as compared to the rest of the brain, and thus, tissue segmentation results commonly show lower accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and 12 weighted images to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance homogeneity is greatly improved by the age of 24 months. The IGM was computed as the coefficient of a voxel-wise linear regression model between corresponding intensities at 1 and 2 years. The proposed IGM method revealed low regression values of 1-10% in GM and CSF regions, as well as in WM regions at maturation stage of myelination at 1 year. However, in the prefrontal and temporal lobes we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes mainly due to myelination. The IGM is applied to cross-sectional MRI datasets of 1-year-old subjects via registration, correction and tissue segmentation of the IGM-corrected dataset. We validated our approach in a small leave-one-out study of images with known, manual 'ground truth' segmentations. Published by Elsevier B.V.

关键词： Myelination expectation maximization algorithm Tissue segmentation Intensity growth map Partial volume estimation

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Mobility Behavior Modeling in UCN

Mobility Behavior Modeling in UCN

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Yildiz, Muersel Khan, Manzoor Ahmed Tech Univ Berlin DAI Lab Berlin Germany

ISBN: (纸本)9781467359399;9781467359382

With the advent of revolutionary wireless technologies and equipments, one may expect a great user mobility. In this paper, we study the mobility of users in user centric networking (UCN) environment. We propose the mobility prediction approach by WLAN group formation. We focus on mobility behavior of users and propose the WLAN group prediction approach. We base the proposed approach on the relatively simple parameters and evaluate the performance of our proposed concept by real implementation. Our approach is based on building the history of encountered WLAN groups, modeling an encounter instance of a new group as arrivals for the proposed model and finally adapting the prediction. In the granular level, the arrival rates are estimated using expectation maximization algorithm. We believe that the proposed approach is important for UCN like environments, where information on the hardware or software capabilities, available network resources, instant AP load and etc are not available for the end-user.

关键词： Markov Arrival Processes expectation maximization algorithm Mobility Prediction Mobility Behavior Modeling AP Selection in UCN

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Automatic Design of a Novel Image Filter Based on the GA-EM algorithm for Vein Shapes

Automatic Design of a Novel Image Filter Based on the GA-EM ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kashihara, Koji Univ Tokushima Inst Sci & Technol Tokushima 7708506 Japan

ISBN: (纸本)9781479906529

Medical doctors and clinical technologists operate specific, complicated diagnostic systems to assess venous diseases. Instead of using such expensive equipment, low-cost infrared cameras could capture vein images noninvasively and simply. On the other hand, the obtained image may have low contrast and a low signal-to-noise (S/N) ratio and this should be sufficiently improved by filtering processes. Therefore, an efficient image filtering method to estimate venous changes will enable the early detection of disease. In this study, a novel filtering method based on the genetic algorithm (GA) with the expectation maximization (EM) algorithm was newly proposed for the visualization of venous shapes;its effectiveness was evaluated by images acquired from a near-infrared (780 nm) charge coupled device (CCD) camera. The novel filter was automatically designed by the GA to efficiently improve the worse S/N ratio of venous images, even with an unknown correct image answer. In future studies, the proposed filtering method could be employed to easily detect peripheral swelling from vein images.

关键词： near infrared camera image filter genetic algorithm expectation maximization algorithm Gaussian mixture model

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Unsupervised Satellite Image Segmentation using a Bivariate Beta type-II Mixture Model

Unsupervised Satellite Image Segmentation using a Bivariate ...

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IEEE 9th International Conference on Computational Cybernetics (ICCC)

作者： Ben Arab, Taher Zribi, Mourad Masmoudi, Afif Lab Informat Signal & Image Cote Opale LISIC EA 4 ULCO50 Rue Ferdinand Buisson BP 719 F-62228 Calais France Univ Sfax Fac Sci Lab Probabilities Statist Sfax Tunisia

ISBN: (纸本)9781479900633;9781479900602

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a. K-Means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a. K-means clustering algorithm and expectation-maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta typeII attributes a specific distribution type according to different parameters. Different adapted algorithms (namely. K-Means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II expectation maximization algorithm K-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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Fully Distributed Acoustic Source Localization in Wireless Sensor Network

Fully Distributed Acoustic Source Localization in Wireless S...

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IEEE International Conference of Region 10 (TENCON)

作者： Feng, Yuwu Huang, Qinghua Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781479928255;9781479928279

Each node in wireless sensor network has limited energy for communication and computation, so how to reduce the energy consumption efficiently is an important research problem. In this paper, we propose a fully distributed expectation maximization algorithm (FDEM) for acoustic source localization to reduce communication cost significantly. It just requires local data transmission and simple computation. Each sensor node transmits energy observations to its nearest neighbors (nodes within one-hop communication range) and also receives energy observations from its nearest neighbors. Long range wireless communication is avoided and the energy is saved. Simulation results for a static source localization and a moving source tracking demonstrate that the FDEM algorithm can provide a good tradeoff between localization accuracy and communication cost.

关键词： acoustic source localization expectation maximization algorithm wireless sensor network fully distributed communication cost

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Noisy Hidden Markov Models for Speech Recognition

Noisy Hidden Markov Models for Speech Recognition

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International Joint Conference on Neural Networks (IJCNN)

作者： Audhkhasi, Kartik Osoba, Osonde Kosko, Bart Univ So Calif Dept Elect Engn Signal & Image Proc Inst Los Angeles CA 90089 USA

ISBN: (纸本)9781467361293;9781467361286

We show that noise can speed training in hidden Markov models (HMMs). The new Noisy expectationmaximization (NEM) algorithm shows how to inject noise when learning the maximum-likelihood estimate of the HMM parameters because the underlying Baum-Welch training algorithm is a special case of the expectation-maximization (EM) algorithm. The NEM theorem gives a sufficient condition for such an average noise boost. The condition is a simple quadratic constraint on the noise when the HMM uses a Gaussian mixture model at each state. Simulations show that a noisy HMM converges faster than a noiseless HMM on the TIMIT data set.

关键词： Hidden Markov model expectation maximization algorithm noisy EM algorithm stochastic resonance speech recognition noise injection

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