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Sample records for hierarchical method based

  1. A Robust Deconvolution Method based on Transdimensional Hierarchical Bayesian Inference

    NASA Astrophysics Data System (ADS)

    Kolb, J.; Lekic, V.

    2012-12-01

    Analysis of P-S and S-P conversions allows us to map receiver side crustal and lithospheric structure. This analysis often involves deconvolution of the parent wave field from the scattered wave field as a means of suppressing source-side complexity. A variety of deconvolution techniques exist including damped spectral division, Wiener filtering, iterative time-domain deconvolution, and the multitaper method. All of these techniques require estimates of noise characteristics as input parameters. We present a deconvolution method based on transdimensional Hierarchical Bayesian inference in which both noise magnitude and noise correlation are used as parameters in calculating the likelihood probability distribution. Because the noise for P-S and S-P conversion analysis in terms of receiver functions is a combination of both background noise - which is relatively easy to characterize - and signal-generated noise - which is much more difficult to quantify - we treat measurement errors as an known quantity, characterized by a probability density function whose mean and variance are model parameters. This transdimensional Hierarchical Bayesian approach has been successfully used previously in the inversion of receiver functions in terms of shear and compressional wave speeds of an unknown number of layers [1]. In our method we used a Markov chain Monte Carlo (MCMC) algorithm to find the receiver function that best fits the data while accurately assessing the noise parameters. In order to parameterize the receiver function we model the receiver function as an unknown number of Gaussians of unknown amplitude and width. The algorithm takes multiple steps before calculating the acceptance probability of a new model, in order to avoid getting trapped in local misfit minima. Using both observed and synthetic data, we show that the MCMC deconvolution method can accurately obtain a receiver function as well as an estimate of the noise parameters given the parent and daughter

  2. Two novel pathway analysis methods based on a hierarchical model

    PubMed Central

    Evangelou, Marina; Dudbridge, Frank; Wernisch, Lorenz

    2014-01-01

    Motivation: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date. Methods: We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways. Results: The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher’s method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved. Availability: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors. Contact: marina.evangelou@cimr.cam.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24123673

  3. Hierarchical flux-based thermal-structural finite element analysis method

    NASA Technical Reports Server (NTRS)

    Polesky, Sandra P.

    1992-01-01

    A hierarchical flux-based finite element method is developed for both a one and two dimensional thermal structural analyses. Derivation of the finite element equations is presented. The resulting finite element matrices associated with the flux based formulation are evaluated in a closed form. The hierarchical finite elements include additional degrees of freedom in the approximation of the element variable distributions by the use of nodeless variables. The nodeless variables offer increased solution accuracy without the need for defining actual nodes and rediscretizing the finite element model. Thermal and structural responses are obtained from a conventional linear finite element method and exact solutions. Results show that the hierarchical flux-based method can provide improved thermal and structural solution accuracy with fewer elements when compared to results for the conventional linear element method.

  4. A new spectral difference method using hierarchical polynomial bases for hyperbolic conservation laws

    NASA Astrophysics Data System (ADS)

    Liang, Xie; Min, Xu; Bin, Zhang; Zihua, Qiu

    2015-03-01

    To solve hyperbolic conservation laws, a new method is developed based on the spectral difference (SD) algorithm. The new scheme adopts hierarchical polynomials to represent the solution in each cell instead of Lagrange interpolation polynomials used by the original one. The degrees of freedom (DOFs) of the present scheme are the coefficients of these polynomials, which do not represent the states at the solution points like the original method. Therefore, the solution points defined in the original SD scheme are discarded, while the flux points are preserved to construct a Lagrange interpolation polynomial to approximate flux function in each cell. To update the DOFs, differential operators are applied to the governing equation as well as the Lagrange interpolation polynomial of flux function to evaluate first and higher order derivatives of both solution and flux at the centroid of the cell. The stability property of the current scheme is proved to be the same as the original SD method when the same solution space is adopted. One dimensional methods are always stable by the use of zeros of Legendre polynomials as inner flux points. For two dimensional problems, the introduction of Raviart-Thomas spaces for the interpolation of flux function proves stable schemes for triangles. Accuracy studies are performed with one- and two-dimensional problems. p-Multigrid algorithm is implemented with orthogonal hierarchical bases. The results verify the high efficiency and low memory requirements of implementation of p-multigrid algorithm with the proposed scheme.

  5. Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

    PubMed

    Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W

    2016-01-01

    Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness. PMID:27618082

  6. 3D CAD model retrieval method based on hierarchical multi-features

    NASA Astrophysics Data System (ADS)

    An, Ran; Wang, Qingwen

    2015-12-01

    The classical "Shape Distribution D2" algorithm takes the distance between two random points on a surface of CAD model as statistical features, and based on that it generates a feature vector to calculate the dissimilarity and achieve the retrieval goal. This algorithm has a simple principle, high computational efficiency and can get a better retrieval results for the simple shape models. Based on the analysis of D2 algorithm's shape distribution curve, this paper enhances the algorithm's descriptive ability for a model's overall shape through the statistics of the angle between two random points' normal vectors, especially for the distinctions between the model's plane features and curved surface features; meanwhile, introduce the ratio that a line between two random points cut off by the model's surface to enhance the algorithm's descriptive ability for a model's detailed features; finally, integrating the two shape describing methods with the original D2 algorithm, this paper proposes a new method based the hierarchical multi-features. Experimental results showed that this method has bigger improvements and could get a better retrieval results compared with the traditional 3D CAD model retrieval method.

  7. Dynamic and quantitative method of analyzing service consistency evolution based on extended hierarchical finite state automata.

    PubMed

    Fan, Linjun; Tang, Jun; Ling, Yunxiang; Li, Benxian

    2014-01-01

    This paper is concerned with the dynamic evolution analysis and quantitative measurement of primary factors that cause service inconsistency in service-oriented distributed simulation applications (SODSA). Traditional methods are mostly qualitative and empirical, and they do not consider the dynamic disturbances among factors in service's evolution behaviors such as producing, publishing, calling, and maintenance. Moreover, SODSA are rapidly evolving in terms of large-scale, reusable, compositional, pervasive, and flexible features, which presents difficulties in the usage of traditional analysis methods. To resolve these problems, a novel dynamic evolution model extended hierarchical service-finite state automata (EHS-FSA) is constructed based on finite state automata (FSA), which formally depict overall changing processes of service consistency states. And also the service consistency evolution algorithms (SCEAs) based on EHS-FSA are developed to quantitatively assess these impact factors. Experimental results show that the bad reusability (17.93% on average) is the biggest influential factor, the noncomposition of atomic services (13.12%) is the second biggest one, and the service version's confusion (1.2%) is the smallest one. Compared with previous qualitative analysis, SCEAs present good effectiveness and feasibility. This research can guide the engineers of service consistency technologies toward obtaining a higher level of consistency in SODSA. PMID:24772033

  8. Identifying hierarchical and overlapping protein complexes based on essential protein-protein interactions and "seed-expanding" method.

    PubMed

    Ren, Jun; Zhou, Wei; Wang, Jianxin

    2014-01-01

    Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and "seed-expanding." First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

  9. Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

    PubMed Central

    Ren, Jun; Zhou, Wei; Wang, Jianxin

    2014-01-01

    Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

  10. A hierarchical method for whole-brain connectivity-based parcellation.

    PubMed

    Moreno-Dominguez, David; Anwander, Alfred; Knösche, Thomas R

    2014-10-01

    In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps. Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function-anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole-brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them. Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole-brain parcellation. PMID:24740833

  11. Parallel hierarchical method in networks

    NASA Astrophysics Data System (ADS)

    Malinochka, Olha; Tymchenko, Leonid

    2007-09-01

    This method of parallel-hierarchical Q-transformation offers new approach to the creation of computing medium - of parallel -hierarchical (PH) networks, being investigated in the form of model of neurolike scheme of data processing [1-5]. The approach has a number of advantages as compared with other methods of formation of neurolike media (for example, already known methods of formation of artificial neural networks). The main advantage of the approach is the usage of multilevel parallel interaction dynamics of information signals at different hierarchy levels of computer networks, that enables to use such known natural features of computations organization as: topographic nature of mapping, simultaneity (parallelism) of signals operation, inlaid cortex, structure, rough hierarchy of the cortex, spatially correlated in time mechanism of perception and training [5].

  12. Hierarchical Ensemble Methods for Protein Function Prediction

    PubMed Central

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

  13. Hierarchical design of an electro-hydraulic actuator based on robust LPV methods

    NASA Astrophysics Data System (ADS)

    Németh, Balázs; Varga, Balázs; Gáspár, Péter

    2015-08-01

    The paper proposes a hierarchical control design of an electro-hydraulic actuator, which is used to improve the roll stability of vehicles. The purpose of the control system is to generate a reference torque, which is required by the vehicle dynamic control. The control-oriented model of the actuator is formulated in two subsystems. The high-level hydromotor is described in a linear form, while the low-level spool valve is a polynomial system. These subsystems require different control strategies. At the high level, a linear parameter-varying control is used to guarantee performance specifications. At the low level, a control Lyapunov-function-based algorithm, which creates discrete control input values of the valve, is proposed. The interaction between the two subsystems is guaranteed by the spool displacement, which is control input at the high level and must be tracked at the low-level control. The spool displacement has physical constraints, which must also be incorporated into the control design. The robust design of the high-level control incorporates the imprecision of the low-level control as an uncertainty of the system.

  14. A novel load balancing method for hierarchical federation simulation system

    NASA Astrophysics Data System (ADS)

    Bin, Xiao; Xiao, Tian-yuan

    2013-07-01

    In contrast with single HLA federation framework, hierarchical federation framework can improve the performance of large-scale simulation system in a certain degree by distributing load on several RTI. However, in hierarchical federation framework, RTI is still the center of message exchange of federation, and it is still the bottleneck of performance of federation, the data explosion in a large-scale HLA federation may cause overload on RTI, It may suffer HLA federation performance reduction or even fatal error. Towards this problem, this paper proposes a load balancing method for hierarchical federation simulation system based on queuing theory, which is comprised of three main module: queue length predicting, load controlling policy, and controller. The method promotes the usage of resources of federate nodes, and improves the performance of HLA simulation system with balancing load on RTIG and federates. Finally, the experiment results are presented to demonstrate the efficient control of the method.

  15. Hierarchical super-resolution-based inpainting.

    PubMed

    Le Meur, Olivier; Ebdelli, Mounira; Guillemot, Christine

    2013-10-01

    This paper introduces a novel framework for examplar-based inpainting. It consists in performing first the inpainting on a coarse version of the input image. A hierarchical super-resolution algorithm is then used to recover details on the missing areas. The advantage of this approach is that it is easier to inpaint low-resolution pictures than high-resolution ones. The gain is both in terms of computational complexity and visual quality. However, to be less sensitive to the parameter setting of the inpainting method, the low-resolution input picture is inpainted several times with different configurations. Results are efficiently combined with a loopy belief propagation and details are recovered by a single-image super-resolution algorithm. Experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method. Results are compared to five state-of-the-art inpainting methods. PMID:23661318

  16. Identification of Nitrogen, Phosphorus, and Potassium Deficiencies in Rice Based on Static Scanning Technology and Hierarchical Identification Method

    PubMed Central

    Chen, Lisu; Lin, Lin; Cai, Guangzhe; Sun, Yuanyuan; Huang, Tao; Wang, Ke; Deng, Jinsong

    2014-01-01

    Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2nd and 3rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%. PMID:25426712

  17. Identification of nitrogen, phosphorus, and potassium deficiencies in rice based on static scanning technology and hierarchical identification method.

    PubMed

    Chen, Lisu; Lin, Lin; Cai, Guangzhe; Sun, Yuanyuan; Huang, Tao; Wang, Ke; Deng, Jinsong

    2014-01-01

    Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2nd and 3rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%. PMID:25426712

  18. Hierarchical bilateral filtering based disparity estimation for view synthesis

    NASA Astrophysics Data System (ADS)

    Shin, Hong-Chang; Lee, Gwangsoon; Cheong, Won-Sik; Hur, Namho

    2016-06-01

    In this paper, we introduce a high efficient and practical disparity estimation using hierarchical bilateral filtering for real-time view synthesis. The proposed method is based on hierarchical stereo matching with hardware-efficient bilateral filtering. Hardware-efficient bilateral filtering is different from the exact bilateral filter. The purpose of the method is to design an edge-preserving filter that can be efficiently parallelized on hardware. The proposed hierarchical bilateral filtering based disparity estimation is essentially a coarse-to-fine use of stereo matching with bilateral filtering. It works as follows: firstly, the hierarchical image pyramid are constructed; the multi-scale algorithm then starts by applying a local stereo matching to the downsampled images at the coarsest level of the hierarchy. After the local stereo matching, the estimated disparity map is refined with the bilateral filtering. And then the refined disparity map will be adaptively upsampled to the next finer level. The upsampled disparity map used as a prior of the corresponding local stereo matching at the next level, and filtered and so on. The method we propose is essentially a combination of hierarchical stereo matching and hardware-efficient bilateral filtering. As a result, visual comparison using real-world stereoscopic video clips shows that the method gives better results than one of state-of-art methods in terms of robustness and computation time.

  19. A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images

    PubMed Central

    Smith, Rebecca; Najarian, Kayvan; Ward, Kevin

    2009-01-01

    Background Traumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models. Results Performance of the automated algorithm is compared with results obtained via manual initialization. An error measures is calculated based on the shapes detected with each method and the gold standard shapes. ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method. Visual inspection by two radiologists and one trauma surgeon also indicates generally accurate performance. Conclusion The hierarchical algorithm described in this paper automatically detects and segments key structures from pelvic X-rays. Unlike various other x-ray segmentation methods, it does not require manual initialization or input. Moreover, it handles the inconsistencies between x-ray images in a clinical environment and performs successfully in the presence of fracture. This method and the segmentation results provide a valuable base for future work in fracture detection. PMID:19891796

  20. A hierarchical classification method for finger knuckle print recognition

    NASA Astrophysics Data System (ADS)

    Kong, Tao; Yang, Gongping; Yang, Lu

    2014-12-01

    Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.

  1. Hierarchical model-based interferometric synthetic aperture radar image registration

    NASA Astrophysics Data System (ADS)

    Wang, Yang; Huang, Haifeng; Dong, Zhen; Wu, Manqing

    2014-01-01

    With the rapid development of spaceborne interferometric synthetic aperture radar technology, classical image registration methods are incompetent for high-efficiency and high-accuracy masses of real data processing. Based on this fact, we propose a new method. This method consists of two steps: coarse registration that is realized by cross-correlation algorithm and fine registration that is realized by hierarchical model-based algorithm. Hierarchical model-based algorithm is a high-efficiency optimization algorithm. The key features of this algorithm are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-to-fine refinement strategy. Experimental results from different kinds of simulated and real data have confirmed that the proposed method is very fast and has high accuracy. Comparing with a conventional cross-correlation method, the proposed method provides markedly improved performance.

  2. Fingerprint analysis of Hibiscus mutabilis L. leaves based on ultra performance liquid chromatography with photodiode array detector combined with similarity analysis and hierarchical clustering analysis methods

    PubMed Central

    Liang, Xianrui; Ma, Meiling; Su, Weike

    2013-01-01

    Background: A method for chemical fingerprint analysis of Hibiscus mutabilis L. leaves was developed based on ultra performance liquid chromatography with photodiode array detector (UPLC-PAD) combined with similarity analysis (SA) and hierarchical clustering analysis (HCA). Materials and Methods: 10 batches of Hibiscus mutabilis L. leaves samples were collected from different regions of China. UPLC-PAD was employed to collect chemical fingerprints of Hibiscus mutabilis L. leaves. Results: The relative standard deviations (RSDs) of the relative retention times (RRT) and relative peak areas (RPA) of 10 characteristic peaks (one of them was identified as rutin) in precision, repeatability and stability test were less than 3%, and the method of fingerprint analysis was validated to be suitable for the Hibiscus mutabilis L. leaves. Conclusions: The chromatographic fingerprints showed abundant diversity of chemical constituents qualitatively in the 10 batches of Hibiscus mutabilis L. leaves samples from different locations by similarity analysis on basis of calculating the correlation coefficients between each two fingerprints. Moreover, the HCA method clustered the samples into four classes, and the HCA dendrogram showed the close or distant relations among the 10 samples, which was consistent to the SA result to some extent. PMID:23930008

  3. A hierarchical method for molecular docking using cloud computing.

    PubMed

    Kang, Ling; Guo, Quan; Wang, Xicheng

    2012-11-01

    Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein-ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs. PMID:23017886

  4. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    EPA Science Inventory

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  5. Parallel iterative solvers and preconditioners using approximate hierarchical methods

    SciTech Connect

    Grama, A.; Kumar, V.; Sameh, A.

    1996-12-31

    In this paper, we report results of the performance, convergence, and accuracy of a parallel GMRES solver for Boundary Element Methods. The solver uses a hierarchical approximate matrix-vector product based on a hybrid Barnes-Hut / Fast Multipole Method. We study the impact of various accuracy parameters on the convergence and show that with minimal loss in accuracy, our solver yields significant speedups. We demonstrate the excellent parallel efficiency and scalability of our solver. The combined speedups from approximation and parallelism represent an improvement of several orders in solution time. We also develop fast and paralellizable preconditioners for this problem. We report on the performance of an inner-outer scheme and a preconditioner based on truncated Green`s function. Experimental results on a 256 processor Cray T3D are presented.

  6. A reliable method of manufacturing metallic hierarchical superhydrophobic surfaces

    SciTech Connect

    Pogreb, Roman; Whyman, Gene; Barayev, Reuven; Bormashenko, Edward; Aurbach, Doron

    2009-06-01

    A method of manufacturing hierarchical metallic surfaces demonstrating superhydrophobic properties is presented. The surfaces showed apparent contact angles as high as 153 deg. and sliding angles of 10 deg. for 50-100 {mu}l droplets. The Cassie-like model [A. B. D. Cassie and S. Baxter, Trans. Faraday Soc. 40, 546 (1944)], considering the hierarchical topography of the relief, predicts apparent contact angles in a satisfactory agreement with the measured values.

  7. Multigrid hierarchical simulated annealing method for reconstructing heterogeneous media.

    PubMed

    Pant, Lalit M; Mitra, Sushanta K; Secanell, Marc

    2015-12-01

    A reconstruction methodology based on different-phase-neighbor (DPN) pixel swapping and multigrid hierarchical annealing is presented. The method performs reconstructions by starting at a coarse image and successively refining it. The DPN information is used at each refinement stage to freeze interior pixels of preformed structures. This preserves the large-scale structures in refined images and also reduces the number of pixels to be swapped, thereby resulting in a decrease in the necessary computational time to reach a solution. Compared to conventional single-grid simulated annealing, this method was found to reduce the required computation time to achieve a reconstruction by around a factor of 70-90, with the potential of even higher speedups for larger reconstructions. The method is able to perform medium sized (up to 300(3) voxels) three-dimensional reconstructions with multiple correlation functions in 36-47 h. PMID:26764849

  8. Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

    PubMed

    Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

    2016-01-01

    Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

  9. Elastic image registration using hierarchical spatially based mean shift.

    PubMed

    Yang, Xuan; Pei, Jihong; Sun, Wei

    2013-09-01

    In this paper, a novel estimation technique for corresponding points using a hierarchical, spatially based mean shift algorithm is proposed. We proposed a spatially based probability estimation using different spatial masks. For a given point on reference image, its corresponding register point is found along the search trajectory generated by optimizing Bhattacharyya coefficient between two windows centered at the points on the register and reference images. The outliers are further eliminated by analyzing statistical information on the displacements of the candidate register points. Experiments on various monomodal medical images show that the proposed method is feasible and fast. PMID:23930802

  10. Carbon composition with hierarchical porosity, and methods of preparation

    DOEpatents

    Mayes, Richard T; Dai, Sheng

    2014-10-21

    A method for fabricating a porous carbon material possessing a hierarchical porosity, the method comprising subjecting a precursor composition to a curing step followed by a carbonization step, the precursor composition comprising: (i) a templating component comprised of a block copolymer, (ii) a phenolic component, (iii) a dione component in which carbonyl groups are adjacent, and (iv) an acidic component, wherein said carbonization step comprises heating the precursor composition at a carbonizing temperature for sufficient time to convert the precursor composition to a carbon material possessing a hierarchical porosity comprised of mesopores and macropores. Also described are the resulting hierarchical porous carbon material, a capacitive deionization device in which the porous carbon material is incorporated, as well as methods for desalinating water by use of said capacitive deionization device.

  11. Hierarchical coefficient of a multifractal based network

    NASA Astrophysics Data System (ADS)

    Moreira, Darlan A.; Lucena, Liacir dos Santos; Corso, Gilberto

    2014-02-01

    The hierarchical property for a general class of networks stands for a power-law relation between clustering coefficient, CC and connectivity k: CC∝kβ. This relation is empirically verified in several biologic and social networks, as well as in random and deterministic network models, in special for hierarchical networks. In this work we show that the hierarchical property is also present in a Lucena network. To create a Lucena network we use the dual of a multifractal lattice ML, the vertices are the sites of the ML and links are established between neighbouring lattices, therefore this network is space filling and planar. Besides a Lucena network shows a scale-free distribution of connectivity. We deduce a relation for the maximal local clustering coefficient CCimax of a vertex i in a planar graph. This condition expresses that the number of links among neighbour, N△, of a vertex i is equal to its connectivity ki, that means: N△=ki. The Lucena network fulfils the condition N△≃ki independent of ki and the anisotropy of ML. In addition, CCmax implies the threshold β=1 for the hierarchical property for any scale-free planar network.

  12. Hierarchical Relations among Three-Way Methods.

    ERIC Educational Resources Information Center

    Kiers, Henk A. L.

    1991-01-01

    Several methods for the analysis of three-way data (data classified three ways) are described and shown to be variants of principal components analysis of the two-way supermatrix in which each two-way slice is strung out into a column vector. Direct fitting and fitting derived data are considered. (SLD)

  13. Low energy isomers of (H2O)25 from a hierarchical method based on Monte Carlo temperature basin paving and molecular tailoring approaches benchmarked by MP2 calculations

    NASA Astrophysics Data System (ADS)

    Sahu, Nityananda; Gadre, Shridhar R.; Rakshit, Avijit; Bandyopadhyay, Pradipta; Miliordos, Evangelos; Xantheas, Sotiris S.

    2014-10-01

    We report new global minimum candidate structures for the (H2O)25 cluster that are lower in energy than the ones reported previously and correspond to hydrogen bonded networks with 42 hydrogen bonds and an interior, fully coordinated water molecule. These were obtained as a result of a hierarchical approach based on initial Monte Carlo Temperature Basin Paving sampling of the cluster's Potential Energy Surface with the Effective Fragment Potential, subsequent geometry optimization using the Molecular Tailoring Approach with the fragments treated at the second order Møller-Plesset (MP2) perturbation (MTA-MP2) and final refinement of the entire cluster at the MP2 level of theory. The MTA-MP2 optimized cluster geometries, constructed from the fragments, were found to be within <0.5 kcal/mol from the minimum geometries obtained from the MP2 optimization of the entire (H2O)25 cluster. In addition, the grafting of the MTA-MP2 energies yields electronic energies that are within <0.3 kcal/mol from the MP2 energies of the entire cluster while preserving their energy rank order. Finally, the MTA-MP2 approach was found to reproduce the MP2 harmonic vibrational frequencies, constructed from the fragments, quite accurately when compared to the MP2 ones of the entire cluster in both the HOH bending and the OH stretching regions of the spectra.

  14. Comparison of the incremental and hierarchical methods for crystalline neon.

    PubMed

    Nolan, S J; Bygrave, P J; Allan, N L; Manby, F R

    2010-02-24

    We present a critical comparison of the incremental and hierarchical methods for the evaluation of the static cohesive energy of crystalline neon. Both of these schemes make it possible to apply the methods of molecular electronic structure theory to crystalline solids, offering a systematically improvable alternative to density functional theory. Results from both methods are compared with previous theoretical and experimental studies of solid neon and potential sources of error are discussed. We explore the similarities of the two methods and demonstrate how they may be used in tandem to study crystalline solids. PMID:21386379

  15. Method for implementation of recursive hierarchical segmentation on parallel computers

    NASA Technical Reports Server (NTRS)

    Tilton, James C. (Inventor)

    2005-01-01

    A method, computer readable storage, and apparatus for implementing a recursive hierarchical segmentation algorithm on a parallel computing platform. The method includes setting a bottom level of recursion that defines where a recursive division of an image into sections stops dividing, and setting an intermediate level of recursion where the recursive division changes from a parallel implementation into a serial implementation. The segmentation algorithm is implemented according to the set levels. The method can also include setting a convergence check level of recursion with which the first level of recursion communicates with when performing a convergence check.

  16. Model-based hierarchical reinforcement learning and human action control

    PubMed Central

    Botvinick, Matthew; Weinstein, Ari

    2014-01-01

    Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

  17. Hierarchical cobalt-based hydroxide microspheres for water oxidation

    NASA Astrophysics Data System (ADS)

    Zhang, Ye; Cui, Bai; Derr, Olivia; Yao, Zhibo; Qin, Zhaotong; Deng, Xiangyun; Li, Jianbao; Lin, Hong

    2014-02-01

    3D hierarchical cobalt hydroxide carbonate hydrate (Co(CO3)0.5(OH).0.11H2O) has been synthesized featuring a hollow urchin-like structure by a one-step hydrothermal method at modest temperature on FTO glass substrates. The functionalities of precursor surfactants were isolated and analyzed. A plausible formation mechanism of the spherical urchin-like microclusters has been furnished through time-dependent investigations. Introduction of other transitional metal doping (Cu, Ni) would give rise to a substantial morphological change associated with a surface area drop. The directly grown cobalt-based hydroxide composite electrodes were found to be capable of catalyzing oxygen evolution reaction (OER) under both neutral pH and alkaline conditions. The favorable 3D dendritic morphology and porous structure provide large surface areas and possible defect sites that are likely responsible for their robust electrochemical activity.

  18. Hierarchic plate and shell models based on p-extension

    NASA Technical Reports Server (NTRS)

    Szabo, Barna A.; Sahrmann, Glenn J.

    1988-01-01

    Formulations of finite element models for beams, arches, plates and shells based on the principle of virtual work was studied. The focus is on computer implementation of hierarchic sequences of finite element models suitable for numerical solution of a large variety of practical problems which may concurrently contain thin and thick plates and shells, stiffeners, and regions where three dimensional representation is required. The approximate solutions corresponding to the hierarchic sequence of models converge to the exact solution of the fully three dimensional model. The stopping criterion is based on: (1) estimation of the relative error in energy norm; (2) equilibrium tests, and (3) observation of the convergence of quantities of interest.

  19. Iris Image Classification Based on Hierarchical Visual Codebook.

    PubMed

    Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

    2014-06-01

    Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection. PMID:26353275

  20. Constructive Epistemic Modeling: A Hierarchical Bayesian Model Averaging Method

    NASA Astrophysics Data System (ADS)

    Tsai, F. T. C.; Elshall, A. S.

    2014-12-01

    Constructive epistemic modeling is the idea that our understanding of a natural system through a scientific model is a mental construct that continually develops through learning about and from the model. Using the hierarchical Bayesian model averaging (HBMA) method [1], this study shows that segregating different uncertain model components through a BMA tree of posterior model probabilities, model prediction, within-model variance, between-model variance and total model variance serves as a learning tool [2]. First, the BMA tree of posterior model probabilities permits the comparative evaluation of the candidate propositions of each uncertain model component. Second, systemic model dissection is imperative for understanding the individual contribution of each uncertain model component to the model prediction and variance. Third, the hierarchical representation of the between-model variance facilitates the prioritization of the contribution of each uncertain model component to the overall model uncertainty. We illustrate these concepts using the groundwater modeling of a siliciclastic aquifer-fault system. The sources of uncertainty considered are from geological architecture, formation dip, boundary conditions and model parameters. The study shows that the HBMA analysis helps in advancing knowledge about the model rather than forcing the model to fit a particularly understanding or merely averaging several candidate models. [1] Tsai, F. T.-C., and A. S. Elshall (2013), Hierarchical Bayesian model averaging for hydrostratigraphic modeling: Uncertainty segregation and comparative evaluation. Water Resources Research, 49, 5520-5536, doi:10.1002/wrcr.20428. [2] Elshall, A.S., and F. T.-C. Tsai (2014). Constructive epistemic modeling of groundwater flow with geological architecture and boundary condition uncertainty under Bayesian paradigm, Journal of Hydrology, 517, 105-119, doi: 10.1016/j.jhydrol.2014.05.027.

  1. Hierarchical semi-numeric method for pairwise fuzzy group decision making.

    PubMed

    Marimin, M; Umano, M; Hatono, I; Tamura, H

    2002-01-01

    Gradual improvements to a single-level semi-numeric method, i.e., linguistic labels preference representation by fuzzy sets computation for pairwise fuzzy group decision making are summarized. The method is extended to solve multiple criteria hierarchical structure pairwise fuzzy group decision-making problems. The problems are hierarchically structured into focus, criteria, and alternatives. Decision makers express their evaluations of criteria and alternatives based on each criterion by using linguistic labels. The labels are converted into and processed in triangular fuzzy numbers (TFNs). Evaluations of criteria yield relative criteria weights. Evaluations of the alternatives, based on each criterion, yield a degree of preference for each alternative or a degree of satisfaction for each preference value. By using a neat ordered weighted average (OWA) or a fuzzy weighted average operator, solutions obtained based on each criterion are aggregated into final solutions. The hierarchical semi-numeric method is suitable for solving a larger and more complex pairwise fuzzy group decision-making problem. The proposed method has been verified and applied to solve some real cases and is compared to Saaty's (1996) analytic hierarchy process (AHP) method. PMID:18244875

  2. Asymmetric capacitors using lignin-based hierarchical porous carbons

    NASA Astrophysics Data System (ADS)

    Salinas-Torres, David; Ruiz-Rosas, Ramiro; Valero-Romero, María José; Rodríguez-Mirasol, José; Cordero, Tomás; Morallón, Emilia; Cazorla-Amorós, Diego

    2016-09-01

    Hierarchical porous carbons (HPC) were fabricated from lignin by hard template method using Beta and Y zeolites as templates. Textural properties were dictated by the hard template, obtaining a bi-modal pore size distribution with similar micropore sizes but different mesopore sizes. These HPCs provide a well-connected and developed porosity that show capacitance values near to 140 F g-1 in 1 M H2SO4 at 1 A g-1 and a capacitance retention of ca. 50% and 40% when the specific current is increased from 1 to 64 A g-1 for the Y and the Beta-based carbons, respectively. A symmetric capacitor working at 1.2 V with energy densities of 4.2 Wh kg-1 at 1.3 kW kg-1 has been obtained using the Beta-based HPC. Asymmetric in mass design allowed to operate the capacitor safely at 1.4 V, yielding an energy density of 6.3 Wh kg-1 at 1.3 kW kg-1, an increase of 50% with respect to the symmetric configuration, while keeping a maximum power near to 50 kW kg-1. This capacitor has an energy density comparable to that of a symmetric supercapacitor built using a commercial activated carbon of much higher porosity development, outperforming it in terms of energy, coulombic efficiencies and maximum power.

  3. A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

    NASA Astrophysics Data System (ADS)

    Šilhavý, Jakub; Minár, Jozef; Mentlík, Pavel; Sládek, Ján

    2016-07-01

    This paper presents a new method of automatic lineament extraction which includes the removal of the 'artefacts effect' which is associated with the process of raster based analysis. The core of the proposed Multi-Hillshade Hierarchic Clustering (MHHC) method incorporates a set of variously illuminated and rotated hillshades in combination with hierarchic clustering of derived 'protolineaments'. The algorithm also includes classification into positive and negative lineaments. MHHC was tested in two different territories in Bohemian Forest and Central Western Carpathians. The original vector-based algorithm was developed for comparison of the individual lineaments proximity. Its use confirms the compatibility of manual and automatic extraction and their similar relationships to structural data in the study areas.

  4. Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

    2011-01-01

    The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

  5. P2MP MPLS-Based Hierarchical Service Management System

    NASA Astrophysics Data System (ADS)

    Kumaki, Kenji; Nakagawa, Ikuo; Nagami, Kenichi; Ogishi, Tomohiko; Ano, Shigehiro

    This paper proposes a point-to-multipoint (P2MP) Multi-Protocol Label Switching (MPLS) based hierarchical service management system. Traditionally, general management systems deployed in some service providers control MPLS Label Switched Paths (LSPs) (e.g., RSVP-TE and LDP) and services (e.g., L2VPN, L3VPN and IP) separately. In order for dedicated management systems for MPLS LSPs and services to cooperate with each other automatically, a hierarchical service management system has been proposed with the main focus on point-to-point (P2P) TE LSPs in MPLS path management. In the case where P2MP TE LSPs and services are deployed in MPLS networks, the dedicated management systems for P2MP TE LSPs and services must work together automatically. Therefore, this paper proposes a new algorithm that uses a correlation between P2MP TE LSPs and multicast VPN services based on a P2MP MPLS-based hierarchical service management architecture. Also, the capacity and performance of the proposed algorithm are evaluated by simulations, which are actually based on certain real MPLS production networks, and are compared to that of the algorithm for P2P TE LSPs. Results show this system is very scalable within real MPLS production networks. This system, with the automatic correlation, appears to be deployable in real MPLS production networks.

  6. Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

    NASA Astrophysics Data System (ADS)

    Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

    2012-11-01

    This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.

  7. Recent global methane trends: an investigation using hierarchical Bayesian methods

    NASA Astrophysics Data System (ADS)

    Rigby, M. L.; Stavert, A.; Ganesan, A.; Lunt, M. F.

    2014-12-01

    Following a decade with little growth, methane concentrations began to increase across the globe in 2007, and have continued to rise ever since. The reasons for this renewed growth are currently the subject of much debate. Here, we discuss the recent observed trends, and highlight some of the strengths and weaknesses in current "inverse" methods for quantifying fluxes using observations. In particular, we focus on the outstanding problems of accurately quantifying uncertainties in inverse frameworks. We examine to what extent the recent methane changes can be explained by the current generation of flux models and inventories. We examine the major modes of variability in wetland models along with the Global Fire Emissions Database (GFED) and the Emissions Database for Global Atmospheric Research (EDGAR). Using the Model for Ozone and Related Tracers (MOZART), we determine whether the spatial and temporal atmospheric trends predicted using these emissions can be brought into consistency with in situ atmospheric observations. We use a novel hierarchical Bayesian methodology in which scaling factors applied to the principal components of the flux fields are estimated simultaneously with the uncertainties associated with the a priori fluxes and with model representations of the observations. Using this method, we examine the predictive power of methane flux models for explaining recent fluctuations.

  8. Hierarchical photocatalysts.

    PubMed

    Li, Xin; Yu, Jiaguo; Jaroniec, Mietek

    2016-05-01

    As a green and sustainable technology, semiconductor-based heterogeneous photocatalysis has received much attention in the last few decades because it has potential to solve both energy and environmental problems. To achieve efficient photocatalysts, various hierarchical semiconductors have been designed and fabricated at the micro/nanometer scale in recent years. This review presents a critical appraisal of fabrication methods, growth mechanisms and applications of advanced hierarchical photocatalysts. Especially, the different synthesis strategies such as two-step templating, in situ template-sacrificial dissolution, self-templating method, in situ template-free assembly, chemically induced self-transformation and post-synthesis treatment are highlighted. Finally, some important applications including photocatalytic degradation of pollutants, photocatalytic H2 production and photocatalytic CO2 reduction are reviewed. A thorough assessment of the progress made in photocatalysis may open new opportunities in designing highly effective hierarchical photocatalysts for advanced applications ranging from thermal catalysis, separation and purification processes to solar cells. PMID:26963902

  9. Hierarchical Information-Based Clustering for Connectivity-Based Cortex Parcellation

    PubMed Central

    Gorbach, Nico S.; Schütte, Christoph; Melzer, Corina; Goldau, Mathias; Sujazow, Olivia; Jitsev, Jenia; Douglas, Tania; Tittgemeyer, Marc

    2011-01-01

    One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas. PMID:21977015

  10. Facile Method toward Hierarchical Fullerene Architectures with Enhanced Hydrophobicity and Photoluminescence.

    PubMed

    Zheng, Shushu; Xu, Meilin; Lu, Xing

    2015-09-16

    A two-step self-assembly strategy has been developed for the preparation of fullerene hierarchical architectures. Typically, the precipitation method is utilized to synthesize the initial fullerene microstructures, and subsequently a drop-drying process is employed to facilitate the fullerene microstructures to self-assemble into the final hierarchical structures. Overall, this methodology is quite simple and feasible, which can be applied to prepare fullerene hierarchical structures with different morphological features, simply by choosing proper solvent. Moreover, the as-obtained C70 hierarchical structures have many superior properties over the original C70 microrods such as superhydrophobicity and unique photoluminescence behaviors, promising their applications as waterproof optoelectronics. PMID:26320882

  11. Automated control of hierarchical systems using value-driven methods

    NASA Technical Reports Server (NTRS)

    Pugh, George E.; Burke, Thomas E.

    1990-01-01

    An introduction is given to the Value-driven methodology, which has been successfully applied to solve a variety of difficult decision, control, and optimization problems. Many real-world decision processes (e.g., those encountered in scheduling, allocation, and command and control) involve a hierarchy of complex planning considerations. For such problems it is virtually impossible to define a fixed set of rules that will operate satisfactorily over the full range of probable contingencies. Decision Science Applications' value-driven methodology offers a systematic way of automating the intuitive, common-sense approach used by human planners. The inherent responsiveness of value-driven systems to user-controlled priorities makes them particularly suitable for semi-automated applications in which the user must remain in command of the systems operation. Three examples of the practical application of the approach in the automation of hierarchical decision processes are discussed: the TAC Brawler air-to-air combat simulation is a four-level computerized hierarchy; the autonomous underwater vehicle mission planning system is a three-level control system; and the Space Station Freedom electrical power control and scheduling system is designed as a two-level hierarchy. The methodology is compared with rule-based systems and with other more widely-known optimization techniques.

  12. Hierarchical Methods for the Generation, Publication and Visualization of Huge Astronomical Data Cube Surveys

    NASA Astrophysics Data System (ADS)

    Fernique, P.; Allen, M.; Boch, T.; Bonnarel, F.; Oberto, A.

    2015-09-01

    The CDS has developed and validated new methods to generate, publish and display huge astronomical image data cubes based on the Hierarchical Progressive Survey (HiPS) framework. Data cubes with two spatial dimensions and an additional spectral or temporal dimension can be mapped onto HEALPix grids at different resolutions, which supports zooming and panning of the data across the sky with the ability to explore the third dimension of the cube. These methods are successfully applied to different sorts of cube data, and surveys of cube data.

  13. Wavelet based hierarchical coding scheme for radar image compression

    NASA Astrophysics Data System (ADS)

    Sheng, Wen; Jiao, Xiaoli; He, Jifeng

    2007-12-01

    This paper presents a wavelet based hierarchical coding scheme for radar image compression. Radar signal is firstly quantized to digital signal, and reorganized as raster-scanned image according to radar's repeated period frequency. After reorganization, the reformed image is decomposed to image blocks with different frequency band by 2-D wavelet transformation, each block is quantized and coded by the Huffman coding scheme. A demonstrating system is developed, showing that under the requirement of real time processing, the compression ratio can be very high, while with no significant loss of target signal in restored radar image.

  14. Hierarchical polypyrrole based composites for high performance asymmetric supercapacitors

    NASA Astrophysics Data System (ADS)

    Chen, Gao-Feng; Liu, Zhao-Qing; Lin, Jia-Ming; Li, Nan; Su, Yu-Zhi

    2015-06-01

    An advanced asymmetric supercapacitor with high energy density, exploiting hierarchical polypyrrole (PPy) based composites as both the anode [three dimensional (3D) chuzzle-like Ni@PPy@MnO2] and (3D cochleate-like Ni@MnO2@PPy) cathode, has been developed. The ultrathin PPy and flower-like MnO2 orderly coating on the high-conductivity 3D-Ni enhance charge storage while the unique 3D chuzzle-like and 3D cochleate-like structures provide storage chambers and fast ion transport pathways for benefiting the transport of electrolyte ions. The 3D cochleate-like Ni@MnO2@PPy possesses excellent pseudocapacitance with a relatively negative voltage window while preserved EDLC and free transmission channels conducive to hold the high power, providing an ideal cathode for the asymmetric supercapacitor. It is the first report of assembling hierarchical PPy based composites as both the anode and cathode for asymmetric supercapacitor, which exhibits wide operation voltage of 1.3-1.5 V with maximum energy and power densities of 59.8 Wh kg-1 and 7500 W kg-1.

  15. Efficiently dense hierarchical graphene based aerogel electrode for supercapacitors

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Lu, Chengxing; Peng, Huifen; Zhang, Xin; Wang, Zhenkun; Wang, Gongkai

    2016-08-01

    Boosting gravimetric and volumetric capacitances simultaneously at a high rate is still a discrepancy in development of graphene based supercapacitors. We report the preparation of dense hierarchical graphene/activated carbon composite aerogels via a reduction induced self-assembly process coupled with a drying post treatment. The compact and porous structures of composite aerogels could be maintained. The drying post treatment has significant effects on increasing the packing density of aerogels. The introduced activated carbons play the key roles of spacers and bridges, mitigating the restacking of adjacent graphene nanosheets and connecting lateral and vertical graphene nanosheets, respectively. The optimized aerogel with a packing density of 0.67 g cm-3 could deliver maximum gravimetric and volumetric capacitances of 128.2 F g-1 and 85.9 F cm-3, respectively, at a current density of 1 A g-1 in aqueous electrolyte, showing no apparent degradation to the specific capacitance at a current density of 10 A g-1 after 20000 cycles. The corresponding gravimetric and volumetric capacitances of 116.6 F g-1 and 78.1 cm-3 with an acceptable cyclic stability are also achieved in ionic liquid electrolyte. The results show a feasible strategy of designing dense hierarchical graphene based aerogels for supercapacitors.

  16. Efficiently dense hierarchical graphene based aerogel electrode for supercapacitors

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Lu, Chengxing; Peng, Huifen; Zhang, Xin; Wang, Zhenkun; Wang, Gongkai

    2016-08-01

    Boosting gravimetric and volumetric capacitances simultaneously at a high rate is still a discrepancy in development of graphene based supercapacitors. We report the preparation of dense hierarchical graphene/activated carbon composite aerogels via a reduction induced self-assembly process coupled with a drying post treatment. The compact and porous structures of composite aerogels could be maintained. The drying post treatment has significant effects on increasing the packing density of aerogels. The introduced activated carbons play the key roles of spacers and bridges, mitigating the restacking of adjacent graphene nanosheets and connecting lateral and vertical graphene nanosheets, respectively. The optimized aerogel with a packing density of 0.67 g cm-3 could deliver maximum gravimetric and volumetric capacitances of 128.2 F g-1 and 85.9 F cm-3, respectively, at a current density of 1 A g-1 in aqueous electrolyte, showing no apparent degradation to the specific capacitance at a current density of 10 A g-1 after 20000 cycles. The corresponding gravimetric and volumetric capacitances of 116.6 F g-1 and 78.1 cm-3 with an acceptable cyclic stability are also achieved in ionic liquid electrolyte. The results show a feasible strategy of designing dense hierarchical graphene based aerogels for supercapacitors.

  17. A hierarchical fuzzy rule-based approach to aphasia diagnosis.

    PubMed

    Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

    2007-10-01

    Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy. PMID:17293167

  18. Facial animation on an anatomy-based hierarchical face model

    NASA Astrophysics Data System (ADS)

    Zhang, Yu; Prakash, Edmond C.; Sung, Eric

    2003-04-01

    In this paper we propose a new hierarchical 3D facial model based on anatomical knowledge that provides high fidelity for realistic facial expression animation. Like real human face, the facial model has a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different types of muscle models have been developed to simulate distribution of the muscle force on the skin due to muscle contraction. By the presence of the skull model, our facial model takes advantage of both more accurate facial deformation and the consideration of facial anatomy during the interactive definition of facial muscles. Under the muscular force, the deformation of the facial skin is evaluated using numerical integration of the governing dynamic equations. The dynamic facial animation algorithm runs at interactive rate with flexible and realistic facial expressions to be generated.

  19. Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

    PubMed

    Jankovic, Marko; Ogawa, Hidemitsu

    2004-10-01

    Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method. PMID:15593379

  20. A Model of Knowledge Based Information Retrieval with Hierarchical Concept Graph.

    ERIC Educational Resources Information Center

    Kim, Young Whan; Kim, Jin H.

    1990-01-01

    Proposes a model of knowledge-based information retrieval (KBIR) that is based on a hierarchical concept graph (HCG) which shows relationships between index terms and constitutes a hierarchical thesaurus as a knowledge base. Conceptual distance between a query and an object is discussed and the use of Boolean operators is described. (25…

  1. Chromatin structure analysis based on a hierarchic texture model.

    PubMed

    Wolf, G; Beil, M; Guski, H

    1995-02-01

    The quantification of chromatin structures is an important part of nuclear grading of malignant and premalignant lesions. In order to achieve high accuracy, computerized image analysis systems have been applied in this process. Chromatin texture analysis of cell nuclei requires a suitable texture model. A hierarchic model seemed to be most compatible for this purpose. It assumes that texture consists of homogeneous regions (textons). Based on this model, two approaches to texture segmentation and feature extraction were investigated using sections of cervical tissue. We examined the reproducibility of the measurement under changing optical conditions. The coefficients of variations of the texture features ranged from 2.1% to 16.9%. The features were tested for their discriminating capability in a pilot study including 30 cases of cervical dysplasia and carcinoma. The overall classification accuracy reached 65%. This study presents an automated technique for texture analysis that is similar to human perception. PMID:7766266

  2. Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

    PubMed Central

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

  3. Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

    PubMed

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

  4. Design and Fabrication of Hierarchically Porous Carbon with a Template-free Method

    PubMed Central

    Gong, Yutong; Wei, Zhongzhe; Wang, Jing; Zhang, Pengfei; Li, Haoran; Wang, Yong

    2014-01-01

    Fabrication of hierarchically porous carbon materials (HPCs) with high surface area and pore volume has always been pursued. However, the currently effective template methods and acid/base activation strategies suffer from the drawbacks of either high costs or tedious steps. Herein, HPCs with 3D macro-mesopores and short-range meso-micropores were fabricated via an easy and sustainable two-step method from biomass. Macro-mesopores were constructed by slightly accumulation/aggregation of carbon spheres ranging from 60 nm to 80 nm, providing efficient mass diffusion pathways. Short-range mesopores and micropores with high electrolyte accessibility were developed in these spheres by air activation. The obtained HPCs showed surface area values up to 1306 m2/g and high mesopore volume proportion (63.9%). They demonstrated excellent capacitance and low equivalent series resistance (ESR) as supercapacitor electrode materials, suggesting the efficient diffusion and adsorption of electrolyte ions in the designed hierarchically porous structure. PMID:25215549

  5. A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

    PubMed Central

    2014-01-01

    Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in

  6. Hierarchical structure analysis describing abnormal base composition of genomes

    NASA Astrophysics Data System (ADS)

    Ouyang, Zhengqing; Liu, Jian-Kun; She, Zhen-Su

    2005-10-01

    Abnormal base compositional patterns of genomic DNA sequences are studied in the framework of a hierarchical structure (HS) model originally proposed for the study of fully developed turbulence [She and Lévêque, Phys. Rev. Lett. 72, 336 (1994)]. The HS similarity law is verified over scales between 103bp and 105bp , and the HS parameter β is proposed to describe the degree of heterogeneity in the base composition patterns. More than one hundred bacteria, archaea, virus, yeast, and human genome sequences have been analyzed and the results show that the HS analysis efficiently captures abnormal base composition patterns, and the parameter β is a characteristic measure of the genome. Detailed examination of the values of β reveals an intriguing link to the evolutionary events of genetic material transfer. Finally, a sequence complexity (S) measure is proposed to characterize gradual increase of organizational complexity of the genome during the evolution. The present study raises several interesting issues in the evolutionary history of genomes.

  7. Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Lv, Tiejun; Gao, Hui; Li, Xiaopeng; Yang, Shaoshi; Hanzo, Lajos

    2016-01-01

    It has been shown that cooperative localization is capable of improving both the positioning accuracy and coverage in scenarios where the global positioning system (GPS) has a poor performance. However, due to its potentially excessive computational complexity, at the time of writing the application of cooperative localization remains limited in practice. In this paper, we address the efficient cooperative positioning problem in wireless sensor networks. A space-time hierarchical-graph based scheme exhibiting fast convergence is proposed for localizing the agent nodes. In contrast to conventional methods, agent nodes are divided into different layers with the aid of the space-time hierarchical-model and their positions are estimated gradually. In particular, an information propagation rule is conceived upon considering the quality of positional information. According to the rule, the information always propagates from the upper layers to a certain lower layer and the message passing process is further optimized at each layer. Hence, the potential error propagation can be mitigated. Additionally, both position estimation and position broadcasting are carried out by the sensor nodes. Furthermore, a sensor activation mechanism is conceived, which is capable of significantly reducing both the energy consumption and the network traffic overhead incurred by the localization process. The analytical and numerical results provided demonstrate the superiority of our space-time hierarchical-graph based cooperative localization scheme over the benchmarking schemes considered.

  8. ESTIMATION OF FAILURE RATES OF DIGITAL COMPONENTS USING A HIERARCHICAL BAYESIAN METHOD.

    SciTech Connect

    YUE, M.; CHU, T.L.

    2006-01-30

    One of the greatest challenges in evaluating reliability of digital I&C systems is how to obtain better failure rate estimates of digital components. A common practice of the digital component failure rate estimation is attempting to use empirical formulae to capture the impacts of various factors on the failure rates. The applicability of an empirical formula is questionable because it is not based on laws of physics and requires good data, which is scarce in general. In this study, the concept of population variability of the Hierarchical Bayesian Method (HBM) is applied to estimating the failure rate of a digital component using available data. Markov Chain Monte Carlo (MCMC) simulation is used to implement the HBM. Results are analyzed and compared by selecting different distribution types and priors distributions. Inspired by the sensitivity calculations and based on review of analytic derivations, it seems reasonable to suggest avoiding the use of gamma distribution in two-stage Bayesian analysis and HBM analysis.

  9. A biological hierarchical model based underwater moving object detection.

    PubMed

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

  10. OBIA based hierarchical image classification for industrial lake water.

    PubMed

    Uca Avci, Z D; Karaman, M; Ozelkan, E; Kumral, M; Budakoglu, M

    2014-07-15

    Water management is very important in water mining regions for the sustainability of the natural environment and for industrial activities. This study focused on Acigol Lake, which is an important wetland for sodium sulphate (Na2SO4) production, a significant natural protection area and habitat for local bird species and endemic species of this saline environment, and a stopover for migrating flamingos. By a hierarchical classification method, ponds representing the industrial part were classified according to in-situ measured Baumé values, and lake water representing the natural part was classified according to in-situ measurements of water depth. The latter is directly related to the water level, which should not exceed a critical level determined by the regulatory authorities. The resulting data, produced at an accuracy of around 80%, illustrates the status in two main regions for a single date. The output of the analysis may be meaningful for firms and environmental researchers, and authorizations can provide a good perspective for decision making for sustainable resource management in the region which has uncommon and specific ecological characteristics. PMID:24813772

  11. A Biological Hierarchical Model Based Underwater Moving Object Detection

    PubMed Central

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

  12. Hierarchical structure of the countries based on electricity consumption and economic growth

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Aslan, Alper; Deviren, Bayram; Keskin, Mustafa

    2016-07-01

    We investigate the hierarchical structures of countries based on electricity consumption and economic growth by using the real amounts of their consumption over a certain time period. We use electricity consumption data to detect the topological properties of 64 countries from 1971 to 2008. These countries are divided into three clusters: low income group, middle income group and high income group countries. Firstly, a relationship between electricity consumption and economic growth is investigated by using the concept of hierarchical structure methods (minimal spanning tree (MST) and hierarchical tree (HT)). Secondly, we perform bootstrap techniques to investigate a value of the statistical reliability to the links of the MST. Finally, we use a clustering linkage procedure in order to observe the cluster structure more clearly. The results of the structural topologies of these trees are as follows: (i) we identified different clusters of countries according to their geographical location and economic growth, (ii) we found a strong relation between energy consumption and economic growth for all the income groups considered in this study and (iii) the results are in good agreement with the causal relationship between electricity consumption and economic growth.

  13. The Common Prescription Patterns Based on the Hierarchical Clustering of Herb-Pairs Efficacies

    PubMed Central

    2016-01-01

    Prescription patterns are rules or regularities used to generate, recognize, or judge a prescription. Most of existing studies focused on the specific prescription patterns for diverse diseases or syndromes, while little attention was paid to the common patterns, which reflect the global view of the regularities of prescriptions. In this paper, we designed a method CPPM to find the common prescription patterns. The CPPM is based on the hierarchical clustering of herb-pair efficacies (HPEs). Firstly, HPEs were hierarchically clustered; secondly, the individual herbs are labeled by the HPEC (the clusters of HPEs); and then the prescription patterns were extracted from the combinations of HPEC; finally the common patterns are recognized statistically. The results showed that HPEs have hierarchical clustering structure. When the clustering level is 2 and the HPEs were classified into two clusters, the common prescription patterns are obvious. Among 332 candidate prescriptions, 319 prescriptions follow the common patterns. The description of the patterns is that if a prescription contains the herbs of the cluster (C 1), it is very likely to have other herbs of another cluster (C 2); while a prescription has the herbs of C 2, it may have no herbs of C 1. Finally, we discussed that the common patterns are mathematically coincident with the Blood-Qi theory. PMID:27190534

  14. A Bayesian, exemplar-based approach to hierarchical shape matching.

    PubMed

    Gavrila, Dariu M

    2007-08-01

    This paper presents a novel probabilistic approach to hierarchical, exemplar-based shape matching. No feature correspondence is needed among exemplars, just a suitable pairwise similarity measure. The approach uses a template tree to efficiently represent and match the variety of shape exemplars. The tree is generated offline by a bottom-up clustering approach using stochastic optimization. Online matching involves a simultaneous coarse-to-fine approach over the template tree and over the transformation parameters. The main contribution of this paper is a Bayesian model to estimate the a posteriori probability of the object class, after a certain match at a node of the tree. This model takes into account object scale and saliency and allows for a principled setting of the matching thresholds such that unpromising paths in the tree traversal process are eliminated early on. The proposed approach was tested in a variety of application domains. Here, results are presented on one of the more challenging domains: real-time pedestrian detection from a moving vehicle. A significant speed-up is obtained when comparing the proposed probabilistic matching approach with a manually tuned nonprobabilistic variant, both utilizing the same template tree structure. PMID:17568144

  15. Using hierarchically structured problem-solving knowledge in a rule-based process planning system

    SciTech Connect

    Hummel, K.E.; Brooks, S.L.

    1987-06-01

    A rule-based expert system, XCUT, currently is being developed which will generate process plans for the production of machined parts, given a feature-based part description. Due to the vast and dynamic nature of process planning knowledge, a technique has been used in the development of XCUT that segments problem solving knowledge into multiple rule bases. These rule bases are structured in a hierarchical manner that is reflective of the problem decomposition procedure used to generate a plan. An inference engine, HERB (Hierarchical Expert Rule Bases), has been developed which supports the manipulation of multiple rule bases during the planning process. This paper illustrates the hierarchical nature of problem-solving knowledge in the XCUT system and describes the use of HERB for programming with hierarchically structured rule bases. 6 refs., 21 figs.

  16. A hierarchical non-iterative extension of the Guyan condensation method for damped structures

    NASA Astrophysics Data System (ADS)

    Soheilifard, Reza

    2015-05-01

    Reduction methods are commonly employed for solving the eigenvalue problems of systems with a large number of degrees of freedom. These methods are based upon dividing the system's degrees of freedom into masters and slaves, and obtaining a reduced system which is in terms of the masters only. Since 1965 when the Guyan condensation method for undamped structures was presented, which neglects the dynamic effects of the slaves entirely, there have been many efforts to overcome this by proposing various forms of dynamic condensation methods. These methods take into account the dynamics of the slaves through an iterative procedure. In this paper, a hierarchical, non-iterative reduction method has been proposed for damped dynamic systems. The method results in explicit forms of the effective stiffness, viscosity and mass, and also introduces higher order properties when third and higher order approximations are used. Furthermore, a procedure for the automatic selection of master degrees of freedom has been proposed which assures the convergence and increases the efficiency of the method. Application of the method for obtaining low-frequency eigenvalues of two example structures, with and without damping, reveals that results with good accuracy are obtained by using higher order approximations, as they consider the dynamics of the slaves properly.

  17. Hierarchical scheduling method of UAV resources for emergency surveying

    NASA Astrophysics Data System (ADS)

    Zhang, Junxiao; Zhu, Qing; Shen, Fuqiang; Miao, Shuangxi; Cao, Zhenyu; Weng, Qiqiang

    2015-12-01

    Traditional mission scheduling methods are unable to meet the timeliness requirements of emergency surveying. Different size and overlaps of different missions lead to inefficient scheduling and poor mission returns. Especially for UAVs, based on their agile and flexible ability, the scheduling result becomes diversiform; as affected by environment and unmanned aerial vehicle performance, different scheduling will lead to different time costs and mission payoffs. An effective scheduling solution is to arrange the UAVs reasonably to complete as many as missions possible with better quality and satisfaction of different demands. This paper proposes a method for mission decomposition or aggregation to generate a mission unit for specific UAVs based on the spatio-temporal constraints of different missions and UAV observation ability demands. In this way, the problems of lack or redundancy of resource scheduling, which can be caused by mission overload, various information demands and spatial overlapping will be effectively reduced. Furthermore, the global efficiency evaluation function is built by considering typical scheduling objectives, such as mission returns, priority and load balancing of resources. Then, an improved ant colony algorithm is designed to acquire an optimal scheduling scheme and the dynamic adjustment strategy is employed. Finally, the correctness and validity are demonstrated by the simulation experiment.

  18. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Artemenko, A. G.; Muratov, Eugene N.; Polischuk, P. G.; Ognichenko, L. N.; Liahovsky, A. V.; Hromov, A. I.; Varlamova, E. V.

    This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it's a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the "molecular alignment" problem, consideration of different physical-chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the "HiT QSAR" software that so includes powerful statistical capabilities and a number of useful utilities.

  19. Vehicle detection based on visual saliency and deep sparse convolution hierarchical model

    NASA Astrophysics Data System (ADS)

    Cai, Yingfeng; Wang, Hai; Chen, Xiaobo; Gao, Li; Chen, Long

    2016-06-01

    Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification. These types of methods generally have high processing times and low vehicle detection performance. To address this issue, a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed. A visual saliency calculation is firstly used to generate a small vehicle candidate area. The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection. The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group, which outperforms the existing state-of-the-art algorithms. More importantly, high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

  20. A novel 3D constellation-masked method for physical security in hierarchical OFDMA system.

    PubMed

    Zhang, Lijia; Liu, Bo; Xin, Xiangjun; Liu, Deming

    2013-07-01

    This paper proposes a novel 3D constellation-masked method to ensure the physical security in hierarchical optical orthogonal frequency division multiplexing access (OFDMA) system. The 3D constellation masking is executed on the two levels of hierarchical modulation and among different OFDM subcarriers, which is realized by the masking vectors. The Lorenz chaotic model is adopted for the generation of masking vectors in the proposed scheme. A 9.85 Gb/s encrypted hierarchical QAM OFDM signal is successfully demonstrated in the experiment. The performance of illegal optical network unit (ONU) with different masking vectors is also investigated. The proposed method is demonstrated to be secure and efficient against the commonly known attacks in the experiment. PMID:23842348

  1. Hierarchical Grid-based Multi-People Tracking-by-Detection With Global Optimization.

    PubMed

    Chen, Lili; Wang, Wei; Panin, Giorgio; Knoll, Alois

    2015-11-01

    We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios. PMID:26151936

  2. Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation.

    PubMed

    Cheng, Kai-Wen; Chen, Yie-Tarng; Fang, Wen-Hsien

    2015-12-01

    This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden. PMID:26394423

  3. Protein structure prediction using a docking-based hierarchical folding scheme.

    PubMed

    Kifer, Ilona; Nussinov, Ruth; Wolfson, Haim J

    2011-06-01

    The pathways by which proteins fold into their specific native structure are still an unsolved mystery. Currently, many methods for protein structure prediction are available, and most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations toward each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method's abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the template-based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for template-based structure prediction, and in particular, the docking-basedranking technique presented here can be incorporated into any profile-profile comparison based method. PMID:21445943

  4. Protein Structure Prediction using a Docking-based Hierarchical Folding scheme

    PubMed Central

    Kifer, Ilona; Nussinov, Ruth; Wolfson, Haim J.

    2011-01-01

    The pathways by which proteins fold into their specific native structure is still an unsolved mystery. Currently many methods for protein structure prediction are available, most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently-folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations towards each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method’s abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the Template-Based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for Template-Based structure prediction, and in particular, the docking-based ranking technique presented here can be incorporated into any profile-profile comparison based method. PMID:21445943

  5. A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning

    NASA Astrophysics Data System (ADS)

    Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei

    2013-03-01

    In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.

  6. A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning

    NASA Astrophysics Data System (ADS)

    Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei

    2012-04-01

    In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.

  7. Global vs. Localized Search: A Comparison of Database Selection Methods in a Hierarchical Environment.

    ERIC Educational Resources Information Center

    Conrad, Jack G.; Claussen, Joanne Smestad; Yang, Changwen

    2002-01-01

    Compares standard global information retrieval searching with more localized techniques to address the database selection problem that users often have when searching for the most relevant database, based on experiences with the Westlaw Directory. Findings indicate that a browse plus search approach in a hierarchical environment produces the most…

  8. Hierarchic Agglomerative Clustering Methods for Automatic Document Classification.

    ERIC Educational Resources Information Center

    Griffiths, Alan; And Others

    1984-01-01

    Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

  9. Calculation of correlated initial state in the hierarchical equations of motion method using an imaginary time path integral approach

    SciTech Connect

    Song, Linze; Shi, Qiang

    2015-11-21

    Based on recent findings in the hierarchical equations of motion (HEOM) for correlated initial state [Y. Tanimura, J. Chem. Phys. 141, 044114 (2014)], we propose a new stochastic method to obtain the initial conditions for the real time HEOM propagation, which can be used further to calculate the equilibrium correlation functions and symmetrized correlation functions. The new method is derived through stochastic unraveling of the imaginary time influence functional, where a set of stochastic imaginary time HEOM are obtained. The validity of the new method is demonstrated using numerical examples including the spin-Boson model, and the Holstein model with undamped harmonic oscillator modes.

  10. Calculation of correlated initial state in the hierarchical equations of motion method using an imaginary time path integral approach.

    PubMed

    Song, Linze; Shi, Qiang

    2015-11-21

    Based on recent findings in the hierarchical equations of motion (HEOM) for correlated initial state [Y. Tanimura, J. Chem. Phys. 141, 044114 (2014)], we propose a new stochastic method to obtain the initial conditions for the real time HEOM propagation, which can be used further to calculate the equilibrium correlation functions and symmetrized correlation functions. The new method is derived through stochastic unraveling of the imaginary time influence functional, where a set of stochastic imaginary time HEOM are obtained. The validity of the new method is demonstrated using numerical examples including the spin-Boson model, and the Holstein model with undamped harmonic oscillator modes. PMID:26590526

  11. Low energy isomers of (H{sub 2}O){sub 25} from a hierarchical method based on Monte Carlo temperature basin paving and molecular tailoring approaches benchmarked by MP2 calculations

    SciTech Connect

    Sahu, Nityananda; Gadre, Shridhar R. E-mail: sotiris.xantheas@pnnl.gov; Rakshit, Avijit; Bandyopadhyay, Pradipta; Miliordos, Evangelos; Xantheas, Sotiris S. E-mail: sotiris.xantheas@pnnl.gov

    2014-10-28

    We report new global minimum candidate structures for the (H{sub 2}O){sub 25} cluster that are lower in energy than the ones reported previously and correspond to hydrogen bonded networks with 42 hydrogen bonds and an interior, fully coordinated water molecule. These were obtained as a result of a hierarchical approach based on initial Monte Carlo Temperature Basin Paving sampling of the cluster's Potential Energy Surface with the Effective Fragment Potential, subsequent geometry optimization using the Molecular Tailoring Approach with the fragments treated at the second order Møller-Plesset (MP2) perturbation (MTA-MP2) and final refinement of the entire cluster at the MP2 level of theory. The MTA-MP2 optimized cluster geometries, constructed from the fragments, were found to be within <0.5 kcal/mol from the minimum geometries obtained from the MP2 optimization of the entire (H{sub 2}O){sub 25} cluster. In addition, the grafting of the MTA-MP2 energies yields electronic energies that are within <0.3 kcal/mol from the MP2 energies of the entire cluster while preserving their energy rank order. Finally, the MTA-MP2 approach was found to reproduce the MP2 harmonic vibrational frequencies, constructed from the fragments, quite accurately when compared to the MP2 ones of the entire cluster in both the HOH bending and the OH stretching regions of the spectra.

  12. Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.

    2012-01-01

    A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

  13. An ontology-based hierarchical semantic modeling approach to clinical pathway workflows.

    PubMed

    Ye, Yan; Jiang, Zhibin; Diao, Xiaodi; Yang, Dong; Du, Gang

    2009-08-01

    This paper proposes an ontology-based approach of modeling clinical pathway workflows at the semantic level for facilitating computerized clinical pathway implementation and efficient delivery of high-quality healthcare services. A clinical pathway ontology (CPO) is formally defined in OWL web ontology language (OWL) to provide common semantic foundation for meaningful representation and exchange of pathway-related knowledge. A CPO-based semantic modeling method is then presented to describe clinical pathways as interconnected hierarchical models including the top-level outcome flow and intervention workflow level along a care timeline. Furthermore, relevant temporal knowledge can be fully represented by combing temporal entities in CPO and temporal rules based on semantic web rule language (SWRL). An illustrative example about a clinical pathway for cesarean section shows the applicability of the proposed methodology in enabling structured semantic descriptions of any real clinical pathway. PMID:19539278

  14. Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

    NASA Astrophysics Data System (ADS)

    Ganesan, A. L.; Rigby, M.; Zammit-Mangion, A.; Manning, A. J.; Prinn, R. G.; Fraser, P. J.; Harth, C. M.; Kim, K.-R.; Krummel, P. B.; Li, S.; Mühle, J.; O'Doherty, S. J.; Park, S.; Salameh, P. K.; Steele, L. P.; Weiss, R. F.

    2014-04-01

    We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.

  15. Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

    NASA Astrophysics Data System (ADS)

    Ganesan, A. L.; Rigby, M.; Zammit-Mangion, A.; Manning, A. J.; Prinn, R. G.; Fraser, P. J.; Harth, C. M.; Kim, K.-R.; Krummel, P. B.; Li, S.; Mühle, J.; O'Doherty, S. J.; Park, S.; Salameh, P. K.; Steele, L. P.; Weiss, R. F.

    2013-12-01

    We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDF) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgement. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties, than traditional methods.

  16. Structural Group-based Auditing of Missing Hierarchical Relationships in UMLS

    PubMed Central

    Chen, Yan; Gu, Huanying(Helen); Perl, Yehoshua; Geller, James

    2009-01-01

    The Metathesaurus of the UMLS was created by integrating various source terminologies. The inter-concept relationships were either integrated into the UMLS from the source terminologies or specially generated. Due to the extensive size and inherent complexity of the Metathesaurus, the accidental omission of some hierarchical relationships was inevitable. We present a recursive procedure which allows a human expert, with the support of an algorithm, to locate missing hierarchical relationships. The procedure starts with a group of concepts with exactly the same (correct) semantic type assignments. It then partitions the concepts, based on child-of hierarchical relationships, into smaller, singly rooted, hierarchically connected subgroups. The auditor only needs to focus on the subgroups with very few concepts and their concepts with semantic type reassignments. The procedure was evaluated by comparing it with a comprehensive manual audit and it exhibits a perfect error recall. PMID:18824248

  17. Spectral/HP Element Method With Hierarchical Reconstruction for Solving Hyperbolic Conservation Laws

    SciTech Connect

    Xu, Zhiliang; Lin, Guang

    2009-12-01

    Hierarchical reconstruction (HR) has been successfully applied to prevent oscillations in solutions computed by finite volume, discontinuous Galerkin, spectral volume schemes when solving hyperbolic conservation laws. In this paper, we demonstrate that HR can also be combined with spectral/hp element methods for solving hyperbolic conservation laws. We show that HR preserves the order of accuracy of spectral/hp element methods for smooth solutions and generate essentially non-oscillatory solution profiles for shock wave problems.

  18. Validity of the t-plot method to assess microporosity in hierarchical micro/mesoporous materials.

    PubMed

    Galarneau, Anne; Villemot, François; Rodriguez, Jeremy; Fajula, François; Coasne, Benoit

    2014-11-11

    The t-plot method is a well-known technique which allows determining the micro- and/or mesoporous volumes and the specific surface area of a sample by comparison with a reference adsorption isotherm of a nonporous material having the same surface chemistry. In this paper, the validity of the t-plot method is discussed in the case of hierarchical porous materials exhibiting both micro- and mesoporosities. Different hierarchical zeolites with MCM-41 type ordered mesoporosity are prepared using pseudomorphic transformation. For comparison, we also consider simple mechanical mixtures of microporous and mesoporous materials. We first show an intrinsic failure of the t-plot method; this method does not describe the fact that, for a given surface chemistry and pressure, the thickness of the film adsorbed in micropores or small mesopores (< 10σ, σ being the diameter of the adsorbate) increases with decreasing the pore size (curvature effect). We further show that such an effect, which arises from the fact that the surface area and, hence, the free energy of the curved gas/liquid interface decreases with increasing the film thickness, is captured using the simple thermodynamical model by Derjaguin. The effect of such a drawback on the ability of the t-plot method to estimate the micro- and mesoporous volumes of hierarchical samples is then discussed, and an abacus is given to correct the underestimated microporous volume by the t-plot method. PMID:25232908

  19. Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization

    NASA Astrophysics Data System (ADS)

    Hui, Zhenyang; Hu, Youjian; Jin, Shuanggen; Yevenyo, Yao Ziggah

    2016-08-01

    Road information acquisition is an important part of city informatization construction. Airborne LiDAR provides a new means of acquiring road information. However, the existing road extraction methods using LiDAR point clouds always decide the road intensity threshold based on experience, which cannot obtain the optimal threshold to extract a road point cloud. Moreover, these existing methods are deficient in removing the interference of narrow roads and several attached areas (e.g., parking lot and bare ground) to main roads extraction, thereby imparting low completeness and correctness to the city road network extraction result. Aiming at resolving the key technical issues of road extraction from airborne LiDAR point clouds, this paper proposes a novel method to extract road centerlines from airborne LiDAR point clouds. The proposed approach is mainly composed of three key algorithms, namely, Skewness balancing, Rotating neighborhood, and Hierarchical fusion and optimization (SRH). The skewness balancing algorithm used for the filtering was adopted as a new method for obtaining an optimal intensity threshold such that the "pure" road point cloud can be obtained. The rotating neighborhood algorithm on the other hand was developed to remove narrow roads (corridors leading to parking lots or sidewalks), which are not the main roads to be extracted. The proposed hierarchical fusion and optimization algorithm caused the road centerlines to be unaffected by certain attached areas and ensured the road integrity as much as possible. The proposed method was tested using the Vaihingen dataset. The results demonstrated that the proposed method can effectively extract road centerlines in a complex urban environment with 91.4% correctness and 80.4% completeness.

  20. Degradable Zinc-Phosphate-Based Hierarchical Nanosubstrates for Capture and Release of Circulating Tumor Cells.

    PubMed

    Guo, Shan; Xu, Jiaquan; Xie, Min; Huang, Wei; Yuan, Erfeng; Liu, Ya; Fan, Liping; Cheng, Shibo; Liu, Songmei; Wang, Fubing; Yuan, Bifeng; Dong, Weiguo; Zhang, Xiaolian; Huang, Weihua; Zhou, Xiang

    2016-06-29

    Circulating tumor cells (CTCs) play a significant role in cancer diagnosis and personalized therapy, and it is still a significant challenge to efficiently capture and gently release CTCs from clinical samples for downstream manipulation and molecular analysis. Many CTC devices incorporating various nanostructures have been developed for CTC isolation with sufficient capture efficiency, however, fabricating such nanostructured substrates often requires elaborate design and complicated procedures. Here we fabricate a degradable zinc-phosphate-based hierarchical nanosubstrate (HZnPNS), and we demonstrate its excellent CTC-capture performance along with effective cell-release capability for downstream molecular analysis. This transparent hierarchical architecture prepared by a low-temperature hydrothermal method, enables substantially enhanced capture efficiency and convenient imaging. Biocompatible sodium citrate could rapidly dissolve the architecture at room temperature, allowing that 88 ± 4% of captured cells are gently released with a high viability of 92 ± 1%. Furthermore, antiepithelial cell adhesion molecule antibody functionalized HZnPNS (anti-EpCAM/HZnPNS) was successfully applied to isolate CTCs from whole blood samples of cancer patients, as well as release CTCs for global DNA methylation analysis, indicating it will serve as a simple and reliable alternative platform for CTC detection. PMID:27265681

  1. Hierarchical structure for audio-video based semantic classification of sports video sequences

    NASA Astrophysics Data System (ADS)

    Kolekar, M. H.; Sengupta, S.

    2005-07-01

    A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

  2. Hierarchical modeling of diffusive transport through nanochannels by coupling molecular dynamics with finite element method

    NASA Astrophysics Data System (ADS)

    Ziemys, A.; Kojic, M.; Milosevic, M.; Kojic, N.; Hussain, F.; Ferrari, M.; Grattoni, A.

    2011-06-01

    We present a successful hierarchical modeling approach which accounts for interface effects on diffusivity, ignored in classical continuum theories. A molecular dynamics derived diffusivity scaling scheme is incorporated into a finite element method to model transport through a nanochannel. In a 5 nm nanochannel, the approach predicts 2.2 times slower mass release than predicted by Fick's law by comparing time spent to release 90% of mass. The scheme was validated by predicting experimental glucose diffusion through a nanofluidic membrane with a correlation coefficient of 0.999. Comparison with experiments through a nanofluidic membrane showed interface effects to be crucial. We show robustness of our discrete continuum model in addressing complex diffusion phenomena in biomedical and engineering applications by providing flexible hierarchical coupling of molecular scale effects and preserving computational finite element method speed.

  3. Hierarchical image-based rendering using texture mapping hardware

    SciTech Connect

    Max, N

    1999-01-15

    Multi-layered depth images containing color and normal information for subobjects in a hierarchical scene model are precomputed with standard z-buffer hardware for six orthogonal views. These are adaptively selected according to the proximity of the viewpoint, and combined using hardware texture mapping to create ''reprojected'' output images for new viewpoints. (If a subobject is too close to the viewpoint, the polygons in the original model are rendered.) Specific z-ranges are selected from the textures with the hardware alpha test to give accurate 3D reprojection. The OpenGL color matrix is used to transform the precomputed normals into their orientations in the final view, for hardware shading.

  4. Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000-2011 period

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Deviren, Bayram; Keskin, Mustafa

    2014-11-01

    We investigate hierarchical structures of the European countries by using debt as a percentage of Gross Domestic Product (GDP) of the countries as they change over a certain period of time. We obtain the topological properties among the countries based on debt as a percentage of GDP of European countries over the period 2000-2011 by using the concept of hierarchical structure methods (minimal spanning tree, (MST) and hierarchical tree, (HT)). This period is also divided into two sub-periods related to 2004 enlargement of the European Union, namely 2000-2004 and 2005-2011, in order to test various time-window and observe the temporal evolution. The bootstrap techniques is applied to see a value of statistical reliability of the links of the MSTs and HTs. The clustering linkage procedure is also used to observe the cluster structure more clearly. From the structural topologies of these trees, we identify different clusters of countries according to their level of debts. Our results show that by the debt crisis, the less and most affected Eurozone’s economies are formed as a cluster with each other in the MSTs and hierarchical trees.

  5. Highly Transparent Water-Repelling Surfaces based on Biomimetic Hierarchical Structure

    NASA Astrophysics Data System (ADS)

    Wooh, Sanghyuk; Koh, Jai; Yoon, Hyunsik; Char, Kookheon

    2013-03-01

    Nature is a great source of inspiration for creating unique structures with special functions. The representative examples of water-repelling surfaces in nature, such as lotus leaves, rose petals, and insect wings, consist of an array of bumps (or long hairs) and nanoscale surface features with different dimension scales. Herein, we introduced a method of realizing multi-dimensional hierarchical structures and water-repellancy of the surfaces with different drop impact scenarios. The multi-dimensional hierarchical structures were fabricated by soft imprinting method with TiO2 nanoparticle pastes. In order to achieve the enhanced hydrophobicity, fluorinated moieties were attached to the etched surfaces to lower the surface energy. As a result, super-hydrophobic surfaces with high transparency were realized (over 176° water contact angle), and for further investigation, these hierarchical surfaces with different drop impact scenarios were characterized by varying the impact speed, drop size, and the geometry of the surfaces.

  6. Superomniphobic, transparent, and antireflection surfaces based on hierarchical nanostructures.

    PubMed

    Mazumder, Prantik; Jiang, Yongdong; Baker, David; Carrilero, Albert; Tulli, Domenico; Infante, Daniel; Hunt, Andrew T; Pruneri, Valerio

    2014-08-13

    Optical surfaces that can repel both water and oil have much potential for applications in a diverse array of technologies including self-cleaning solar panels, anti-icing windows and windshields for automobiles and aircrafts, low-drag surfaces, and antismudge touch screens. By exploiting a hierarchical geometry made of two-tier nanostructures, primary nanopillars of length scale ∼ 100-200 nm superposed with secondary branching nanostructures made of nanoparticles of length scale ∼ 10-30 nm, we have achieved static contact angles of more than 170° and 160° for water and oil, respectively, while the sliding angles were lower than 4°. At the same time, with respect to the initial flat bare glass, the nanotextured surface presented significantly reduced reflection (<0.5%), increased transmission (93.8% average over the 400 to 700 nm wavelength range), and very low scattering values (about 1% haze). To the authors' knowledge, these are the highest optical performances in conjunction with superomniphobicity reported to date in the literature. The primary nanopillars are monolithically integrated in the glass surface using lithography-free metal dewetting followed by reactive ion etching,1 while the smaller and higher surface area branching structure made of secondary nanoparticles are deposited by the NanoSpray2 combustion chemical vapor deposition (CCVD). PMID:24988148

  7. Hierarchical Multiscale Adaptive Variable Fidelity Wavelet-based Turbulence Modeling with Lagrangian Spatially Variable Thresholding

    NASA Astrophysics Data System (ADS)

    Nejadmalayeri, Alireza

    The current work develops a wavelet-based adaptive variable fidelity approach that integrates Wavelet-based Direct Numerical Simulation (WDNS), Coherent Vortex Simulations (CVS), and Stochastic Coherent Adaptive Large Eddy Simulations (SCALES). The proposed methodology employs the notion of spatially and temporarily varying wavelet thresholding combined with hierarchical wavelet-based turbulence modeling. The transition between WDNS, CVS, and SCALES regimes is achieved through two-way physics-based feedback between the modeled SGS dissipation (or other dynamically important physical quantity) and the spatial resolution. The feedback is based on spatio-temporal variation of the wavelet threshold, where the thresholding level is adjusted on the fly depending on the deviation of local significant SGS dissipation from the user prescribed level. This strategy overcomes a major limitation for all previously existing wavelet-based multi-resolution schemes: the global thresholding criterion, which does not fully utilize the spatial/temporal intermittency of the turbulent flow. Hence, the aforementioned concept of physics-based spatially variable thresholding in the context of wavelet-based numerical techniques for solving PDEs is established. The procedure consists of tracking the wavelet thresholding-factor within a Lagrangian frame by exploiting a Lagrangian Path-Line Diffusive Averaging approach based on either linear averaging along characteristics or direct solution of the evolution equation. This innovative technique represents a framework of continuously variable fidelity wavelet-based space/time/model-form adaptive multiscale methodology. This methodology has been tested and has provided very promising results on a benchmark with time-varying user prescribed level of SGS dissipation. In addition, a longtime effort to develop a novel parallel adaptive wavelet collocation method for numerical solution of PDEs has been completed during the course of the current work

  8. Hierarchical Assemblies of Block-Copolymer-Based Supramolecules in Thin Films

    SciTech Connect

    Tung, Shih-Huang; Kalarickal, Nisha C.; Mays, Jimmy W.; Xu, Ting

    2009-09-08

    The hierarchical assemblies of supramolecules, which consisted of polystyrene-b-poly(4-vinylpyridine) (PS-b-P4VP) with 3-pentadecylphenol (PDP) hydrogen-bonded to the 4VP, were investigated in thin films after solvent annealing in a chloroform atmosphere. The synergistic coassembly of PS-b-P4VP and PDP was utilized to generate oriented hierarchical structures in thin films. Hierarchical assemblies, including lamellae-within-lamellae and cylinders-within-lamellae, were simultaneously ordered and oriented from a few to several tens of nanometers over macroscopic length scales. The macroscopic orientation of supramolecular assembly depends on the P4VP(PDP) fraction and can be tailored by varying the PDP to P4VP ratio without interfering with the supramolecular morphologies. The lamellar and cylindrical microdomains, with a periodicity of {approx}40 nm, could be oriented normal to the surface, while the assembly of comb blocks, P4VP(PDP), with a periodicity of {approx}4 nm, were oriented parallel to the surface. Furthermore, using one PS-b-P4VP copolymer, thin films with different hierarchical structures, i.e., lamellae-within-lamellae and cylinders-within-lamellae, were obtained by varying the ratio of PDP to 4VP units. The concepts described in these studies can be potentially applied to other BCP-based supramolecular thin films, thus creating an avenue to functional, hierarchically ordered thin films.

  9. Investigation of major international and Turkish companies via hierarchical methods and bootstrap approach

    NASA Astrophysics Data System (ADS)

    Kantar, E.; Deviren, B.; Keskin, M.

    2011-11-01

    We present a study, within the scope of econophysics, of the hierarchical structure of 98 among the largest international companies including 18 among the largest Turkish companies, namely Banks, Automobile, Software-hardware, Telecommunication Services, Energy and the Oil-Gas sectors, viewed as a network of interacting companies. We analyze the daily time series data of the Boerse-Frankfurt and Istanbul Stock Exchange. We examine the topological properties among the companies over the period 2006-2010 by using the concept of hierarchical structure methods (the minimal spanning tree (MST) and the hierarchical tree (HT)). The period is divided into three subperiods, namely 2006-2007, 2008 which was the year of global economic crisis, and 2009-2010, in order to test various time-windows and observe temporal evolution. We carry out bootstrap analyses to associate the value of statistical reliability to the links of the MSTs and HTs. We also use average linkage clustering analysis (ALCA) in order to better observe the cluster structure. From these studies, we find that the interactions among the Banks/Energy sectors and the other sectors were reduced after the global economic crisis; hence the effects of the Banks and Energy sectors on the correlations of all companies were decreased. Telecommunication Services were also greatly affected by the crisis. We also observed that the Automobile and Banks sectors, including Turkish companies as well as some companies from the USA, Japan and Germany were strongly correlated with each other in all periods.

  10. A Hierarchical Control Architecture for a PEBB-Based ILC Marx Modulator

    SciTech Connect

    Macken, K.; Burkhart, C.; Larsen, R.; Nguyen, M.N.; Olsen, J.; /SLAC

    2011-12-15

    The idea of building power conversion systems around Power Electronic Building Blocks (PEBBs) was initiated by the U.S. Office of Naval Research in the mid 1990s. A PEBB-based design approach is advantageous in terms of power density, modularity, reliability, and serviceability. It is obvious that this approach has much appeal for pulsed power conversion including the International Linear Collider (ILC) klystron modulator application. A hierarchical control architecture has the inherent capability to support the integration of PEBBs. This has already been successfully demonstrated in a number of industrial applications in the recent past. This paper outlines the underlying concepts of a hierarchical control architecture for a PEBB-based Marx-topology ILC klystron modulator. The control in PEBB-based power conversion systems can be functionally partitioned into (three) hierarchical layers; system layer, application layer, and PEBB layer. This has been adopted here. Based on such a hierarchical partition, the interfaces are clearly identified and defined and, consequently, are easily characterised. A conceptual design of the hardware manager, executing low-level hardware oriented tasks, is detailed. In addition, the idea of prognostics is briefly discussed.

  11. The Impact of Standards-Based Reform: Applying Brantlinger's Critique of "Hierarchical Ideologies"

    ERIC Educational Resources Information Center

    Bacon, Jessica; Ferri, Beth

    2013-01-01

    Brantlinger's [2004b. "Ideologies Discerned, Values Determined: Getting past the Hierarchies of Special Education." In "Ideology and the Politics of (in)Exclusion," edited by L. Ware, 11-31. New York: Peter Lang Publishing] critique of hierarchical ideologies lays bare the logics embedded in standards-based reform. Drawing…

  12. Risk Assessment and Hierarchical Risk Management of Enterprises in Chemical Industrial Parks Based on Catastrophe Theory

    PubMed Central

    Chen, Yu; Song, Guobao; Yang, Fenglin; Zhang, Shushen; Zhang, Yun; Liu, Zhenyu

    2012-01-01

    According to risk systems theory and the characteristics of the chemical industry, an index system was established for risk assessment of enterprises in chemical industrial parks (CIPs) based on the inherent risk of the source, effectiveness of the prevention and control mechanism, and vulnerability of the receptor. A comprehensive risk assessment method based on catastrophe theory was then proposed and used to analyze the risk levels of ten major chemical enterprises in the Songmu Island CIP, China. According to the principle of equal distribution function, the chemical enterprise risk level was divided into the following five levels: 1.0 (very safe), 0.8 (safe), 0.6 (generally recognized as safe, GRAS), 0.4 (unsafe), 0.2 (very unsafe). The results revealed five enterprises (50%) with an unsafe risk level, and another five enterprises (50%) at the generally recognized as safe risk level. This method solves the multi-objective evaluation and decision-making problem. Additionally, this method involves simple calculations and provides an effective technique for risk assessment and hierarchical risk management of enterprises in CIPs. PMID:23208298

  13. Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection.

    PubMed

    Li, Zhifeng; Gong, Dihong; Li, Xuelong; Tao, Dacheng

    2016-05-01

    Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods. PMID:26930681

  14. Nhs: Network-based Hierarchical Segmentation for Cryo-EM Density Maps

    PubMed Central

    Burger, Virginia; Chennubhotla, Chakra

    2012-01-01

    Electron cryo-microscopy (cryo-EM) experiments yield low-resolution (3–30Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and sub-units. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semi-supervised computational methods, while validation of resulting segmentations has remained an open problem in this field. Our network-based bias-free segmentation method for cryo-EM density map segmentation, Nhs (Network-based hierarchical segmentation), provides the user with a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant sub-regions at multiple scales. PMID:22696408

  15. Hierarchical flight control system synthesis for rotorcraft-based unmanned aerial vehicles

    NASA Astrophysics Data System (ADS)

    Shim, Hyunchul

    The Berkeley Unmanned Aerial Vehicle (UAV) research aims to design, implement, and analyze a group of autonomous intelligent UAVs and UGVs (Unmanned Ground Vehicles). The goal of this dissertation is to provide a comprehensive procedural methodology to design, implement, and test rotorcraft-based unmanned aerial vehicles (RUAVs). We choose the rotorcraft as the base platform for our aerial agents because it offers ideal maneuverability for our target scenarios such as the pursuit-evasion game. Aided by many enabling technologies such as lightweight and powerful computers, high-accuracy navigation sensors and communication devices, it is now possible to construct RUAVs capable of precise navigation and intelligent behavior by the decentralized onboard control system. Building a fully functioning RUAV requires a deep understanding of aeronautics, control theory and computer science as well as a tremendous effort for implementation. These two aspects are often inseparable and therefore equally highlighted throughout this research. The problem of multiple vehicle coordination is approached through the notion of a hierarchical system. The idea behind the proposed architecture is to build a hierarchical multiple-layer system that gradually decomposes the abstract mission objectives into the physical quantities of control input. Each RUAV incorporated into this system performs the given tasks and reports the results through the hierarchical communication channel back to the higher-level coordinator. In our research, we provide a theoretical and practical approach to build a number of RUAVs based on commercially available navigation sensors, computer systems, and radio-controlled helicopters. For the controller design, the dynamic model of the helicopter is first built. The helicopter exhibits a very complicated multi-input multi-output, nonlinear, time-varying and coupled dynamics, which is exposed to severe exogenous disturbances. This poses considerable difficulties for

  16. Fast pseudo-semantic segmentation for joint region-based hierarchical and multiresolution representation

    NASA Astrophysics Data System (ADS)

    Sekkal, Rafiq; Strauss, Clement; Pasteau, François; Babel, Marie; Deforges, Olivier

    2012-01-01

    In this paper, we present a new scalable segmentation algorithm called JHMS (Joint Hierarchical and Multiresolution Segmentation) that is characterized by region-based hierarchy and resolution scalability. Most of the proposed algorithms either apply a multiresolution segmentation or a hierarchical segmentation. The proposed approach combines both multiresolution and hierarchical segmentation processes. Indeed, the image is considered as a set of images at different levels of resolution, where at each level a hierarchical segmentation is performed. Multiresolution implies that a segmentation of a given level is reused in further segmentation processes operated at next levels so that to insure contour consistency between different resolutions. Each level of resolution provides a Region Adjacency Graph (RAG) that describes the neighborhood relationships between regions within a given level of the multiresolution representation. Region label consistency is preserved thanks to a dedicated projection algorithm based on inter-level relationships. Moreover, a preprocess based on a quadtree partitioning reduces the amount of input data thus leading to a lower overall complexity of the segmentation framework. Experiments show that we obtain effective results when compared to the state of the art together with a lower complexity.

  17. Photocatalytic properties of hierarchical ZnO flowers synthesized by a sucrose-assisted hydrothermal method

    NASA Astrophysics Data System (ADS)

    Lv, Wei; Wei, Bo; Xu, Lingling; Zhao, Yan; Gao, Hong; Liu, Jia

    2012-10-01

    In this work, hierarchical ZnO flowers were synthesized via a sucrose-assisted urea hydrothermal method. The thermogravimetric analysis/differential thermal analysis (TGA-DTA) and Fourier transform infrared spectra (FTIR) showed that sucrose acted as a complexing agent in the synthesis process and assisted combustion during annealing. Photocatalytic activity was evaluated using the degradation of organic dye methyl orange. The sucrose added ZnO flowers showed improved activity, which was mainly attributed to the better crystallinity as confirmed by X-ray photoelectron spectroscopy (XPS) analysis. The effect of sucrose amount on photocatalytic activity was also studied.

  18. Method of forecasting energy center positions of laser beam spot images using a parallel hierarchical network for optical communication systems

    NASA Astrophysics Data System (ADS)

    Timchenko, Leonid I.; Kokryatskaya, Natalia I.; Melnikov, Viktor V.; Kosenko, Galina L.

    2013-05-01

    A forecasting method, based on the parallel-hierarchical (PH) network and hyperbolic smoothing of empirical data, is presented in this paper. Preceding values of the time series, hyperbolic smoothing, and PH network data are used for forecasting. To determine a position of the next route fragment in relation to X and Y axes, hyperbola parameters are sent to the route parameter forecasting system. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the PH network. An average prediction error is 0.55% for the developed method and 1.62% for neural networks. That is why, due to the use of the PH network and hyperbolic smoothing, the developed method is more efficient for real-time systems than traditional neural networks in forecasting energy center positions of laser beam spot images for optical communication systems.

  19. Low crosstalk optical hierarchical authentication with a fixed random phase lock based on two beams interference

    NASA Astrophysics Data System (ADS)

    Lu, Dajiang; He, Wenqi; Peng, Xiang

    2015-09-01

    We propose a novel method to achieve the purpose of hierarchical authentication based on two beams interference. In this method, different target images indicating different authentication levels are analytically encoded into corresponding phase-only masks (phase keys) and amplitude-only masks (amplitude keys) with the help of a random phase mask, which is created in advance and acts as the fixed lock of this authentication system. For the authentication process, a legal user can obtain a specified target image at the output plane if his/her phase key, and amplitude key, which should be settled close against the fixed internal phase lock, are respectively illuminated by two coherent beams. By comparing the target image with all the standard certification images in the database, the system can thus verify the user's identity. In simple terms, this system can not only confirm the legality of a user but also distinguish his/her identity level. Moreover, in despite of the internal phase lock of this system being fixed, the crosstalk between different pairs of keys hold by different users is low. Theoretical analysis and numerical simulation are both provided to demonstrate the validity of this method.

  20. Hierarchical content-based image retrieval by dynamic indexing and guided search

    NASA Astrophysics Data System (ADS)

    You, Jane; Cheung, King H.; Liu, James; Guo, Linong

    2003-12-01

    This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

  1. A Functional Networks Estimation Method of Resting-State fMRI Using a Hierarchical Markov Random Field

    PubMed Central

    Liu, Wei; Awate, Suyash P.; Anderson, Jeffrey S.; Fletcher, P. Thomas

    2014-01-01

    We propose a hierarchical Markov random field model that estimates both group and subject functional networks simultaneously. The model takes into account the within-subject spatial coherence as well as the between-subject consistency of the network label maps. The statistical dependency between group and subject networks acts as a regularization, which helps the network estimation on both layers. We use Gibbs sampling to approximate the posterior density of the network labels and Monte Carlo expectation maximization to estimate the model parameters. We compare our method with two alternative segmentation methods based on K-Means and normalized cuts, using synthetic and real fMRI data. The experimental results show our proposed model is able to identify both group and subject functional networks with higher accuracy, more robustness, and inter-session consistency. PMID:24954282

  2. Facile one-step photolithographic method for engineering hierarchically nano/microstructured transparent superamphiphobic surfaces.

    PubMed

    Li, Tingjie; Paliy, Maxim; Wang, Xiaolong; Kobe, Brad; Lau, Woon-Ming; Yang, Jun

    2015-05-27

    It is of great value to develop a simple, controllable, and scalable method of making superamphiphobic surfaces. Here we present a facile one-step photolithographic method to engineer superamphiphobic surfaces consisting of photoresist micropillars decorated with nanoparticles of the same photoresist. The surface or coating is optically transparent and versatile, and can be fabricated on a broad range of substrates including stretchable elastomers. During the development of the micropillar array, photoresist nanoparticles are spontaneously grown on the micropillars by a well-controlled emulsification process of the un-cross-linked residual photoresist. This creates a hierarchical structure with a re-entrant and convex morphology which is the key for superoleophobicity. The chemical bonding between the nanoparticles and the micropillars is strong producing a robust and durable coating. This facile method is scalable and industry-applicable for a variety of applications such as self-cleaning, antifouling, and deicing/antifrosting. PMID:25942618

  3. Hierarchical decomposition of burn body diagram based on cutaneous functional units and its utility.

    PubMed

    Richard, Reg; Jones, John A; Parshley, Philip

    2015-01-01

    A burn body diagram (BBD) is a common feature used in the delivery of burn care for estimating the TBSA burn as well as calculating fluid resuscitation and nutritional requirements, wound healing, and rehabilitation intervention. However, little change has occurred for over seven decades in the configuration of the BBD. The purpose of this project was to develop a computerized model using hierarchical decomposition (HD) to more precisely determine the percentage burn within a BBD based on cutaneous functional units (CFUs). HD is a process by which a system is degraded into smaller parts that are more precise in their use. CFUs were previously identified fields of the skin involved in the range of motion. A standard Lund/Browder (LB) BBD template was used as the starting point to apply the CFU segments. LB body divisions were parceled down into smaller body area divisions through a HD process based on the CFU concept. A numerical pattern schema was used to label the various segments in a cephalo/caudal, anterior/posterior, medial/lateral manner. Hand/fingers were divided based on anatomical landmarks and known cutaneokinematic function. The face was considered using aesthetic units. Computer code was written to apply the numeric hierarchical schema to CFUs and applied within the context of the surface area graphic evaluation BBD program. Each segmented CFU was coded to express 100% of itself. The CFU/HD method refined the standard LB diagram from 13 body segments and 33 subdivisions into 182 isolated CFUs. Associated CFUs were reconstituted into 219 various surface area combinations totaling 401 possible surface segments. The CFU/HD schema of the body surface mapping is applicable to measuring and calculating percent wound healing in a more precise manner. It eliminates subjective assessment of the percentage wound healing and the need for additional devices such as planimetry. The development of CFU/HD body mapping schema has rendered a technologically advanced

  4. Correlation Based Hierarchical Clustering in Financial Time Series

    NASA Astrophysics Data System (ADS)

    Micciche', S.; Lillo, F.; Mantegna, R. N.

    2005-09-01

    We review a correlation based clustering procedure applied to a portfolio of assets synchronously traded in a financial market. The portfolio considered consists of the set of 500 highly capitalized stocks traded at the New York Stock Exchange during the time period 1987-1998. We show that meaningful economic information can be extracted from correlation matrices.

  5. Chelating agent-free, vapor-assisted crystallization method to synthesize hierarchical microporous/mesoporous MIL-125 (Ti).

    PubMed

    McNamara, Nicholas D; Hicks, Jason C

    2015-03-11

    Titanium-based microporous heterogeneous catalysts are widely studied but are often limited by the accessibility of reactants to active sites. Metal-organic frameworks (MOFs), such as MIL-125 (Ti), exhibit enhanced surface areas due to their high intrinsic microporosity, but the pore diameters of most microporous MOFs are often too small to allow for the diffusion of larger reactants (>7 Å) relevant to petroleum and biomass upgrading. In this work, hierarchical microporous MIL-125 exhibiting significantly enhanced interparticle mesoporosity was synthesized using a chelating-free, vapor-assisted crystallization method. The resulting hierarchical MOF was examined as an active catalyst for the oxidation of dibenzothiophene (DBT) with tert-butyl hydroperoxide and outperformed the solely microporous analogue. This was attributed to greater access of the substrate to surface active sites, as the pores in the microporous analogues were of inadequate size to accommodate DBT. Moreover, thiophene adsorption studies suggested the mesoporous MOF contained larger amounts of unsaturated metal sites that could enhance the observed catalytic activity. PMID:25695286

  6. Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Keskin, Mustafa; Deviren, Bayram

    2012-04-01

    We have analyzed the topology of 50 important Turkish companies for the period 2006-2010 using the concept of hierarchical methods (the minimal spanning tree (MST) and hierarchical tree (HT)). We investigated the statistical reliability of links between companies in the MST by using the bootstrap technique. We also used the average linkage cluster analysis (ALCA) technique to observe the cluster structures much better. The MST and HT are known as useful tools to perceive and detect global structure, taxonomy, and hierarchy in financial data. We obtained four clusters of companies according to their proximity. We also observed that the Banks and Holdings cluster always forms in the centre of the MSTs for the periods 2006-2007, 2008, and 2009-2010. The clusters match nicely with their common production activities or their strong interrelationship. The effects of the Automobile sector increased after the global financial crisis due to the temporary incentives provided by the Turkish government. We find that Turkish companies were not very affected by the global financial crisis.

  7. Facile method for preparing superoleophobic surfaces with hierarchical microcubic/nanowire structures.

    PubMed

    Kwak, Wonshik; Hwang, Woonbong

    2016-02-01

    To facilitate the fabrication of superoleophobic surfaces having hierarchical microcubic/nanowire structures (HMNS), even for low surface tension liquids including octane (surface tension = 21.1 mN m(-1)), and to understand the influences of surface structures on the oleophobicity, we developed a convenient method to achieve superoleophobic surfaces on aluminum substrates using chemical acid etching, anodization and fluorination treatment. The liquid repellency of the structured surface was validated through observable experimental results; the contact and sliding angle measurements. The etching condition required to ensure high surface roughness was established, and an optimal anodizing condition was determined, as a critical parameter in building the superoleophobicity. The microcubic structures formed by acid etching are essential for achieving the formation of the hierarchical structure, and therefore, the nanowire structures formed by anodization lead to an enhancement of the superoleophobicity for low surface tension liquids. Under optimized morphology by microcubic/nanowire structures with fluorination treatment, the contact angle over 150° and the sliding angle less than 10° are achieved even for octane. PMID:26670869

  8. Facile method for preparing superoleophobic surfaces with hierarchical microcubic/nanowire structures

    NASA Astrophysics Data System (ADS)

    Kwak, Wonshik; Hwang, Woonbong

    2016-02-01

    To facilitate the fabrication of superoleophobic surfaces having hierarchical microcubic/nanowire structures (HMNS), even for low surface tension liquids including octane (surface tension = 21.1 mN m-1), and to understand the influences of surface structures on the oleophobicity, we developed a convenient method to achieve superoleophobic surfaces on aluminum substrates using chemical acid etching, anodization and fluorination treatment. The liquid repellency of the structured surface was validated through observable experimental results the contact and sliding angle measurements. The etching condition required to ensure high surface roughness was established, and an optimal anodizing condition was determined, as a critical parameter in building the superoleophobicity. The microcubic structures formed by acid etching are essential for achieving the formation of the hierarchical structure, and therefore, the nanowire structures formed by anodization lead to an enhancement of the superoleophobicity for low surface tension liquids. Under optimized morphology by microcubic/nanowire structures with fluorination treatment, the contact angle over 150° and the sliding angle less than 10° are achieved even for octane.

  9. Hierarchical and joint site-edge methods for medicare hospice service region boundary analysis.

    PubMed

    Ma, Haijun; Carlin, Bradley P; Banerjee, Sudipto

    2010-06-01

    Hospice service offers a convenient and ethically preferable health-care option for terminally ill patients. However, this option is unavailable to patients in remote areas not served by any hospice system. In this article, we seek to determine the service areas of two particular cancer hospice systems in northeastern Minnesota based only on death counts abstracted from Medicare billing records. The problem is one of spatial boundary analysis, a field that appears statistically underdeveloped for irregular areal (lattice) data, even though most publicly available human health data are of this type. In this article, we suggest a variety of hierarchical models for areal boundary analysis that hierarchically or jointly parameterize both the areas and the edge segments. This leads to conceptually appealing solutions for our data that remain computationally feasible. While our approaches parallel similar developments in statistical image restoration using Markov random fields, important differences arise due to the irregular nature of our lattices, the sparseness and high variability of our data, the existence of important covariate information, and most importantly, our desire for full posterior inference on the boundary. Our results successfully delineate service areas for our two Minnesota hospice systems that sometimes conflict with the hospices' self-reported service areas. We also obtain boundaries for the spatial residuals from our fits, separating regions that differ for reasons yet unaccounted for by our model. PMID:19645704

  10. Hierarchical and Joint Site-Edge Methods for Medicare Hospice Service Region Boundary Analysis

    PubMed Central

    Ma, Haijun; Carlin, Bradley P.; Banerjee, Sudipto

    2011-01-01

    Summary Hospice service offers a convenient and ethically preferable health care option for terminally ill patients. However, this option is unavailable to patients in remote areas not served by any hospice system. In this paper we seek to determine the service areas of two particular cancer hospice systems in northeastern Minnesota based only on death counts abstracted from Medicare billing records. The problem is one of spatial boundary analysis, a field that appears statistically underdeveloped for irregular areal (lattice) data, even though most publicly available human health data are of this type. In this paper, we suggest a variety of hierarchical models for areal boundary analysis that hierarchically or jointly parameterize both the areas and the edge segments. This leads to conceptually appealing solutions for our data that remain computationally feasible. While our approaches parallel similar developments in statistical image restoration using Markov random fields, important differences arise due to the irregular nature of our lattices, the sparseness and high variability of our data, the existence of important covariate information, and most importantly, our desire for full posterior inference on the boundary. Our results successfully delineate service areas for our two Minnesota hospice systems that sometimes conflict with the hospices' self-reported service areas. We also obtain boundaries for the spatial residuals from our fits, separating regions that differ for reasons yet unaccounted for by our model. PMID:19645704

  11. Inferring a District-Based Hierarchical Structure of Social Contacts from Census Data

    PubMed Central

    Yu, Zhiwen; Liu, Jiming; Zhu, Xianjun

    2015-01-01

    Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model. PMID:25679787

  12. Multi-Hierarchical Modeling of Driving Behavior Using Dynamics-Based Mode Segmentation

    NASA Astrophysics Data System (ADS)

    Okuda, Hiroyuki; Suzuki, Tatsuya; Nakano, Ato; Inagaki, Shinkichi; Hayakawa, Soichiro

    This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.

  13. 3D Pharmacophore, hierarchical methods, and 5-HT4 receptor binding data.

    PubMed

    Varin, Thibault; Saettel, Nicolas; Villain, Jonathan; Lesnard, Aurelien; Dauphin, François; Bureau, Ronan; Rault, Sylvain

    2008-10-01

    5-Hydroxytryptamine subtype-4 (5-HT(4)) receptors have stimulated considerable interest amongst scientists and clinicians owing to their importance in neurophysiology and potential as therapeutic targets. A comparative analysis of hierarchical methods applied to data from one thousand 5-HT(4) receptor-ligand binding interactions was carried out. The chemical structures were described as chemical and pharmacophore fingerprints. The definitions of indices, related to the quality of the hierarchies in being able to distinguish between active and inactive compounds, revealed two interesting hierarchies with the Unity (1 active cluster) and pharmacophore fingerprints (4 active clusters). The results of this study also showed the importance of correct choice of metrics as well as the effectiveness of a new alternative of the Ward clustering algorithm named Energy (Minimum E-Distance method). In parallel, the relationship between these classifications and a previously defined 3D 5-HT(4) antagonist pharmacophore was established. PMID:18821249

  14. Robust Superhydrophobic Foam: A Graphdiyne-Based Hierarchical Architecture for Oil/Water Separation.

    PubMed

    Gao, Xin; Zhou, Jingyuan; Du, Ran; Xie, Ziqian; Deng, Shibin; Liu, Rong; Liu, Zhongfan; Zhang, Jin

    2016-01-01

    Robust superhydrophobic foam is fabricated by combining an ordered graphdiyne-based hierarchical structure with a low-surface-energy coating. This foam shows not only superhydrophobicity both in air (≈160.1°) and in oil (≈171.0°), but also high resistance toward abrasion cycles. Owing to its 3D porous structures and numerous superhydrophobic surfaces, it can easily separate oil from water with high efficiency and good recyclability. PMID:26551876

  15. A medical cloud-based platform for respiration rate measurement and hierarchical classification of breath disorders.

    PubMed

    Fekr, Atena Roshan; Janidarmian, Majid; Radecka, Katarzyna; Zilic, Zeljko

    2014-01-01

    The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot's breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM's performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel

  16. A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

    PubMed Central

    Fekr, Atena Roshan; Janidarmian, Majid; Radecka, Katarzyna; Zilic, Zeljko

    2014-01-01

    The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot's breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM's performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel

  17. A nanotectonics approach to produce hierarchically organized bioactive glass nanoparticles-based macrospheres

    NASA Astrophysics Data System (ADS)

    Luz, Gisela M.; Mano, João F.

    2012-09-01

    Bioactive particles have been widely used in a series of biomedical applications due to their ability to promote bone-bonding and elicit favorable biological responses in therapies associated with the replacement and regeneration of mineralized tissues. In this work hierarchical architectures are prepared by an innovative methodology using SiO2-CaO sol-gel based nanoparticles. Inspired by colloidal crystals, spherical aggregates were formed on biomimetic superhydrophobic surfaces using bioactive glass nanoparticles (BG-NPs) able to promote bone regeneration. A highly ordered organization, a common feature of mineralized structures in Nature, was achieved at both nano- and microlevels, being the crystallization degree of the structures controlled by the evaporation rates taking place at room temperature (RT) or at 4 °C. The crystallization degree of the structures influenced the Ca/P ratio of the apatitic film formed at their surface, after 7 days of immersion in SBF. This allows the regulation of bioactive properties and the ability to release potential additives that could be also incorporated in such particles with a high efficiency. Such a versatile method to produce bioactive particles with controlled size and internal structure could open new possibilities in designing new spherical devices for orthopaedic applications, including tissue engineering.

  18. GDS-based Mask Data Preparation Flow: Data Volume Containment by Hierarchical Data Processing

    NASA Astrophysics Data System (ADS)

    Schulze, Steffen F.; LaCour, Pat; Buck, Peter D.

    2002-12-01

    As the industry enters the development of the 65nm node the pressure on the data path and tapeout flow is growing. Design complexity and increased deployment of resolution enhancement techniques (RET) result in rapidly growing file sizes, which turns what used to be the relatively simple task of mask data preparation into a real bottleneck. This discussion introduces the data preparation scheme in the mask house and analyzes its evolution. Mask data preparation (MDP) has evolved from a flow that only needed to support a single mask lithography tool data format (MEBES) with minimal data alteration steps to one which requires the support of many mask lithography tool data formats and at the same time requires significant data alteration to support the increased precision necessary for today"s advanced masks.. However, the MDP flow developed around the MEBES format and it"s derivatives still exists. The design community has migrated towards the use of hierarchical data formats and processes to control file size and processing time. MDP, which from a file size and process complexity point of view is beginning to look more and more like the advanced RET operations performed on the data prior to mask manufacturing, is still standardized on a flat data format that is poorly optimized for a growing number of mask lithography tools. Based on examples it will be shown how this complicates the data handling further. An alternate data preparation flow accommodating the larger files and re-gaining flexibility for turnaround time (TAT) and throughput management is suggested. This flow utilizes the hierarchical GDS-II format as the exchange format for mask data preparation. It complements the existing flow for the most complex designs. The introduction of a hierarchical exchange format enables the transfer of a number of necessary data preparation steps into the hierarchical domain. Data processing strategies are discussed. The paper illustrates the benefit of hierarchical

  19. Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

    USGS Publications Warehouse

    Graves, T.A.; Kendall, K.C.; Royle, J. Andrew; Stetz, J.B.; Macleod, A.C.

    2011-01-01

    Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system. ?? 2011 The Authors. Animal Conservation ?? 2011 The Zoological Society of London.

  20. Hierarchical CdS Nanowires Based Rigid and Flexible Photodetectors with Ultrahigh Sensitivity.

    PubMed

    Li, Ludong; Lou, Zheng; Shen, Guozhen

    2015-10-28

    Hierarchical CdS nanowires were synthesized via a facile vapor transport method, which were used to fabricate both rigid and flexible visible-light photodetectors. Studies found that the rigid photodetectors on SiO2/Si substrate showed ultrahigh photo-dark current ratio up to 1.96 × 10(4), several orders of magnitude higher than previously reported CdS nanostructures, as well as high specific detectivity (4.27 × 10(12) Jones), fast response speed and excellent environmental stability. Highly flexible photodetectors were also fabricated on polyimide substrate, which exhibited comparable photoresponse performance as the rigid one. In addition, the as-prepared flexible devices displayed excellent mechanical flexibility, electrical stability and folding endurance. The results indicate that the hierarchical CdS nanowires may be good candidates for nanoscale optoelectronic devices such as high-efficiency photoswitches and highly photosensitive detectors. PMID:26439364

  1. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    NASA Astrophysics Data System (ADS)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-11-01

    In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs) by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen-Rocky Mountain Biogenic Aerosol Study) ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of

  2. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    NASA Astrophysics Data System (ADS)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-07-01

    In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP) by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution due to poor

  3. Integrated photonic reservoir computing based on hierarchical time-multiplexing structure.

    PubMed

    Zhang, Hong; Feng, Xue; Li, Boxun; Wang, Yu; Cui, Kaiyu; Liu, Fang; Dou, Weibei; Huang, Yidong

    2014-12-15

    An integrated photonic reservoir computing (RC) based on hierarchical time-multiplexing structure is proposed by numerical simulations. A micro-ring array (MRA) is employed as a typical time delay implementation of RC. At the output port of the MRA, a secondary time-multiplexing is achieved by multi-mode interference (MMI) splitter and delay line array. This hierarchical time-multiplexing structure can ensure a large reservoir size with fast processing speed. Simulation results indicate that the proposed RC system yields better performance than previously reported ones. The achieved normalized mean square error between the system output and target sequence are 0.5% and 2.7% for signal classification and chaotic time series prediction, respectively, while the sample rate is as high as 1.3 Gbps. PMID:25607084

  4. A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

    NASA Technical Reports Server (NTRS)

    Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

    2012-01-01

    The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

  5. A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

    PubMed Central

    Luo, Yurong; Hargraves, Rosalyn H.; Bai, Ou; Qi, Xuguang; Ward, Kevin R.; Pfaffenberger, Michael Paul

    2013-01-01

    Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander. PMID:23766720

  6. Evolution of platinum hierarchical microstructure amine - Assisted growth via solvothermal method

    NASA Astrophysics Data System (ADS)

    Ooi, Mahayatun Dayana Johan; Aziz, Azlan Abdul

    2015-04-01

    Here we studied the formation of Platinum hierarchical microstructure by varying the synthesis time using amine assisted growth via solvothermal method. A small cluster of particles was produced at a shorter synthesis time (5h) while fully grown flower-like microstructure were formed at 9h of reaction. The synthesized Pt particles exhibit high absorption peak at 230 nm corresponding to Pt absorption peak. The catalytic property of the synthesized Pt is greatly influenced by its geometrical shape. The fully grown flower-like particles exhibit large electrochemical surface area (4.88 cm-2 g-1) and catalytic stability at a longer period, which can serve as a potential catalyst for electro-oxidation of formic acid.

  7. A hierarchical lattice spring model to simulate the mechanics of 2-D materials-based composites

    NASA Astrophysics Data System (ADS)

    Brely, Lucas; Bosia, Federico; Pugno, Nicola

    2015-07-01

    In the field of engineering materials, strength and toughness are typically two mutually exclusive properties. Structural biological materials such as bone, tendon or dentin have resolved this conflict and show unprecedented damage tolerance, toughness and strength levels. The common feature of these materials is their hierarchical heterogeneous structure, which contributes to increased energy dissipation before failure occurring at different scale levels. These structural properties are the key to exceptional bioinspired material mechanical properties, in particular for nanocomposites. Here, we develop a numerical model in order to simulate the mechanisms involved in damage progression and energy dissipation at different size scales in nano- and macro-composites, which depend both on the heterogeneity of the material and on the type of hierarchical structure. Both these aspects have been incorporated into a 2-dimensional model based on a Lattice Spring Model, accounting for geometrical nonlinearities and including statistically-based fracture phenomena. The model has been validated by comparing numerical results to continuum and fracture mechanics results as well as finite elements simulations, and then employed to study how structural aspects impact on hierarchical composite material properties. Results obtained with the numerical code highlight the dependence of stress distributions on matrix properties and reinforcement dispersion, geometry and properties, and how failure of sacrificial elements is directly involved in the damage tolerance of the material. Thanks to the rapidly developing field of nanocomposite manufacture, it is already possible to artificially create materials with multi-scale hierarchical reinforcements. The developed code could be a valuable support in the design and optimization of these advanced materials, drawing inspiration and going beyond biological materials with exceptional mechanical properties.

  8. Hierarchical graphs for better annotations of rule-based models of biochemical systems

    SciTech Connect

    Hu, Bin; Hlavacek, William

    2009-01-01

    In the graph-based formalism of the BioNetGen language (BNGL), graphs are used to represent molecules, with a colored vertex representing a component of a molecule, a vertex label representing the internal state of a component, and an edge representing a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions, with a rule that specifies addition (removal) of an edge representing a class of association (dissociation) reactions and with a rule that specifies a change of vertex label representing a class of reactions that affect the internal state of a molecular component. A set of rules comprises a mathematical/computational model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Here, for purposes of model annotation, we propose an extension of BNGL that involves the use of hierarchical graphs to represent (1) relationships among components and subcomponents of molecules and (2) relationships among classes of reactions defined by rules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR)/CD3 complex. Likewise, we illustrate how hierarchical graphs can be used to document the similarity of two related rules for kinase-catalyzed phosphorylation of a protein substrate. We also demonstrate how a hierarchical graph representing a protein can be encoded in an XML-based format.

  9. A facile method to fabricate porous Co{sub 3}O{sub 4} hierarchical microspheres

    SciTech Connect

    Cheng, J.P. Chen, X.; Ma, R.; Liu, F.; Zhang, X.B.

    2011-08-15

    Flower-like Co{sub 3}O{sub 4} hierarchical microspheres composed of self-assembled porous nanoplates have been prepared by a two-step method without employing templates. The first step involves the synthesis of flower-like Co(OH){sub 2} microspheres by a solution route at low temperatures. The second step includes the calcination of the as-prepared Co(OH){sub 2} microspheres at 200 deg. C for 1 h, causing their decomposition to form porous Co{sub 3}O{sub 4} microspheres without destruction of their original morphology. The samples were characterized by scanning electron microscope, transmission electron microscope, X-ray diffractormeter and Fourier transform infrared spectroscope. Some experimental factors including solution temperature and surfactant on the morphologies of the final products have been investigated. The magnetic properties of Co{sub 3}O{sub 4} microspheres were also investigated. - Graphical Abstract: Flower-like Co{sub 3}O{sub 4} microspheres are composed of self-assembled nanoplates and these nanoplates appear to be closely packed in the microspheres. These nanoplates consist of a large number of nanocrystallites less than 5 nm in size with a porous structure, in which the connection between nanocrystallites is random. Research Highlights: {yields} Flower-like Co{sub 3}O{sub 4} hierarchical microspheres composed of self-assembled porous nanoplates have been prepared by a two-step method without employing templates. {yields} Layered Co(OH){sub 2} microspheres were prepared with an appropriate approach under low temperatures for 1 h reaction. {yields} Calcination caused Co(OH){sub 2} decomposition to form porous Co{sub 3}O{sub 4} microspheres without destruction of their original morphology.

  10. A Subspace Pursuit–based Iterative Greedy Hierarchical Solution to the Neuromagnetic Inverse Problem

    PubMed Central

    Babadi, Behtash; Obregon-Henao, Gabriel; Lamus, Camilo; Hämäläinen, Matti S.; Brown, Emery N.; Purdon, Patrick L.

    2013-01-01

    Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy

  11. An Adaptive Algebra Test: A Testlet-Based, Hierarchically-Structured Test with Validity-Based Scoring. Technical Report No. 90-92.

    ERIC Educational Resources Information Center

    Wainer, Howard; And Others

    The initial development of a testlet-based algebra test was previously reported (Wainer and Lewis, 1990). This account provides the details of this excursion into the use of hierarchical testlets and validity-based scoring. A pretest of two 15-item hierarchical testlets was carried out in which examinees' performance on a 4-item subset of each…

  12. Aerial surveillance based on hierarchical object classification for ground target detection

    NASA Astrophysics Data System (ADS)

    Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

    2015-03-01

    Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

  13. Transparent, 3-dimensional light-collected, and flexible fiber-type dye-sensitized solar cells based on highly ordered hierarchical anatase TiO2 nanorod arrays

    NASA Astrophysics Data System (ADS)

    Liang, Jia; Zhang, Gengmin; Yin, Jianbo; Yang, Yingchao

    2014-12-01

    Two kinds of hierarchical anatase TiO2 structures are synthesized by a facile hydrothermal method in this report. A new transparent, 3D light-collected, and flexible fiber-type dye-sensitized solar cell (FF-DSSC) with such hierarchical TiO2 structures is developed. The conversion efficiency of the FF-DSSC based on a TiCl4-treated TiO2 nanorod array (hierarchical structure I) exhibits about 4 times higher than that based on a HCl-treated TiO2 nanorod array, and further rises to 4.4% when the TiCl4-treated TiO2 nanorod array is treated in a mixed solution of (NH4)2TiF6 and H3BO3 three times (hierarchical structure II). The obvious enhancement in conversion efficiency can be ascribed to the dye adsorption promotion benefiting from their hierarchical structures. Beyond the attractive conversion efficiency, the new designed FF-DSSC possesses several advantages including good flexibility, excellent stability, and 3D light-collection. The conversion efficiencies of the FF-DSSCs can still keep 85%-90% even the FF-DSSCs are bent for 1000 times. The maximum power outputs of the FF-DSSCs characterized by Diffuse Illumination Mode using home-made Al reflector exhibit about 3 times higher than that done by Standard Illumination Mode due to 3D light-collections. The FF-DSSCs based on highly ordered hierarchical anatase TiO2 nanorod arrays hold great promise in future energy harvest.

  14. The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods

    NASA Astrophysics Data System (ADS)

    Deviren, Seyma Akkaya; Deviren, Bayram

    2016-06-01

    Carbon dioxide (CO2) emission has an essential role in the current debate on sustainable development and environmental protection. CO2 emission is also directly linked with use of energy which plays a focal role both for production and consumption in the world economy. Therefore the relationship between the CO2 emission and economic growth has a significant implication for the environmental and economical policies. In this study, within the scope of sociophysics, the topology, taxonomy and relationships among the 33 countries, which have almost the high CO2 emission and economic growth values, are investigated by using the hierarchical structure methods, such as the minimal spanning tree (MST) and hierarchical tree (HT), over the period of 1970-2010. The average linkage cluster analysis (ALCA) is also used to examine the cluster structure more clearly in HTs. According to their proximity, economic ties and economic growth, different clusters of countries are identified from the structural topologies of these trees. We have found that the high income & OECD countries are closely connected to each other and are isolated from the upper middle and lower middle income countries from the MSTs, which are obtained both for the CO2 emission and economic growth. Moreover, the high income & OECD clusters are homogeneous with respect to the economic activities and economic ties of the countries. It is also mentioned that the Group of Seven (G7) countries (CAN, ENG, FRA, GER, ITA, JPN, USA) are connected to each other and these countries are located at the center of the MST for the results of CO2 emission. The same analysis may also successfully apply to the other environmental sources and different countries.

  15. Hierarchical graphene nanocones over 3D platform of carbon fabrics: A route towards fully foldable graphene based electron source

    NASA Astrophysics Data System (ADS)

    Maiti, Uday N.; Maiti, Soumen; Das, Nirmalya S.; Chattopadhyay, Kalyan K.

    2011-10-01

    A three dimensional field emitter comprising hierarchical nanostructures of graphene over flexible fabric substrate is presented. The nanostructuring is realized through plasma treatment of graphene, coaxially deposited over individual carbon fiber by means of simple aqueous phase electrophoretic deposition technique. Hierarchical graphene nanocone, acting as a cold electron emitter, exhibits outstanding electron emission performance with a turn-on field as low as 0.41 V μm-1 and a threshold field down to 0.81 V μm-1. Electric field modification around the special woven like geometry of the underlying base fabric substrate serves as the booster to the nanostructured graphene related field amplification at the electron emission site. Superb robustness in the emission stability can be attributed to suppressed joule heating on behalf of higher inborn accessible surface area of graphene nanocone as well as excellent electrical and thermal conductivity of both the graphene and carbon fabrics. Superior flexibility of this high-performance graphene based emitter ensures their potential use in completely foldable and wearable field emission devices.A three dimensional field emitter comprising hierarchical nanostructures of graphene over flexible fabric substrate is presented. The nanostructuring is realized through plasma treatment of graphene, coaxially deposited over individual carbon fiber by means of simple aqueous phase electrophoretic deposition technique. Hierarchical graphene nanocone, acting as a cold electron emitter, exhibits outstanding electron emission performance with a turn-on field as low as 0.41 V μm-1 and a threshold field down to 0.81 V μm-1. Electric field modification around the special woven like geometry of the underlying base fabric substrate serves as the booster to the nanostructured graphene related field amplification at the electron emission site. Superb robustness in the emission stability can be attributed to suppressed joule heating on

  16. Genetic Network Inference Using Hierarchical Structure.

    PubMed

    Kimura, Shuhei; Tokuhisa, Masato; Okada-Hatakeyama, Mariko

    2016-01-01

    Many methods for inferring genetic networks have been proposed, but the regulations they infer often include false-positives. Several researchers have attempted to reduce these erroneous regulations by proposing the use of a priori knowledge about the properties of genetic networks such as their sparseness, scale-free structure, and so on. This study focuses on another piece of a priori knowledge, namely, that biochemical networks exhibit hierarchical structures. Based on this idea, we propose an inference approach that uses the hierarchical structure in a target genetic network. To obtain a reasonable hierarchical structure, the first step of the proposed approach is to infer multiple genetic networks from the observed gene expression data. We take this step using an existing method that combines a genetic network inference method with a bootstrap method. The next step is to extract a hierarchical structure from the inferred networks that is consistent with most of the networks. Third, we use the hierarchical structure obtained to assign confidence values to all candidate regulations. Numerical experiments are also performed to demonstrate the effectiveness of using the hierarchical structure in the genetic network inference. The improvement accomplished by the use of the hierarchical structure is small. However, the hierarchical structure could be used to improve the performances of many existing inference methods. PMID:26941653

  17. Hierarchical development of three direct-design methods for two-dimensional axial-turbomachinery cascades

    SciTech Connect

    Korakianitis, T. )

    1993-04-01

    The direct and inverse blade-design iterations for the selection of isolated airfoils and gas turbine blade cascades are enormously reduced if the initial blade shape has performance characteristics near the desirable ones. This paper presents the hierarchical development of three direct blade-design methods of increasing utility for generating two-dimensional blade shapes. The methods can be used to generate inputs to the direct- or inverse-blade-design sequences for subsonic or supersonic airfoils for compressors and turbines, or isolated airfoils. The first method specifies the airfoil shapes with analytical polynomials. It shows that continuous curvature and continuous slope of curvature are necessary conditions to minimize the possibility of flow separation, and to lead to improved blade designs. The second method specifies the airfoil shapes with parametric fourth-order polynomials, which result in continuous-slope-of-curvature airfoils, with smooth Mach number and pressure distributions. This method is time consuming. The third method specifies the airfoil shapes by using a mixture of analytical polynomials and mapping the airfoil surfaces from a desirable curvature distribution. The third method provides blade surfaces with desirable performance in very few direct-design iterations. In all methods the geometry near the leading edge is specified by a thickness distribution added to a construction line, which eliminates the leading edge overspeed and laminar-separation regions. The blade-design methods presented in this paper can be used to improve the aerodynamic and heat transfer performance of turbomachinery cascades, and they can result in high-performance airfoils in very few iterations.

  18. Shape-based human detection and segmentation via hierarchical part-template matching.

    PubMed

    Lin, Zhe; Davis, Larry S

    2010-04-01

    We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human/nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets. PMID:20224118

  19. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    PubMed Central

    Sherrill, Delsey M; Moy, Marilyn L; Reilly, John J; Bonato, Paolo

    2005-01-01

    Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical clustering methods are relevant

  20. Recombination reduction on lead halide perovskite solar cells based on low temperature synthesized hierarchical TiO₂ nanorods.

    PubMed

    Jaramillo-Quintero, Oscar A; Solís de la Fuente, Mauricio; Sanchez, Rafael S; Recalde, Ileana B; Juarez-Perez, Emilio J; Rincón, Marina E; Mora-Seró, Iván

    2016-03-28

    Intensive research on the electron transport material (ETM) has been pursued to improve the efficiency of perovskite solar cells (PSCs) and decrease their cost. More importantly, the role of the ETM layer is not yet fully understood, and research on new device architectures is still needed. Here, we report the use of three-dimensional (3D) TiO2 with a hierarchical architecture based on rutile nanorods (NR) as photoanode material for PSCs. The proposed hierarchical nanorod (HNR) films were synthesized by a two-step low temperature (180 °C) hydrothermal method, and consist of TiO2 nanorod trunks with optimal lengths of 540 nm and TiO2 nanobranches with lengths of 45 nm. Different device configurations were fabricated with TiO2 structures (compact layer, NR and HNR) and CH3NH3PbI3, using different synthetic routes, as the active material. PSCs based on HNR-CH3NH3PbI3 achieved the highest power conversion efficiency compared to PSCs with other TiO2 structures. This result can be ascribed mainly to lower charge recombination as determined by impedance spectroscopy. Furthermore, we have observed that the CH3NH3PbI3 perovskite deposited by the two-step route shows higher efficiency, surface coverage and infiltration within the structure of 3D HNR than the one-step CH3NH3PbI(3-x)Cl(x) perovskite. PMID:26616491

  1. Hierarchical clustering of EMD based interest points for road sign detection

    NASA Astrophysics Data System (ADS)

    Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza

    2014-04-01

    This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.

  2. An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks.

    PubMed

    Butun, Ismail; Ra, In-Ho; Sankar, Ravi

    2015-01-01

    In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) "downward-IDS (D-IDS)" to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) "upward-IDS (U-IDS)" to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented. PMID:26593915

  3. Security clustering algorithm based on reputation in hierarchical peer-to-peer network

    NASA Astrophysics Data System (ADS)

    Chen, Mei; Luo, Xin; Wu, Guowen; Tan, Yang; Kita, Kenji

    2013-03-01

    For the security problems of the hierarchical P2P network (HPN), the paper presents a security clustering algorithm based on reputation (CABR). In the algorithm, we take the reputation mechanism for ensuring the security of transaction and use cluster for managing the reputation mechanism. In order to improve security, reduce cost of network brought by management of reputation and enhance stability of cluster, we select reputation, the historical average online time, and the network bandwidth as the basic factors of the comprehensive performance of node. Simulation results showed that the proposed algorithm improved the security, reduced the network overhead, and enhanced stability of cluster.

  4. An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks

    PubMed Central

    Butun, Ismail; Ra, In-Ho; Sankar, Ravi

    2015-01-01

    In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) “downward-IDS (D-IDS)” to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) “upward-IDS (U-IDS)” to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented. PMID:26593915

  5. Hierarchical image enhancement

    NASA Astrophysics Data System (ADS)

    Qi, Wei; Han, Jing; Zhang, Yi; Bai, Lian-fa

    2016-05-01

    Image enhancement is an important technique in computer vision. In this paper, we propose a hierarchical image enhancement approach based on the structure layer and texture layer. In the structure layer, we propose a structure-based method based on GMM, which better exploits structure details with fewer noise. In the texture layer, we present a structure-filtering method to filter unwanted texture with keeping completeness of detected salient structure. Next, we introduce a structure constraint prior to integrate them, leading to an improved enhancement result. Extensive experiments demonstrate that the proposed approach achieves higher quality results than previous approaches.

  6. Predicting Hydrologic Response Through a Pooled Watershed Knowledge Base: A Hierarchical Bayesian Approach

    NASA Astrophysics Data System (ADS)

    Smith, T. J.; Marshall, L. A.; Sharma, A.

    2011-12-01

    Hydrologic modelers are confronted with the challenge of producing estimates of the uncertainty associated with model predictions across a wide array of watersheds, often under very limited data conditions. Statistical methods for hydrologic modeling have evolved rapidly over the recent past in response to these challenges, from improved strategies to both estimate optimal parameter values and predictive uncertainty to approaches that aim to link model parameters to watershed characteristics and allow parameters to be transferred to data-poor watersheds. However, despite the advances that have been made in the application of such statistical tools there remains significant work to be done, particularly regarding the quantification/transfer of predictive uncertainty at/to data-limited locations. Here, we present a hierarchical Bayesian modeling technique referred to as Bayes Empirical Bayes (BEB) as a means of addressing the difficulties in making reliable hydrologic predictions under uncertainty in data-limited watersheds. The BEB technique utilizes formal hierarchical Bayesian analysis (specifically the resultant posterior probability distributions for each estimated model parameter) to pool information from auxiliary watersheds to generate informed probability distributions for each parameter at a data-limited watershed of interest. The application of such a method has thus far been untested in hydrologic applications but has been used more extensively in ecological studies. This technique represents a significant departure from earlier attempts to make predictions in data-limited watersheds in both its usage of available data and its ability to simultaneously quantify predictive uncertainty directly. By utilizing the Bayesian toolkit under a hierarchical approach, information available from auxiliary watersheds can be integrated and summarized into the prediction at the site of interest.

  7. Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm

    PubMed Central

    Ji, Zhiwei; Wang, Bing

    2014-01-01

    Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC. PMID:24745007

  8. Deterministic hierarchical networks

    NASA Astrophysics Data System (ADS)

    Barrière, L.; Comellas, F.; Dalfó, C.; Fiol, M. A.

    2016-06-01

    It has been shown that many networks associated with complex systems are small-world (they have both a large local clustering coefficient and a small diameter) and also scale-free (the degrees are distributed according to a power law). Moreover, these networks are very often hierarchical, as they describe the modularity of the systems that are modeled. Most of the studies for complex networks are based on stochastic methods. However, a deterministic method, with an exact determination of the main relevant parameters of the networks, has proven useful. Indeed, this approach complements and enhances the probabilistic and simulation techniques and, therefore, it provides a better understanding of the modeled systems. In this paper we find the radius, diameter, clustering coefficient and degree distribution of a generic family of deterministic hierarchical small-world scale-free networks that has been considered for modeling real-life complex systems.

  9. Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Wang, Fang-Fang; Su, Chao-Ton

    2014-11-01

    The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

  10. Object class recognition based on compressive sensing with sparse features inspired by hierarchical model in visual cortex

    NASA Astrophysics Data System (ADS)

    Lu, Pei; Xu, Zhiyong; Yu, Huapeng; Chang, Yongxin; Fu, Chengyu; Shao, Jianxin

    2012-11-01

    According to models of object recognition in cortex, the brain uses a hierarchical approach in which simple, low-level features having high position and scale specificity are pooled and combined into more complex, higher-level features having greater location invariance. At higher levels, spatial structure becomes implicitly encoded into the features themselves, which may overlap, while explicit spatial information is coded more coarsely. In this paper, the importance of sparsity and localized patch features in a hierarchical model inspired by visual cortex is investigated. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. In order to improve generalization performance, the sparsity is proposed and data dimension is reduced by means of compressive sensing theory and sparse representation algorithm. Similarly, within computational neuroscience, adding the sparsity on the number of feature inputs and feature selection is critical for learning biologically model from the statistics of natural images. Then, a redundancy dictionary of patch-based features that could distinguish object class from other categories is designed and then object recognition is implemented by the process of iterative optimization. The method is test on the UIUC car database. The success of this approach suggests a proof for the object class recognition in visual cortex.

  11. ASTEROID FAMILY IDENTIFICATION USING THE HIERARCHICAL CLUSTERING METHOD AND WISE/NEOWISE PHYSICAL PROPERTIES

    SciTech Connect

    Masiero, Joseph R.; Mainzer, A. K.; Bauer, J. M.; Nugent, C. R.; Stevenson, R.

    2013-06-10

    Using albedos from WISE/NEOWISE to separate distinct albedo groups within the Main Belt asteroids, we apply the Hierarchical Clustering Method to these subpopulations and identify dynamically associated clusters of asteroids. While this survey is limited to the {approx}35% of known Main Belt asteroids that were detected by NEOWISE, we present the families linked from these objects as higher confidence associations than can be obtained from dynamical linking alone. We find that over one-third of the observed population of the Main Belt is represented in the high-confidence cores of dynamical families. The albedo distribution of family members differs significantly from the albedo distribution of background objects in the same region of the Main Belt; however, interpretation of this effect is complicated by the incomplete identification of lower-confidence family members. In total we link 38,298 asteroids into 76 distinct families. This work represents a critical step necessary to debias the albedo and size distributions of asteroids in the Main Belt and understand the formation and history of small bodies in our solar system.

  12. Enhanced field electron emission properties of hierarchically structured MWCNT-based cold cathodes

    NASA Astrophysics Data System (ADS)

    Gautier, Loïck-Alexandre; Le Borgne, Vincent; Al Moussalami, Samir; El Khakani, My Ali

    2014-02-01

    Hierarchically structured MWCNT (h-MWCNT)-based cold cathodes were successfully achieved by means of a relatively simple and highly effective approach consisting of the appropriate combination of KOH-based pyramidal texturing of Si (100) substrates and PECVD growth of vertically aligned MWCNTs. By controlling the aspect ratio (AR) of the Si pyramids, we were able to tune the field electron emission (FEE) properties of the h-MWCNT cathodes. Indeed, when the AR is increased from 0 (flat Si) to 0.6, not only the emitted current density was found to increase exponentially, but more importantly its associated threshold field (TF) was reduced from 3.52 V/μm to reach a value as low as 1.95 V/μm. The analysis of the J- E emission curves in the light of the conventional Fowler-Nordheim model revealed the existence of two distinct low-field (LF) and high-field (HF) FEE regimes. In both regimes, the hierarchical structuring was found to increase significantly the associated β LF and β HF field enhancement factors of the h-MWCNT cathodes (by a factor of 1.7 and 2.2, respectively). Pyramidal texturing of the cathodes is believed to favor vacuum space charge effects, which could be invoked to account for the significant enhancement of the FEE, particularly in the HF regime where a β HF as high as 6,980 was obtained for the highest AR value of 0.6.

  13. Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening.

    PubMed

    Cao, Ran; Liu, Minyu; Yin, Min; Liu, Quanhai; Wang, Yanli; Huang, Niu

    2012-10-22

    To discover novel tubulin inhibitors, we performed structure-based virtual screening against the colchicine binding pocket. In combination with a hierarchical docking and scoring procedure, the structural information of an additional subpocket in colchicine site was applied to filter out the undesired docking hits. This strategy automatically resulted in 63 candidates meeting the structural and energetic criteria from a screening library containing approximately 100,000 diverse druglike compounds. Among them, nine molecules were chosen for experimental validation, which all share the similar binding pose and contain an enriched scaffold bearing thiophene core. Encouragingly, five compounds are active in tubulin polymerization assay. The most potent inhibitor, 2-(2-fluorobenzamido)-3-carboxamide-4,5-dimethylthiophene, is structurally distinct to any known colchicine site binders and has higher ligand efficiency than colchicine. On the basis of its predicted binding pose, we systematically probed its binding characteristics by testing series of structural modifications. The obtained structure-activity relationship results are consistent with our binding model, and the inhibition activities of two analogues are improved by 2-fold. We expect that the novel structure discovered in the present study may serve as a starting point for developing tubulin inhibitors with improved efficacy and fewer side effects. We also expect that our hierarchical strategy may be generally applicable in structure-based virtual screening campaigns. PMID:22992059

  14. Managing the systems approach to training using a flexible Hierarchical data base

    SciTech Connect

    Housman, E. ); Bush, E.R. )

    1993-01-01

    Task analysis/curriculum design for a nuclear power station results in a massive amount of data, which must be sequenced and ordered to create an effective program design. This is an almost impossible task without the use of computerized data base. Beginning in 1989, San Onofre nuclear generating station (SONGS) undertook a task analysis/program design project to verify the structure and sequence (design) of all accredited training program. A flex hierarchical data-base management system was designed to store and manage the data collected during the project. For the Operations Training Programm alone [approx]8000 tasks, 90,000 knowledges and abilities, and 10,000 learning objectives were entered into this data base.

  15. Layered HEVC/H.265 video transmission scheme based on hierarchical QAM optimization

    NASA Astrophysics Data System (ADS)

    Feng, Weidong; Zhou, Cheng; Xiong, Chengyi; Chen, Shaobo; Wang, Junxi

    2015-12-01

    High Efficiency Video Coding (HEVC) is the state-of-art video compression standard which fully support scalability features and is able to generate layered video streams with unequal importance. Unfortunately, when the base layer (BL) which is more importance to the stream is lost during the transmission, the enhancement layer (EL) based on the base layer must be discarded by receiver. Obviously, using the same transmittal strategies for BL and EL is unreasonable. This paper proposed an unequal error protection (UEP) system using different hierarchical amplitude modulation (HQAM). The BL data with high priority are mapped into the most reliable HQAM mode and the EL data with low priority are mapped into HQAM mode with fast transmission efficiency. Simulations on scalable HEVC codec show that the proposed optimized video transmission system is more attractive than the traditional equal error protection (EEP) scheme because it effectively balances the transmission efficiency and reconstruction video quality.

  16. A novel method for a multi-level hierarchical composite with brick-and-mortar structure

    NASA Astrophysics Data System (ADS)

    Brandt, Kristina; Wolff, Michael F. H.; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A.

    2013-07-01

    The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships.

  17. A novel method for a multi-level hierarchical composite with brick-and-mortar structure.

    PubMed

    Brandt, Kristina; Wolff, Michael F H; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A

    2013-01-01

    The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships. PMID:23900554

  18. A novel method for a multi-level hierarchical composite with brick-and-mortar structure

    PubMed Central

    Brandt, Kristina; Wolff, Michael F. H.; Salikov, Vitalij; Heinrich, Stefan; Schneider, Gerold A.

    2013-01-01

    The fascination for hierarchically structured hard tissues such as enamel or nacre arises from their unique structure-properties-relationship. During the last decades this numerously motivated the synthesis of composites, mimicking the brick-and-mortar structure of nacre. However, there is still a lack in synthetic engineering materials displaying a true hierarchical structure. Here, we present a novel multi-step processing route for anisotropic 2-level hierarchical composites by combining different coating techniques on different length scales. It comprises polymer-encapsulated ceramic particles as building blocks for the first level, followed by spouted bed spray granulation for a second level, and finally directional hot pressing to anisotropically consolidate the composite. The microstructure achieved reveals a brick-and-mortar hierarchical structure with distinct, however not yet optimized mechanical properties on each level. It opens up a completely new processing route for the synthesis of multi-level hierarchically structured composites, giving prospects to multi-functional structure-properties relationships. PMID:23900554

  19. Compression of multispectral fluorescence microscopic images based on a modified set partitioning in hierarchal trees

    NASA Astrophysics Data System (ADS)

    Mansoor, Awais; Robinson, J. Paul; Rajwa, Bartek

    2009-02-01

    Modern automated microscopic imaging techniques such as high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging are capable of producing hundreds to thousands of images per experiment. For quick retrieval, fast transmission, and storage economy, these images should be saved in a compressed format. A considerable number of techniques based on interband and intraband redundancies of multispectral images have been proposed in the literature for the compression of multispectral and 3D temporal data. However, these works have been carried out mostly in the elds of remote sensing and video processing. Compression for multispectral optical microscopy imaging, with its own set of specialized requirements, has remained under-investigated. Digital photography{oriented 2D compression techniques like JPEG (ISO/IEC IS 10918-1) and JPEG2000 (ISO/IEC 15444-1) are generally adopted for multispectral images which optimize visual quality but do not necessarily preserve the integrity of scientic data, not to mention the suboptimal performance of 2D compression techniques in compressing 3D images. Herein we report our work on a new low bit-rate wavelet-based compression scheme for multispectral fluorescence biological imaging. The sparsity of signicant coefficients in high-frequency subbands of multispectral microscopic images is found to be much greater than in natural images; therefore a quad-tree concept such as Said et al.'s SPIHT1 along with correlation of insignicant wavelet coefficients has been proposed to further exploit redundancy at high-frequency subbands. Our work propose a 3D extension to SPIHT, incorporating a new hierarchal inter- and intra-spectral relationship amongst the coefficients of 3D wavelet-decomposed image. The new relationship, apart from adopting the parent-child relationship of classical SPIHT, also brought forth the conditional "sibling" relationship by relating only the insignicant wavelet coefficients of subbands

  20. A debugging system for azimuthally acoustic logging tools based on modular and hierarchical design ideas

    NASA Astrophysics Data System (ADS)

    Zhang, K.; Ju, X. D.; Lu, J. Q.; Men, B. Y.

    2016-08-01

    On the basis of modular and hierarchical design ideas, this study presents a debugging system for an azimuthally sensitive acoustic bond tool (AABT). The debugging system includes three parts: a personal computer (PC), embedded front-end machine and function expansion boards. Modular and hierarchical design ideas are conducted in all design and debug processes. The PC communicates with the front-end machine via the Internet, and the front-end machine and function expansion boards connect each other by the extended parallel bus. In this method, the three parts of the debugging system form stable and high-speed data communication. This study not only introduces the system-level debugging and sub-system level debugging of the tool but also the debugging of the analogue signal processing board, which is important and greatly used in logging tools. Experiments illustrate that the debugging system can greatly improve AABT verification and calibration efficiency and that, board-level debugging can examine and improve analogue signal processing boards. The design thinking is clear and the design structure is reasonable, thus making it easy to extend and upgrade the debugging system.

  1. Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism

    PubMed Central

    2015-01-01

    , cofactors and vitamins. Conclusions The ECemble method is able to hierarchically assign high quality enzyme annotations to genomic and metagenomic data. This study demonstrated the real application of ECemble to understand the indispensable role played by microbe-encoded enzymes in the healthy functioning of human metabolic systems. PMID:26099921

  2. Evaluation of B2C website based on the usability factors by using fuzzy AHP & hierarchical fuzzy TOPSIS

    NASA Astrophysics Data System (ADS)

    Masudin, I.; Saputro, T. E.

    2016-02-01

    In today's technology, electronic trading transaction via internet has been utilized properly with rapid growth. This paper intends to evaluate related to B2C e-commerce website in order to find out the one which meets the usability factors better than another. The influential factors to B2C e-commerce website are determined for two big retailer websites. The factors are investigated based on the consideration of several studies and conformed to the website characteristics. The evaluation is conducted by using different methods namely fuzzy AHP and hierarchical fuzzy TOPSIS so that the final evaluation can be compared. Fuzzy triangular number is adopted to deal with imprecise judgment under fuzzy environment.

  3. A hierarchical P2P overlay network for interest-based media contents lookup

    NASA Astrophysics Data System (ADS)

    Lee, HyunRyong; Kim, JongWon

    2006-10-01

    We propose a P2P (peer-to-peer) overlay architecture, called IGN (interest grouping network), for contents lookup in the DHC (digital home community), which aims to provide a formalized home-network-extended construction of current P2P file sharing community. The IGN utilizes the Chord and de Bruijn graph for its hierarchical overlay network construction. By combining two schemes and by inheriting its features, the IGN efficiently supports contents lookup. More specifically, by introducing metadata-based lookup keyword, the IGN offers detailed contents lookup that can reflect the user interests. Moreover, the IGN tries to reflect home network environments of DHC by utilizing HG (home gateway) of each home network as a participating node of the IGN. Through experimental and analysis results, we show that the IGN is more efficient than Chord, a well-known DHT (distributed hash table)-based lookup protocol.

  4. Hierarchical wavelet-based image model for pattern analysis and synthesis

    NASA Astrophysics Data System (ADS)

    Scott, Clayton D.; Nowak, Robert D.

    2000-12-01

    Despite their success in other areas of statistical signal processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations inherent in most pattern observations. In this paper we introduce a hierarchical wavelet-based framework for modeling patterns in digital images. This framework takes advantage of the efficient image representations afforded by wavelets, while accounting for unknown pattern transformations. Given a trained model, we can use this framework to synthesize pattern observations. If the model parameters are unknown, we can infer them from labeled training data using TEMPLAR, a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length complexity regularization to learn a template with a sparse representation in the wavelet domain. We illustrate template learning with examples, and discuss how TEMPLAR applies to pattern classification and denoising from multiple, unaligned observations.

  5. 3D face recognition based on the hierarchical score-level fusion classifiers

    NASA Astrophysics Data System (ADS)

    Mráček, Štěpán.; Váša, Jan; Lankašová, Karolína; Drahanský, Martin; Doležel, Michal

    2014-05-01

    This paper describes the 3D face recognition algorithm that is based on the hierarchical score-level fusion clas-sifiers. In a simple (unimodal) biometric pipeline, the feature vector is extracted from the input data and subsequently compared with the template stored in the database. In our approachm, we utilize several feature extraction algorithms. We use 6 different image representations of the input 3D face data. Moreover, we are using Gabor and Gauss-Laguerre filter banks applied on the input image data that yield to 12 resulting feature vectors. Each representation is compared with corresponding counterpart from the biometric database. We also add the recognition based on the iso-geodesic curves. The final score-level fusion is performed on 13 comparison scores using the Support Vector Machine (SVM) classifier.

  6. Predicting the Pro-Longevity or Anti-Longevity Effect of Model Organism Genes with New Hierarchical Feature Selection Methods.

    PubMed

    Wan, Cen; Freitas, Alex A; de Magalhães, João Pedro

    2015-01-01

    Ageing is a highly complex biological process that is still poorly understood. With the growing amount of ageing-related data available on the web, in particular concerning the genetics of ageing, it is timely to apply data mining methods to that data, in order to try to discover novel patterns that may assist ageing research. In this work, we introduce new hierarchical feature selection methods for the classification task of data mining and apply them to ageing-related data from four model organisms: Caenorhabditis elegans (worm), Saccharomyces cerevisiae (yeast), Drosophila melanogaster (fly), and Mus musculus (mouse). The main novel aspect of the proposed feature selection methods is that they exploit hierarchical relationships in the set of features (Gene Ontology terms) in order to improve the predictive accuracy of the Naïve Bayes and 1-Nearest Neighbour (1-NN) classifiers, which are used to classify model organisms' genes into pro-longevity or anti-longevity genes. The results show that our hierarchical feature selection methods, when used together with Naïve Bayes and 1-NN classifiers, obtain higher predictive accuracy than the standard (without feature selection) Naïve Bayes and 1-NN classifiers, respectively. We also discuss the biological relevance of a number of Gene Ontology terms very frequently selected by our algorithms in our datasets. PMID:26357215

  7. Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

    PubMed Central

    Azad, Ariful; Rajwa, Bartek; Pothen, Alex

    2016-01-01

    We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are

  8. An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data

    PubMed Central

    Ross, Beth E.; Hooten, Mevin B.; Koons, David N.

    2012-01-01

    A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time. PMID:23166658

  9. An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data

    USGS Publications Warehouse

    Ross, Beth E.; Hooten, Melvin B.; Koons, David N.

    2012-01-01

    A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

  10. A pollutant load hierarchical allocation method integrated in an environmental capacity management system for Zhushan Bay, Taihu Lake.

    PubMed

    Liang, Shidong; Jia, Haifeng; Yang, Cong; Melching, Charles; Yuan, Yongping

    2015-11-15

    An environmental capacity management (ECM) system was developed to help practically implement a Total Maximum Daily Load (TMDL) for a key bay in a highly eutrophic lake in China. The ECM system consists of a simulation platform for pollutant load calculation and a pollutant load hierarchical allocation (PLHA) system. The simulation platform was developed by linking the Environmental Fluid Dynamics Code (EFDC) and Water Quality Analysis Simulation Program (WASP). In the PLHA, pollutant loads were allocated top-down in several levels based on characteristics of the pollutant sources. Different allocation methods could be used for the different levels with the advantages of each method combined over the entire allocation. Zhushan Bay of Taihu Lake, one of the most eutrophic lakes in China, was selected as a case study. The allowable loads of total nitrogen, total phosphorus, ammonia, and chemical oxygen demand were found to be 2122.2, 94.9, 1230.4, and 5260.0 t·yr(-1), respectively. The PLHA for the case study consists of 5 levels. At level 0, loads are allocated to those from the lakeshore direct drainage, atmospheric deposition, internal release, and tributary inflows. At level 1 the loads allocated to tributary inflows are allocated to the 3 tributaries. At level 2, the loads allocated to one inflow tributary are allocated to upstream areas and local sources along the tributary. At level 3, the loads allocated to local sources are allocated to the point and non-point sources from different towns. At level 4, the loads allocated to non-point sources in each town are allocated to different villages. Compared with traditional forms of pollutant load allocation methods, PLHA can combine the advantages of different methods which put different priority weights on equity and efficiency, and the PLHA is easy to understand for stakeholders and more flexible to adjust when applied in practical cases. PMID:26172589

  11. A hierarchical pyramid method for managing large-scale high-resolution drainage networks extracted from DEM

    NASA Astrophysics Data System (ADS)

    Bai, Rui; Tiejian, Li; Huang, Yuefei; Jiaye, Li; Wang, Guangqian; Yin, Dongqin

    2015-12-01

    The increasing resolution of Digital Elevation Models (DEMs) and the development of drainage network extraction algorithms make it possible to develop high-resolution drainage networks for large river basins. These vector networks contain massive numbers of river reaches with associated geographical features, including topological connections and topographical parameters. These features create challenges for efficient map display and data management. Of particular interest are the requirements of data management for multi-scale hydrological simulations using multi-resolution river networks. In this paper, a hierarchical pyramid method is proposed, which generates coarsened vector drainage networks from the originals iteratively. The method is based on the Horton-Strahler's (H-S) order schema. At each coarsening step, the river reaches with the lowest H-S order are pruned, and their related sub-basins are merged. At the same time, the topological connections and topographical parameters of each coarsened drainage network are inherited from the former level using formulas that are presented in this study. The method was applied to the original drainage networks of a watershed in the Huangfuchuan River basin extracted from a 1-m-resolution airborne LiDAR DEM and applied to the full Yangtze River basin in China, which was extracted from a 30-m-resolution ASTER GDEM. In addition, a map-display and parameter-query web service was published for the Mississippi River basin, and its data were extracted from the 30-m-resolution ASTER GDEM. The results presented in this study indicate that the developed method can effectively manage and display massive amounts of drainage network data and can facilitate multi-scale hydrological simulations.

  12. Photo-driven autonomous hydrogen generation system based on hierarchically shelled ZnO nanostructures

    SciTech Connect

    Kim, Heejin; Yong, Kijung

    2013-11-25

    A quantum dot semiconductor sensitized hierarchically shelled one-dimensional ZnO nanostructure has been applied as a quasi-artificial leaf for hydrogen generation. The optimized ZnO nanostructure consists of one dimensional nanowire as a core and two-dimensional nanosheet on the nanowire surface. Furthermore, the quantum dot semiconductors deposited on the ZnO nanostructures provide visible light harvesting properties. To realize the artificial leaf, we applied the ZnO based nanostructure as a photoelectrode with non-wired Z-scheme system. The demonstrated un-assisted photoelectrochemical system showed the hydrogen generation properties under 1 sun condition irradiation. In addition, the quantum dot modified photoelectrode showed 2 mA/cm{sup 2} current density at the un-assisted condition.

  13. Recombination reduction on lead halide perovskite solar cells based on low temperature synthesized hierarchical TiO2 nanorods

    NASA Astrophysics Data System (ADS)

    Jaramillo-Quintero, Oscar A.; Solís de La Fuente, Mauricio; Sanchez, Rafael S.; Recalde, Ileana B.; Juarez-Perez, Emilio J.; Rincón, Marina E.; Mora-Seró, Iván

    2016-03-01

    Intensive research on the electron transport material (ETM) has been pursued to improve the efficiency of perovskite solar cells (PSCs) and decrease their cost. More importantly, the role of the ETM layer is not yet fully understood, and research on new device architectures is still needed. Here, we report the use of three-dimensional (3D) TiO2 with a hierarchical architecture based on rutile nanorods (NR) as photoanode material for PSCs. The proposed hierarchical nanorod (HNR) films were synthesized by a two-step low temperature (180 °C) hydrothermal method, and consist of TiO2 nanorod trunks with optimal lengths of 540 nm and TiO2 nanobranches with lengths of 45 nm. Different device configurations were fabricated with TiO2 structures (compact layer, NR and HNR) and CH3NH3PbI3, using different synthetic routes, as the active material. PSCs based on HNR-CH3NH3PbI3 achieved the highest power conversion efficiency compared to PSCs with other TiO2 structures. This result can be ascribed mainly to lower charge recombination as determined by impedance spectroscopy. Furthermore, we have observed that the CH3NH3PbI3 perovskite deposited by the two-step route shows higher efficiency, surface coverage and infiltration within the structure of 3D HNR than the one-step CH3NH3PbI3-xClx perovskite.Intensive research on the electron transport material (ETM) has been pursued to improve the efficiency of perovskite solar cells (PSCs) and decrease their cost. More importantly, the role of the ETM layer is not yet fully understood, and research on new device architectures is still needed. Here, we report the use of three-dimensional (3D) TiO2 with a hierarchical architecture based on rutile nanorods (NR) as photoanode material for PSCs. The proposed hierarchical nanorod (HNR) films were synthesized by a two-step low temperature (180 °C) hydrothermal method, and consist of TiO2 nanorod trunks with optimal lengths of 540 nm and TiO2 nanobranches with lengths of 45 nm. Different

  14. Enhanced field electron emission properties of hierarchically structured MWCNT-based cold cathodes

    PubMed Central

    2014-01-01

    Hierarchically structured MWCNT (h-MWCNT)-based cold cathodes were successfully achieved by means of a relatively simple and highly effective approach consisting of the appropriate combination of KOH-based pyramidal texturing of Si (100) substrates and PECVD growth of vertically aligned MWCNTs. By controlling the aspect ratio (AR) of the Si pyramids, we were able to tune the field electron emission (FEE) properties of the h-MWCNT cathodes. Indeed, when the AR is increased from 0 (flat Si) to 0.6, not only the emitted current density was found to increase exponentially, but more importantly its associated threshold field (TF) was reduced from 3.52 V/μm to reach a value as low as 1.95 V/μm. The analysis of the J-E emission curves in the light of the conventional Fowler-Nordheim model revealed the existence of two distinct low-field (LF) and high-field (HF) FEE regimes. In both regimes, the hierarchical structuring was found to increase significantly the associated βLF and βHF field enhancement factors of the h-MWCNT cathodes (by a factor of 1.7 and 2.2, respectively). Pyramidal texturing of the cathodes is believed to favor vacuum space charge effects, which could be invoked to account for the significant enhancement of the FEE, particularly in the HF regime where a βHF as high as 6,980 was obtained for the highest AR value of 0.6. PMID:24484649

  15. Hierarchical control of ride height system for electronically controlled air suspension based on variable structure and fuzzy control theory

    NASA Astrophysics Data System (ADS)

    Xu, Xing; Zhou, Kongkang; Zou, Nannan; Jiang, Hong; Cui, Xiaoli

    2015-09-01

    The current research of air suspension mainly focuses on the characteristics and design of the air spring. In fact, electronically controlled air suspension (ECAS) has excellent performance in flexible height adjustment during different driving conditions. However, the nonlinearity of the ride height adjusting system and the uneven distribution of payload affect the control accuracy of ride height and the body attitude. Firstly, the three-point measurement system of three height sensors is used to establish the mathematical model of the ride height adjusting system. The decentralized control of ride height and the centralized control of body attitude are presented to design the ride height control system for ECAS. The exact feedback linearization method is adopted for the nonlinear mathematical model of the ride height system. Secondly, according to the hierarchical control theory, the variable structure control (VSC) technique is used to design a controller that is able to adjust the ride height for the quarter-vehicle anywhere, and each quarter-vehicle height control system is independent. Meanwhile, the three-point height signals obtained by three height sensors are tracked to calculate the body pitch and roll attitude over time, and then by calculating the deviation of pitch and roll and its rates, the height control correction is reassigned based on the fuzzy algorithm. Finally, to verify the effectiveness and performance of the proposed combined control strategy, a validating test of ride height control system with and without road disturbance is carried out. Testing results show that the height adjusting time of both lifting and lowering is over 5 s, and the pitch angle and the roll angle of body attitude are less than 0.15°. This research proposes a hierarchical control method that can guarantee the attitude stability, as well as satisfy the ride height tracking system.

  16. Hierarchical Co-based Porous Layered Double Hydroxide Arrays Derived via Alkali Etching for High-performance Supercapacitors

    PubMed Central

    Abushrenta, Nasser; Wu, Xiaochao; Wang, Junnan; Liu, Junfeng; Sun, Xiaoming

    2015-01-01

    Hierarchical nanoarchitecture and porous structure can both provide advantages for improving the electrochemical performance in energy storage electrodes. Here we report a novel strategy to synthesize new electrode materials, hierarchical Co-based porous layered double hydroxide (PLDH) arrays derived via alkali etching from Co(OH)2@CoAl LDH nanoarrays. This structure not only has the benefits of hierarchical nanoarrays including short ion diffusion path and good charge transport, but also possesses a large contact surface area owing to its porous structure which lead to a high specific capacitance (23.75 F cm−2 or 1734 F g−1 at 5 mA cm−2) and excellent cycling performance (over 85% after 5000 cycles). The enhanced electrode material is a promising candidate for supercapacitors in future application. PMID:26278334

  17. Hierarchical Co-based Porous Layered Double Hydroxide Arrays Derived via Alkali Etching for High-performance Supercapacitors

    NASA Astrophysics Data System (ADS)

    Abushrenta, Nasser; Wu, Xiaochao; Wang, Junnan; Liu, Junfeng; Sun, Xiaoming

    2015-08-01

    Hierarchical nanoarchitecture and porous structure can both provide advantages for improving the electrochemical performance in energy storage electrodes. Here we report a novel strategy to synthesize new electrode materials, hierarchical Co-based porous layered double hydroxide (PLDH) arrays derived via alkali etching from Co(OH)2@CoAl LDH nanoarrays. This structure not only has the benefits of hierarchical nanoarrays including short ion diffusion path and good charge transport, but also possesses a large contact surface area owing to its porous structure which lead to a high specific capacitance (23.75 F cm-2 or 1734 F g-1 at 5 mA cm-2) and excellent cycling performance (over 85% after 5000 cycles). The enhanced electrode material is a promising candidate for supercapacitors in future application.

  18. Supramolecular Alternating Polymer from Crown Ether and Pillar[5]arene-Based Double Molecular Recognition for Preparation of Hierarchical Materials.

    PubMed

    Li, Hui; Fan, Xiaodong; Qi, Miao; Yang, Zhen; Zhang, Haitao; Tian, Wei

    2016-01-01

    A novel supramolecular alternating polymer is constructed based on double molecular recognition events of benzo-21-crown-7 with a secondary ammonium salt and of pillar[5]arene with a neutral guest. The resulting polymer is utilized to prepare hierarchical materials with different dimensionalities for the first time. These materials included zero-dimensional spherical aggregates, one-dimensional nanofibers, two-dimensional microstructured films, and three-dimensional ordered glue. This development will be helpful for designing and preparing supramolecular hierarchical materials with different dimensionalities. PMID:26555439

  19. The potential of near-surface geophysical methods in a hierarchical monitoring approach for the detection of shallow CO2 seeps at geological storage sites

    NASA Astrophysics Data System (ADS)

    Sauer, U.; Schuetze, C.; Dietrich, P.

    2013-12-01

    The MONACO project (Monitoring approach for geological CO2 storage sites using a hierarchic observation concept) aims to find reliable monitoring tools that work on different spatial and temporal scales at geological CO2 storage sites. This integrative hierarchical monitoring approach based on different levels of coverage and resolutions is proposed as a means of reliably detecting CO2 degassing areas at ground surface level and for identifying CO2 leakages from storage formations into the shallow subsurface, as well as CO2 releases into the atmosphere. As part of this integrative hierarchical monitoring concept, several methods and technologies from ground-based remote sensing (Open-path Fourier-transform infrared (OP-FTIR) spectroscopy), regional measurements (near-surface geophysics, chamber-based soil CO2 flux measurement) and local in-situ measurements (using shallow boreholes) will either be combined or used complementary to one another. The proposed combination is a suitable concept for investigating CO2 release sites. This also presents the possibility of adopting a modular monitoring concept whereby our monitoring approach can be expanded to incorporate other methods in various coverage scales at any temporal resolution. The link between information obtained from large-scale surveys and local in-situ monitoring can be realized by sufficient geophysical techniques for meso-scale monitoring, such as geoelectrical and self-potential (SP) surveys. These methods are useful for characterizing fluid flow and transport processes in permeable near-surface sedimentary layers and can yield important information concerning CO2-affected subsurface structures. Results of measurements carried out a natural analogue site in the Czech Republic indicate that the hierarchical monitoring approach represents a successful multidisciplinary modular concept that can be used to monitor both physical and chemical processes taking place during CO2 migration and seepage. The

  20. Structural system identification using degree of freedom-based reduction and hierarchical clustering algorithm

    NASA Astrophysics Data System (ADS)

    Chang, Seongmin; Baek, Sungmin; Kim, Ki-Ook; Cho, Maenghyo

    2015-06-01

    A system identification method has been proposed to validate finite element models of complex structures using measured modal data. Finite element method is used for the system identification as well as the structural analysis. In perturbation methods, the perturbed system is expressed as a combination of the baseline structure and the related perturbations. The changes in dynamic responses are applied to determine the structural modifications so that the equilibrium may be satisfied in the perturbed system. In practical applications, the dynamic measurements are carried out on a limited number of accessible nodes and associated degrees of freedom. The equilibrium equation is, in principle, expressed in terms of the measured (master, primary) and unmeasured (slave, secondary) degrees of freedom. Only the specified degrees of freedom are included in the equation formulation for identification and the unspecified degrees of freedom are eliminated through the iterative improved reduction scheme. A large number of system parameters are included as the unknown variables in the system identification of large-scaled structures. The identification problem with large number of system parameters requires a large amount of computation time and resources. In the present study, a hierarchical clustering algorithm is applied to reduce the number of system parameters effectively. Numerical examples demonstrate that the proposed method greatly improves the accuracy and efficiency in the inverse problem of identification.

  1. Hierarchical construction of stratigraphic elements in surface-based reservoir models

    NASA Astrophysics Data System (ADS)

    Wang, Y.; Xu, S.; Mukerji, T.

    2013-12-01

    We present a surface-based simulation algorithm connecting stratigraphic hierarchy with surface-based reservoir models through statistical metrics. Geostatistical simulation algorithms provide tools for modeling spatial complexity and the resulting uncertainties for energy resource assessments. As a new family within a wide array of stochastic geological models, surface-based models and rule-based algorithms effectively represent stratigraphic responses to geological events in both time and space by assigning depositional and erosional surfaces with predefined geometries and rules. Recent advances in surface-based modeling focus on simulating morphological evolution of deep-water depositional systems and constraining models to available well and seismic data. However, especially in deep-water plays, scarce well data can only bring information about local stratal features rather than relatively general information such as hierarchy or organization, when these features are below seismic resolution. Without such information, surface-based models lack geological realism and may not be reliable even when conditioned to data. Our proposed surface-based simulation algorithm links stratigraphic hierarchy with surface-based reservoir modeling through spatial statistical tools. Ripley's K-function is used to quantitatively describe the stratigraphic distribution patterns of channel deposits. We also use the compensation index metric for quantifying the strength of compensational stacking in stratigraphic elements. These two metrics help us to extract information about sedimentary hierarchy and element organization from a set of experimental strata, and bridge physical tank experiments with numerical models. We utilize these two geostatistical metrics in conjunction with a surface-based simulation algorithm to 1) integrate clustering and compensational stacking patterns of channel deposits into reservoir modeling 2) make numerical models represent a stratigraphic hierarchical

  2. Analysis of the twin spacing and grain size effects on mechanical properties in hierarchically nanotwinned face-centered cubic metals based on a mechanism-based plasticity model

    NASA Astrophysics Data System (ADS)

    Zhu, Linli; Qu, Shaoxing; Guo, Xiang; Lu, Jian

    2015-03-01

    Hierarchical twin lamellae in polycrystalline face-centered cubic (fcc) metals possess a possibility to achieve higher strength with keeping an acceptable elongation. The present work is concerned with the analysis of twin spacing and grain size-dependent plastic performance in hierarchically nanotwinned fcc metals using a generalized strain-gradient plasticity model. The dislocation density-based physical model for constitutive description of nanotwinned fcc metals is expanded for the hierarchical structures of nanotwins. The strengthening mechanism and the failure behavior in these hierarchical nanostructures are studied to evaluate the strength and ductility. Moreover, the transition twin spacing between the strengthening and softening is obtained in different order of twin lamellae. A dislocation-based model on nucleating deformation twins is presented to predict the critical twin spacing in the lowest twin lamellae for generating the subordinate twin lamellae. Our simulation results demonstrate that the existence of the hierarchical nanotwins gives rise to a significant enhancement in the strength, and the resulting global flow stresses are sensitive to the twin spacings of the hierarchical twin lamellae and the grain size. Two softening stages are observed with variation of twin spacing, and the relevant transition twin spacing depends on the microstructural size in hierarchically nanotwinned metals. We further find that the predicted failure strain decreases with decreasing the twin spacing, which is quite different from the case of the individually nanotwinned fcc metals. The critical twin spacing for generating subordinate twins also depends on the twin spacing of superordinate twin lamellae and the grain size. These findings suggest that the high yield strength and good ductility can be achieved by optimizing the grain size and the twin spacings in the hierarchical twins.

  3. A novel vehicle dynamics stability control algorithm based on the hierarchical strategy with constrain of nonlinear tyre forces

    NASA Astrophysics Data System (ADS)

    Li, Liang; Jia, Gang; Chen, Jie; Zhu, Hongjun; Cao, Dongpu; Song, Jian

    2015-08-01

    Direct yaw moment control (DYC), which differentially brakes the wheels to produce a yaw moment for the vehicle stability in a steering process, is an important part of electric stability control system. In this field, most control methods utilise the active brake pressure with a feedback controller to adjust the braked wheel. However, the method might lead to a control delay or overshoot because of the lack of a quantitative project relationship between target values from the upper stability controller to the lower pressure controller. Meanwhile, the stability controller usually ignores the implementing ability of the tyre forces, which might be restrained by the combined-slip dynamics of the tyre. Therefore, a novel control algorithm of DYC based on the hierarchical control strategy is brought forward in this paper. As for the upper controller, a correctional linear quadratic regulator, which not only contains feedback control but also contains feed forward control, is introduced to deduce the object of the stability yaw moment in order to guarantee the yaw rate and side-slip angle stability. As for the medium and lower controller, the quantitative relationship between the vehicle stability object and the target tyre forces of controlled wheels is proposed to achieve smooth control performance based on a combined-slip tyre model. The simulations with the hardware-in-the-loop platform validate that the proposed algorithm can improve the stability of the vehicle effectively.

  4. An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

    PubMed

    Booma, P M; Prabhakaran, S; Dhanalakshmi, R

    2014-01-01

    Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

  5. Expansion of the Hierarchical Terminology Auditing Framework Through Usage of Levenshtein Distance-Based Criterion.

    PubMed

    Zakharchenko, Aleksandr; Geller, James

    2016-01-01

    We continue our previous work of creating and improving a framework for detecting the differences between hierarchical terminologies and suggesting ways to connect them, which is implemented in an automated tool. PMID:27577431

  6. Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

    PubMed

    Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

    2014-12-01

    This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies. PMID:25406641

  7. The construction of a FBG-based hierarchical AOFSN with high reliability and scalability

    NASA Astrophysics Data System (ADS)

    Peng, Li-mei; Yang, Won-Hyuk; Li, Xin-wan; Kim, Young-Chon

    2008-11-01

    To improve the reliability and scalability that are very important for large-scale all optical fiber sensor networks (AOFSN), three-level hierarchical sensor network architectures are proposed. The first two levels consist of active interrogation and RNs, respectively. The third level called sensor subnet (SSN) consists of passive FBGs and a few switches. As AOFSN is mainly multiplexed by wired and passive FBGs, the routing algorithm for scanning sensors is determined by the virtual topology of SSN due to the passivity. Therefore, the research concentrates on the construction of SSN and aims at proposing regular and unicursal virtual topology to realize reliable and scalable routing schemes. Two regular types of SSNs are proposed. Each type consists of several sensor cells (SC), square-based SC (SSC) or pentagon-based SC (PSC) and is scaled several times from the SCs. The virtual topologies maintain the self-similar square- or pentagon-like architecture so as to gain simple routing. Finally, the switch architecture of RN is proposed for the reliability of the first two levels; and then, the reliability and scalability of SSN are discussed in view of how much link failures can be tolerant, and how each SC is scaled to maintain the self-similarity, respectively.

  8. Humic acids-based hierarchical porous carbons as high-rate performance electrodes for symmetric supercapacitors.

    PubMed

    Qiao, Zhi-jun; Chen, Ming-ming; Wang, Cheng-yang; Yuan, Yun-cai

    2014-07-01

    Two kinds of hierarchical porous carbons (HPCs) with specific surface areas of 2000 m(2)g(-1) were synthesized using leonardite humic acids (LHA) or biotechnology humic acids (BHA) precursors via a KOH activation process. Humic acids have a high content of oxygen-containing groups which enabled them to dissolve in aqueous KOH and facilitated the homogeneous KOH activation. The LHA-based HPC is made up of abundant micro-, meso-, and macropores and in 6M KOH it has a specific capacitance of 178 F g(-1) at 100 Ag(-1) and its capacitance retention on going from 0.05 to 100 A g(-1) is 64%. In contrast, the BHA-based HPC exhibits a lower capacitance retention of 54% and a specific capacitance of 157 F g(-1) at 100 A g(-1) which is due to the excessive micropores in the BHA-HPC. Moreover, LHA-HPC is produced in a higher yield than BHA-HPC (51 vs. 17 wt%). PMID:24851713

  9. Bayesian methods for estimating the reliability in complex hierarchical networks (interim report).

    SciTech Connect

    Marzouk, Youssef M.; Zurn, Rena M.; Boggs, Paul T.; Diegert, Kathleen V.; Red-Horse, John Robert; Pebay, Philippe Pierre

    2007-05-01

    Current work on the Integrated Stockpile Evaluation (ISE) project is evidence of Sandia's commitment to maintaining the integrity of the nuclear weapons stockpile. In this report, we undertake a key element in that process: development of an analytical framework for determining the reliability of the stockpile in a realistic environment of time-variance, inherent uncertainty, and sparse available information. This framework is probabilistic in nature and is founded on a novel combination of classical and computational Bayesian analysis, Bayesian networks, and polynomial chaos expansions. We note that, while the focus of the effort is stockpile-related, it is applicable to any reasonably-structured hierarchical system, including systems with feedback.

  10. Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

    PubMed Central

    Dura-Bernal, Salvador; Wennekers, Thomas; Denham, Susan L.

    2012-01-01

    Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance). Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom-up interactions, for

  11. A Survey of Model Evaluation Approaches with a Tutorial on Hierarchical Bayesian Methods

    ERIC Educational Resources Information Center

    Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan

    2008-01-01

    This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…

  12. Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy.

    PubMed

    Menze, Bjoern H; Petrich, Wolfgang; Hamprecht, Fred A

    2007-03-01

    A hierarchical scheme has been developed for detection of bovine spongiform encephalopathy (BSE) in serum on the basis of its infrared spectral signature. In the first stage, binary subsets between samples originating from diseased and non-diseased cattle are defined along known covariates within the data set. Random forests are then used to select spectral channels on each subset, on the basis of a multivariate measure of variable importance, the Gini importance. The selected features are then used to establish binary discriminations within each subset by means of ridge regression. In the second stage of the hierarchical procedure the predictions from all linear classifiers are used as input to another random forest that provides the final classification. When applied to an independent, blinded validation set of 160 further spectra (84 BSE-positives, 76 BSE-negatives), the hierarchical classifier achieves a sensitivity of 92% and a specificity of 95%. Compared with results from an earlier study based on the same data, the hierarchical scheme performs better than linear discriminant analysis with features selected by genetic optimization and robust linear discriminant analysis, and performs as well as a neural network and a support vector machine. PMID:17237926

  13. Multiple target tracking by learning-based hierarchical association of detection responses.

    PubMed

    Huang, Chang; Li, Yuan; Nevatia, Ramakant

    2013-04-01

    We propose a hierarchical association approach to multiple target tracking from a single camera by progressively linking detection responses into longer track fragments (i.e., tracklets). Given frame-by-frame detection results, a conservative dual-threshold method that only links very similar detection responses between consecutive frames is adopted to generate initial tracklets with minimum identity switches. Further association of these highly fragmented tracklets at each level of the hierarchy is formulated as a Maximum A Posteriori (MAP) problem that considers initialization, termination, and transition of tracklets as well as the possibility of them being false alarms, which can be efficiently computed by the Hungarian algorithm. The tracklet affinity model, which measures the likelihood of two tracklets belonging to the same target, is a linear combination of automatically learned weak nonparametric models upon various features, which is distinct from most of previous work that relies on heuristic selection of parametric models and manual tuning of their parameters. For this purpose, we develop a novel bag ranking method and train the crucial tracklet affinity models by the boosting algorithm. This bag ranking method utilizes the soft max function to relax the oversufficient objective function used by the conventional instance ranking method. It provides a tighter upper bound of empirical errors in distinguishing correct associations from the incorrect ones, and thus yields more accurate tracklet affinity models for the tracklet association problem. We apply this approach to the challenging multiple pedestrian tracking task. Systematic experiments conducted on two real-life datasets show that the proposed approach outperforms previous state-of-the-art algorithms in terms of tracking accuracy, in particular, considerably reducing fragmentations and identity switches. PMID:23428432

  14. Web-based Hierarchical Ordering Mechanism (WHOM) tool for MODIS data from Terra

    NASA Astrophysics Data System (ADS)

    Sikder, M. S.; Eaton, P.; Leptoukh, G.; McCrimmon, N.; Zhou, B.

    2001-05-01

    At the NASA Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC), we have substantially enhanced the popular Web-based Hierarchical Ordering Mechanism (WHOM) to include data from the Earth Observing System (EOS). The GES DAAC archives unprecedented volumes of remotely sensed data and large number of geophysical products derived from the MODIS instrument on board Terra satellite, and distributes them to the world scientific and applications user community. These products are currently divided into three groups: Radiometric and Geolocation, Atmosphere, and Ocean data products. The so-called Terra-WHOM (http://acdisx.gsfc.nasa.gov/data/dataset/MODIS/index.html) is a GES DAAC developed search and order user interface which is a smaller segment of the WHOM interface that provides access to all other GES DAAC data holdings. Terra-WHOM specifically provides user access to MODIS data archived at the GES DAAC. It allows users to navigate through all the available data products and submit a data request with minimal effort. The WHOM modular design and hierarchical architecture makes this tool unique, user-friendly, and very efficient to complete the search and order. The main principle of WHOM is that it advertises the available data products, so, users always know what they can get. The WHOM design includes: simple point & click, flexible, web pages generated from templates, consistent look and feel throughout interface, and easy configuration management due to contents being encapsulated and separated from software. Modular search algorithms provide dynamic Spatial and Temporal search capability and return the search results as html pages using CGI scripts. In Terra-WHOM, calendar pages show the actual number of data granules archived for each day for high-resolution local scenes, and from there the user can go to a page showing the geo-coverage for every granule for a given day. This feature significantly optimizes user's effort for selecting data. The

  15. High performance solid state flexible supercapacitor based on molybdenum sulfide hierarchical nanospheres

    NASA Astrophysics Data System (ADS)

    Javed, Muhammad Sufyan; Dai, Shuge; Wang, Mingjun; Guo, Donglin; Chen, Lin; Wang, Xue; Hu, Chenguo; Xi, Yi

    2015-07-01

    Molybdenum sulfide (MoS2) hierarchical nanospheres are synthesized using a hydrothermal method and characterized by X-ray powder diffraction, Brunauer-Emmett-Teller, scanning electron microscopy and transmission electron microscopy. The prepared MoS2 is used to fabricate solid state flexible supercapacitors which show excellent electrochemical performance such as high capacitance 368 F g-1 at a scan rate of 5 mV s-1 and high power density of 128 W kg-1 at energy density of 5.42 Wh kg-1. The fabricated supercapacitor presents good characteristics such as lightweight, low cast, portability, high flexibility, and long term cycling stability by retaining 96.5% after 5000 cycles at constant discharge current of 0.8 mA. Electrochemical impedance spectroscopy (EIS) results reveal low resistance and suggest that MoS2 nanospheres would be a promising candidate for supercapacitors. Three charged supercapacitors connected in series can light 8 red color commercial light emitting diodes (LEDs) for 2 min, demonstrating its capability as a good storage device.

  16. Hierarchical densification and negative thermal expansion in Ce-based metallic glass under high pressure.

    PubMed

    Luo, Qiang; Garbarino, Gaston; Sun, Baoan; Fan, Dawei; Zhang, Yue; Wang, Zhi; Sun, Yajuan; Jiao, Jin; Li, Xiaodong; Li, Pengshan; Mattern, Norbert; Eckert, Jürgen; Shen, Jun

    2015-01-01

    The polyamorphsim in amorphous materials is one of the most fascinating topics in condensed matter physics. In amorphous metals, the nature of polyamorphic transformation is poorly understood. Here we investigate the structural evolution of a Ce-based metallic glass (MG) with pressure at room temperature (RT) and near the glass transition temperature by synchrotron X-ray diffraction, uncovering novel behaviours. The MG shows hierarchical densification processes at both temperatures, arising from the hierarchy of interatomic interactions. In contrast with a continuous and smooth process for the low- to medium-density amorphous state transformation at RT, a relatively abrupt and discontinuous transformation around 5.5 GPa is observed at 390 K, suggesting a possible weak first-order nature. Furthermore, both positive and abnormal-negative thermal expansion behaviours on medium-range order are observed in different pressure windows, which could be related to the low-energy vibrational motions and relaxation of the weakly linked solute-centred clusters. PMID:25641091

  17. Hierarchical Conformational Analysis of Native Lysozyme Based on Sub-Millisecond Molecular Dynamics Simulations

    PubMed Central

    Wang, Kai; Long, Shiyang; Tian, Pu

    2015-01-01

    Hierarchical organization of free energy landscape (FEL) for native globular proteins has been widely accepted by the biophysics community. However, FEL of native proteins is usually projected onto one or a few dimensions. Here we generated collectively 0.2 milli-second molecular dynamics simulation trajectories in explicit solvent for hen egg white lysozyme (HEWL), and carried out detailed conformational analysis based on backbone torsional degrees of freedom (DOF). Our results demonstrated that at micro-second and coarser temporal resolutions, FEL of HEWL exhibits hub-like topology with crystal structures occupying the dominant structural ensemble that serves as the hub of conformational transitions. However, at 100ns and finer temporal resolutions, conformational substates of HEWL exhibit network-like topology, crystal structures are associated with kinetic traps that are important but not dominant ensembles. Backbone torsional state transitions on time scales ranging from nanoseconds to beyond microseconds were found to be associated with various types of molecular interactions. Even at nanoseconds temporal resolution, the number of conformational substates that are of statistical significance is quite limited. These observations suggest that detailed analysis of conformational substates at multiple temporal resolutions is both important and feasible. Transition state ensembles among various conformational substates at microsecond temporal resolution were observed to be considerably disordered. Life times of these transition state ensembles are found to be nearly independent of the time scales of the participating torsional DOFs. PMID:26057625

  18. Wyner-Ziv video coding based on a new hierarchical block matching algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Rong Ke; Zhao, Hong Bo; Yue, Zhi

    2008-02-01

    Distributed video coding (DVC) is a new video coding paradigm that shifts the complexity from the encoder side to the decoder side. One particular case of DVC, the Wyner-Ziv coding scheme, encodes each video frame separately and decodes the video sequence jointly with side information. This paper presents a new Wyner-Ziv video coding scheme based on hierarchical block matching algorithm (HBMA). In this proposed scheme, the side information is greatly refined to assist the reconstruction of the Wyner-Ziv frames. The bidirectional motion estimation and the forward motion estimation are associated to generate the interpolated frame from temporally adjacent key frames to attain the high fidelity side information. During the bidirectional motion estimation, the size of the block and the search area vary at different levels of hierarchy. In additional, the motion vectors are inherited from big blocks to small blocks by choosing the smallest mean-of-the-absolute-difference value among neighboring blocks. Preliminary experiment results show that the proposed scheme can achieve better rate-distortion performance by 0.5-1 dB compared to the existing Wyner-Ziv video coding with the slightly increased decoding complexity.

  19. Hierarchical Control Scheme for Improving Transient Voltage Recovery of a DFIG-Based WPP

    SciTech Connect

    Kim, Jinho; Muljadi, Eduard; Kang, Yong Cheol

    2015-06-05

    Modern grid codes require that wind power plants (WPPs) inject reactive power according to the voltage dip at a point of interconnection (POI). This requirement helps to support a POI voltage during a fault. However, if a fault is cleared, the POI and wind turbine generator (WTG) voltages are likely to exceed acceptable levels unless the WPP reduces the injected reactive power quickly. This might deteriorate the stability of a grid by allowing the disconnection of WTGs to avoid any damage. This paper proposes a hierarchical control scheme of a doubly-fed induction generator (DFIG)-based WPP. The proposed scheme aims to improve the reactive power injecting capability during the fault and suppress the overvoltage after the fault clearance. To achieve the former, an adaptive reactive power-to-voltage scheme is implemented in each DFIG controller so that a DFIG with a larger reactive power capability will inject more reactive power. To achieve the latter, a washout filter is used to capture a high frequency component contained in the WPP voltage, which is used to remove the accumulated values in the proportional-integral controllers. Test results indicate that the scheme successfully supports the grid voltage during the fault, and recovers WPP voltages without exceeding the limit after the fault clearance.

  20. Prolonging the Lifetime of Wireless Sensor Networks Interconnected to Fixed Network Using Hierarchical Energy Tree Based Routing Algorithm

    PubMed Central

    Kalpana, M.; Dhanalakshmi, R.; Parthiban, P.

    2014-01-01

    This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN). It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA) based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP). The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV. PMID:25535626

  1. Prolonging the lifetime of wireless sensor networks interconnected to fixed network using hierarchical energy tree based routing algorithm.

    PubMed

    Kalpana, M; Dhanalakshmi, R; Parthiban, P

    2014-01-01

    This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN). It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA) based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP). The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV. PMID:25535626

  2. Quality assurance controls in research data base management: nonsense codes in hierarchical file structures

    SciTech Connect

    Farrell, M.P.; Strand, R.H.; Magoun, A.D.; Pennington, C.H.; Schramm, H.; Cobb, S.P.; Daniels, K.

    1980-01-01

    In complex studies using multiple data bases composed of hierarchical file structures, there is a high probability that errors may be perpetuated into summary reports unless some form of quality assurance is integrated into the research data base management program. In studies that substitute numeric codes for variable values, this problem of error propagation is even more acute. This paper addresses the problem of error propagation in those studies employing a coding scheme to represent longer alphanumeric values. Several approaches are available that minimize errors in coding variables. Numeric codes with embedded information allocated to positions within the value codes are widely used. Such smart codes require a full knowledge of the universe the variables describe as well as the potential classification schemes for each variable. Nonsense codes, or codes without embedded information, efficiently circumvent the problems associated with smart codes. Alphanumeric variable values are assigned a sequential numeric code as new values are encountered in the data base. With nonsense codes, the management approach is open-ended and does not require a knowledge of the number of potential classification levels for the variables. Experience indicates that coding errors appear to be less frequent with nonsense codes. The use of the FORMAT procedure in SAS/version 79.2 complements the nonsense code approach using variable labeling. Current restrictions in the use of the FORMAT statement and the sort order of the labels in BY statements or PRINT requests can be circumvented by using the PUT function to assign format values to a new character variable. 1 figure.

  3. Influence of geometry on mechanical properties of bio-inspired silica-based hierarchical materials.

    PubMed

    Dimas, Leon S; Buehler, Markus J

    2012-09-01

    Diatoms, bone, nacre and deep-sea sponges are mineralized natural structures found abundantly in nature. They exhibit mechanical properties on par with advanced engineering materials, yet their fundamental building blocks are brittle and weak. An intriguing characteristic of these structures is their heterogeneous distribution of mechanical properties. Specifically, diatoms exhibit nanoscale porosity in specific geometrical configurations to create regions with distinct stress strain responses, notably based on a single and simple building block, silica. The study reported here, using models derived from first principles based full atomistic studies with the ReaxFF reactive force field, focuses on the mechanics and deformation mechanisms of silica-based nanocomposites inspired by mineralized structures. We examine single edged notched tensile specimens and analyze stress and strain fields under varied sample size in order to gain fundamental insights into the deformation mechanisms of structures with distinct ordered arrangements of soft and stiff phases. We find that hierarchical arrangements of silica nanostructures markedly change the stress and strain transfer in the samples. The combined action of strain transfer in the deformable phase, and stress transfer in the strong phase, acts synergistically to reduce the intensity of stress concentrations around a crack tip, and renders the resulting composites less sensitive to the presence of flaws, for certain geometrical configurations it even leads to stable crack propagation. A systematic study allows us to identify composite structures with superior fracture mechanical properties relative to their constituents, akin to many natural biomineralized materials that turn the weaknesses of building blocks into a strength of the overall system. PMID:22740585

  4. 3 D Hierarchical Porous Carbon for Supercapacitors Prepared from Lignin through a Facile Template-Free Method.

    PubMed

    Zhang, Wenli; Lin, Haibo; Lin, Zheqi; Yin, Jian; Lu, Haiyan; Liu, Dechen; Zhao, Mingzhu

    2015-06-22

    Lignin-derived hierarchical porous carbon (LHPC) was prepared through a facile template-free method. Solidification of the lignin-KOH solution resulted in KOH crystalizing within lignin. The crystalized KOH particles in solid lignin acted both as template and activating agent in the heat-treatment process. The obtained LHPC, exhibiting a 3D network, consisted of macroporous cores, mesoporous channels, and micropores. The LHPC comprised 12.27 at % oxygen-containing groups, which resulted in pseudocapacitance. The LHPC displayed a capacitance of 165.0 F g(-1) in 1 M H2 SO4 at 0.05 A g(-1) , and the capacitance was still 123.5 F g(-1) even at 10 A g(-1) . The LHPC also displayed excellent cycling stability with capacitance retention of 97.3 % after 5000 galvanostatic charge-discharge cycles. On account of the facile preparation of LHPC, this paper offers a facile alternative method for the preparation of hierarchical porous carbon for electrochemical energy storage devices. PMID:26033894

  5. Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging

    NASA Astrophysics Data System (ADS)

    Wainwright, Haruko M.; Flores Orozco, Adrian; Bücker, Matthias; Dafflon, Baptiste; Chen, Jinsong; Hubbard, Susan S.; Williams, Kenneth H.

    2016-01-01

    In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this study, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys and drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain.

  6. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    NASA Astrophysics Data System (ADS)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  7. Optimizing an estuarine water quality monitoring program through an entropy-based hierarchical spatiotemporal Bayesian framework

    NASA Astrophysics Data System (ADS)

    Alameddine, Ibrahim; Karmakar, Subhankar; Qian, Song S.; Paerl, Hans W.; Reckhow, Kenneth H.

    2013-10-01

    The total maximum daily load program aims to monitor more than 40,000 standard violations in around 20,000 impaired water bodies across the United States. Given resource limitations, future monitoring efforts have to be hedged against the uncertainties in the monitored system, while taking into account existing knowledge. In that respect, we have developed a hierarchical spatiotemporal Bayesian model that can be used to optimize an existing monitoring network by retaining stations that provide the maximum amount of information, while identifying locations that would benefit from the addition of new stations. The model assumes the water quality parameters are adequately described by a joint matrix normal distribution. The adopted approach allows for a reduction in redundancies, while emphasizing information richness rather than data richness. The developed approach incorporates the concept of entropy to account for the associated uncertainties. Three different entropy-based criteria are adopted: total system entropy, chlorophyll-a standard violation entropy, and dissolved oxygen standard violation entropy. A multiple attribute decision making framework is adopted to integrate the competing design criteria and to generate a single optimal design. The approach is implemented on the water quality monitoring system of the Neuse River Estuary in North Carolina, USA. The model results indicate that the high priority monitoring areas identified by the total system entropy and the dissolved oxygen violation entropy criteria are largely coincident. The monitoring design based on the chlorophyll-a standard violation entropy proved to be less informative, given the low probabilities of violating the water quality standard in the estuary.

  8. Hierarchical CaCO3 chromatography: a stationary phase based on biominerals.

    PubMed

    Sato, Kosuke; Oaki, Yuya; Takahashi, Daisuke; Toshima, Kazunobu; Imai, Hiroaki

    2015-03-23

    In biomineralization, acidic macromolecules play important roles for the growth control of crystals through a specific interaction. Inspired by this interaction, we report on an application of the hierarchical structures in CaCO3 biominerals to a stationary phase of chromatography. The separation and purification of acidic small organic molecules are achieved by thin-layer chromatography and flash chromatography using the powder of biominerals as the stationary phase. The unit nanocrystals and their oriented assembly, the hierarchical structure, are suitable for the adsorption site of the target organic molecules and the flow path of the elution solvents, respectively. The separation mode is ascribed to the specific adsorption of the acidic molecules on the crystal face and the coordination of the functional groups to the calcium ions. The results imply that a new family of stationary phase of chromatography can be developed by the fine tuning of hierarchical structures in CaCO3 materials. PMID:25677568

  9. A Hierarchical Clustering Methodology for the Estimation of Toxicity

    EPA Science Inventory

    A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

  10. Density-based hierarchical clustering of pyro-sequences on a large scale—the case of fungal ITS1

    PubMed Central

    Pagni, Marco; Niculita-Hirzel, Hélène; Pellissier, Loïc; Dubuis, Anne; Xenarios, Ioannis; Guisan, Antoine; Sanders, Ian R.; Goudet, Jérôme; Guex, Nicolas

    2013-01-01

    Motivation: Analysis of millions of pyro-sequences is currently playing a crucial role in the advance of environmental microbiology. Taxonomy-independent, i.e. unsupervised, clustering of these sequences is essential for the definition of Operational Taxonomic Units. For this application, reproducibility and robustness should be the most sought after qualities, but have thus far largely been overlooked. Results: More than 1 million hyper-variable internal transcribed spacer 1 (ITS1) sequences of fungal origin have been analyzed. The ITS1 sequences were first properly extracted from 454 reads using generalized profiles. Then, otupipe, cd-hit-454, ESPRIT-Tree and DBC454, a new algorithm presented here, were used to analyze the sequences. A numerical assay was developed to measure the reproducibility and robustness of these algorithms. DBC454 was the most robust, closely followed by ESPRIT-Tree. DBC454 features density-based hierarchical clustering, which complements the other methods by providing insights into the structure of the data. Availability: An executable is freely available for non-commercial users at ftp://ftp.vital-it.ch/tools/dbc454. It is designed to run under MPI on a cluster of 64-bit Linux machines running Red Hat 4.x, or on a multi-core OSX system. Contact: dbc454@vital-it.ch or nicolas.guex@isb-sib.ch PMID:23539304

  11. A classification framework for content-based extraction of biomedical objects from hierarchically decomposed images

    NASA Astrophysics Data System (ADS)

    Thies, Christian; Schmidt Borreda, Marcel; Seidl, Thomas; Lehmann, Thomas M.

    2006-03-01

    Multiscale analysis provides a complete hierarchical partitioning of images into visually plausible regions. Each of them is formally characterized by a feature vector describing shape, texture and scale properties. Consequently, object extraction becomes a classification of the feature vectors. Classifiers are trained by relevant and irrelevant regions labeled as object and remaining partitions, respectively. A trained classifier is applicable to yet uncategorized partitionings to identify the corresponding region's classes. Such an approach enables retrieval of a-priori unknown objects within a point-and-click interface. In this work, the classification pipeline consists of a framework for data selection, feature selection, classifier training, classification of testing data, and evaluation. According to the no-free-lunch-theorem of supervised learning, the appropriate classification pipeline is determined experimentally. Therefore, each of the steps is varied by state-of-the-art methods and the respective classification quality is measured. Selection of training data from the ground truth is supported by bootstrapping, variance pooling, virtual training data, and cross validation. Feature selection for dimension reduction is performed by linear discriminant analysis, principal component analysis, and greedy selection. Competing classifiers are k-nearest-neighbor, Bayesian classifier, and the support vector machine. Quality is measured by precision and recall to reflect the retrieval task. A set of 105 hand radiographs from clinical routine serves as ground truth, where the metacarpal bones have been labeled manually. In total, 368 out of 39.017 regions are identified as relevant. In initial experiments for feature selection with the support vector machine have been obtained recall, precision and F-measure of 0.58, 0.67, and 0,62, respectively.

  12. A hierarchical network-based algorithm for multi-scale watershed delineation

    NASA Astrophysics Data System (ADS)

    Castronova, Anthony M.; Goodall, Jonathan L.

    2014-11-01

    Watershed delineation is a process for defining a land area that contributes surface water flow to a single outlet point. It is a commonly used in water resources analysis to define the domain in which hydrologic process calculations are applied. There has been a growing effort over the past decade to improve surface elevation measurements in the U.S., which has had a significant impact on the accuracy of hydrologic calculations. Traditional watershed processing on these elevation rasters, however, becomes more burdensome as data resolution increases. As a result, processing of these datasets can be troublesome on standard desktop computers. This challenge has resulted in numerous works that aim to provide high performance computing solutions to large data, high resolution data, or both. This work proposes an efficient watershed delineation algorithm for use in desktop computing environments that leverages existing data, U.S. Geological Survey (USGS) National Hydrography Dataset Plus (NHD+), and open source software tools to construct watershed boundaries. This approach makes use of U.S. national-level hydrography data that has been precomputed using raster processing algorithms coupled with quality control routines. Our approach uses carefully arranged data and mathematical graph theory to traverse river networks and identify catchment boundaries. We demonstrate this new watershed delineation technique, compare its accuracy with traditional algorithms that derive watershed solely from digital elevation models, and then extend our approach to address subwatershed delineation. Our findings suggest that the open-source hierarchical network-based delineation procedure presented in the work is a promising approach to watershed delineation that can be used summarize publicly available datasets for hydrologic model input pre-processing. Through our analysis, we explore the benefits of reusing the NHD+ datasets for watershed delineation, and find that the our technique

  13. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

    USGS Publications Warehouse

    Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

    2015-01-01

    Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

  14. Aspects Of Multicriterial Mathematical Modeling And Of The Fuzzy Formalism For The Hierarchization Of Study Programs Based On Several Quality Characteristics

    NASA Astrophysics Data System (ADS)

    Bucur, Amelia

    2015-09-01

    The aim of this paper is to present aspects of mathematical modeling for the hierarchization of study programs from universities, based on several quality characteristics. The tools used pertain to multicriterial optimization, to the different methods of assessing importance coefficients, to the utility theory, the fuzzy formalism, and to the fuzzy simple additive weighting method. The conclusion is that multicriterial decision-making methods can be efficiently used in assessing the quality of study programs, noting that, just like other methods from the decision theory, the multicriterial decision-making methods highlight aspects of problems differently, therefore, there can be no comparison or competitiveness between them, and choosing one over the other is up to the decision-maker.

  15. Hierarchical multiple binary image encryption based on a chaos and phase retrieval algorithm in the Fresnel domain

    NASA Astrophysics Data System (ADS)

    Wang, Zhipeng; Lv, Xiaodong; Wang, Hongjuan; Hou, Chenxia; Gong, Qiong; Qin, Yi

    2016-03-01

    Based on the chaos and phase retrieval algorithm, a hierarchical multiple binary image encryption is proposed. In the encryption process, each plaintext is encrypted into a diffraction intensity pattern by two chaos-generated random phase masks (RPMs). Thereafter, the captured diffraction intensity patterns are partially selected by different binary masks and then combined together to form a single intensity pattern. The combined intensity pattern is saved as ciphertext. For decryption, an iterative phase retrieval algorithm is performed, in which a support constraint in the output plane and a median filtering operation are utilized to achieve a rapid convergence rate without a stagnation problem. The proposed scheme has a simple optical setup and large encryption capacity. In particular, it is well suited for constructing a hierarchical security system. The security and robustness of the proposal are also investigated.

  16. Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items

    ERIC Educational Resources Information Center

    Huang, Hung-Yu; Wang, Wen-Chung

    2013-01-01

    Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian…

  17. Systematic hierarchical coarse-graining with the inverse Monte Carlo method.

    PubMed

    Lyubartsev, Alexander P; Naômé, Aymeric; Vercauteren, Daniel P; Laaksonen, Aatto

    2015-12-28

    We outline our coarse-graining strategy for linking micro- and mesoscales of soft matter and biological systems. The method is based on effective pairwise interaction potentials obtained in detailed ab initio or classical atomistic Molecular Dynamics (MD) simulations, which can be used in simulations at less accurate level after scaling up the size. The effective potentials are obtained by applying the inverse Monte Carlo (IMC) method [A. P. Lyubartsev and A. Laaksonen, Phys. Rev. E 52(4), 3730-3737 (1995)] on a chosen subset of degrees of freedom described in terms of radial distribution functions. An in-house software package MagiC is developed to obtain the effective potentials for arbitrary molecular systems. In this work we compute effective potentials to model DNA-protein interactions (bacterial LiaR regulator bound to a 26 base pairs DNA fragment) at physiological salt concentration at a coarse-grained (CG) level. Normally the IMC CG pair-potentials are used directly as look-up tables but here we have fitted them to five Gaussians and a repulsive wall. Results show stable association between DNA and the model protein as well as similar position fluctuation profile. PMID:26723605

  18. Systematic hierarchical coarse-graining with the inverse Monte Carlo method

    NASA Astrophysics Data System (ADS)

    Lyubartsev, Alexander P.; Naômé, Aymeric; Vercauteren, Daniel P.; Laaksonen, Aatto

    2015-12-01

    We outline our coarse-graining strategy for linking micro- and mesoscales of soft matter and biological systems. The method is based on effective pairwise interaction potentials obtained in detailed ab initio or classical atomistic Molecular Dynamics (MD) simulations, which can be used in simulations at less accurate level after scaling up the size. The effective potentials are obtained by applying the inverse Monte Carlo (IMC) method [A. P. Lyubartsev and A. Laaksonen, Phys. Rev. E 52(4), 3730-3737 (1995)] on a chosen subset of degrees of freedom described in terms of radial distribution functions. An in-house software package MagiC is developed to obtain the effective potentials for arbitrary molecular systems. In this work we compute effective potentials to model DNA-protein interactions (bacterial LiaR regulator bound to a 26 base pairs DNA fragment) at physiological salt concentration at a coarse-grained (CG) level. Normally the IMC CG pair-potentials are used directly as look-up tables but here we have fitted them to five Gaussians and a repulsive wall. Results show stable association between DNA and the model protein as well as similar position fluctuation profile.

  19. Systematic hierarchical coarse-graining with the inverse Monte Carlo method

    SciTech Connect

    Lyubartsev, Alexander P.; Naômé, Aymeric; Vercauteren, Daniel P.; Laaksonen, Aatto

    2015-12-28

    We outline our coarse-graining strategy for linking micro- and mesoscales of soft matter and biological systems. The method is based on effective pairwise interaction potentials obtained in detailed ab initio or classical atomistic Molecular Dynamics (MD) simulations, which can be used in simulations at less accurate level after scaling up the size. The effective potentials are obtained by applying the inverse Monte Carlo (IMC) method [A. P. Lyubartsev and A. Laaksonen, Phys. Rev. E 52(4), 3730–3737 (1995)] on a chosen subset of degrees of freedom described in terms of radial distribution functions. An in-house software package MagiC is developed to obtain the effective potentials for arbitrary molecular systems. In this work we compute effective potentials to model DNA-protein interactions (bacterial LiaR regulator bound to a 26 base pairs DNA fragment) at physiological salt concentration at a coarse-grained (CG) level. Normally the IMC CG pair-potentials are used directly as look-up tables but here we have fitted them to five Gaussians and a repulsive wall. Results show stable association between DNA and the model protein as well as similar position fluctuation profile.

  20. Morphology-controlled hydrothermal synthesis of MnCO{sub 3} hierarchical superstructures with Schiff base as stabilizer

    SciTech Connect

    Hu, He; Xu, Jie-yan; Yang, Hong; Liang, Jie; Yang, Shiping; Wu, Huixia

    2011-11-15

    Graphical abstract: MnCO3 microcrystals with hierarchical superstructures were synthesized by using the CO2 in atmosphere as carbonate ions source and Schiff base as shape guiding-agent in water/ethanol system under hydrothermal condition. Highlights: {yields} The most interesting in this work is the use of the greenhouse gases CO{sub 2} in atmosphere as carbonate ions source to precipitate with Mn{sup 2+} for producing MnCO{sub 3} crystals. {yields} This work is the first report related to the small organic molecule Schiff base as shape guiding-agent to produce different MnCO{sub 3} hierarchical superstructures. {yields} We are controllable synthesis of the MnCO{sub 3} hierarchical superstructures such as chrysanthemum, straw-bundle, dumbbell and sphere-like microcrystals. {yields} The as-prepared MnCO{sub 3} could be used precursor to fabricate the Mn{sub 2}O{sub 3} hierarchical superstructures after thermal decomposition at high temperature. -- Abstract: MnCO{sub 3} with hierarchical superstructures such as chrysanthemum, straw-bundle, dumbbell and sphere-like were synthesized in water/ethanol system under environment-friendly hydrothermal condition. In the synthesis process, the CO{sub 2} in atmosphere was used as the source of carbonate ions and Schiff base was used as shape guiding-agent. The different superstructures of MnCO{sub 3} could be obtained by controlling the hydrothermal temperature, the molar ratio of manganous ions to the Schiff base, or the volume ratio of water to ethanol. A tentative growth mechanism for the generation of MnCO{sub 3} superstructures was proposed based on the rod-dumbbell-sphere model. Furthermore, the MnCO{sub 3} as precursor could be further successfully transferred to Mn{sub 2}O{sub 3} microstructure after heating in the atmosphere at 500 {sup o}C, and the morphology of the Mn{sub 2}O{sub 3} was directly determined by that of the MnCO{sub 3} precursor.

  1. Hybrid hierarchical bio-based materials: Development and characterization through experimentation and computational simulations

    NASA Astrophysics Data System (ADS)

    Haq, Mahmoodul

    but seems to decrease toughness. Thus, the traditionally seen opposite measures of stiffness and toughness can be brought to an efficient balance through the combination of bio-resin and nanoclay. A multiscale computational approach, namely a multi-FE based approach, was implemented to the developed materials to extrapolate the experimental matrix, to provide insight into nano-scale behavior beyond measurements and to hopefully serve as a tool for computational design of hybrid materials. Additionally, an enhanced RVE for modeling the three-phase material was determined by solving a topology optimization based material layout problem to determine the distribution of bio-resin, thereby allowing modeling the nanostructure in greater detail and closer to reality. Overall, eco-friendly, tailorable, cost-effective and multiscale reinforced bio-based composites were successfully developed. The improved multifaceted features possible for these sustainable bio-based materials are likely to increase their appeal for use in transportation and housing structural applications. Additionally, it is believed that the approach of understanding complex materials by integrating simulations and experiments, as attempted in this work, holds great promise, and a similar methodology can be applied for other types of hierarchical materials, thereby providing guidance in designing those materials.

  2. A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

    PubMed Central

    Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

    2014-01-01

    Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification

  3. Modified distance in average linkage based on M-estimator and MADn criteria in hierarchical cluster analysis

    NASA Astrophysics Data System (ADS)

    Muda, Nora; Othman, Abdul Rahman

    2015-10-01

    The process of grouping a set of objects into classes of similar objects is called clustering. It divides a large group of observations into smaller groups so that the observations within each group are relatively similar and the observations in different groups are relatively dissimilar. In this study, an agglomerative method in hierarchical cluster analysis is chosen and clusters were constructed by using an average linkage technique. An average linkage technique requires distance between clusters, which is calculated based on the average distance between all pairs of points, one group with another group. In calculating the average distance, the distance will not be robust when there is an outlier. Therefore, the average distance in average linkage needs to be modified in order to overcome the problem of outlier. Therefore, the criteria of outlier detection based on MADn criteria is used and the average distance is recalculated without the outlier. Next, the distance in average linkage is calculated based on a modified one step M-estimator (MOM). The groups of cluster are presented in dendrogram graph. To evaluate the goodness of a modified distance in the average linkage clustering, the bootstrap analysis is conducted on the dendrogram graph and the bootstrap value (BP) are assessed for each branch in dendrogram that formed the group, to ensure the reliability of the branches constructed. This study found that the average linkage technique with modified distance is significantly superior than the usual average linkage technique, if there is an outlier. Both of these techniques are said to be similar if there is no outlier.

  4. Hierarchical Auxetic Mechanical Metamaterials

    PubMed Central

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I.; Azzopardi, Keith M.; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N.

    2015-01-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts. PMID:25670400

  5. Hierarchical Auxetic Mechanical Metamaterials

    NASA Astrophysics Data System (ADS)

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I.; Azzopardi, Keith M.; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N.

    2015-02-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts.

  6. Hierarchical auxetic mechanical metamaterials.

    PubMed

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I; Azzopardi, Keith M; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N

    2015-01-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts. PMID:25670400

  7. Short prokaryotic DNA fragment binning using a hierarchical classifier based on linear discriminant analysis and principal component analysis.

    PubMed

    Zheng, Hao; Wu, Hongwei

    2010-12-01

    Metagenomics is an emerging field in which the power of genomic analysis is applied to an entire microbial community, bypassing the need to isolate and culture individual microbial species. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed n-mer oligonucleotide frequencies as the features and developed a hierarchical classifier (PCAHIER) for binning short (≤ 1,000 bps) metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space. The hierarchical classifier consists of four layers of local classifiers that are implemented based on the linear discriminant analysis. These local classifiers are responsible for binning prokaryotic DNA fragments into superkingdoms, of the same superkingdom into phyla, of the same phylum into genera, and of the same genus into species, respectively. We evaluated the performance of the PCAHIER by using our own simulated data sets as well as the widely used simHC synthetic metagenome data set from the IMG/M system. The effectiveness of the PCAHIER was demonstrated through comparisons against a non-hierarchical classifier, and two existing binning algorithms (TETRA and Phylopythia). PMID:21121023

  8. Performance Modeling of Network-Attached Storage Device Based Hierarchical Mass Storage Systems

    NASA Technical Reports Server (NTRS)

    Menasce, Daniel A.; Pentakalos, Odysseas I.

    1995-01-01

    Network attached storage devices improve I/O performance by separating control and data paths and eliminating host intervention during the data transfer phase. Devices are attached to both a high speed network for data transfer and to a slower network for control messages. Hierarchical mass storage systems use disks to cache the most recently used files and a combination of robotic and manually mounted tapes to store the bulk of the files in the file system. This paper shows how queuing network models can be used to assess the performance of hierarchical mass storage systems that use network attached storage devices as opposed to host attached storage devices. Simulation was used to validate the model. The analytic model presented here can be used, among other things, to evaluate the protocols involved in 1/0 over network attached devices.

  9. Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

    NASA Astrophysics Data System (ADS)

    Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

    2014-12-01

    The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are

  10. Hierarchical Bayesian spatio-temporal modeling and entropy-based network design

    NASA Astrophysics Data System (ADS)

    Wu, Y.; Jin, B.; Chan, E.

    2012-12-01

    Typical spatio-temporal data include temperature, precipitation, atmospheric pressure, ozone concentration, personal income, infection prevalence, mosquito populations, among others. To model such data in a given region by hierarchical Bayesian kriging is undertaken in this paper. In addition, an environmental network design problem is also explored. For demonstration, we consider the ozone concentrations in the Toronto region of Ontario, Canada. There are many missing observations in the data. To proceed, we first formulate the hierarchical spatio-temporal model in terms of observed data. We then fill in some missing observations such that the data has the staircase structure. Thus, in light of Le and Zidek (2006), we model the ozone concentrations in Toronto region by hierarchical Bayesian kriging and derive a conditional predictive distribution of the ozone concentrations over unknown locations. To decide if a new monitoring station needs to be added or an existing station can be closed down, we solve this environmental network design problem by using the principle of maximum entropy.