Adaptive clustering algorithm for community detection in complex networks.
Ye, Zhenqing; Hu, Songnian; Yu, Jun
2008-10-01
Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality. PMID:18999501
NASA Astrophysics Data System (ADS)
Wu, Xia; Wu, Genhua
2014-08-01
Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron.
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
NASA Technical Reports Server (NTRS)
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
Anticipation versus adaptation in Evolutionary Algorithms: The case of Non-Stationary Clustering
NASA Astrophysics Data System (ADS)
González, A. I.; Graña, M.; D'Anjou, A.; Torrealdea, F. J.
1998-07-01
From the technological point of view is usually more important to ensure the ability to react promptly to changing environmental conditions than to try to forecast them. Evolution Algorithms were proposed initially to drive the adaptation of complex systems to varying or uncertain environments. In the general setting, the adaptive-anticipatory dilemma reduces itself to the placement of the interaction with the environment in the computational schema. Adaptation consists of the estimation of the proper parameters from present data in order to react to a present environment situation. Anticipation consists of the estimation from present data in order to react to a future environment situation. This duality is expressed in the Evolutionary Computation paradigm by the precise location of the consideration of present data in the computation of the individuals fitness function. In this paper we consider several instances of Evolutionary Algorithms applied to precise problem and perform an experiment that test their response as anticipative and adaptive mechanisms. The non stationary problem considered is that of Non Stationary Clustering, more precisely the adaptive Color Quantization of image sequences. The experiment illustrates our ideas and gives some quantitative results that may support the proposition of the Evolutionary Computation paradigm for other tasks that require the interaction with a Non-Stationary environment.
Adaptive Clustering of Hypermedia Documents.
ERIC Educational Resources Information Center
Johnson, Andrew; Fotouhi, Farshad
1996-01-01
Discussion of hypermedia systems focuses on a comparison of two types of adaptive algorithm (genetic algorithm and neural network) in clustering hypermedia documents. These clusters allow the user to index into the nodes to find needed information more quickly, since clustering is "personalized" based on the user's paths rather than representing…
Basic cluster compression algorithm
NASA Technical Reports Server (NTRS)
Hilbert, E. E.; Lee, J.
1980-01-01
Feature extraction and data compression of LANDSAT data is accomplished by BCCA program which reduces costs associated with transmitting, storing, distributing, and interpreting multispectral image data. Algorithm uses spatially local clustering to extract features from image data to describe spectral characteristics of data set. Approach requires only simple repetitive computations, and parallel processing can be used for very high data rates. Program is written in FORTRAN IV for batch execution and has been implemented on SEL 32/55.
Performance Comparison Of Evolutionary Algorithms For Image Clustering
NASA Astrophysics Data System (ADS)
Civicioglu, P.; Atasever, U. H.; Ozkan, C.; Besdok, E.; Karkinli, A. E.; Kesikoglu, A.
2014-09-01
Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
Adaptive continuous twisting algorithm
NASA Astrophysics Data System (ADS)
Moreno, Jaime A.; Negrete, Daniel Y.; Torres-González, Victor; Fridman, Leonid
2016-09-01
In this paper, an adaptive continuous twisting algorithm (ACTA) is presented. For double integrator, ACTA produces a continuous control signal ensuring finite time convergence of the states to zero. Moreover, the control signal generated by ACTA compensates the Lipschitz perturbation in finite time, i.e. its value converges to the opposite value of the perturbation. ACTA also keeps its convergence properties, even in the case that the upper bound of the derivative of the perturbation exists, but it is unknown.
NASA Astrophysics Data System (ADS)
Friedenberg, David
2010-10-01
the rate of falsely detected active regions. Additionally we examine the more general field of clustering and develop a framework for clustering algorithms based around diffusion maps. Diffusion maps can be used to project high-dimensional data into a lower dimensional space while preserving much of the structure in the data. We demonstrate how diffusion maps can be used to solve clustering problems and examine the influence of tuning parameters on the results. We introduce two novel methods, the self-tuning diffusion map which replaces the global scaling parameter in the typical diffusion map framework with a local scaling parameter and an algorithm for automatically selecting tuning parameters based on a cross-validation style score called prediction strength. The methods are tested on several example datasets.
Parallel Clustering Algorithms for Structured AMR
Gunney, B T; Wissink, A M; Hysom, D A
2005-10-26
We compare several different parallel implementation approaches for the clustering operations performed during adaptive gridding operations in patch-based structured adaptive mesh refinement (SAMR) applications. Specifically, we target the clustering algorithm of Berger and Rigoutsos (BR91), which is commonly used in many SAMR applications. The baseline for comparison is a simplistic parallel extension of the original algorithm that works well for up to O(10{sup 2}) processors. Our goal is a clustering algorithm for machines of up to O(10{sup 5}) processors, such as the 64K-processor IBM BlueGene/Light system. We first present an algorithm that avoids the unneeded communications of the simplistic approach to improve the clustering speed by up to an order of magnitude. We then present a new task-parallel implementation to further reduce communication wait time, adding another order of magnitude of improvement. The new algorithms also exhibit more favorable scaling behavior for our test problems. Performance is evaluated on a number of large scale parallel computer systems, including a 16K-processor BlueGene/Light system.
Ghorbanzadeh, Leila; Torshabi, Ahmad Esmaili; Nabipour, Jamshid Soltani; Arbatan, Moslem Ahmadi
2016-04-01
In image guided radiotherapy, in order to reach a prescribed uniform dose in dynamic tumors at thorax region while minimizing the amount of additional dose received by the surrounding healthy tissues, tumor motion must be tracked in real-time. Several correlation models have been proposed in recent years to provide tumor position information as a function of time in radiotherapy with external surrogates. However, developing an accurate correlation model is still a challenge. In this study, we proposed an adaptive neuro-fuzzy based correlation model that employs several data clustering algorithms for antecedent parameters construction to avoid over-fitting and to achieve an appropriate performance in tumor motion tracking compared with the conventional models. To begin, a comparative assessment is done between seven nuero-fuzzy correlation models each constructed using a unique data clustering algorithm. Then, each of the constructed models are combined within an adaptive sevenfold synthetic model since our tumor motion database has high degrees of variability and that each model has its intrinsic properties at motion tracking. In the proposed sevenfold synthetic model, best model is selected adaptively at pre-treatment. The model also updates the steps for each patient using an automatic model selectivity subroutine. We tested the efficacy of the proposed synthetic model on twenty patients (divided equally into two control and worst groups) treated with CyberKnife synchrony system. Compared to Cyberknife model, the proposed synthetic model resulted in 61.2% and 49.3% reduction in tumor tracking error in worst and control group, respectively. These results suggest that the proposed model selection program in our synthetic neuro-fuzzy model can significantly reduce tumor tracking errors. Numerical assessments confirmed that the proposed synthetic model is able to track tumor motion in real time with high accuracy during treatment. PMID:25765021
The SMART CLUSTER METHOD - adaptive earthquake cluster analysis and declustering
NASA Astrophysics Data System (ADS)
Schaefer, Andreas; Daniell, James; Wenzel, Friedemann
2016-04-01
Earthquake declustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity with usual applications comprising of probabilistic seismic hazard assessments (PSHAs) and earthquake prediction methods. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation. Various methods have been developed to address this issue from other researchers. These have differing ranges of complexity ranging from rather simple statistical window methods to complex epidemic models. This study introduces the smart cluster method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal identification. Hereby, an adaptive search algorithm for data point clusters is adopted. It uses the earthquake density in the spatio-temporal neighbourhood of each event to adjust the search properties. The identified clusters are subsequently analysed to determine directional anisotropy, focussing on a strong correlation along the rupture plane and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010/2011 Darfield-Christchurch events, an adaptive classification procedure is applied to disassemble subsequent ruptures which may have been grouped into an individual cluster using near-field searches, support vector machines and temporal splitting. The steering parameters of the search behaviour are linked to local earthquake properties like magnitude of completeness, earthquake density and Gutenberg-Richter parameters. The method is capable of identifying and classifying earthquake clusters in space and time. It is tested and validated using earthquake data from California and New Zealand. As a result of the cluster identification process, each event in
Cubit Adaptive Meshing Algorithm Library
2004-09-01
CAMAL (Cubit adaptive meshing algorithm library) is a software component library for mesh generation. CAMAL 2.0 includes components for triangle, quad and tetrahedral meshing. A simple Application Programmers Interface (API) takes a discrete boundary definition and CAMAL computes a quality interior unstructured grid. The triangle and quad algorithms may also import a geometric definition of a surface on which to define the grid. CAMALs triangle meshing uses a 3D space advancing front method, the quadmore » meshing algorithm is based upon Sandias patented paving algorithm and the tetrahedral meshing algorithm employs the GHS3D-Tetmesh component developed by INRIA, France.« less
Adaptive protection algorithm and system
Hedrick, Paul [Pittsburgh, PA; Toms, Helen L [Irwin, PA; Miller, Roger M [Mars, PA
2009-04-28
An adaptive protection algorithm and system for protecting electrical distribution systems traces the flow of power through a distribution system, assigns a value (or rank) to each circuit breaker in the system and then determines the appropriate trip set points based on the assigned rank.
Streamlining algorithms for complete adaptation
NASA Technical Reports Server (NTRS)
Erickson, J. C., Jr. (Editor); Chevallier, J. P.; Goodyer, Michael J.; Hornung, Hans G.; Mignosi, Andre; Sears, William R.; Smith, J.; Wedemeyer, Erich H.
1990-01-01
For purposes of the adaptive-wall algorithms to be described, the modern era is considered to have begun with the simultaneous, independent recognition of the concept of matching an experimental inner flow across an interface to a computed outer flow by Chevallier, Ferri, Goodyer, Lissaman, Rubbert, and Sears. Fundamental investigations of the adaptive-wall matching concept by means of numerical simulations and theoretical considerations are described. An overview of the development and operation of 2D adaptive-wall facilities from about 1970 until the present is given, followed by similar material for 3D adaptive-wall facilities from approximately 1978 until the present. A general formulation of adaptation strategy is presented, with a theoretical basis for adaptation followed by 2D flexible, impermeable-wall applications; 2D ventilated-wall applications; 3D flexible, impermeable-wall applications; and 3D ventilated-wall applications. Representative experimental and 3D results are given, with 2D, followed by a discussion of limitations and open questions.
Hesitant fuzzy agglomerative hierarchical clustering algorithms
NASA Astrophysics Data System (ADS)
Zhang, Xiaolu; Xu, Zeshui
2015-02-01
Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results.
Basic firefly algorithm for document clustering
NASA Astrophysics Data System (ADS)
Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza
2015-12-01
The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process. To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simplicity, such an approach tends to be trapped in a local minimum during its search for an optimal solution. To address the shortcoming, this paper proposes a Basic Firefly (Basic FA) algorithm to cluster text documents. The algorithm employs the Average Distance to Document Centroid (ADDC) as the objective function of the search. Experiments utilizing the proposed algorithm were conducted on the 20Newsgroups benchmark dataset. Results demonstrate that the Basic FA generates a more robust and compact clusters than the ones produced by K-means and Particle Swarm Optimization (PSO).
A survey of fuzzy clustering algorithms for pattern recognition. II.
Baraldi, A; Blonda, P
1999-01-01
For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms. PMID:18252358
Color sorting algorithm based on K-means clustering algorithm
NASA Astrophysics Data System (ADS)
Zhang, BaoFeng; Huang, Qian
2009-11-01
In the process of raisin production, there were a variety of color impurities, which needs be removed effectively. A new kind of efficient raisin color-sorting algorithm was presented here. First, the technology of image processing basing on the threshold was applied for the image pre-processing, and then the gray-scale distribution characteristic of the raisin image was found. In order to get the chromatic aberration image and reduce some disturbance, we made the flame image subtraction that the target image data minus the background image data. Second, Haar wavelet filter was used to get the smooth image of raisins. According to the different colors and mildew, spots and other external features, the calculation was made to identify the characteristics of their images, to enable them to fully reflect the quality differences between the raisins of different types. After the processing above, the image were analyzed by K-means clustering analysis method, which can achieve the adaptive extraction of the statistic features, in accordance with which, the image data were divided into different categories, thereby the categories of abnormal colors were distinct. By the use of this algorithm, the raisins of abnormal colors and ones with mottles were eliminated. The sorting rate was up to 98.6%, and the ratio of normal raisins to sorted grains was less than one eighth.
Self-organization and clustering algorithms
NASA Technical Reports Server (NTRS)
Bezdek, James C.
1991-01-01
Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed.
Open cluster membership probability based on K-means clustering algorithm
NASA Astrophysics Data System (ADS)
El Aziz, Mohamed Abd; Selim, I. M.; Essam, A.
2016-08-01
In the field of galaxies images, the relative coordinate positions of each star with respect to all the other stars are adapted. Therefore the membership of star cluster will be adapted by two basic criterions, one for geometric membership and other for physical (photometric) membership. So in this paper, we presented a new method for the determination of open cluster membership based on K-means clustering algorithm. This algorithm allows us to efficiently discriminate the cluster membership from the field stars. To validate the method we applied it on NGC 188 and NGC 2266, membership stars in these clusters have been obtained. The color-magnitude diagram of the membership stars is significantly clearer and shows a well-defined main sequence and a red giant branch in NGC 188, which allows us to better constrain the cluster members and estimate their physical parameters. The membership probabilities have been calculated and compared to those obtained by the other methods. The results show that the K-means clustering algorithm can effectively select probable member stars in space without any assumption about the spatial distribution of stars in cluster or field. The similarity of our results is in a good agreement with results derived by previous works.
Research on algorithms for adaptive antenna arrays
NASA Astrophysics Data System (ADS)
Widrow, B.; Newman, W.; Gooch, R.; Duvall, K.; Shur, D.
1981-08-01
The fundamental efficiency of adaptive algorithms is analyzed. It is found that noise in the adaptive weights increases with convergence speed. This causes loss in mean-square-error performance. Efficiency is considered from the point of view of misadjustment versus speed of convergence. A new version of the LMS algorithm based on Newton's method is analyzed and shown to make maximally efficient use of real-time input data. The performance of this algorithm is not affected by eigenvalue disparity. Practical algorithms can be devised that closely approximate Newton's method. In certain cases, the steepest descent version of LMS performs as well as Newton's method. The efficiency of adaptive algorithms with nonstationary input environments is analyzed where signals, jammers, and background noises can be of a transient and nonstationary nature. A new adaptive filtering method for broadband adaptive beamforming is described which uses both poles and zeros in the adaptive signal filtering paths from the antenna elements to the final array output.
An algorithm for spatial heirarchy clustering
NASA Technical Reports Server (NTRS)
Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.
1981-01-01
A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.
An adaptive algorithm for noise rejection.
Lovelace, D E; Knoebel, S B
1978-01-01
An adaptive algorithm for the rejection of noise artifact in 24-hour ambulatory electrocardiographic recordings is described. The algorithm is based on increased amplitude distortion or increased frequency of fluctuations associated with an episode of noise artifact. The results of application of the noise rejection algorithm on a high noise population of test tapes are discussed.
Adaptive-clustering optical neural net.
Casasent, D P; Barnard, E
1990-06-10
Pattern recognition techniques (for clustering and linear discriminant function selection) are combined with neural net methods (that provide an automated method to combine linear discriminant functions into piecewise linear discriminant surfaces). The resulting adaptive-clustering neural net is suitable for optical implementation and has certain desirable properties in comparison with other neural nets. Simulation results are provided.
User-Based Document Clustering by Redescribing Subject Descriptions with a Genetic Algorithm.
ERIC Educational Resources Information Center
Gordon, Michael D.
1991-01-01
Discussion of clustering of documents and queries in information retrieval systems focuses on the use of a genetic algorithm to adapt subject descriptions so that documents become more effective in matching relevant queries. Various types of clustering are explained, and simulation experiments used to test the genetic algorithm are described. (27…
A Task-parallel Clustering Algorithm for Structured AMR
Gunney, B N; Wissink, A M
2004-11-02
A new parallel algorithm, based on the Berger-Rigoutsos algorithm for clustering grid points into logically rectangular regions, is presented. The clustering operation is frequently performed in the dynamic gridding steps of structured adaptive mesh refinement (SAMR) calculations. A previous study revealed that although the cost of clustering is generally insignificant for smaller problems run on relatively few processors, the algorithm scaled inefficiently in parallel and its cost grows with problem size. Hence, it can become significant for large scale problems run on very large parallel machines, such as the new BlueGene system (which has {Omicron}(10{sup 4}) processors). We propose a new task-parallel algorithm designed to reduce communication wait times. Performance was assessed using dynamic SAMR re-gridding operations on up to 16K processors of currently available computers at Lawrence Livermore National Laboratory. The new algorithm was shown to be up to an order of magnitude faster than the baseline algorithm and had better scaling trends.
QPSO-based adaptive DNA computing algorithm.
Karakose, Mehmet; Cigdem, Ugur
2013-01-01
DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.
Adaptive sensor fusion using genetic algorithms
Fitzgerald, D.S.; Adams, D.G.
1994-08-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.
Noise-enhanced clustering and competitive learning algorithms.
Osoba, Osonde; Kosko, Bart
2013-01-01
Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.
Cluster compression algorithm: A joint clustering/data compression concept
NASA Technical Reports Server (NTRS)
Hilbert, E. E.
1977-01-01
The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.
Chaotic map clustering algorithm for EEG analysis
NASA Astrophysics Data System (ADS)
Bellotti, R.; De Carlo, F.; Stramaglia, S.
2004-03-01
The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.
An incremental clustering algorithm based on Mahalanobis distance
NASA Astrophysics Data System (ADS)
Aik, Lim Eng; Choon, Tan Wee
2014-12-01
Classical fuzzy c-means clustering algorithm is insufficient to cluster non-spherical or elliptical distributed datasets. The paper replaces classical fuzzy c-means clustering euclidean distance with Mahalanobis distance. It applies Mahalanobis distance to incremental learning for its merits. A Mahalanobis distance based fuzzy incremental clustering learning algorithm is proposed. Experimental results show the algorithm is an effective remedy for the defect in fuzzy c-means algorithm but also increase training accuracy.
AIDA: Adaptive Image Deconvolution Algorithm
NASA Astrophysics Data System (ADS)
Hom, Erik; Haase, Sebastian; Marchis, Franck
2013-10-01
AIDA is an implementation and extension of the MISTRAL myopic deconvolution method developed by Mugnier et al. (2004) (see J. Opt. Soc. Am. A 21:1841-1854). The MISTRAL approach has been shown to yield object reconstructions with excellent edge preservation and photometric precision when used to process astronomical images. AIDA improves upon the original MISTRAL implementation. AIDA, written in Python, can deconvolve multiple frame data and three-dimensional image stacks encountered in adaptive optics and light microscopic imaging.
Adaptive link selection algorithms for distributed estimation
NASA Astrophysics Data System (ADS)
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
Clustering of noisy image data using an adaptive neuro-fuzzy system
NASA Technical Reports Server (NTRS)
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Synchronous Firefly Algorithm for Cluster Head Selection in WSN
Baskaran, Madhusudhanan; Sadagopan, Chitra
2015-01-01
Wireless Sensor Network (WSN) consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH) offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC. PMID:26495431
Synchronous Firefly Algorithm for Cluster Head Selection in WSN.
Baskaran, Madhusudhanan; Sadagopan, Chitra
2015-01-01
Wireless Sensor Network (WSN) consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH) offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC. PMID:26495431
Classification of posture maintenance data with fuzzy clustering algorithms
NASA Technical Reports Server (NTRS)
Bezdek, James C.
1991-01-01
Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various conditions were collected in conjunction with JSC postural control studies using a Tilt-Translation Device (TTD). The University of West Florida proposed applying the Fuzzy C-Means Clustering (FCM) Algorithms to this data with a view towards identifying various states and stages. Data supplied by NASA/JSC were submitted to the FCM algorithms in an attempt to identify and characterize cluster substructure in a mixed ensemble of pre- and post-adaptational TTD data. Following several unsuccessful trials with FCM using a full 11 dimensional data set, a set of two channels (features) were found to enable FCM to separate pre- from post-adaptational TTD data. The main conclusions are that: (1) FCM seems able to separate pre- from post-TTD subject no. 2 on the one trial that was used, but only in certain subintervals of time; and (2) Channels 2 (right rear transducer force) and 8 (hip sway bar) contain better discrimination information than other supersets and combinations of the data that were tried so far.
Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters
Tellaroli, Paola; Bazzi, Marco; Donato, Michele; Brazzale, Alessandra R.; Drăghici, Sorin
2016-01-01
Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository. PMID:27015427
Improved Ant Colony Clustering Algorithm and Its Performance Study.
Gao, Wei
2016-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533
Improved Ant Colony Clustering Algorithm and Its Performance Study
Gao, Wei
2016-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533
Adaptation dynamics in densely clustered chemoreceptors.
Pontius, William; Sneddon, Michael W; Emonet, Thierry
2013-01-01
In many sensory systems, transmembrane receptors are spatially organized in large clusters. Such arrangement may facilitate signal amplification and the integration of multiple stimuli. However, this organization likely also affects the kinetics of signaling since the cytoplasmic enzymes that modulate the activity of the receptors must localize to the cluster prior to receptor modification. Here we examine how these spatial considerations shape signaling dynamics at rest and in response to stimuli. As a model system, we use the chemotaxis pathway of Escherichia coli, a canonical system for the study of how organisms sense, respond, and adapt to environmental stimuli. In bacterial chemotaxis, adaptation is mediated by two enzymes that localize to the clustered receptors and modulate their activity through methylation-demethylation. Using a novel stochastic simulation, we show that distributive receptor methylation is necessary for successful adaptation to stimulus and also leads to large fluctuations in receptor activity in the steady state. These fluctuations arise from noise in the number of localized enzymes combined with saturated modification kinetics between the localized enzymes and the receptor substrate. An analytical model explains how saturated enzyme kinetics and large fluctuations can coexist with an adapted state robust to variation in the expression levels of the pathway constituents, a key requirement to ensure the functionality of individual cells within a population. This contrasts with the well-mixed covalent modification system studied by Goldbeter and Koshland in which mean activity becomes ultrasensitive to protein abundances when the enzymes operate at saturation. Large fluctuations in receptor activity have been quantified experimentally and may benefit the cell by enhancing its ability to explore empty environments and track shallow nutrient gradients. Here we clarify the mechanistic relationship of these large fluctuations to well
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Parallelization of Edge Detection Algorithm using MPI on Beowulf Cluster
NASA Astrophysics Data System (ADS)
Haron, Nazleeni; Amir, Ruzaini; Aziz, Izzatdin A.; Jung, Low Tan; Shukri, Siti Rohkmah
In this paper, we present the design of parallel Sobel edge detection algorithm using Foster's methodology. The parallel algorithm is implemented using MPI message passing library and master/slave algorithm. Every processor performs the same sequential algorithm but on different part of the image. Experimental results conducted on Beowulf cluster are presented to demonstrate the performance of the parallel algorithm.
A hybrid monkey search algorithm for clustering analysis.
Chen, Xin; Zhou, Yongquan; Luo, Qifang
2014-01-01
Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.
Adaptive computation algorithm for RBF neural network.
Han, Hong-Gui; Qiao, Jun-Fei
2012-02-01
A novel learning algorithm is proposed for nonlinear modelling and identification using radial basis function neural networks. The proposed method simplifies neural network training through the use of an adaptive computation algorithm (ACA). In addition, the convergence of the ACA is analyzed by the Lyapunov criterion. The proposed algorithm offers two important advantages. First, the model performance can be significantly improved through ACA, and the modelling error is uniformly ultimately bounded. Secondly, the proposed ACA can reduce computational cost and accelerate the training speed. The proposed method is then employed to model classical nonlinear system with limit cycle and to identify nonlinear dynamic system, exhibiting the effectiveness of the proposed algorithm. Computational complexity analysis and simulation results demonstrate its effectiveness.
Robust growing neural gas algorithm with application in cluster analysis.
Qin, A K; Suganthan, P N
2004-01-01
We propose a novel robust clustering algorithm within the Growing Neural Gas (GNG) framework, called Robust Growing Neural Gas (RGNG) network.The Matlab codes are available from . By incorporating several robust strategies, such as outlier resistant scheme, adaptive modulation of learning rates and cluster repulsion method into the traditional GNG framework, the proposed RGNG network possesses better robustness properties. The RGNG is insensitive to initialization, input sequence ordering and the presence of outliers. Furthermore, the RGNG network can automatically determine the optimal number of clusters by seeking the extreme value of the Minimum Description Length (MDL) measure during network growing process. The resulting center positions of the optimal number of clusters represented by prototype vectors are close to the actual ones irrespective of the existence of outliers. Topology relationships among these prototypes can also be established. Experimental results have shown the superior performance of our proposed method over the original GNG incorporating MDL method, called GNG-M, in static data clustering tasks on both artificial and UCI data sets. PMID:15555857
Controlling cluster synchronization by adapting the topology.
Lehnert, Judith; Hövel, Philipp; Selivanov, Anton; Fradkov, Alexander; Schöll, Eckehard
2014-10-01
We suggest an adaptive control scheme for the control of in-phase and cluster synchronization in delay-coupled networks. Based on the speed-gradient method, our scheme adapts the topology of a network such that the target state is realized. It is robust towards different initial conditions as well as changes in the coupling parameters. The emerging topology is characterized by a delicate interplay of excitatory and inhibitory links leading to the stabilization of the desired cluster state. As a crucial parameter determining this interplay we identify the delay time. Furthermore, we show how to construct networks such that they exhibit not only a given cluster state but also with a given oscillation frequency. We apply our method to coupled Stuart-Landau oscillators, a paradigmatic normal form that naturally arises in an expansion of systems close to a Hopf bifurcation. The successful and robust control of this generic model opens up possible applications in a wide range of systems in physics, chemistry, technology, and life science.
Classification of posture maintenance data with fuzzy clustering algorithms
NASA Technical Reports Server (NTRS)
Bezdek, James C.
1992-01-01
Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various sensory organization test (SOT) conditions were collected in conjunction with Johnson Space Center postural control studies using a tilt-translation device (TTD). The University of West Florida applied the fuzzy c-meams (FCM) clustering algorithms to this data with a view towards identifying various states and stages of subjects experiencing such changes. Feature analysis, time step analysis, pooling data, response of the subjects, and the algorithms used are discussed.
A novel clustering algorithm inspired by membrane computing.
Peng, Hong; Luo, Xiaohui; Gao, Zhisheng; Wang, Jun; Pei, Zheng
2015-01-01
P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.
A Novel Clustering Algorithm Inspired by Membrane Computing
Luo, Xiaohui; Gao, Zhisheng; Wang, Jun; Pei, Zheng
2015-01-01
P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature. PMID:25874264
A parallel adaptive mesh refinement algorithm
NASA Technical Reports Server (NTRS)
Quirk, James J.; Hanebutte, Ulf R.
1993-01-01
Over recent years, Adaptive Mesh Refinement (AMR) algorithms which dynamically match the local resolution of the computational grid to the numerical solution being sought have emerged as powerful tools for solving problems that contain disparate length and time scales. In particular, several workers have demonstrated the effectiveness of employing an adaptive, block-structured hierarchical grid system for simulations of complex shock wave phenomena. Unfortunately, from the parallel algorithm developer's viewpoint, this class of scheme is quite involved; these schemes cannot be distilled down to a small kernel upon which various parallelizing strategies may be tested. However, because of their block-structured nature such schemes are inherently parallel, so all is not lost. In this paper we describe the method by which Quirk's AMR algorithm has been parallelized. This method is built upon just a few simple message passing routines and so it may be implemented across a broad class of MIMD machines. Moreover, the method of parallelization is such that the original serial code is left virtually intact, and so we are left with just a single product to support. The importance of this fact should not be underestimated given the size and complexity of the original algorithm.
SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM
A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme?the piecewise parabolic method (PPM)?for computing advective solution fields; a weight function capable of promoting grid node clustering ...
Fully implicit adaptive mesh refinement MHD algorithm
NASA Astrophysics Data System (ADS)
Philip, Bobby
2005-10-01
In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former results in stiffness due to the presence of very fast waves. The latter requires one to resolve the localized features that the system develops. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. To our knowledge, a scalable, fully implicit AMR algorithm has not been accomplished before for MHD. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technologyootnotetextL. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite --FAC-- algorithms) for scalability. We will demonstrate that the concept is indeed feasible, featuring optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations will be presented on a variety of problems.
Finite element adaptive mesh analysis using a cluster of workstations
NASA Astrophysics Data System (ADS)
Wang, K. P.; Bruch, J. C., Jr.
1998-01-01
Parallel computation on clusters of workstations is becoming one of the major trends in the study of parallel computations, because of their high computing speed, cost effectiveness and scalability. This paper presents studies of using a cluster of workstations for the finite element adaptive mesh analysis of a free surface seepage problem. A parallel algorithm proven to be simple to implement and efficient is used to perform the analysis. A network of workstations is used as the hardware of a parallel system. Two parallel software packages, P4 and PVM (parallel virtual machine), are used to handle communications among networked workstations. Computational issues to be discussed are domain decomposition, load balancing, and communication time.
Multi-Parent Clustering Algorithms from Stochastic Grammar Data Models
NASA Technical Reports Server (NTRS)
Mjoisness, Eric; Castano, Rebecca; Gray, Alexander
1999-01-01
We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several "parent" clusters. For each data vector the algorithm makes a discrete decision among these alternatives. Thus, a recursive version of this algorithm would place data clusters in a Directed Acyclic Graph rather than a tree. We test the algorithm with synthetic data generated according to the statistical data model. We also illustrate the algorithm using real data from large-scale gene expression assays.
Adaptive path planning: Algorithm and analysis
Chen, Pang C.
1993-03-01
Path planning has to be fast to support real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To alleviate this problem, we present a learning algorithm that uses past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful subgoals is learned to support faster planning. The algorithm is suitable for both stationary and incrementally-changing environments. To analyze our algorithm, we use a previously developed stochastic model that quantifies experience utility. Using this model, we characterize the situations in which the adaptive planner is useful, and provide quantitative bounds to predict its behavior. The results are demonstrated with problems in manipulator planning. Our algorithm and analysis are sufficiently general that they may also be applied to task planning or other planning domains in which experience is useful.
Algorithms for adaptive nonlinear pattern recognition
NASA Astrophysics Data System (ADS)
Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary
2011-09-01
In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral
Adaptive Trajectory Prediction Algorithm for Climbing Flights
NASA Technical Reports Server (NTRS)
Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz
2012-01-01
Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.
A Flocking Based algorithm for Document Clustering Analysis
Cui, Xiaohui; Gao, Jinzhu; Potok, Thomas E
2006-01-01
Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.
Adaptive snakes using the EM algorithm.
Nascimento, Jacinto C; Marques, Jorge S
2005-11-01
Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A new algorithm is proposed in this paper to overcome this difficulty. The algorithm is based on two key ideas. First, edge points are associated in strokes. Second, each stroke is classified as valid (inlier) or invalid (outlier) and a confidence degree is associated to each stroke. The expectation maximization algorithm is used to update the confidence degrees and to estimate the object contour. It is shown that this is equivalent to the use of an adaptive potential function which varies during the optimization process. Valid strokes receive high confidence degrees while confidence degrees of invalid strokes tend to zero during the optimization process. Experimental results are presented to illustrate the performance of the proposed algorithm in the presence of clutter, showing a remarkable robustness.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
MLEM algorithm adaptation for improved SPECT scintimammography
NASA Astrophysics Data System (ADS)
Krol, Andrzej; Feiglin, David H.; Lee, Wei; Kunniyur, Vikram R.; Gangal, Kedar R.; Coman, Ioana L.; Lipson, Edward D.; Karczewski, Deborah A.; Thomas, F. Deaver
2005-04-01
Standard MLEM and OSEM algorithms used in SPECT Tc-99m sestamibi scintimammography produce hot-spot artifacts (HSA) at the image support peripheries. We investigated a suitable adaptation of MLEM and OSEM algorithms needed to reduce HSA. Patients with suspicious breast lesions were administered 10 mCi of Tc-99m sestamibi and SPECT scans were acquired for patients in prone position with uncompressed breasts. In addition, to simulate breast lesions, some patients were imaged with a number of breast skin markers each containing 1 mCi of Tc-99m. In order to reduce HSA in reconstruction, we removed from the backprojection step the rays that traverse the periphery of the support region on the way to a detector bin, when their path length through this region was shorter than some critical length. Such very short paths result in a very low projection counts contributed to the detector bin, and consequently to overestimation of the activity in the peripheral voxels in the backprojection step-thus creating HSA. We analyzed the breast-lesion contrast and suppression of HSA in the images reconstructed using standard and modified MLEM and OSEM algorithms vs. critical path length (CPL). For CPL >= 0.01 pixel size, we observed improved breast-lesion contrast and lower noise in the reconstructed images, and a very significant reduction of HSA in the maximum intensity projection (MIP) images.
Adaptive Numerical Algorithms in Space Weather Modeling
NASA Technical Reports Server (NTRS)
Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav
2010-01-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical
Adaptive numerical algorithms in space weather modeling
NASA Astrophysics Data System (ADS)
Tóth, Gábor; van der Holst, Bart; Sokolov, Igor V.; De Zeeuw, Darren L.; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Najib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav
2012-02-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit
AIDA: An Adaptive Image Deconvolution Algorithm
NASA Astrophysics Data System (ADS)
Hom, Erik; Marchis, F.; Lee, T. K.; Haase, S.; Agard, D. A.; Sedat, J. W.
2007-10-01
We recently described an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging [Hom et al., J. Opt. Soc. Am. A 24, 1580 (2007)]. AIDA is a reimplementation and extension of the MISTRAL method developed by Mugnier and co-workers and shown to yield object reconstructions with excellent edge preservation and photometric precision [J. Opt. Soc. Am. A 21, 1841 (2004)]. Written in Numerical Python with calls to a robust constrained conjugate gradient method, AIDA has significantly improved run times over the original MISTRAL implementation. AIDA includes a scheme to automatically balance maximum-likelihood estimation and object regularization, which significantly decreases the amount of time and effort needed to generate satisfactory reconstructions. Here, we present a gallery of results demonstrating the effectiveness of AIDA in processing planetary science images acquired using adaptive-optics systems. Offered as an open-source alternative to MISTRAL, AIDA is available for download and further development at: http://msg.ucsf.edu/AIDA. This work was supported in part by the W. M. Keck Observatory, the National Institutes of Health, NASA, the National Science Foundation Science and Technology Center for Adaptive Optics at UC-Santa Cruz, and the Howard Hughes Medical Institute.
A Poisson-based adaptive affinity propagation clustering for SAGE data.
Tang, DongMing; Zhu, QingXin; Yang, Fan
2010-02-01
Serial analysis of gene expression (SAGE) is a powerful tool to obtain gene expression profiles. Clustering analysis is a valuable technique for analyzing SAGE data. In this paper, we propose an adaptive clustering method for SAGE data analysis, namely, PoissonAPS. The method incorporates a novel clustering algorithm, Affinity Propagation (AP). While AP algorithm has demonstrated good performance on many different data sets, it also faces several limitations. PoissonAPS overcomes the limitations of AP using the clustering validation measure as a cost function of merging and splitting, and as a result, it can automatically cluster SAGE data without user-specified parameters. We evaluated PoissonAPS and compared its performance with other methods on several real life SAGE datasets. The experimental results show that PoissonAPS can produce meaningful and interpretable clusters for SAGE data.
An Adaptive Path Planning Algorithm for Cooperating Unmanned Air Vehicles
Cunningham, C.T.; Roberts, R.S.
2000-09-12
An adaptive path planning algorithm is presented for cooperating Unmanned Air Vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.
Adaptive path planning algorithm for cooperating unmanned air vehicles
Cunningham, C T; Roberts, R S
2001-02-08
An adaptive path planning algorithm is presented for cooperating Unmanned Air Vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.
A systematic comparison of genome-scale clustering algorithms
2012-01-01
Background A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further
The Enhanced Hoshen-Kopelman Algorithm for Cluster Analysis
NASA Astrophysics Data System (ADS)
Hoshen, Joseph
1997-08-01
In 1976 Hoshen and Kopelman(J. Hoshen and R. Kopelman, Phys. Rev. B, 14, 3438 (1976).) introduced a breakthrough algorithm, known today as the Hoshen-Kopelman algorithm, for cluster analysis. This algorithm revolutionized Monte Carlo cluster calculations in percolation theory as it enables analysis of very large lattices containing 10^11 or more sites. Initially the HK algorithm primary use was in the domain of pure and basic sciences. Later it began finding applications in diverse fields of technology and applied sciences. Example of such applications are two and three dimensional image analysis, composite material modeling, polymers, remote sensing, brain modeling and food processing. While the original HK algorithm provides only cluster size data for only one class of sites, the Enhanced HK (EHK) algorithm, presented in this paper, enables calculations of cluster spatial moments -- characteristics of cluster shapes -- for multiple classes of sites. These enhancements preserve the time and space complexities of the original HK algorithm, such that very large lattices could be still analyzed simultaneously in a single pass through the lattice for cluster sizes, classes and shapes.
Adaptive RED algorithm based on minority game
NASA Astrophysics Data System (ADS)
Wei, Jiaolong; Lei, Ling; Qian, Jingjing
2007-11-01
With more and more applications appearing and the technology developing in the Internet, only relying on terminal system can not satisfy the complicated demand of QoS network. Router mechanisms must be participated into protecting responsive flows from the non-responsive. Routers mainly use active queue management mechanism (AQM) to avoid congestion. In the point of interaction between the routers, the paper applies minority game to describe the interaction of the users and observes the affection on the length of average queue. The parameters α, β of ARED being hard to confirm, adaptive RED based on minority game can depict the interactions of main body and amend the parameter α, β of ARED to the best. Adaptive RED based on minority game optimizes ARED and realizes the smoothness of average queue length. At the same time, this paper extends the network simulator plat - NS by adding new elements. Simulation has been implemented and the results show that new algorithm can reach the anticipative objects.
Efficient Cluster Algorithm for Spin Glasses in Any Space Dimension
NASA Astrophysics Data System (ADS)
Zhu, Zheng; Ochoa, Andrew J.; Katzgraber, Helmut G.
2015-08-01
Spin systems with frustration and disorder are notoriously difficult to study, both analytically and numerically. While the simulation of ferromagnetic statistical mechanical models benefits greatly from cluster algorithms, these accelerated dynamics methods remain elusive for generic spin-glass-like systems. Here, we present a cluster algorithm for Ising spin glasses that works in any space dimension and speeds up thermalization by at least one order of magnitude at temperatures where thermalization is typically difficult. Our isoenergetic cluster moves are based on the Houdayer cluster algorithm for two-dimensional spin glasses and lead to a speedup over conventional state-of-the-art methods that increases with the system size. We illustrate the benefits of the isoenergetic cluster moves in two and three space dimensions, as well as the nonplanar chimera topology found in the D-Wave Inc. quantum annealing machine.
Efficient Cluster Algorithm for Spin Glasses in Any Space Dimension.
Zhu, Zheng; Ochoa, Andrew J; Katzgraber, Helmut G
2015-08-14
Spin systems with frustration and disorder are notoriously difficult to study, both analytically and numerically. While the simulation of ferromagnetic statistical mechanical models benefits greatly from cluster algorithms, these accelerated dynamics methods remain elusive for generic spin-glass-like systems. Here, we present a cluster algorithm for Ising spin glasses that works in any space dimension and speeds up thermalization by at least one order of magnitude at temperatures where thermalization is typically difficult. Our isoenergetic cluster moves are based on the Houdayer cluster algorithm for two-dimensional spin glasses and lead to a speedup over conventional state-of-the-art methods that increases with the system size. We illustrate the benefits of the isoenergetic cluster moves in two and three space dimensions, as well as the nonplanar chimera topology found in the D-Wave Inc. quantum annealing machine.
A space-time cluster algorithm for stochastic processes.
Gulbahce, N.
2003-01-01
We introduce a space-time cluster algorithm that will generate histories of stochastic processes. Michael Zimmer introduced a spacetime MC algorithm for stochastic classical dynamics and he applied it to simulate Ising model with Glauber dynamics. Following his steps, we extended Brower and Tamayo's embedded {phi}{sup 4} dynamics to space and time. We believe our algorithm can be applied to more general stochastic systems. Why space-time? To be able to study nonequilibrium systems, we need to know the probability of the 'history' of a nonequilibrium state. Histories are the entire space-time configurations. Cluster algorithms first introduced by SW, are useful to overcome critical slowing down. Brower and Tamayo have mapped continous field variables to Ising spins, and have grown and flipped SW clusters to gain speed. Our algorithm is an extended version of theirs to space and time.
A Fast Implementation of the ISODATA Clustering Algorithm
NASA Technical Reports Server (NTRS)
Memarsadeghi, Nargess; Mount, David M.; Netanyahu, Nathan S.; LeMoigne, Jacqueline
2005-01-01
Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.
An adaptive algorithm for motion compensated color image coding
NASA Technical Reports Server (NTRS)
Kwatra, Subhash C.; Whyte, Wayne A.; Lin, Chow-Ming
1987-01-01
This paper presents an adaptive algorithm for motion compensated color image coding. The algorithm can be used for video teleconferencing or broadcast signals. Activity segmentation is used to reduce the bit rate and a variable stage search is conducted to save computations. The adaptive algorithm is compared with the nonadaptive algorithm and it is shown that with approximately 60 percent savings in computing the motion vector and 33 percent additional compression, the performance of the adaptive algorithm is similar to the nonadaptive algorithm. The adaptive algorithm results also show improvement of up to 1 bit/pel over interframe DPCM coding with nonuniform quantization. The test pictures used for this study were recorded directly from broadcast video in color.
Efficient Record Linkage Algorithms Using Complete Linkage Clustering
Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar
2016-01-01
Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. PMID:27124604
Clustering of Hadronic Showers with a Structural Algorithm
Charles, M.J.; /SLAC
2005-12-13
The internal structure of hadronic showers can be resolved in a high-granularity calorimeter. This structure is described in terms of simple components and an algorithm for reconstruction of hadronic clusters using these components is presented. Results from applying this algorithm to simulated hadronic Z-pole events in the SiD concept are discussed.
Lee, Chongdeuk; Jeong, Taegwon
2011-01-01
Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms. PMID:22163905
Lee, Chongdeuk; Jeong, Taegwon
2011-01-01
Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.
CCL: an algorithm for the efficient comparison of clusters
Hundt, R.; Schön, J. C.; Neelamraju, S.; Zagorac, J.; Jansen, M.
2013-01-01
The systematic comparison of the atomic structure of solids and clusters has become an important task in crystallography, chemistry, physics and materials science, in particular in the context of structure prediction and structure determination of nanomaterials. In this work, an efficient and robust algorithm for the comparison of cluster structures is presented, which is based on the mapping of the point patterns of the two clusters onto each other. This algorithm has been implemented as the module CCL in the structure visualization and analysis program KPLOT. PMID:23682193
A modified density-based clustering algorithm and its implementation
NASA Astrophysics Data System (ADS)
Ban, Zhihua; Liu, Jianguo; Yuan, Lulu; Yang, Hua
2015-12-01
This paper presents an improved density-based clustering algorithm based on the paper of clustering by fast search and find of density peaks. A distance threshold is introduced for the purpose of economizing memory. In order to reduce the probability that two points share the same density value, similarity is utilized to define proximity measure. We have tested the modified algorithm on a large data set, several small data sets and shape data sets. It turns out that the proposed algorithm can obtain acceptable results and can be applied more wildly.
Measuring Constraint-Set Utility for Partitional Clustering Algorithms
NASA Technical Reports Server (NTRS)
Davidson, Ian; Wagstaff, Kiri L.; Basu, Sugato
2006-01-01
Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves the performance of a variety of algorithms. However, in most of these experiments, results are averaged over different randomly chosen constraint sets from a given set of labels, thereby masking interesting properties of individual sets. We demonstrate that constraint sets vary significantly in how useful they are for constrained clustering; some constraint sets can actually decrease algorithm performance. We create two quantitative measures, informativeness and coherence, that can be used to identify useful constraint sets. We show that these measures can also help explain differences in performance for four particular constrained clustering algorithms.
A Geometric Clustering Algorithm with Applications to Structural Data
Xu, Shutan; Zou, Shuxue
2015-01-01
Abstract An important feature of structural data, especially those from structural determination and protein-ligand docking programs, is that their distribution could be mostly uniform. Traditional clustering algorithms developed specifically for nonuniformly distributed data may not be adequate for their classification. Here we present a geometric partitional algorithm that could be applied to both uniformly and nonuniformly distributed data. The algorithm is a top-down approach that recursively selects the outliers as the seeds to form new clusters until all the structures within a cluster satisfy a classification criterion. The algorithm has been evaluated on a diverse set of real structural data and six sets of test data. The results show that it is superior to the previous algorithms for the clustering of structural data and is similar to or better than them for the classification of the test data. The algorithm should be especially useful for the identification of the best but minor clusters and for speeding up an iterative process widely used in NMR structure determination. PMID:25517067
Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering.
He, Zhaoshui; Xie, Shengli; Zdunek, Rafal; Zhou, Guoxu; Cichocki, Andrzej
2011-12-01
Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
NASA Astrophysics Data System (ADS)
Sadr, Ali; Momtaz, Amirkeyvan
2012-01-01
Clustering is one of the image-processing methods used in non-destructive testing (NDT). As one of the initializing parameters, most clustering algorithms, like fuzzy C means (FCM), Iterative self-organization data analysis (ISODATA), K-means, and their derivatives, require the number of clusters. This paper proposes an algorithm for clustering the pixels in C-scan images without any initializing parameters. In this state-of-the-art method, an image is sampled based on the rosette pattern and according to the pattern characteristics, and extracted samples are clustered and then the number of clusters is determined. The centroids of the classes are computed by means of a method used to calculate the distribution function. Based on different data sets, the results show that the algorithm improves the clustering capability by 92.93% and 91.93% in comparison with FCM and K-means algorithms, respectively. Moreover, when dealing with high-resolution data sets, the efficiency of the algorithm in terms of cluster detection and run time improves considerably.
Sampling Within k-Means Algorithm to Cluster Large Datasets
Bejarano, Jeremy; Bose, Koushiki; Brannan, Tyler; Thomas, Anita; Adragni, Kofi; Neerchal, Nagaraj; Ostrouchov, George
2011-08-01
Due to current data collection technology, our ability to gather data has surpassed our ability to analyze it. In particular, k-means, one of the simplest and fastest clustering algorithms, is ill-equipped to handle extremely large datasets on even the most powerful machines. Our new algorithm uses a sample from a dataset to decrease runtime by reducing the amount of data analyzed. We perform a simulation study to compare our sampling based k-means to the standard k-means algorithm by analyzing both the speed and accuracy of the two methods. Results show that our algorithm is significantly more efficient than the existing algorithm with comparable accuracy. Further work on this project might include a more comprehensive study both on more varied test datasets as well as on real weather datasets. This is especially important considering that this preliminary study was performed on rather tame datasets. Also, these datasets should analyze the performance of the algorithm on varied values of k. Lastly, this paper showed that the algorithm was accurate for relatively low sample sizes. We would like to analyze this further to see how accurate the algorithm is for even lower sample sizes. We could find the lowest sample sizes, by manipulating width and confidence level, for which the algorithm would be acceptably accurate. In order for our algorithm to be a success, it needs to meet two benchmarks: match the accuracy of the standard k-means algorithm and significantly reduce runtime. Both goals are accomplished for all six datasets analyzed. However, on datasets of three and four dimension, as the data becomes more difficult to cluster, both algorithms fail to obtain the correct classifications on some trials. Nevertheless, our algorithm consistently matches the performance of the standard algorithm while becoming remarkably more efficient with time. Therefore, we conclude that analysts can use our algorithm, expecting accurate results in considerably less time.
A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering
ERIC Educational Resources Information Center
Chahine, Firas Safwan
2012-01-01
Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…
A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics.
Oyana, Tonny J
2010-01-01
The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique-the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines.
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.
Aadil, Farhan; Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET
Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
Effective FCM noise clustering algorithms in medical images.
Kannan, S R; Devi, R; Ramathilagam, S; Takezawa, K
2013-02-01
The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
A Novel Complex Networks Clustering Algorithm Based on the Core Influence of Nodes
Dai, Bin; Xie, Zhongyu
2014-01-01
In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow convergence. In this paper, we propose a clustering algorithm by calculating the core influence of nodes. The clustering process is a simulation of the process of cluster formation in sociology. The algorithm detects the nodes with core influence through their betweenness centrality, and builds the cluster's core structure by discriminant functions. Next, the algorithm gets the final cluster structure after clustering the rest of the nodes in the network by optimizing method. Experiments on different datasets show that the clustering accuracy of this algorithm is superior to the classical clustering algorithm (Fast-Newman algorithm). It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely. PMID:24741359
Adaptive DNA Computing Algorithm by Using PCR and Restriction Enzyme
NASA Astrophysics Data System (ADS)
Kon, Yuji; Yabe, Kaoru; Rajaee, Nordiana; Ono, Osamu
In this paper, we introduce an adaptive DNA computing algorithm by using polymerase chain reaction (PCR) and restriction enzyme. The adaptive algorithm is designed based on Adleman-Lipton paradigm[3] of DNA computing. In this work, however, unlike the Adleman- Lipton architecture a cutting operation has been introduced to the algorithm and the mechanism in which the molecules used by computation were feedback to the next cycle devised. Moreover, the amplification by PCR is performed in the molecule used by feedback and the difference concentration arisen in the base sequence can be used again. By this operation the molecules which serve as a solution candidate can be reduced down and the optimal solution is carried out in the shortest path problem. The validity of the proposed adaptive algorithm is considered with the logical simulation and finally we go on to propose applying adaptive algorithm to the chemical experiment which used the actual DNA molecules for solving an optimal network problem.
Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao
2015-01-01
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. PMID:26793269
Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao
2015-01-01
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Functional clustering algorithm for the analysis of dynamic network data
NASA Astrophysics Data System (ADS)
Feldt, S.; Waddell, J.; Hetrick, V. L.; Berke, J. D.; Żochowski, M.
2009-05-01
We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.
Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale
Kobourov, Stephen; Gallant, Mike; Börner, Katy
2016-01-01
Overview Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. Cluster Quality Metrics We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Network Clustering Algorithms Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large
An efficient method of key-frame extraction based on a cluster algorithm.
Zhang, Qiang; Yu, Shao-Pei; Zhou, Dong-Sheng; Wei, Xiao-Peng
2013-12-18
This paper proposes a novel method of key-frame extraction for use with motion capture data. This method is based on an unsupervised cluster algorithm. First, the motion sequence is clustered into two classes by the similarity distance of the adjacent frames so that the thresholds needed in the next step can be determined adaptively. Second, a dynamic cluster algorithm called ISODATA is used to cluster all the frames and the frames nearest to the center of each class are automatically extracted as key-frames of the sequence. Unlike many other clustering techniques, the present improved cluster algorithm can automatically address different motion types without any need for specified parameters from users. The proposed method is capable of summarizing motion capture data reliably and efficiently. The present work also provides a meaningful comparison between the results of the proposed key-frame extraction technique and other previous methods. These results are evaluated in terms of metrics that measure reconstructed motion and the mean absolute error value, which are derived from the reconstructed data and the original data.
Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong
2015-12-10
In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network's running and the degree of candidate nodes' effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime.
Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong
2015-01-01
In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network’s running and the degree of candidate nodes’ effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime. PMID:26690440
Adaptive Routing Algorithm in Wireless Communication Networks Using Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Yan, Xuesong; Wu, Qinghua; Cai, Zhihua
At present, mobile communications traffic routing designs are complicated because there are more systems inter-connecting to one another. For example, Mobile Communication in the wireless communication networks has two routing design conditions to consider, i.e. the circuit switching and the packet switching. The problem in the Packet Switching routing design is its use of high-speed transmission link and its dynamic routing nature. In this paper, Evolutionary Algorithms is used to determine the best solution and the shortest communication paths. We developed a Genetic Optimization Process that can help network planners solving the best solutions or the best paths of routing table in wireless communication networks are easily and quickly. From the experiment results can be noted that the evolutionary algorithm not only gets good solutions, but also a more predictable running time when compared to sequential genetic algorithm.
The C4 clustering algorithm: Clusters of galaxies in the Sloan Digital Sky Survey
Miller, Christopher J.; Nichol, Robert; Reichart, Dan; Wechsler, Risa H.; Evrard, August; Annis, James; McKay, Timothy; Bahcall, Neta; Bernardi, Mariangela; Boehringer, Hans; Connolly, Andrew; Goto, Tomo; Kniazev, Alexie; Lamb, Donald; Postman, Marc; Schneider, Donald; Sheth, Ravi; Voges, Wolfgang; /Cerro-Tololo InterAmerican Obs. /Portsmouth U., ICG /North Carolina U. /Chicago U., Astron. Astrophys. Ctr. /Chicago U., EFI /Michigan U. /Fermilab /Princeton U. Observ. /Garching, Max Planck Inst., MPE /Pittsburgh U. /Tokyo U., ICRR /Baltimore, Space Telescope Sci. /Penn State U. /Chicago U. /Stavropol, Astrophys. Observ. /Heidelberg, Max Planck Inst. Astron. /INI, SAO
2005-03-01
We present the ''C4 Cluster Catalog'', a new sample of 748 clusters of galaxies identified in the spectroscopic sample of the Second Data Release (DR2) of the Sloan Digital Sky Survey (SDSS). The C4 cluster-finding algorithm identifies clusters as overdensities in a seven-dimensional position and color space, thus minimizing projection effects that have plagued previous optical cluster selection. The present C4 catalog covers {approx}2600 square degrees of sky and ranges in redshift from z = 0.02 to z = 0.17. The mean cluster membership is 36 galaxies (with redshifts) brighter than r = 17.7, but the catalog includes a range of systems, from groups containing 10 members to massive clusters with over 200 cluster members with redshifts. The catalog provides a large number of measured cluster properties including sky location, mean redshift, galaxy membership, summed r-band optical luminosity (L{sub r}), velocity dispersion, as well as quantitative measures of substructure and the surrounding large-scale environment. We use new, multi-color mock SDSS galaxy catalogs, empirically constructed from the {Lambda}CDM Hubble Volume (HV) Sky Survey output, to investigate the sensitivity of the C4 catalog to the various algorithm parameters (detection threshold, choice of passbands and search aperture), as well as to quantify the purity and completeness of the C4 cluster catalog. These mock catalogs indicate that the C4 catalog is {approx_equal}90% complete and 95% pure above M{sub 200} = 1 x 10{sup 14} h{sup -1}M{sub {circle_dot}} and within 0.03 {le} z {le} 0.12. Using the SDSS DR2 data, we show that the C4 algorithm finds 98% of X-ray identified clusters and 90% of Abell clusters within 0.03 {le} z {le} 0.12. Using the mock galaxy catalogs and the full HV dark matter simulations, we show that the L{sub r} of a cluster is a more robust estimator of the halo mass (M{sub 200}) than the galaxy line-of-sight velocity dispersion or the richness of the cluster. However, if we
Coupled cluster algorithms for networks of shared memory parallel processors
NASA Astrophysics Data System (ADS)
Bentz, Jonathan L.; Olson, Ryan M.; Gordon, Mark S.; Schmidt, Michael W.; Kendall, Ricky A.
2007-05-01
As the popularity of using SMP systems as the building blocks for high performance supercomputers increases, so too increases the need for applications that can utilize the multiple levels of parallelism available in clusters of SMPs. This paper presents a dual-layer distributed algorithm, using both shared-memory and distributed-memory techniques to parallelize a very important algorithm (often called the "gold standard") used in computational chemistry, the single and double excitation coupled cluster method with perturbative triples, i.e. CCSD(T). The algorithm is presented within the framework of the GAMESS [M.W. Schmidt, K.K. Baldridge, J.A. Boatz, S.T. Elbert, M.S. Gordon, J.J. Jensen, S. Koseki, N. Matsunaga, K.A. Nguyen, S. Su, T.L. Windus, M. Dupuis, J.A. Montgomery, General atomic and molecular electronic structure system, J. Comput. Chem. 14 (1993) 1347-1363]. (General Atomic and Molecular Electronic Structure System) program suite and the Distributed Data Interface [M.W. Schmidt, G.D. Fletcher, B.M. Bode, M.S. Gordon, The distributed data interface in GAMESS, Comput. Phys. Comm. 128 (2000) 190]. (DDI), however, the essential features of the algorithm (data distribution, load-balancing and communication overhead) can be applied to more general computational problems. Timing and performance data for our dual-level algorithm is presented on several large-scale clusters of SMPs.
An adaptive strategy for single- and multi-cluster gene assignment.
Garg, Sanjeev; Hansen, Marc F; Rowe, David W; Achenie, Luke E K
2003-01-01
Strict assignment of genes to one class, dimensionality reduction, a priori specification of the number of classes, the need for a training set, nonunique solution, and complex learning mechanisms are some of the inadequacies of current clustering algorithms. Existing algorithms cluster genes on the basis of high positive correlations between their expression patterns. However, genes with strong negative correlations can also have similar functions and are most likely to have a role in the same pathways. To address some of these issues, we propose the adaptive centroid algorithm (ACA), which employs an analysis of variance (ANOVA)-based performance criterion. The ACA also uses Euclidian distances, the center-of-mass principle for heterogeneously distributed mass elements, and the given data set to give unique solutions. The proposed approach involves three stages. In the first stage a two-way ANOVA of the gene expression matrix is performed. The two factors in the ANOVA are gene expression and experimental condition. The residual mean squared error (MSE) from the ANOVA is used as a performance criterion in the ACA. Finally, correlated clusters are found based on the Pearson correlation coefficients. To validate the proposed approach, a two-way ANOVA is again performed on the discovered clusters. The results from this last step indicate that MSEs of the clusters are significantly lower compared to that of the fibroblast-serum gene expression matrix. The ACA is employed in this study for single- as well as multi-cluster gene assignments.
NASA Technical Reports Server (NTRS)
Lennington, R. K.; Johnson, J. K.
1979-01-01
An efficient procedure which clusters data using a completely unsupervised clustering algorithm and then uses labeled pixels to label the resulting clusters or perform a stratified estimate using the clusters as strata is developed. Three clustering algorithms, CLASSY, AMOEBA, and ISOCLS, are compared for efficiency. Three stratified estimation schemes and three labeling schemes are also considered and compared.
Adaptive fuzzy leader clustering of complex data sets in pattern recognition
NASA Technical Reports Server (NTRS)
Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.
Adaptive path planning: Algorithm and analysis
Chen, Pang C.
1995-03-01
To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions difficult tasks. From these solutions, an evolving sparse work of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a framework in which a slow but effective planner may be improved both cost-wise and capability-wise by a faster but less effective planner coupled with experience. We analyze algorithm by formalizing the concept of improvability and deriving conditions under which a planner can be improved within the framework. The analysis is based on two stochastic models, one pessimistic (on task complexity), the other randomized (on experience utility). Using these models, we derive quantitative bounds to predict the learning behavior. We use these estimation tools to characterize the situations in which the algorithm is useful and to provide bounds on the training time. In particular, we show how to predict the maximum achievable speedup. Additionally, our analysis techniques are elementary and should be useful for studying other types of probabilistic learning as well.
An adaptive inverse kinematics algorithm for robot manipulators
NASA Technical Reports Server (NTRS)
Colbaugh, R.; Glass, K.; Seraji, H.
1990-01-01
An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.
Adaptive impulsive cluster synchronization in community network with nonidentical nodes
NASA Astrophysics Data System (ADS)
Gong, Xiaoli; Gan, Luyining; Wu, Zhaoyan
2016-07-01
In this paper, cluster synchronization in community network with nonidentical nodes is investigated. Through introducing proper adaptive strategy into impulsive control scheme, adaptive impulsive controllers are designed for achieving the cluster synchronization. In this adaptive impulsive control scheme, for any given networks, the impulsive gains can adjust themselves to proper values according to the proposed adaptive strategy when the impulsive intervals are fixed. The impulsive instants can be estimated by solving a sequence of maximum value problems when the impulsive gains are fixed. Both community networks without and with coupling delay are considered. Based on the Lyapunov function method and mathematical analysis technique, two synchronization criteria are derived. Several numerical examples are performed to verify the effectiveness of the derived theoretical results.
Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering
Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang
2012-01-01
Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043
Adaptively resizing populations: Algorithm, analysis, and first results
NASA Technical Reports Server (NTRS)
Smith, Robert E.; Smuda, Ellen
1993-01-01
Deciding on an appropriate population size for a given Genetic Algorithm (GA) application can often be critical to the algorithm's success. Too small, and the GA can fall victim to sampling error, affecting the efficacy of its search. Too large, and the GA wastes computational resources. Although advice exists for sizing GA populations, much of this advice involves theoretical aspects that are not accessible to the novice user. An algorithm for adaptively resizing GA populations is suggested. This algorithm is based on recent theoretical developments that relate population size to schema fitness variance. The suggested algorithm is developed theoretically, and simulated with expected value equations. The algorithm is then tested on a problem where population sizing can mislead the GA. The work presented suggests that the population sizing algorithm may be a viable way to eliminate the population sizing decision from the application of GA's.
ABCluster: the artificial bee colony algorithm for cluster global optimization.
Zhang, Jun; Dolg, Michael
2015-10-01
Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters. PMID:26327507
A novel hybrid self-adaptive bat algorithm.
Fister, Iztok; Fong, Simon; Brest, Janez; Fister, Iztok
2014-01-01
Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.
A Novel Hybrid Self-Adaptive Bat Algorithm
Fister, Iztok; Brest, Janez
2014-01-01
Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction. PMID:25187904
An adaptive algorithm for low contrast infrared image enhancement
NASA Astrophysics Data System (ADS)
Liu, Sheng-dong; Peng, Cheng-yuan; Wang, Ming-jia; Wu, Zhi-guo; Liu, Jia-qi
2013-08-01
An adaptive infrared image enhancement algorithm for low contrast is proposed in this paper, to deal with the problem that conventional image enhancement algorithm is not able to effective identify the interesting region when dynamic range is large in image. This algorithm begin with the human visual perception characteristics, take account of the global adaptive image enhancement and local feature boost, not only the contrast of image is raised, but also the texture of picture is more distinct. Firstly, the global image dynamic range is adjusted from the overall, the dynamic range of original image and display grayscale form corresponding relationship, the gray scale of bright object is raised and the the gray scale of dark target is reduced at the same time, to improve the overall image contrast. Secondly, the corresponding filtering algorithm is used on the current point and its neighborhood pixels to extract image texture information, to adjust the brightness of the current point in order to enhance the local contrast of the image. The algorithm overcomes the default that the outline is easy to vague in traditional edge detection algorithm, and ensure the distinctness of texture detail in image enhancement. Lastly, we normalize the global luminance adjustment image and the local brightness adjustment image, to ensure a smooth transition of image details. A lot of experiments is made to compare the algorithm proposed in this paper with other convention image enhancement algorithm, and two groups of vague IR image are taken in experiment. Experiments show that: the contrast ratio of the picture is boosted after handled by histogram equalization algorithm, but the detail of the picture is not clear, the detail of the picture can be distinguished after handled by the Retinex algorithm. The image after deal with by self-adaptive enhancement algorithm proposed in this paper becomes clear in details, and the image contrast is markedly improved in compared with Retinex
Adaptive dimension reduction for clustering high dimensional data
Ding, Chris; He, Xiaofeng; Zha, Hongyuan; Simon, Horst
2002-10-01
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. many initialization methods were proposed to tackle this problem, but with only limited success. In this paper they propose a new approach to resolve this problem by repeated dimension reductions such that K-means or EM are performed only in very low dimensions. Cluster membership is utilized as a bridge between the reduced dimensional sub-space and the original space, providing flexibility and ease of implementation. Clustering analysis performed on highly overlapped Gaussians, DNA gene expression profiles and internet newsgroups demonstrate the effectiveness of the proposed algorithm.
Vetoed jet clustering: the mass-jump algorithm
NASA Astrophysics Data System (ADS)
Stoll, Martin
2015-04-01
A new class of jet clustering algorithms is introduced. A criterion inspired by successful mass-drop taggers is applied that prevents the recombination of two hard prongs if their combined jet mass is substantially larger than the masses of the separate prongs. This "mass jump" veto effectively results in jets with variable radii in dense environments. Differences to existing methods are investigated. It is shown for boosted top quarks that the new algorithm has beneficial properties which can lead to improved tagging purity.
Areibi, Shawki; Yang, Zhen
2004-01-01
Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem. PMID:15355604
Areibi, Shawki; Yang, Zhen
2004-01-01
Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem.
Adaptive image contrast enhancement algorithm for point-based rendering
NASA Astrophysics Data System (ADS)
Xu, Shaoping; Liu, Xiaoping P.
2015-03-01
Surgical simulation is a major application in computer graphics and virtual reality, and most of the existing work indicates that interactive real-time cutting simulation of soft tissue is a fundamental but challenging research problem in virtual surgery simulation systems. More specifically, it is difficult to achieve a fast enough graphic update rate (at least 30 Hz) on commodity PC hardware by utilizing traditional triangle-based rendering algorithms. In recent years, point-based rendering (PBR) has been shown to offer the potential to outperform the traditional triangle-based rendering in speed when it is applied to highly complex soft tissue cutting models. Nevertheless, the PBR algorithms are still limited in visual quality due to inherent contrast distortion. We propose an adaptive image contrast enhancement algorithm as a postprocessing module for PBR, providing high visual rendering quality as well as acceptable rendering efficiency. Our approach is based on a perceptible image quality technique with automatic parameter selection, resulting in a visual quality comparable to existing conventional PBR algorithms. Experimental results show that our adaptive image contrast enhancement algorithm produces encouraging results both visually and numerically compared to representative algorithms, and experiments conducted on the latest hardware demonstrate that the proposed PBR framework with the postprocessing module is superior to the conventional PBR algorithm and that the proposed contrast enhancement algorithm can be utilized in (or compatible with) various variants of the conventional PBR algorithm.
Locally adaptive bilateral clustering for universal image denoising
NASA Astrophysics Data System (ADS)
Toh, K. K. V.; Mat Isa, N. A.
2012-12-01
This paper presents a novel and efficient locally adaptive denoising method based on clustering of pixels into regions of similar geometric and radiometric structures. Clustering is performed by adaptively segmenting pixels in the local kernel based on their augmented variational series. Then, noise pixels are restored by selectively considering the radiometric and spatial properties of every pixel in the formed clusters. The proposed method is exceedingly robust in conveying reliable local structural information even in the presence of noise. As a result, the proposed method substantially outperforms other state-of-the-art methods in terms of image restoration and computational cost. We support our claims with ample simulated and real data experiments. The relatively fast runtime from extensive simulations also suggests that the proposed method is suitable for a variety of image-based products — either embedded in image capturing devices or applied as image enhancement software.
Mapping cultivable land from satellite imagery with clustering algorithms
NASA Astrophysics Data System (ADS)
Arango, R. B.; Campos, A. M.; Combarro, E. F.; Canas, E. R.; Díaz, I.
2016-07-01
Open data satellite imagery provides valuable data for the planning and decision-making processes related with environmental domains. Specifically, agriculture uses remote sensing in a wide range of services, ranging from monitoring the health of the crops to forecasting the spread of crop diseases. In particular, this paper focuses on a methodology for the automatic delimitation of cultivable land by means of machine learning algorithms and satellite data. The method uses a partition clustering algorithm called Partitioning Around Medoids and considers the quality of the clusters obtained for each satellite band in order to evaluate which one better identifies cultivable land. The proposed method was tested with vineyards using as input the spectral and thermal bands of the Landsat 8 satellite. The experimental results show the great potential of this method for cultivable land monitoring from remote-sensed multispectral imagery.
ICANP2: Isoenergetic cluster algorithm for NP-complete Problems
NASA Astrophysics Data System (ADS)
Zhu, Zheng; Fang, Chao; Katzgraber, Helmut G.
NP-complete optimization problems with Boolean variables are of fundamental importance in computer science, mathematics and physics. Most notably, the minimization of general spin-glass-like Hamiltonians remains a difficult numerical task. There has been a great interest in designing efficient heuristics to solve these computationally difficult problems. Inspired by the rejection-free isoenergetic cluster algorithm developed for Ising spin glasses, we present a generalized cluster update that can be applied to different NP-complete optimization problems with Boolean variables. The cluster updates allow for a wide-spread sampling of phase space, thus speeding up optimization. By carefully tuning the pseudo-temperature (needed to randomize the configurations) of the problem, we show that the method can efficiently tackle problems on topologies with a large site-percolation threshold. We illustrate the ICANP2 heuristic on paradigmatic optimization problems, such as the satisfiability problem and the vertex cover problem.
Adaptive-mesh algorithms for computational fluid dynamics
NASA Technical Reports Server (NTRS)
Powell, Kenneth G.; Roe, Philip L.; Quirk, James
1993-01-01
The basic goal of adaptive-mesh algorithms is to distribute computational resources wisely by increasing the resolution of 'important' regions of the flow and decreasing the resolution of regions that are less important. While this goal is one that is worthwhile, implementing schemes that have this degree of sophistication remains more of an art than a science. In this paper, the basic pieces of adaptive-mesh algorithms are described and some of the possible ways to implement them are discussed and compared. These basic pieces are the data structure to be used, the generation of an initial mesh, the criterion to be used to adapt the mesh to the solution, and the flow-solver algorithm on the resulting mesh. Each of these is discussed, with particular emphasis on methods suitable for the computation of compressible flows.
A Bayesian Adaptive Basis Algorithm for Single Particle Reconstruction
Kucukelbir, Alp; Sigworth, Fred J.; Tagare, Hemant D.
2012-01-01
Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. This paper proposes a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad-hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background. PMID:22564910
NIC-based Reduction Algorithms for Large-scale Clusters
Petrini, F; Moody, A T; Fernandez, J; Frachtenberg, E; Panda, D K
2004-07-30
Efficient algorithms for reduction operations across a group of processes are crucial for good performance in many large-scale, parallel scientific applications. While previous algorithms limit processing to the host CPU, we utilize the programmable processors and local memory available on modern cluster network interface cards (NICs) to explore a new dimension in the design of reduction algorithms. In this paper, we present the benefits and challenges, design issues and solutions, analytical models, and experimental evaluations of a family of NIC-based reduction algorithms. Performance and scalability evaluations were conducted on the ASCI Linux Cluster (ALC), a 960-node, 1920-processor machine at Lawrence Livermore National Laboratory, which uses the Quadrics QsNet interconnect. We find NIC-based reductions on modern interconnects to be more efficient than host-based implementations in both scalability and consistency. In particular, at large-scale--1812 processes--NIC-based reductions of small integer and floating-point arrays provided respective speedups of 121% and 39% over the host-based, production-level MPI implementation.
Adaptive NUC algorithm for uncooled IRFPA based on neural networks
NASA Astrophysics Data System (ADS)
Liu, Ziji; Jiang, Yadong; Lv, Jian; Zhu, Hongbin
2010-10-01
With developments in uncooled infrared plane array (UFPA) technology, many new advanced uncooled infrared sensors are used in defensive weapons, scientific research, industry and commercial applications. A major difference in imaging techniques between infrared IRFPA imaging system and a visible CCD camera is that, IRFPA need nonuniformity correction and dead pixel compensation, we usually called it infrared image pre-processing. Two-point or multi-point correction algorithms based on calibration commonly used may correct the non-uniformity of IRFPAs, but they are limited by pixel linearity and instability. Therefore, adaptive non-uniformity correction techniques are developed. Two of these adaptive non-uniformity correction algorithms are mostly discussed, one is based on temporal high-pass filter, and another is based on neural network. In this paper, a new NUC algorithm based on improved neural networks is introduced, and involves the compare result between improved neural networks and other adaptive correction techniques. A lot of different will discussed in different angle, like correction effects, calculation efficiency, hardware implementation and so on. According to the result and discussion, it could be concluding that the adaptive algorithm offers improved performance compared to traditional calibration mode techniques. This new algorithm not only provides better sensitivity, but also increases the system dynamic range. As the sensor application expended, it will be very useful in future infrared imaging systems.
NASA Astrophysics Data System (ADS)
Bo, Yizhou; Shifa, Naima
2013-09-01
An estimator for finding the abundance of a rare, clustered and mobile population has been introduced. This model is based on adaptive cluster sampling (ACS) to identify the location of the population and negative binomial distribution to estimate the total in each site. To identify the location of the population we consider both sampling with replacement (WR) and sampling without replacement (WOR). Some mathematical properties of the model are also developed.
Adaptive improved natural gradient algorithm for blind source separation.
Liu, Jian-Qiang; Feng, Da-Zheng; Zhang, Wei-Wei
2009-03-01
We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor. The proposed natural gradient algorithm with variable step-size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time-varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time-invariant and time-varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g., the number of sources is 10) in the complete case.
Sparse Gamma Rhythms Arising through Clustering in Adapting Neuronal Networks
Kilpatrick, Zachary P.; Ermentrout, Bard
2011-01-01
Gamma rhythms (30–100 Hz) are an extensively studied synchronous brain state responsible for a number of sensory, memory, and motor processes. Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm, while regular-spiking pyramidal neurons fire sparsely. We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior. Excitatory neurons form several clusters that fire every few cycles of the fast oscillation. This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model. We exploit the fact that the timescale of adaptation is much slower than that of the other variables. Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit. This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant. We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced. Both approaches predict the same scaling of cluster number with respect to the adaptation time constant, which is corroborated in numerical simulations of the full system. Thus, we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons. PMID:22125486
Data-adaptive algorithms for calling alleles in repeat polymorphisms.
Stoughton, R; Bumgarner, R; Frederick, W J; McIndoe, R A
1997-01-01
Data-adaptive algorithms are presented for separating overlapping signatures of heterozygotic allele pairs in electrophoresis data. Application is demonstrated for human microsatellite CA-repeat polymorphisms in LiCor 4000 and ABI 373 data. The algorithms allow overlapping alleles to be called correctly in almost every case where a trained observer could do so, and provide a fast automated objective alternative to human reading of the gels. The algorithm also supplies an indication of confidence level which can be used to flag marginal cases for verification by eye, or as input to later stages of statistical analysis. PMID:9059812
Automated Tract Extraction via Atlas Based Adaptive Clustering
Tunç, Birkan; Parker, William A.; Ingalhalikar, Madhura; Verma, Ragini
2014-01-01
Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of the connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber clustering atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, the reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection or the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples. PMID:25134977
Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal
2015-01-01
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191
Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal
2015-01-01
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191
Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal
2015-08-13
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.
Mammographic images segmentation based on chaotic map clustering algorithm
2014-01-01
Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766
An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation
NASA Astrophysics Data System (ADS)
Son, Seokho; Sim, Kwang Mong
Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).
Adaptation algorithms for 2-D feedforward neural networks.
Kaczorek, T
1995-01-01
The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).
Fast randomized Hough transformation track initiation algorithm based on multi-scale clustering
NASA Astrophysics Data System (ADS)
Wan, Minjie; Gu, Guohua; Chen, Qian; Qian, Weixian; Wang, Pengcheng
2015-10-01
A fast randomized Hough transformation track initiation algorithm based on multi-scale clustering is proposed to overcome existing problems in traditional infrared search and track system(IRST) which cannot provide movement information of the initial target and select the threshold value of correlation automatically by a two-dimensional track association algorithm based on bearing-only information . Movements of all the targets are presumed to be uniform rectilinear motion throughout this new algorithm. Concepts of space random sampling, parameter space dynamic linking table and convergent mapping of image to parameter space are developed on the basis of fast randomized Hough transformation. Considering the phenomenon of peak value clustering due to shortcomings of peak detection itself which is built on threshold value method, accuracy can only be ensured on condition that parameter space has an obvious peak value. A multi-scale idea is added to the above-mentioned algorithm. Firstly, a primary association is conducted to select several alternative tracks by a low-threshold .Then, alternative tracks are processed by multi-scale clustering methods , through which accurate numbers and parameters of tracks are figured out automatically by means of transforming scale parameters. The first three frames are processed by this algorithm in order to get the first three targets of the track , and then two slightly different gate radius are worked out , mean value of which is used to be the global threshold value of correlation. Moreover, a new model for curvilinear equation correction is applied to the above-mentioned track initiation algorithm for purpose of solving the problem of shape distortion when a space three-dimensional curve is mapped to a two-dimensional bearing-only space. Using sideways-flying, launch and landing as examples to build models and simulate, the application of the proposed approach in simulation proves its effectiveness , accuracy , and adaptivity
Sweeney, Timothy E; Chen, Albert C; Gevaert, Olivier
2015-11-19
In order to discover new subsets (clusters) of a data set, researchers often use algorithms that perform unsupervised clustering, namely, the algorithmic separation of a dataset into some number of distinct clusters. Deciding whether a particular separation (or number of clusters, K) is correct is a sort of 'dark art', with multiple techniques available for assessing the validity of unsupervised clustering algorithms. Here, we present a new technique for unsupervised clustering that uses multiple clustering algorithms, multiple validity metrics, and progressively bigger subsets of the data to produce an intuitive 3D map of cluster stability that can help determine the optimal number of clusters in a data set, a technique we call COmbined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL). COMMUNAL locally optimizes algorithms and validity measures for the data being used. We show its application to simulated data with a known K, and then apply this technique to several well-known cancer gene expression datasets, showing that COMMUNAL provides new insights into clustering behavior and stability in all tested cases. COMMUNAL is shown to be a useful tool for determining K in complex biological datasets, and is freely available as a package for R.
Dynamically Incremental K-means++ Clustering Algorithm Based on Fuzzy Rough Set Theory
NASA Astrophysics Data System (ADS)
Li, Wei; Wang, Rujing; Jia, Xiufang; Jiang, Qing
Being classic K-means++ clustering algorithm only for static data, dynamically incremental K-means++ clustering algorithm (DK-Means++) is presented based on fuzzy rough set theory in this paper. Firstly, in DK-Means++ clustering algorithm, the formula of similar degree is improved by weights computed by using of the important degree of attributes which are reduced on the basis of rough fuzzy set theory. Secondly, new data only need match granular which was clustered by K-means++ algorithm or seldom new data is clustered by classic K-means++ algorithm in global data. In this way, that all data is re-clustered each time in dynamic data set is avoided, so the efficiency of clustering is improved. Throughout our experiments showing, DK-Means++ algorithm can objectively and efficiently deal with clustering problem of dynamically incremental data.
Gravitation field algorithm and its application in gene cluster
2010-01-01
Background Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena. Results This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus. Conclusions The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at http://ccst.jlu.edu.cn/CSBG/GFA. PMID:20854683
Novel similarity-based clustering algorithm for grouping broadcast news
NASA Astrophysics Data System (ADS)
Ibrahimov, Oktay V.; Sethi, Ishwar K.; Dimitrova, Nevenka
2002-03-01
The goal of the current paper is to introduce a novel clustering algorithm that has been designed for grouping transcribed textual documents obtained out of audio, video segments. Since audio transcripts are normally highly erroneous documents, one of the major challenges at the text processing stage is to reduce the negative impacts of errors gained at the speech recognition stage. Other difficulties come from the nature of conversational speech. In the paper we describe the main difficulties of the spoken documents and suggest an approach restricting their negative effects. In our paper we also present a clustering algorithm that groups transcripts on the base of informative closeness of documents. To carry out such partitioning we give an intuitive definition of informative field of a transcript and use it in our algorithm. To assess informative closeness of the transcripts, we apply Chi-square similarity measure, which is also described in the paper. Our experiments with Chi-square similarity measure showed its robustness and high efficacy. In particular, the performance analysis that have been carried out in regard to Chi-square and three other similarity measures such as Cosine, Dice, and Jaccard showed that Chi-square is more robust to specific features of spoken documents.
An Adaptive Immune Genetic Algorithm for Edge Detection
NASA Astrophysics Data System (ADS)
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Flight data processing with the F-8 adaptive algorithm
NASA Technical Reports Server (NTRS)
Hartmann, G.; Stein, G.; Petersen, K.
1977-01-01
An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
A new adaptive GMRES algorithm for achieving high accuracy
Sosonkina, M.; Watson, L.T.; Kapania, R.K.; Walker, H.F.
1996-12-31
GMRES(k) is widely used for solving nonsymmetric linear systems. However, it is inadequate either when it converges only for k close to the problem size or when numerical error in the modified Gram-Schmidt process used in the GMRES orthogonalization phase dramatically affects the algorithm performance. An adaptive version of GMRES (k) which tunes the restart value k based on criteria estimating the GMRES convergence rate for the given problem is proposed here. The essence of the adaptive GMRES strategy is to adapt the parameter k to the problem, similar in spirit to how a variable order ODE algorithm tunes the order k. With FORTRAN 90, which provides pointers and dynamic memory management, dealing with the variable storage requirements implied by varying k is not too difficult. The parameter k can be both increased and decreased-an increase-only strategy is described next followed by pseudocode.
Adaptive bad pixel correction algorithm for IRFPA based on PCNN
NASA Astrophysics Data System (ADS)
Leng, Hanbing; Zhou, Zuofeng; Cao, Jianzhong; Yi, Bo; Yan, Aqi; Zhang, Jian
2013-10-01
Bad pixels and response non-uniformity are the primary obstacles when IRFPA is used in different thermal imaging systems. The bad pixels of IRFPA include fixed bad pixels and random bad pixels. The former is caused by material or manufacture defect and their positions are always fixed, the latter is caused by temperature drift and their positions are always changing. Traditional radiometric calibration-based bad pixel detection and compensation algorithm is only valid to the fixed bad pixels. Scene-based bad pixel correction algorithm is the effective way to eliminate these two kinds of bad pixels. Currently, the most used scene-based bad pixel correction algorithm is based on adaptive median filter (AMF). In this algorithm, bad pixels are regarded as image noise and then be replaced by filtered value. However, missed correction and false correction often happens when AMF is used to handle complex infrared scenes. To solve this problem, a new adaptive bad pixel correction algorithm based on pulse coupled neural networks (PCNN) is proposed. Potential bad pixels are detected by PCNN in the first step, then image sequences are used periodically to confirm the real bad pixels and exclude the false one, finally bad pixels are replaced by the filtered result. With the real infrared images obtained from a camera, the experiment results show the effectiveness of the proposed algorithm.
A new detection algorithm for microcalcification clusters in mammographic screening
NASA Astrophysics Data System (ADS)
Xie, Weiying; Ma, Yide; Li, Yunsong
2015-05-01
A novel approach for microcalcification clusters detection is proposed. At the first time, we make a short analysis of mammographic images with microcalcification lesions to confirm these lesions have much greater gray values than normal regions. After summarizing the specific feature of microcalcification clusters in mammographic screening, we make more focus on preprocessing step including eliminating the background, image enhancement and eliminating the pectoral muscle. In detail, Chan-Vese Model is used for eliminating background. Then, we do the application of combining morphology method and edge detection method. After the AND operation and Sobel filter, we use Hough Transform, it can be seen that the result have outperformed for eliminating the pectoral muscle which is approximately the gray of microcalcification. Additionally, the enhancement step is achieved by morphology. We make effort on mammographic image preprocessing to achieve lower computational complexity. As well known, it is difficult to robustly achieve mammograms analysis due to low contrast between normal and lesion tissues, there are also much noise in such images. After a serious preprocessing algorithm, a method based on blob detection is performed to microcalcification clusters according their specific features. The proposed algorithm has employed Laplace operator to improve Difference of Gaussians (DoG) function in terms of low contrast images. A preliminary evaluation of the proposed method performs on a known public database namely MIAS, rather than synthetic images. The comparison experiments and Cohen's kappa coefficients all demonstrate that our proposed approach can potentially obtain better microcalcification clusters detection results in terms of accuracy, sensitivity and specificity.
Adaptive Flocking of Robot Swarms: Algorithms and Properties
NASA Astrophysics Data System (ADS)
Lee, Geunho; Chong, Nak Young
This paper presents a distributed approach for adaptive flocking of swarms of mobile robots that enables to navigate autonomously in complex environments populated with obstacles. Based on the observation of the swimming behavior of a school of fish, we propose an integrated algorithm that allows a swarm of robots to navigate in a coordinated manner, split into multiple swarms, or merge with other swarms according to the environment conditions. We prove the convergence of the proposed algorithm using Lyapunov stability theory. We also verify the effectiveness of the algorithm through extensive simulations, where a swarm of robots repeats the process of splitting and merging while passing around multiple stationary and moving obstacles. The simulation results show that the proposed algorithm is scalable, and robust to variations in the sensing capability of individual robots.
Adaptive Tempering Monte Carlo Optimization of Calcium Clusters.
NASA Astrophysics Data System (ADS)
Dong, X.; Blaisten-Barojas, E.
2006-03-01
The global minimum energy structures of calcium clusters with 15 to 34 atoms were obtained by the Adaptive Tempering Monte Carlo (ATMC) method. The cluster binding energy was obtained within a tight binding approach with parameters reported in previous work[1]. The ATMC optimization process is fast and drives the system across configuration space very effectively reaching the global minimum within a small number of tempering events. The structure of six cluster sizes 15, 16, 18, 21, 23 and 25 corresponding to the global minimum has not been reported in the literature for any other metals. Three clusters Ca15, Ca21 and Ca23 are relatively more stable than the others in this size range. Melting of these clusters are further studied with the weighted histogram analysis method and the free energy profile is predicted. The melting transition is monitored with a novel structural order parameter that reflects the mobility of surface atoms, their bonding order and bonding directionality. [1] X. Dong, G. M. Wang, E. Blaisten-Barojas, Phys. Rev. B 70, 205409 (2004).
An adaptive grid algorithm for one-dimensional nonlinear equations
NASA Technical Reports Server (NTRS)
Gutierrez, William E.; Hills, Richard G.
1990-01-01
Richards' equation, which models the flow of liquid through unsaturated porous media, is highly nonlinear and difficult to solve. Step gradients in the field variables require the use of fine grids and small time step sizes. The numerical instabilities caused by the nonlinearities often require the use of iterative methods such as Picard or Newton interation. These difficulties result in large CPU requirements in solving Richards equation. With this in mind, adaptive and multigrid methods are investigated for use with nonlinear equations such as Richards' equation. Attention is focused on one-dimensional transient problems. To investigate the use of multigrid and adaptive grid methods, a series of problems are studied. First, a multigrid program is developed and used to solve an ordinary differential equation, demonstrating the efficiency with which low and high frequency errors are smoothed out. The multigrid algorithm and an adaptive grid algorithm is used to solve one-dimensional transient partial differential equations, such as the diffusive and convective-diffusion equations. The performance of these programs are compared to that of the Gauss-Seidel and tridiagonal methods. The adaptive and multigrid schemes outperformed the Gauss-Seidel algorithm, but were not as fast as the tridiagonal method. The adaptive grid scheme solved the problems slightly faster than the multigrid method. To solve nonlinear problems, Picard iterations are introduced into the adaptive grid and tridiagonal methods. Burgers' equation is used as a test problem for the two algorithms. Both methods obtain solutions of comparable accuracy for similar time increments. For the Burgers' equation, the adaptive grid method finds the solution approximately three times faster than the tridiagonal method. Finally, both schemes are used to solve the water content formulation of the Richards' equation. For this problem, the adaptive grid method obtains a more accurate solution in fewer work units and
Adaptive sensor array algorithm for structural health monitoring of helmet
NASA Astrophysics Data System (ADS)
Zou, Xiaotian; Tian, Ye; Wu, Nan; Sun, Kai; Wang, Xingwei
2011-04-01
The adaptive neural network is a standard technique used in nonlinear system estimation and learning applications for dynamic models. In this paper, we introduced an adaptive sensor fusion algorithm for a helmet structure health monitoring system. The helmet structure health monitoring system is used to study the effects of ballistic/blast events on the helmet and human skull. Installed inside the helmet system, there is an optical fiber pressure sensors array. After implementing the adaptive estimation algorithm into helmet system, a dynamic model for the sensor array has been developed. The dynamic response characteristics of the sensor network are estimated from the pressure data by applying an adaptive control algorithm using artificial neural network. With the estimated parameters and position data from the dynamic model, the pressure distribution of the whole helmet can be calculated following the Bazier Surface interpolation method. The distribution pattern inside the helmet will be very helpful for improving helmet design to provide better protection to soldiers from head injuries.
Efficient implementation of the adaptive scale pixel decomposition algorithm
NASA Astrophysics Data System (ADS)
Zhang, L.; Bhatnagar, S.; Rau, U.; Zhang, M.
2016-08-01
Context. Most popular algorithms in use to remove the effects of a telescope's point spread function (PSF) in radio astronomy are variants of the CLEAN algorithm. Most of these algorithms model the sky brightness using the delta-function basis, which results in undesired artefacts when used to image extended emission. The adaptive scale pixel decomposition (Asp-Clean) algorithm models the sky brightness on a scale-sensitive basis and thus gives a significantly better imaging performance when imaging fields that contain both resolved and unresolved emission. Aims: However, the runtime cost of Asp-Clean is higher than that of scale-insensitive algorithms. In this paper, we identify the most expensive step in the original Asp-Clean algorithm and present an efficient implementation of it, which significantly reduces the computational cost while keeping the imaging performance comparable to the original algorithm. The PSF sidelobe levels of modern wide-band telescopes are significantly reduced, allowing us to make approximations to reduce the computational cost, which in turn allows for the deconvolution of larger images on reasonable timescales. Methods: As in the original algorithm, scales in the image are estimated through function fitting. Here we introduce an analytical method to model extended emission, and a modified method for estimating the initial values used for the fitting procedure, which ultimately leads to a lower computational cost. Results: The new implementation was tested with simulated EVLA data and the imaging performance compared well with the original Asp-Clean algorithm. Tests show that the current algorithm can recover features at different scales with lower computational cost.
An adaptive mesh refinement algorithm for the discrete ordinates method
Jessee, J.P.; Fiveland, W.A.; Howell, L.H.; Colella, P.; Pember, R.B.
1996-03-01
The discrete ordinates form of the radiative transport equation (RTE) is spatially discretized and solved using an adaptive mesh refinement (AMR) algorithm. This technique permits the local grid refinement to minimize spatial discretization error of the RTE. An error estimator is applied to define regions for local grid refinement; overlapping refined grids are recursively placed in these regions; and the RTE is then solved over the entire domain. The procedure continues until the spatial discretization error has been reduced to a sufficient level. The following aspects of the algorithm are discussed: error estimation, grid generation, communication between refined levels, and solution sequencing. This initial formulation employs the step scheme, and is valid for absorbing and isotopically scattering media in two-dimensional enclosures. The utility of the algorithm is tested by comparing the convergence characteristics and accuracy to those of the standard single-grid algorithm for several benchmark cases. The AMR algorithm provides a reduction in memory requirements and maintains the convergence characteristics of the standard single-grid algorithm; however, the cases illustrate that efficiency gains of the AMR algorithm will not be fully realized until three-dimensional geometries are considered.
Fast frequency acquisition via adaptive least squares algorithm
NASA Technical Reports Server (NTRS)
Kumar, R.
1986-01-01
A new least squares algorithm is proposed and investigated for fast frequency and phase acquisition of sinusoids in the presence of noise. This algorithm is a special case of more general, adaptive parameter-estimation techniques. The advantages of the algorithms are their conceptual simplicity, flexibility and applicability to general situations. For example, the frequency to be acquired can be time varying, and the noise can be nonGaussian, nonstationary and colored. As the proposed algorithm can be made recursive in the number of observations, it is not necessary to have a priori knowledge of the received signal-to-noise ratio or to specify the measurement time. This would be required for batch processing techniques, such as the fast Fourier transform (FFT). The proposed algorithm improves the frequency estimate on a recursive basis as more and more observations are obtained. When the algorithm is applied in real time, it has the extra advantage that the observations need not be stored. The algorithm also yields a real time confidence measure as to the accuracy of the estimator.
PHURBAS: AN ADAPTIVE, LAGRANGIAN, MESHLESS, MAGNETOHYDRODYNAMICS CODE. I. ALGORITHM
Maron, Jason L.; McNally, Colin P.; Mac Low, Mordecai-Mark E-mail: cmcnally@amnh.org
2012-05-01
We present an algorithm for simulating the equations of ideal magnetohydrodynamics and other systems of differential equations on an unstructured set of points represented by sample particles. Local, third-order, least-squares, polynomial interpolations (Moving Least Squares interpolations) are calculated from the field values of neighboring particles to obtain field values and spatial derivatives at the particle position. Field values and particle positions are advanced in time with a second-order predictor-corrector scheme. The particles move with the fluid, so the time step is not limited by the Eulerian Courant-Friedrichs-Lewy condition. Full spatial adaptivity is implemented to ensure the particles fill the computational volume, which gives the algorithm substantial flexibility and power. A target resolution is specified for each point in space, with particles being added and deleted as needed to meet this target. Particle addition and deletion is based on a local void and clump detection algorithm. Dynamic artificial viscosity fields provide stability to the integration. The resulting algorithm provides a robust solution for modeling flows that require Lagrangian or adaptive discretizations to resolve. This paper derives and documents the Phurbas algorithm as implemented in Phurbas version 1.1. A following paper presents the implementation and test problem results.
jClustering, an Open Framework for the Development of 4D Clustering Algorithms
Mateos-Pérez, José María; García-Villalba, Carmen; Pascau, Javier; Desco, Manuel; Vaquero, Juan J.
2013-01-01
We present jClustering, an open framework for the design of clustering algorithms in dynamic medical imaging. We developed this tool because of the difficulty involved in manually segmenting dynamic PET images and the lack of availability of source code for published segmentation algorithms. Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code. The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing. This tool has been coded in Java and is presented as an ImageJ plugin in order to take advantage of all the functionalities offered by this imaging analysis platform. Both binary packages and source code have been published, the latter under a free software license (GNU General Public License) to allow modification if necessary. PMID:23990913
Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description
Schmidt, Gail; Jenkerson, Calli; Masek, Jeffrey; Vermote, Eric; Gao, Feng
2013-01-01
The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from LandsatThematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product.The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record.This report discusses the LEDAPS algorithm, which was implemented by the USGS.
Inconsistent Denoising and Clustering Algorithms for Amplicon Sequence Data.
Koskinen, Kaisa; Auvinen, Petri; Björkroth, K Johanna; Hultman, Jenni
2015-08-01
Natural microbial communities have been studied for decades using the 16S rRNA gene as a marker. In recent years, the application of second-generation sequencing technologies has revolutionized our understanding of the structure and function of microbial communities in complex environments. Using these highly parallel techniques, a detailed description of community characteristics are constructed, and even the rare biosphere can be detected. The new approaches carry numerous advantages and lack many features that skewed the results using traditional techniques, but we are still facing serious bias, and the lack of reliable comparability of produced results. Here, we contrasted publicly available amplicon sequence data analysis algorithms by using two different data sets, one with defined clone-based structure, and one with food spoilage community with well-studied communities. We aimed to assess which software and parameters produce results that resemble the benchmark community best, how large differences can be detected between methods, and whether these differences are statistically significant. The results suggest that commonly accepted denoising and clustering methods used in different combinations produce significantly different outcome: clustering method impacts greatly on the number of operational taxonomic units (OTUs) and denoising algorithm influences more on taxonomic affiliations. The magnitude of the OTU number difference was up to 40-fold and the disparity between results seemed highly dependent on the community structure and diversity. Statistically significant differences in taxonomies between methods were seen even at phylum level. However, the application of effective denoising method seemed to even out the differences produced by clustering. PMID:25525895
Adaptive vector quantization of MR images using online k-means algorithm
NASA Astrophysics Data System (ADS)
Shademan, Azad; Zia, Mohammad A.
2001-12-01
The k-means algorithm is widely used to design image codecs using vector quantization (VQ). In this paper, we focus on an adaptive approach to implement a VQ technique using the online version of k-means algorithm, in which the size of the codebook is adapted continuously to the statistical behavior of the image. Based on the statistical analysis of the feature space, a set of thresholds are designed such that those codewords corresponding to the low-density clusters would be removed from the codebook and hence, resulting in a higher bit-rate efficiency. Applications of this approach would be in telemedicine, where sequences of highly correlated medical images, e.g. consecutive brain slices, are transmitted over a low bit-rate channel. We have applied this algorithm on magnetic resonance (MR) images and the simulation results on a sample sequence are given. The proposed method has been compared to the standard k-means algorithm in terms of PSNR, MSE, and elapsed time to complete the algorithm.
[Adaptive algorithm for automatic measurement of retinal vascular diameter].
Münch, K; Vilser, W; Senff, I
1995-11-01
A new adaptive computer-aided method for the measurement of blood vessel diameters has been developed. Within areas of interest in the image, the algorithm detects, line-wise, the edges of the vessels, which are then used for image-wise approximation and noise filtration. A high level of adaptivity with respect to numerous measuring parameters ensures its use in a wide range of applications. Thus, it has been shown to significantly improve clinically relevant reproducibility in the area of follow-up observations. The standard deviation for vessel diameter was (2.2 +/- 0.7)% in the case of arteries and (1.8 +/- 0.5)% in the case of veins. Testing the algorithm in images of poor quality revealed its high level of reliability and sensitivity.
An adaptive phase alignment algorithm for cartesian feedback loops
NASA Astrophysics Data System (ADS)
Gimeno-Martin, A.; Pardo-Martin, J.; Ortega-Gonzalez, F.
2010-01-01
An adaptive algorithm to correct phase misalignments in Cartesian feedback linearization loops for power amplifiers has been presented. It yields an error smaller than 0.035 rad between forward and feedback loop signals once convergence is reached. Because this algorithm enables a feedback system to process forward and feedback samples belonging to almost the same algorithm iteration, it is suitable to improve the performance not only of power amplifiers but also any other digital feedback system for communications systems and circuits such as all digital phase locked loops. Synchronizing forward and feedback paths of Cartesian feedback loops takes a small period of time after the system starts up. The phase alignment algorithm needs to converge before the feedback Cartesian loop can start its ideal behavior. However, once the steady state is reached, both paths can be considered synchronized, and the Cartesian feedback loop will only depend on the loop parameters (open-loop gain, loop bandwidth, etc.). It means that the linearization process will also depend only on these parameters since the misalignment effect disappears. Therefore, this algorithm relieves the power amplifier linearizer circuit design of any task required for solving phase misalignment effects inherent to Cartesian feedback systems. Furthermore, when a feedback Cartesian loop has to be designed, the designer can consider that forward and feedback paths are synchronized, since the phase alignment algorithm will do this task. This will reduce the simulation complexity. Then, all efforts are applied to determining the suitable loop parameters that will make the linearization process more efficient.
An efficient sampling algorithm with adaptations for Bayesian variable selection.
Araki, Takamitsu; Ikeda, Kazushi; Akaho, Shotaro
2015-01-01
In Bayesian variable selection, indicator model selection (IMS) is a class of well-known sampling algorithms, which has been used in various models. The IMS is a class of methods that uses pseudo-priors and it contains specific methods such as Gibbs variable selection (GVS) and Kuo and Mallick's (KM) method. However, the efficiency of the IMS strongly depends on the parameters of a proposal distribution and the pseudo-priors. Specifically, the GVS determines their parameters based on a pilot run for a full model and the KM method sets their parameters as those of priors, which often leads to slow mixings of them. In this paper, we propose an algorithm that adapts the parameters of the IMS during running. The parameters obtained on the fly provide an appropriate proposal distribution and pseudo-priors, which improve the mixing of the algorithm. We also prove the convergence theorem of the proposed algorithm, and confirm that the algorithm is more efficient than the conventional algorithms by experiments of the Bayesian variable selection.
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.
de Brito, Daniel M; Maracaja-Coutinho, Vinicius; de Farias, Savio T; Batista, Leonardo V; do Rêgo, Thaís G
2016-01-01
Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me. PMID:26731657
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm
de Brito, Daniel M.; Maracaja-Coutinho, Vinicius; de Farias, Savio T.; Batista, Leonardo V.; do Rêgo, Thaís G.
2016-01-01
Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP—Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me. PMID:26731657
Adaptive Load-Balancing Algorithms Using Symmetric Broadcast Networks
NASA Technical Reports Server (NTRS)
Das, Sajal K.; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)
1997-01-01
In a distributed-computing environment, it is important to ensure that the processor workloads are adequately balanced. Among numerous load-balancing algorithms, a unique approach due to Dam and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three novel SBN-based load-balancing algorithms, and implement them on an SP2. A thorough experimental study with Poisson-distributed synthetic loads demonstrates that these algorithms are very effective in balancing system load while minimizing processor idle time. They also compare favorably with several other existing load-balancing techniques. Additional experiments performed with real data demonstrate that the SBN approach is effective in adaptive computational science and engineering applications where dynamic load balancing is extremely crucial.
A kernel adaptive algorithm for quaternion-valued inputs.
Paul, Thomas K; Ogunfunmi, Tokunbo
2015-10-01
The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations. PMID:25594982
Hierarchically clustered adaptive quantization CMAC and its learning convergence.
Teddy, S D; Lai, E M K; Quek, C
2007-11-01
The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically
Analysis of hypersonic aircraft inlets using flow adaptive mesh algorithms
NASA Astrophysics Data System (ADS)
Neaves, Michael Dean
The numerical investigation into the dynamics of unsteady inlet flowfields is applied to a three-dimensional scramjet inlet-isolator-diffuser geometry designed for hypersonic type applications. The Reynolds-Averaged Navier-Stokes equations are integrated in time using a subiterating, time-accurate implicit algorithm. Inviscid fluxes are calculated using the Low Diffusion Flux Splitting Scheme of Edwards. A modified version of the dynamic solution-adaptive point movement algorithm of Benson and McRae is used in a coupled mode to dynamically resolve the features of the flow by enhancing the spatial accuracy of the simulations. The unsteady mesh terms are incorporated into the flow solver via the inviscid fluxes. The dynamic solution-adaptive grid algorithm of Benson and McRae is modified to improve orthogonality at the boundaries to ensure accurate application of boundary conditions and properly resolve turbulent boundary layers. Shock tube simulations are performed to ascertain the effectiveness of the algorithm for unsteady flow situations on fixed and moving grids. Unstarts due to a combustor and freestream angle of attack perturbations are simulated in a three-dimensional inlet-isolator-diffuser configuration.
An adaptive gyroscope-based algorithm for temporal gait analysis.
Greene, Barry R; McGrath, Denise; O'Neill, Ross; O'Donovan, Karol J; Burns, Adrian; Caulfield, Brian
2010-12-01
Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.
Data-adaptive Shrinkage via the Hyperpenalized EM Algorithm
Boonstra, Philip S.; Taylor, Jeremy M. G.; Mukherjee, Bhramar
2015-01-01
We propose an extension of the expectation-maximization (EM) algorithm, called the hyperpenalized EM (HEM) algorithm, that maximizes a penalized log-likelihood, for which some data are missing or unavailable, using a data-adaptive estimate of the penalty parameter. This is potentially useful in applications for which the analyst is unable or unwilling to choose a single value of a penalty parameter but instead can posit a plausible range of values. The HEM algorithm is conceptually straightforward and also very effective, and we demonstrate its utility in the analysis of a genomic data set. Gene expression measurements and clinical covariates were used to predict survival time. However, many survival times are censored, and some observations only contain expression measurements derived from a different assay, which together constitute a difficult missing data problem. It is desired to shrink the genomic contribution in a data-adaptive way. The HEM algorithm successfully handles both the missing data and shrinkage aspects of the problem. PMID:26834856
Discrete-time minimal control synthesis adaptive algorithm
NASA Astrophysics Data System (ADS)
di Bernardo, M.; di Gennaro, F.; Olm, J. M.; Santini, S.
2010-12-01
This article proposes a discrete-time Minimal Control Synthesis (MCS) algorithm for a class of single-input single-output discrete-time systems written in controllable canonical form. As it happens with the continuous-time MCS strategy, the algorithm arises from the family of hyperstability-based discrete-time model reference adaptive controllers introduced in (Landau, Y. (1979), Adaptive Control: The Model Reference Approach, New York: Marcel Dekker, Inc.) and is able to ensure tracking of the states of a given reference model with minimal knowledge about the plant. The control design shows robustness to parameter uncertainties, slow parameter variation and matched disturbances. Furthermore, it is proved that the proposed discrete-time MCS algorithm can be used to control discretised continuous-time plants with the same performance features. Contrary to previous discrete-time implementations of the continuous-time MCS algorithm, here a formal proof of asymptotic stability is given for generic n-dimensional plants in controllable canonical form. The theoretical approach is validated by means of simulation results.
Adaptive firefly algorithm: parameter analysis and its application.
Cheung, Ngaam J; Ding, Xue-Ming; Shen, Hong-Bin
2014-01-01
As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise. PMID:25397812
Adaptive firefly algorithm: parameter analysis and its application.
Cheung, Ngaam J; Ding, Xue-Ming; Shen, Hong-Bin
2014-01-01
As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.
Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
Jiang, Peng; Feng, Yang; Wu, Feng; Yu, Shanen; Xu, Huan
2016-01-01
Aimed at the limited energy of nodes in underwater wireless sensor networks (UWSNs) and the heavy load of cluster heads in clustering routing algorithms, this paper proposes a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs. Cluster size is first decided by the distance between the cluster head nodes and sink node, and a dynamic layered mechanism is established to avoid the repeated selection of the same cluster head nodes. Using Krill Herd optimization algorithm selects the optimal and second optimal cluster heads, and its Lagrange model directs nodes to a high likelihood area. It ultimately realizes the functions of data collection and data transition. The simulation results show that the proposed algorithm can effectively decrease cluster energy consumption, balance the network energy consumption, and prolong the network lifetime. PMID:27589744
Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs.
Jiang, Peng; Feng, Yang; Wu, Feng; Yu, Shanen; Xu, Huan
2016-01-01
Aimed at the limited energy of nodes in underwater wireless sensor networks (UWSNs) and the heavy load of cluster heads in clustering routing algorithms, this paper proposes a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs. Cluster size is first decided by the distance between the cluster head nodes and sink node, and a dynamic layered mechanism is established to avoid the repeated selection of the same cluster head nodes. Using Krill Herd optimization algorithm selects the optimal and second optimal cluster heads, and its Lagrange model directs nodes to a high likelihood area. It ultimately realizes the functions of data collection and data transition. The simulation results show that the proposed algorithm can effectively decrease cluster energy consumption, balance the network energy consumption, and prolong the network lifetime. PMID:27589744
Generalized pattern search algorithms with adaptive precision function evaluations
Polak, Elijah; Wetter, Michael
2003-05-14
In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters. Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to a form that uses adaptive precision approximations to the cost function. These numerical approximations need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. Such an approach leads to substantial time savings in minimizing computationally expensive functions.
A local adaptive discretization algorithm for Smoothed Particle Hydrodynamics
NASA Astrophysics Data System (ADS)
Spreng, Fabian; Schnabel, Dirk; Mueller, Alexandra; Eberhard, Peter
2014-06-01
In this paper, an extension to the Smoothed Particle Hydrodynamics (SPH) method is proposed that allows for an adaptation of the discretization level of a simulated continuum at runtime. By combining a local adaptive refinement technique with a newly developed coarsening algorithm, one is able to improve the accuracy of the simulation results while reducing the required computational cost at the same time. For this purpose, the number of particles is, on the one hand, adaptively increased in critical areas of a simulation model. Typically, these are areas that show a relatively low particle density and high gradients in stress or temperature. On the other hand, the number of SPH particles is decreased for domains with a high particle density and low gradients. Besides a brief introduction to the basic principle of the SPH discretization method, the extensions to the original formulation providing such a local adaptive refinement and coarsening of the modeled structure are presented in this paper. After having introduced its theoretical background, the applicability of the enhanced formulation, as well as the benefit gained from the adaptive model discretization, is demonstrated in the context of four different simulation scenarios focusing on solid continua. While presenting the results found for these examples, several properties of the proposed adaptive technique are discussed, e.g. the conservation of momentum as well as the existing correlation between the chosen refinement and coarsening patterns and the observed quality of the results.
Adaptive Mesh Refinement Algorithms for Parallel Unstructured Finite Element Codes
Parsons, I D; Solberg, J M
2006-02-03
This project produced algorithms for and software implementations of adaptive mesh refinement (AMR) methods for solving practical solid and thermal mechanics problems on multiprocessor parallel computers using unstructured finite element meshes. The overall goal is to provide computational solutions that are accurate to some prescribed tolerance, and adaptivity is the correct path toward this goal. These new tools will enable analysts to conduct more reliable simulations at reduced cost, both in terms of analyst and computer time. Previous academic research in the field of adaptive mesh refinement has produced a voluminous literature focused on error estimators and demonstration problems; relatively little progress has been made on producing efficient implementations suitable for large-scale problem solving on state-of-the-art computer systems. Research issues that were considered include: effective error estimators for nonlinear structural mechanics; local meshing at irregular geometric boundaries; and constructing efficient software for parallel computing environments.
NASA Astrophysics Data System (ADS)
Park, Sang Ha; Lee, Seokjin; Sung, Koeng-Mo
Non-negative matrix factorization (NMF) is widely used for monaural musical sound source separation because of its efficiency and good performance. However, an additional clustering process is required because the musical sound mixture is separated into more signals than the number of musical tracks during NMF separation. In the conventional method, manual clustering or training-based clustering is performed with an additional learning process. Recently, a clustering algorithm based on the mel-frequency cepstrum coefficient (MFCC) was proposed for unsupervised clustering. However, MFCC clustering supplies limited information for clustering. In this paper, we propose various timbre features for unsupervised clustering and a clustering algorithm with these features. Simulation experiments are carried out using various musical sound mixtures. The results indicate that the proposed method improves clustering performance, as compared to conventional MFCC-based clustering.
NASA Technical Reports Server (NTRS)
Rogers, David
1991-01-01
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
NASA Astrophysics Data System (ADS)
Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok
2015-01-01
This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.
Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation.
Liu, Guoying; Zhang, Yun; Wang, Aimin
2015-11-01
Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods.
Industry Cluster's Adaptive Co-competition Behavior Modeling Inspired by Swarm Intelligence
NASA Astrophysics Data System (ADS)
Xiang, Wei; Ye, Feifan
Adaptation helps the individual enterprise to adjust its behavior to uncertainties in environment and hence determines a healthy growth of both the individuals and the whole industry cluster as well. This paper is focused on the study on co-competition adaptation behavior of industry cluster, which is inspired by swarm intelligence mechanisms. By referencing to ant cooperative transportation and ant foraging behavior and their related swarm intelligence approaches, the cooperative adaptation and competitive adaptation behavior are studied and relevant models are proposed. Those adaptive co-competition behaviors model can be integrated to the multi-agent system of industry cluster to make the industry cluster model more realistic.
Analysis of adaptive algorithms for an integrated communication network
NASA Technical Reports Server (NTRS)
Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim
1985-01-01
Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.
Statistical behaviour of adaptive multilevel splitting algorithms in simple models
Rolland, Joran Simonnet, Eric
2015-02-15
Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection–mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations.
Adaptivity and smart algorithms for fluid-structure interaction
NASA Technical Reports Server (NTRS)
Oden, J. Tinsley
1990-01-01
This paper reviews new approaches in CFD which have the potential for significantly increasing current capabilities of modeling complex flow phenomena and of treating difficult problems in fluid-structure interaction. These approaches are based on the notions of adaptive methods and smart algorithms, which use instantaneous measures of the quality and other features of the numerical flowfields as a basis for making changes in the structure of the computational grid and of algorithms designed to function on the grid. The application of these new techniques to several problem classes are addressed, including problems with moving boundaries, fluid-structure interaction in high-speed turbine flows, flow in domains with receding boundaries, and related problems.
Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms
NASA Astrophysics Data System (ADS)
Cervone, Guido; Franzese, Pasquale; Grajdeanu, Adrian
2010-10-01
The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source. The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.
A hybrid skull-stripping algorithm based on adaptive balloon snake models
NASA Astrophysics Data System (ADS)
Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua
2013-02-01
Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.
NASA Astrophysics Data System (ADS)
Zu, Yun-Xiao; Zhou, Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.
Fully implicit adaptive mesh refinement algorithm for reduced MHD
NASA Astrophysics Data System (ADS)
Philip, Bobby; Pernice, Michael; Chacon, Luis
2006-10-01
In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technology to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite grid --FAC-- algorithms) for scalability. We demonstrate that the concept is indeed feasible, featuring near-optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations in challenging dissipation regimes will be presented on a variety of problems that benefit from this capability, including tearing modes, the island coalescence instability, and the tilt mode instability. L. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) B. Philip, M. Pernice, and L. Chac'on, Lecture Notes in Computational Science and Engineering, accepted (2006)
Contributions to "k"-Means Clustering and Regression via Classification Algorithms
ERIC Educational Resources Information Center
Salman, Raied
2012-01-01
The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
On the impact of dissimilarity measure in k-modes clustering algorithm.
Ng, Michael K; Li, Mark Junjie; Huang, Joshua Zhexue; He, Zengyou
2007-03-01
This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework. PMID:17224620
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.
Application of adaptive cluster sampling to low-density populations of freshwater mussels
Smith, D.R.; Villella, R.F.; Lemarie, D.P.
2003-01-01
Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.
Path Planning Algorithms for the Adaptive Sensor Fleet
NASA Technical Reports Server (NTRS)
Stoneking, Eric; Hosler, Jeff
2005-01-01
The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.
Adham, Manal T; Bentley, Peter J
2016-08-01
This paper proposes and evaluates a solution to the truck redistribution problem prominent in London's Santander Cycle scheme. Due to the complexity of this NP-hard combinatorial optimisation problem, no efficient optimisation techniques are known to solve the problem exactly. This motivates our use of the heuristic Artificial Ecosystem Algorithm (AEA) to find good solutions in a reasonable amount of time. The AEA is designed to take advantage of highly distributed computer architectures and adapt to changing problems. In the AEA a problem is first decomposed into its relative sub-components; they then evolve solution building blocks that fit together to form a single optimal solution. Three variants of the AEA centred on evaluating clustering methods are presented: the baseline AEA, the community-based AEA which groups stations according to journey flows, and the Adaptive AEA which actively modifies clusters to cater for changes in demand. We applied these AEA variants to the redistribution problem prominent in bike share schemes (BSS). The AEA variants are empirically evaluated using historical data from Santander Cycles to validate the proposed approach and prove its potential effectiveness.
SOAR Adaptive Optics Observations of the Globular Cluster NGC 6496
NASA Astrophysics Data System (ADS)
Fraga, Luciano; Kunder, Andrea; Tokovinin, Andrei
2013-06-01
We present high-quality BVRI photometric data in the field of globular cluster NGC 6496 obtained with the SOAR Telescope Adaptive Module (SAM). Our observations were collected as part of the ongoing SAM commissioning. The distance modulus and cluster color excess as found from the red clump are (m - M) V = 15.71 ± 0.02 mag and E(V - I) = 0.28 ± 0.02 mag. An age of 10.5 ± 0.5 Gyr is determined from the difference in magnitude between the red clump and the subgiant branch. These parameters are in excellent agreement with the values derived from isochrone fitting. From the color-magnitude diagram we find a metallicity of [Fe/H] = -0.65 dex and hence support a disk classification for NGC 6496. The complete BVRI data set for NGC 6469 is made available in the electronic edition of the Journal. Based on observations obtained at the Southern Astrophysical Research (SOAR) telescope, which is a joint project of the Ministério da Ciência, Tecnologia, e Inovação (MCTI) da República Federativa do Brasil, the U.S. National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC), and Michigan State University (MSU).
NASA Technical Reports Server (NTRS)
Lambeck, P. F.; Rice, D. P.
1976-01-01
Signature extension is intended to increase the space-time range over which a set of training statistics can be used to classify data without significant loss of recognition accuracy. A first cluster matching algorithm MASC (Multiplicative and Additive Signature Correction) was developed at the Environmental Research Institute of Michigan to test the concept of using associations between training and recognition area cluster statistics to define an average signature transformation. A more recent signature extension module CROP-A (Cluster Regression Ordered on Principal Axis) has shown evidence of making significant associations between training and recognition area cluster statistics, with the clusters to be matched being selected automatically by the algorithm.
A fast readout algorithm for Cluster Counting/Timing drift chambers on a FPGA board
NASA Astrophysics Data System (ADS)
Cappelli, L.; Creti, P.; Grancagnolo, F.; Pepino, A.; Tassielli, G.
2013-08-01
A fast readout algorithm for Cluster Counting and Timing purposes has been implemented and tested on a Virtex 6 core FPGA board. The algorithm analyses and stores data coming from a Helium based drift tube instrumented by 1 GSPS fADC and represents the outcome of balancing between cluster identification efficiency and high speed performance. The algorithm can be implemented in electronics boards serving multiple fADC channels as an online preprocessing stage for drift chamber signals.
Parallelization of the Wolff single-cluster algorithm.
Kaupuzs, J; Rimsāns, J; Melnik, R V N
2010-02-01
A parallel [open multiprocessing (OpenMP)] implementation of the Wolff single-cluster algorithm has been developed and tested for the three-dimensional (3D) Ising model. The developed procedure is generalizable to other lattice spin models and its effectiveness depends on the specific application at hand. The applicability of the developed methodology is discussed in the context of the applications, where a sophisticated shuffling scheme is used to generate pseudorandom numbers of high quality, and an iterative method is applied to find the critical temperature of the 3D Ising model with a great accuracy. For the lattice with linear size L=1024, we have reached the speedup about 1.79 times on two processors and about 2.67 times on four processors, as compared to the serial code. According to our estimation, the speedup about three times on four processors is reachable for the O(n) models with n> or =2. Furthermore, the application of the developed OpenMP code allows us to simulate larger lattices due to greater operative (shared) memory available.
Parallelization of the Wolff single-cluster algorithm
NASA Astrophysics Data System (ADS)
Kaupužs, J.; Rimšāns, J.; Melnik, R. V. N.
2010-02-01
A parallel [open multiprocessing (OpenMP)] implementation of the Wolff single-cluster algorithm has been developed and tested for the three-dimensional (3D) Ising model. The developed procedure is generalizable to other lattice spin models and its effectiveness depends on the specific application at hand. The applicability of the developed methodology is discussed in the context of the applications, where a sophisticated shuffling scheme is used to generate pseudorandom numbers of high quality, and an iterative method is applied to find the critical temperature of the 3D Ising model with a great accuracy. For the lattice with linear size L=1024 , we have reached the speedup about 1.79 times on two processors and about 2.67 times on four processors, as compared to the serial code. According to our estimation, the speedup about three times on four processors is reachable for the O(n) models with n≥2 . Furthermore, the application of the developed OpenMP code allows us to simulate larger lattices due to greater operative (shared) memory available.
Using Clustering Algorithms to Identify Brown Dwarf Characteristics
NASA Astrophysics Data System (ADS)
Choban, Caleb
2016-06-01
Brown dwarfs are stars that are not massive enough to sustain core hydrogen fusion, and thus fade and cool over time. The molecular composition of brown dwarf atmospheres can be determined by observing absorption features in their infrared spectrum, which can be quantified using spectral indices. Comparing these indices to one another, we can determine what kind of brown dwarf it is, and if it is young or metal-poor. We explored a new method for identifying these subgroups through the expectation-maximization machine learning clustering algorithm, which provides a quantitative and statistical way of identifying index pairs which separate rare populations. We specifically quantified two statistics, completeness and concentration, to identify the best index pairs. Starting with a training set, we defined selection regions for young, metal-poor and binary brown dwarfs, and tested these on a large sample of L dwarfs. We present the results of this analysis, and demonstrate that new objects in these classes can be found through these methods.
A novel adaptive multi-resolution combined watermarking algorithm
NASA Astrophysics Data System (ADS)
Feng, Gui; Lin, QiWei
2008-04-01
The rapid development of IT and WWW technique, causing person frequently confronts with various kinds of authorized identification problem, especially the copyright problem of digital products. The digital watermarking technique was emerged as one kind of solutions. The balance between robustness and imperceptibility is always the object sought by related researchers. In order to settle the problem of robustness and imperceptibility, a novel adaptive multi-resolution combined digital image watermarking algorithm was proposed in this paper. In the proposed algorithm, we first decompose the watermark into several sub-bands, and according to its significance to embed the sub-band to different DWT coefficient of the carrier image. While embedding, the HVS was considered. So under the precondition of keeping the quality of image, the larger capacity of watermark can be embedding. The experimental results have shown that the proposed algorithm has better performance in the aspects of robustness and security. And with the same visual quality, the technique has larger capacity. So the unification of robustness and imperceptibility was achieved.
An adaptive correspondence algorithm for modeling scenes with strong interreflections.
Xu, Yi; Aliaga, Daniel G
2009-01-01
Modeling real-world scenes, beyond diffuse objects, plays an important role in computer graphics, virtual reality, and other commercial applications. One active approach is projecting binary patterns in order to obtain correspondence and reconstruct a densely sampled 3D model. In such structured-light systems, determining whether a pixel is directly illuminated by the projector is essential to decoding the patterns. When a scene has abundant indirect light, this process is especially difficult. In this paper, we present a robust pixel classification algorithm for this purpose. Our method correctly establishes the lower and upper bounds of the possible intensity values of an illuminated pixel and of a non-illuminated pixel. Based on the two intervals, our method classifies a pixel by determining whether its intensity is within one interval but not in the other. Our method performs better than standard method due to the fact that it avoids gross errors during decoding process caused by strong inter-reflections. For the remaining uncertain pixels, we apply an iterative algorithm to reduce the inter-reflection within the scene. Thus, more points can be decoded and reconstructed after each iteration. Moreover, the iterative algorithm is carried out in an adaptive fashion for fast convergence.
A vector reconstruction based clustering algorithm particularly for large-scale text collection.
Liu, Ming; Wu, Chong; Chen, Lei
2015-03-01
Along with the fast evolvement of internet technology, internet users have to face the large amount of textual data every day. Apparently, organizing texts into categories can help users dig the useful information from large-scale text collection. Clustering is one of the most promising tools for categorizing texts due to its unsupervised characteristic. Unfortunately, most of traditional clustering algorithms lose their high qualities on large-scale text collection, which mainly attributes to the high-dimensional vector space and semantic similarity among texts. To effectively and efficiently cluster large-scale text collection, this paper puts forward a vector reconstruction based clustering algorithm. Only the features that can represent the cluster are preserved in cluster's representative vector. This algorithm alternately repeats two sub-processes until it converges. One process is partial tuning sub-process, where feature's weight is fine-tuned by iterative process similar to self-organizing-mapping (SOM) algorithm. To accelerate clustering velocity, an intersection based similarity measurement and its corresponding neuron adjustment function are proposed and implemented in this sub-process. The other process is overall tuning sub-process, where the features are reallocated among different clusters. In this sub-process, the features useless to represent the cluster are removed from cluster's representative vector. Experimental results on the three text collections (including two small-scale and one large-scale text collections) demonstrate that our algorithm obtains high-quality performances on both small-scale and large-scale text collections.
A Competency-Based Guided-Learning Algorithm Applied on Adaptively Guiding E-Learning
ERIC Educational Resources Information Center
Hsu, Wei-Chih; Li, Cheng-Hsiu
2015-01-01
This paper presents a new algorithm called competency-based guided-learning algorithm (CBGLA), which can be applied on adaptively guiding e-learning. Computational process analysis and mathematical derivation of competency-based learning (CBL) were used to develop the CBGLA. The proposed algorithm could generate an effective adaptively guiding…
Karayiannis, Nicolaos B; Randolph-Gips, Mary M
2005-03-01
This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non-Euclidean algorithms which are computationally more demanding.
Clustering performance comparison using K-means and expectation maximization algorithms
Jung, Yong Gyu; Kang, Min Soo; Heo, Jun
2014-01-01
Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K-means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K-means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results. PMID:26019610
Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji
2014-01-01
An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.
A highly efficient multi-core algorithm for clustering extremely large datasets
2010-01-01
Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer. PMID:20370922
Skillman, Samuel W.; Hallman, Eric J.; Burns, Jack O.; Smith, Britton D.; O'Shea, Brian W.; Turk, Matthew J.
2011-07-10
Cosmological shocks are a critical part of large-scale structure formation, and are responsible for heating the intracluster medium in galaxy clusters. In addition, they are capable of accelerating non-thermal electrons and protons. In this work, we focus on the acceleration of electrons at shock fronts, which is thought to be responsible for radio relics-extended radio features in the vicinity of merging galaxy clusters. By combining high-resolution adaptive mesh refinement/N-body cosmological simulations with an accurate shock-finding algorithm and a model for electron acceleration, we calculate the expected synchrotron emission resulting from cosmological structure formation. We produce synthetic radio maps of a large sample of galaxy clusters and present luminosity functions and scaling relationships. With upcoming long-wavelength radio telescopes, we expect to see an abundance of radio emission associated with merger shocks in the intracluster medium. By producing observationally motivated statistics, we provide predictions that can be compared with observations to further improve our understanding of magnetic fields and electron shock acceleration.
NASA Astrophysics Data System (ADS)
Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Li, Xiang; Yan, Ruqiang
2016-04-01
Fault information of aero-engine bearings presents two particular phenomena, i.e., waveform distortion and impulsive feature frequency band dispersion, which leads to a challenging problem for current techniques of bearing fault diagnosis. Moreover, although many progresses of sparse representation theory have been made in feature extraction of fault information, the theory also confronts inevitable performance degradation due to the fact that relatively weak fault information has not sufficiently prominent and sparse representations. Therefore, a novel nonlocal sparse model (coined NLSM) and its algorithm framework has been proposed in this paper, which goes beyond simple sparsity by introducing more intrinsic structures of feature information. This work adequately exploits the underlying prior information that feature information exhibits nonlocal self-similarity through clustering similar signal fragments and stacking them together into groups. Within this framework, the prior information is transformed into a regularization term and a sparse optimization problem, which could be solved through block coordinate descent method (BCD), is formulated. Additionally, the adaptive structural clustering sparse dictionary learning technique, which utilizes k-Nearest-Neighbor (kNN) clustering and principal component analysis (PCA) learning, is adopted to further enable sufficient sparsity of feature information. Moreover, the selection rule of regularization parameter and computational complexity are described in detail. The performance of the proposed framework is evaluated through numerical experiment and its superiority with respect to the state-of-the-art method in the field is demonstrated through the vibration signals of experimental rig of aircraft engine bearings.
A Special Local Clustering Algorithm for Identifying the Genes Associated With Alzheimer’s Disease
Pang, Chao-Yang; Hu, Wei; Hu, Ben-Qiong; Shi, Ying; Vanderburg, Charles R.; Rogers, Jack T.
2010-01-01
Clustering is the grouping of similar objects into a class. Local clustering feature refers to the phenomenon whereby one group of data is separated from another, and the data from these different groups are clustered locally. A compact class is defined as one cluster in which all similar elements cluster tightly within the cluster. Herein, the essence of the local clustering feature, revealed by mathematical manipulation, results in a novel clustering algorithm termed as the special local clustering (SLC) algorithm that was used to process gene microarray data related to Alzheimer’s disease (AD). SLC algorithm was able to group together genes with similar expression patterns and identify significantly varied gene expression values as isolated points. If a gene belongs to a compact class in control data and appears as an isolated point in incipient, moderate and/or severe AD gene microarray data, this gene is possibly associated with AD. Application of a clustering algorithm in disease-associated gene identification such as in AD is rarely reported. PMID:20089478
Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730
Deb, Suash; Yang, Xin-She
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730
Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
Block clustering based on difference of convex functions (DC) programming and DC algorithms.
Le, Hoai Minh; Le Thi, Hoai An; Dinh, Tao Pham; Huynh, Van Ngai
2013-10-01
We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.
Region Adaptive Color Demosaicing Algorithm Using Color Constancy
NASA Astrophysics Data System (ADS)
Kim, Chang Won; Oh, Hyun Mook; Yoo, Du Sic; Kang, Moon Gi
2010-12-01
This paper proposes a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In this paper, in order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is examined during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. In order to prevent color failure in conventional edge-based AWB methods, we propose a modified edge-based AWB method that used a predefined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. Simulated and real Bayer color filter array (CFA) data are used to evaluate the performance of the proposed method. When compared to conventional methods, the proposed method shows significant improvements in terms of visual and numerical criteria.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation.
Design of infrasound-detection system via adaptive LMSTDE algorithm
NASA Technical Reports Server (NTRS)
Khalaf, C. S.; Stoughton, J. W.
1984-01-01
A proposed solution to an aviation safety problem is based on passive detection of turbulent weather phenomena through their infrasonic emission. This thesis describes a system design that is adequate for detection and bearing evaluation of infrasounds. An array of four sensors, with the appropriate hardware, is used for the detection part. Bearing evaluation is based on estimates of time delays between sensor outputs. The generalized cross correlation (GCC), as the conventional time-delay estimation (TDE) method, is first reviewed. An adaptive TDE approach, using the least mean square (LMS) algorithm, is then discussed. A comparison between the two techniques is made and the advantages of the adaptive approach are listed. The behavior of the GCC, as a Roth processor, is examined for the anticipated signals. It is shown that the Roth processor has the desired effect of sharpening the peak of the correlation function. It is also shown that the LMSTDE technique is an equivalent implementation of the Roth processor in the time domain. A LMSTDE lead-lag model, with a variable stability coefficient and a convergence criterion, is designed.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661
The Adaptive Analysis of Visual Cognition using Genetic Algorithms
Cook, Robert G.; Qadri, Muhammad A. J.
2014-01-01
Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from six parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, distractor brightness). Display composition changed over time, or evolved, as a function of the bird’s differential accuracy within the population of values for each gene. Testing three randomized initial conditions and one set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological “attractor” represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally. PMID:24000905
Self-adaptive algorithm for segmenting skin regions
NASA Astrophysics Data System (ADS)
Kawulok, Michal; Kawulok, Jolanta; Nalepa, Jakub; Smolka, Bogdan
2014-12-01
In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.
Huang, Xiaohui; Ye, Yunming; Zhang, Haijun
2014-08-01
Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated. However, most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation. In this paper, a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation. First, a set of new objective functions for clustering is developed. Based on these objective functions, the corresponding updating rules for the algorithms are then derived analytically. The properties and performances of these algorithms are investigated on several synthetic and real-life data sets. Experimental studies demonstrate that our proposed algorithms outperform the state-of-the-art kmeans-type clustering algorithms with respect to four metrics: accuracy, RandIndex, Fscore, and normal mutual information.
A scalable and practical one-pass clustering algorithm for recommender system
NASA Astrophysics Data System (ADS)
Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali
2015-12-01
KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.
Ju, Ying; Zhang, Songming; Ding, Ningxiang; Zeng, Xiangxiang; Zhang, Xingyi
2016-01-01
The field of complex network clustering is gaining considerable attention in recent years. In this study, a multi-objective evolutionary algorithm based on membranes is proposed to solve the network clustering problem. Population are divided into different membrane structures on average. The evolutionary algorithm is carried out in the membrane structures. The population are eliminated by the vector of membranes. In the proposed method, two evaluation objectives termed as Kernel J-means and Ratio Cut are to be minimized. Extensive experimental studies comparison with state-of-the-art algorithms proves that the proposed algorithm is effective and promising. PMID:27670156
NASA Technical Reports Server (NTRS)
Mach, Douglas M.; Christian, Hugh J.; Blakeslee, Richard; Boccippio, Dennis J.; Goodman, Steve J.; Boeck, William
2006-01-01
We describe the clustering algorithm used by the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) for combining the lightning pulse data into events, groups, flashes, and areas. Events are single pixels that exceed the LIS/OTD background level during a single frame (2 ms). Groups are clusters of events that occur within the same frame and in adjacent pixels. Flashes are clusters of groups that occur within 330 ms and either 5.5 km (for LIS) or 16.5 km (for OTD) of each other. Areas are clusters of flashes that occur within 16.5 km of each other. Many investigators are utilizing the LIS/OTD flash data; therefore, we test how variations in the algorithms for the event group and group-flash clustering affect the flash count for a subset of the LIS data. We divided the subset into areas with low (1-3), medium (4-15), high (16-63), and very high (64+) flashes to see how changes in the clustering parameters affect the flash rates in these different sizes of areas. We found that as long as the cluster parameters are within about a factor of two of the current values, the flash counts do not change by more than about 20%. Therefore, the flash clustering algorithm used by the LIS and OTD sensors create flash rates that are relatively insensitive to reasonable variations in the clustering algorithms.
A new clustering algorithm for scanning electron microscope images
NASA Astrophysics Data System (ADS)
Yousef, Amr; Duraisamy, Prakash; Karim, Mohammad
2016-04-01
A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons. The electrons interact with the sample atoms, producing various signals that are collected by detectors. The gathered signals contain information about the sample's surface topography and composition. The electron beam is generally scanned in a raster scan pattern, and the beam's position is combined with the detected signal to produce an image. The most common configuration for an SEM produces a single value per pixel, with the results usually rendered as grayscale images. The captured images may be produced with insufficient brightness, anomalous contrast, jagged edges, and poor quality due to low signal-to-noise ratio, grained topography and poor surface details. The segmentation of the SEM images is a tackling problems in the presence of the previously mentioned distortions. In this paper, we are stressing on the clustering of these type of images. In that sense, we evaluate the performance of the well-known unsupervised clustering and classification techniques such as connectivity based clustering (hierarchical clustering), centroid-based clustering, distribution-based clustering and density-based clustering. Furthermore, we propose a new spatial fuzzy clustering technique that works efficiently on this type of images and compare its results against these regular techniques in terms of clustering validation metrics.
A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data
2015-01-01
Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data. PMID:25993469
NASA Astrophysics Data System (ADS)
Sun, Xu; Yang, Lina; Gao, Lianru; Zhang, Bing; Li, Shanshan; Li, Jun
2015-01-01
Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability.
NASA Astrophysics Data System (ADS)
Zhang, Xian-Kun; Tian, Xue; Li, Ya-Nan; Song, Chen
2014-08-01
The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the algorithm.
A fast general-purpose clustering algorithm based on FPGAs for high-throughput data processing
NASA Astrophysics Data System (ADS)
Annovi, A.; Beretta, M.
2010-05-01
We present a fast general-purpose algorithm for high-throughput clustering of data "with a two-dimensional organization". The algorithm is designed to be implemented with FPGAs or custom electronics. The key feature is a processing time that scales linearly with the amount of data to be processed. This means that clustering can be performed in pipeline with the readout, without suffering from combinatorial delays due to looping multiple times through all the data. This feature makes this algorithm especially well suited for problems where the data have high density, e.g. in the case of tracking devices working under high-luminosity condition such as those of LHC or super-LHC. The algorithm is organized in two steps: the first step (core) clusters the data; the second step analyzes each cluster of data to extract the desired information. The current algorithm is developed as a clustering device for modern high-energy physics pixel detectors. However, the algorithm has much broader field of applications. In fact, its core does not specifically rely on the kind of data or detector it is working for, while the second step can and should be tailored for a given application. For example, in case of spatial measurement with silicon pixel detectors, the second step performs center of charge calculation. Applications can thus be foreseen to other detectors and other scientific fields ranging from HEP calorimeters to medical imaging. An additional advantage of this two steps approach is that the typical clustering related calculations (second step) are separated from the combinatorial complications of clustering. This separation simplifies the design of the second step and it enables it to perform sophisticated calculations achieving offline quality in online applications. The algorithm is general purpose in the sense that only minimal assumptions on the kind of clustering to be performed are made.
Evaluating Knowledge Structure-Based Adaptive Testing Algorithms and System Development
ERIC Educational Resources Information Center
Wu, Huey-Min; Kuo, Bor-Chen; Yang, Jinn-Min
2012-01-01
In recent years, many computerized test systems have been developed for diagnosing students' learning profiles. Nevertheless, it remains a challenging issue to find an adaptive testing algorithm to both shorten testing time and precisely diagnose the knowledge status of students. In order to find a suitable algorithm, four adaptive testing…
Multi-element array signal reconstruction with adaptive least-squares algorithms
NASA Technical Reports Server (NTRS)
Kumar, R.
1992-01-01
Two versions of the adaptive least-squares algorithm are presented for combining signals from multiple feeds placed in the focal plane of a mechanical antenna whose reflector surface is distorted due to various deformations. Coherent signal combining techniques based on the adaptive least-squares algorithm are examined for nearly optimally and adaptively combining the outputs of the feeds. The performance of the two versions is evaluated by simulations. It is demonstrated for the example considered that both of the adaptive least-squares algorithms are capable of offsetting most of the loss in the antenna gain incurred due to reflector surface deformations.
Learning assignment order of instances for the constrained K-means clustering algorithm.
Hong, Yi; Kwong, Sam
2009-04-01
The sensitivity of the constrained K-means clustering algorithm (Cop-Kmeans) to the assignment order of instances is studied, and a novel assignment order learning method for Cop-Kmeans, termed as clustering Uncertainty-based Assignment order Learning Algorithm (UALA), is proposed in this paper. The main idea of UALA is to rank all instances in the data set according to their clustering uncertainties calculated by using the ensembles of multiple clustering algorithms. Experimental results on several real data sets with artificial instance-level constraints demonstrate that UALA can identify a good assignment order of instances for Cop-Kmeans. In addition, the effects of ensemble sizes on the performance of UALA are analyzed, and the generalization property of Cop-Kmeans is also studied.
Ju, Chunhua
2013-01-01
Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network
Vimalarani, C.; Subramanian, R.; Sivanandam, S. N.
2016-01-01
Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption. PMID:26881273
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.
Vimalarani, C; Subramanian, R; Sivanandam, S N
2016-01-01
Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption. PMID:26881273
A hybrid algorithm for clustering of time series data based on affinity search technique.
Aghabozorgi, Saeed; Ying Wah, Teh; Herawan, Tutut; Jalab, Hamid A; Shaygan, Mohammad Amin; Jalali, Alireza
2014-01-01
Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.
A randomized algorithm for two-cluster partition of a set of vectors
NASA Astrophysics Data System (ADS)
Kel'manov, A. V.; Khandeev, V. I.
2015-02-01
A randomized algorithm is substantiated for the strongly NP-hard problem of partitioning a finite set of vectors of Euclidean space into two clusters of given sizes according to the minimum-of-the sum-of-squared-distances criterion. It is assumed that the centroid of one of the clusters is to be optimized and is determined as the mean value over all vectors in this cluster. The centroid of the other cluster is fixed at the origin. For an established parameter value, the algorithm finds an approximate solution of the problem in time that is linear in the space dimension and the input size of the problem for given values of the relative error and failure probability. The conditions are established under which the algorithm is asymptotically exact and runs in time that is linear in the space dimension and quadratic in the input size of the problem.
NASA Astrophysics Data System (ADS)
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm. PMID:24697395
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
An approximation polynomial-time algorithm for a sequence bi-clustering problem
NASA Astrophysics Data System (ADS)
Kel'manov, A. V.; Khamidullin, S. A.
2015-06-01
We consider a strongly NP-hard problem of partitioning a finite sequence of vectors in Euclidean space into two clusters using the criterion of the minimal sum of the squared distances from the elements of the clusters to the centers of the clusters. The center of one of the clusters is to be optimized and is determined as the mean value over all vectors in this cluster. The center of the other cluster is fixed at the origin. Moreover, the partition is such that the difference between the indices of two successive vectors in the first cluster is bounded above and below by prescribed constants. A 2-approximation polynomial-time algorithm is proposed for this problem.
Uy, D.L.
1996-02-01
An algorithm for detection and identification of image clusters or {open_quotes}blobs{close_quotes} based on color information for an autonomous mobile robot is developed. The input image data are first processed using a crisp color fuszzyfier, a binary smoothing filter, and a median filter. The processed image data is then inputed to the image clusters detection and identification program. The program employed the concept of {open_quotes}elastic rectangle{close_quotes}that stretches in such a way that the whole blob is finally enclosed in a rectangle. A C-program is develop to test the algorithm. The algorithm is tested only on image data of 8x8 sizes with different number of blobs in them. The algorithm works very in detecting and identifying image clusters.
The "Juggler" algorithm: a hybrid deformable image registration algorithm for adaptive radiotherapy
NASA Astrophysics Data System (ADS)
Xia, Junyi; Chen, Yunmei; Samant, Sanjiv S.
2007-03-01
Fast deformable registration can potentially facilitate the clinical implementation of adaptive radiation therapy (ART), which allows for daily organ deformations not accounted for in radiotherapy treatment planning, which typically utilizes a static organ model, to be incorporated into the fractionated treatment. Existing deformable registration algorithms typically utilize a specific diffusion model, and require a large number of iterations to achieve convergence. This limits the online applications of deformable image registration for clinical radiotherapy, such as daily patient setup variations involving organ deformation, where high registration precision is required. We propose a hybrid algorithm, the "Juggler", based on a multi-diffusion model to achieve fast convergence. The Juggler achieves fast convergence by applying two different diffusion models: i) one being optimized quickly for matching high gradient features, i.e. bony anatomies; and ii) the other being optimized for further matching low gradient features, i.e. soft tissue. The regulation of these 2 competing criteria is achieved using a threshold of a similarity measure, such as cross correlation or mutual information. A multi-resolution scheme was applied for faster convergence involving large deformations. Comparisons of the Juggler algorithm were carried out with demons method, accelerated demons method, and free-form deformable registration using 4D CT lung imaging from 5 patients. Based on comparisons of difference images and similarity measure computations, the Juggler produced a superior registration result. It achieved the desired convergence within 30 iterations, and typically required <90sec to register two 3D image sets of size 256×256×40 using a 3.2 GHz PC. This hybrid registration strategy successfully incorporates the benefits of different diffusion models into a single unified model.
NASA Astrophysics Data System (ADS)
Guo, Z.; Xiong, S. M.
2015-05-01
An algorithm comprising adaptive mesh refinement (AMR) and parallel (Para-) computing capabilities was developed to efficiently solve the coupled phase field equations in 3-D. The AMR was achieved based on a gradient criterion and the point clustering algorithm introduced by Berger (1991). To reduce the time for mesh generation, a dynamic regridding approach was developed based on the magnitude of the maximum phase advancing velocity. Local data at each computing process was then constructed and parallel computation was realized based on the hierarchical grid structure created during the AMR. Numerical tests and simulations on single and multi-dendrite growth were performed and results show that the proposed algorithm could shorten the computing time for 3-D phase field simulation for about two orders of magnitude and enable one to gain much more insight in understanding the underlying physics during dendrite growth in solidification.
A fast density-based clustering algorithm for real-time Internet of Things stream.
Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut
2014-01-01
Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.
A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream
Ying Wah, Teh
2014-01-01
Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753
An Adaptable Power System with Software Control Algorithm
NASA Technical Reports Server (NTRS)
Castell, Karen; Bay, Mike; Hernandez-Pellerano, Amri; Ha, Kong
1998-01-01
A low cost, flexible and modular spacecraft power system design was developed in response to a call for an architecture that could accommodate multiple missions in the small to medium load range. Three upcoming satellites will use this design, with one launch date in 1999 and two in the year 2000. The design consists of modular hardware that can be scaled up or down, without additional cost, to suit missions in the 200 to 600 Watt orbital average load range. The design will be applied to satellite orbits that are circular, polar elliptical and a libration point orbit. Mission unique adaptations are accomplished in software and firmware. In designing this advanced, adaptable power system, the major goals were reduction in weight volume and cost. This power system design represents reductions in weight of 78 percent, volume of 86 percent and cost of 65 percent from previous comparable systems. The efforts to miniaturize the electronics without sacrificing performance has created streamlined power electronics with control functions residing in the system microprocessor. The power system design can handle any battery size up to 50 Amp-hour and any battery technology. The three current implementations will use both nickel cadmium and nickel hydrogen batteries ranging in size from 21 to 50 Amp-hours. Multiple batteries can be used by adding another battery module. Any solar cell technology can be used and various array layouts can be incorporated with no change in Power System Electronics (PSE) hardware. Other features of the design are the standardized interfaces between cards and subsystems and immunity to radiation effects up to 30 krad Total Ionizing Dose (TID) and 35 Mev/cm(exp 2)-kg for Single Event Effects (SEE). The control algorithm for the power system resides in a radiation-hardened microprocessor. A table driven software design allows for flexibility in mission specific requirements. By storing critical power system constants in memory, modifying the system
A Community Detection Algorithm Based on Topology Potential and Spectral Clustering
Wang, Zhixiao; Chen, Zhaotong; Zhao, Ya; Chen, Shaoda
2014-01-01
Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes' topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods. PMID:25147846
NASA Astrophysics Data System (ADS)
Ball, R. C.; Lee, J. R.
1996-03-01
We prove that a new, irreversible growth algorithm, Non-Deletion Reaction-Limited Cluster-cluster Aggregation (NDRLCA), produces equilibrium Branched Polymers, expected to exhibit Lattice Animal statistics [1]. We implement NDRLCA, off-lattice, as a computer simulation for embedding dimension d=2 and 3, obtaining values for critical exponents, fractal dimension D and cluster mass distribution exponent tau: d=2, D≈ 1.53± 0.05, tau = 1.09± 0.06; d=3, D=1.96± 0.04, tau =1.50± 0.04 in good agreement with theoretical LA values. The simulation results do not support recent suggestions [2] that BPs may be in the same universality class as percolation. We also obtain values for a model-dependent critical “fugacity”, z_c and investigate the finite-size effects of our simulation, quantifying notions of “inbreeding” that occur in this algorithm. Finally we use an extension of the NDRLCA proof to show that standard Reaction-Limited Cluster-cluster Aggregation is very unlikely to be in the same universality class as Branched Polymers/Lattice Animals unless the backnone dimension for the latter is considerably less than the published value.
Fuzzy-rough supervised attribute clustering algorithm and classification of microarray data.
Maji, Pradipta
2011-02-01
One of the major tasks with gene expression data is to find groups of coregulated genes whose collective expression is strongly associated with sample categories. In this regard, a new clustering algorithm, termed as fuzzy-rough supervised attribute clustering (FRSAC), is proposed to find such groups of genes. The proposed algorithm is based on the theory of fuzzy-rough sets, which directly incorporates the information of sample categories into the gene clustering process. A new quantitative measure is introduced based on fuzzy-rough sets that incorporates the information of sample categories to measure the similarity among genes. The proposed algorithm is based on measuring the similarity between genes using the new quantitative measure, whereby redundancy among the genes is removed. The clusters are refined incrementally based on sample categories. The effectiveness of the proposed FRSAC algorithm, along with a comparison with existing supervised and unsupervised gene selection and clustering algorithms, is demonstrated on six cancer and two arthritis data sets based on the class separability index and predictive accuracy of the naive Bayes' classifier, the K-nearest neighbor rule, and the support vector machine. PMID:20542768
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm. PMID:25265622
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
NASA Astrophysics Data System (ADS)
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
Vectorizable algorithms for adaptive schemes for rapid analysis of SSME flows
NASA Technical Reports Server (NTRS)
Oden, J. Tinsley
1987-01-01
An initial study into vectorizable algorithms for use in adaptive schemes for various types of boundary value problems is described. The focus is on two key aspects of adaptive computational methods which are crucial in the use of such methods (for complex flow simulations such as those in the Space Shuttle Main Engine): the adaptive scheme itself and the applicability of element-by-element matrix computations in a vectorizable format for rapid calculations in adaptive mesh procedures.
Statistical physics based heuristic clustering algorithms with an application to econophysics
NASA Astrophysics Data System (ADS)
Baldwin, Lucia Liliana
Three new approaches to the clustering of data sets are presented. They are heuristic methods and represent forms of unsupervised (non-parametric) clustering. Applied to an unknown set of data these methods automatically determine the number of clusters and their location using no a priori assumptions. All are based on analogies with different physical phenomena. The first technique, named the Percolation Clustering Algorithm, embodies a novel variation on the nearest-neighbor algorithm focusing on the connectivity between sample points. Exploiting the equivalence with a percolation process, this algorithm considers data points to be surrounded by expanding hyperspheres, which bond when they touch each other. Once a sequence of joined spheres spans an entire cluster, percolation occurs and the cluster size remains constant until it merges with a neighboring cluster. The second procedure, named Nucleation and Growth Clustering, exploits the analogy with nucleation and growth which occurs in island formation during epitaxial growth of solids. The original data points are nucleation centers, around which aggregation will occur. Additional "ad-data" that are introduced into the sample space, interact with the data points and stick if located within a threshold distance. These "ad-data" are used as a tool to facilitate the detection of clusters. The third method, named Discrete Deposition Clustering Algorithm, constrains deposition to occur on a grid, which has the advantage of computational efficiency as opposed to the continuous deposition used in the previous method. The original data form the vertexes of a sparse graph and the deposition sites are defined to be the middle points of this graphs edges. Ad-data are introduced on the deposition site and the system is allowed to evolve in a self-organizing regime. This allows the simulation of a phase transition and by monitoring the specific heat capacity of the system one can mark out a "natural" criterion for
An efficient clustering algorithm for partitioning Y-short tandem repeats data
2012-01-01
Background Y-Short Tandem Repeats (Y-STR) data consist of many similar and almost similar objects. This characteristic of Y-STR data causes two problems with partitioning: non-unique centroids and local minima problems. As a result, the existing partitioning algorithms produce poor clustering results. Results Our new algorithm, called k-Approximate Modal Haplotypes (k-AMH), obtains the highest clustering accuracy scores for five out of six datasets, and produces an equal performance for the remaining dataset. Furthermore, clustering accuracy scores of 100% are achieved for two of the datasets. The k-AMH algorithm records the highest mean accuracy score of 0.93 overall, compared to that of other algorithms: k-Population (0.91), k-Modes-RVF (0.81), New Fuzzy k-Modes (0.80), k-Modes (0.76), k-Modes-Hybrid 1 (0.76), k-Modes-Hybrid 2 (0.75), Fuzzy k-Modes (0.74), and k-Modes-UAVM (0.70). Conclusions The partitioning performance of the k-AMH algorithm for Y-STR data is superior to that of other algorithms, owing to its ability to solve the non-unique centroids and local minima problems. Our algorithm is also efficient in terms of time complexity, which is recorded as O(km(n-k)) and considered to be linear. PMID:23039132
An Adaptive Digital Image Watermarking Algorithm Based on Morphological Haar Wavelet Transform
NASA Astrophysics Data System (ADS)
Huang, Xiaosheng; Zhao, Sujuan
At present, much more of the wavelet-based digital watermarking algorithms are based on linear wavelet transform and fewer on non-linear wavelet transform. In this paper, we propose an adaptive digital image watermarking algorithm based on non-linear wavelet transform--Morphological Haar Wavelet Transform. In the algorithm, the original image and the watermark image are decomposed with multi-scale morphological wavelet transform respectively. Then the watermark information is adaptively embedded into the original image in different resolutions, combining the features of Human Visual System (HVS). The experimental results show that our method is more robust and effective than the ordinary wavelet transform algorithms.
Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems
Claassen, J.P.; Patterson, M.M.
1981-01-01
Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals.
Clustering WHO-ART terms using semantic distance and machine learning algorithms.
Iavindrasana, Jimison; Bousquet, Cedric; Degoulet, Patrice; Jaulent, Marie-Christine
2006-01-01
WHO-ART was developed by the WHO collaborating centre for international drug monitoring in order to code adverse drug reactions. We assume that computation of semantic distance between WHO-ART terms may be an efficient way to group related medical conditions in the WHO database in order to improve signal detection. Our objective was to develop a method for clustering WHO-ART terms according to some proximity of their meanings. Our material comprises 758 WHO-ART terms. A formal definition was acquired for each term as a list of elementary concepts belonging to SNOMED international axes and characterized by modifier terms in some cases. Clustering was implemented as a terminology service on a J2EE server. Two different unsupervised machine learning algorithms (KMeans, Pvclust) clustered WHO-ART terms according to a semantic distance operator previously described. Pvclust grouped 51% of WHO-ART terms. K-Means grouped 100% of WHO-ART terms but 25% clusters were heterogeneous with k = 180 clusters and 6% clusters were heterogeneous with k = 32 clusters. Clustering algorithms associated to semantic distance could suggest potential groupings of WHO-ART terms that need validation according to the user's requirements.
Plot enchaining algorithm: a novel approach for clustering flocks of birds
NASA Astrophysics Data System (ADS)
Büyükaksoy Kaplan, Gülay; Lana, Adnan
2014-06-01
In this study, an intuitive way for tracking flocks of birds is proposed and compared to simple cluster-seeking algorithm for real radar observations. For group of targets such as flock of birds, there is no need to track each target individually. Instead a cluster can be used to represent closely spaced tracks of a possible group. Considering a group of targets as a single target for tracking provides significant performance improvement with almost no loss of information.
A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.
Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue
2016-01-01
Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency. PMID:26907272
A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.
Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue
2016-02-19
Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.
A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network
Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue
2016-01-01
Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency. PMID:26907272
Ergen, Burhan
2014-01-01
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Node Non-Uniform Deployment Based on Clustering Algorithm for Underwater Sensor Networks.
Jiang, Peng; Liu, Jun; Wu, Feng
2015-12-01
A node non-uniform deployment based on clustering algorithm for underwater sensor networks (UWSNs) is proposed in this study. This algorithm is proposed because optimizing network connectivity rate and network lifetime is difficult for the existing node non-uniform deployment algorithms under the premise of improving the network coverage rate for UWSNs. A high network connectivity rate is achieved by determining the heterogeneous communication ranges of nodes during node clustering. Moreover, the concept of aggregate contribution degree is defined, and the nodes with lower aggregate contribution degrees are used to substitute the dying nodes to decrease the total movement distance of nodes and prolong the network lifetime. Simulation results show that the proposed algorithm can achieve a better network coverage rate and network connectivity rate, as well as decrease the total movement distance of nodes and prolong the network lifetime.
Node Non-Uniform Deployment Based on Clustering Algorithm for Underwater Sensor Networks.
Jiang, Peng; Liu, Jun; Wu, Feng
2015-01-01
A node non-uniform deployment based on clustering algorithm for underwater sensor networks (UWSNs) is proposed in this study. This algorithm is proposed because optimizing network connectivity rate and network lifetime is difficult for the existing node non-uniform deployment algorithms under the premise of improving the network coverage rate for UWSNs. A high network connectivity rate is achieved by determining the heterogeneous communication ranges of nodes during node clustering. Moreover, the concept of aggregate contribution degree is defined, and the nodes with lower aggregate contribution degrees are used to substitute the dying nodes to decrease the total movement distance of nodes and prolong the network lifetime. Simulation results show that the proposed algorithm can achieve a better network coverage rate and network connectivity rate, as well as decrease the total movement distance of nodes and prolong the network lifetime. PMID:26633408
Node Non-Uniform Deployment Based on Clustering Algorithm for Underwater Sensor Networks
Jiang, Peng; Liu, Jun; Wu, Feng
2015-01-01
A node non-uniform deployment based on clustering algorithm for underwater sensor networks (UWSNs) is proposed in this study. This algorithm is proposed because optimizing network connectivity rate and network lifetime is difficult for the existing node non-uniform deployment algorithms under the premise of improving the network coverage rate for UWSNs. A high network connectivity rate is achieved by determining the heterogeneous communication ranges of nodes during node clustering. Moreover, the concept of aggregate contribution degree is defined, and the nodes with lower aggregate contribution degrees are used to substitute the dying nodes to decrease the total movement distance of nodes and prolong the network lifetime. Simulation results show that the proposed algorithm can achieve a better network coverage rate and network connectivity rate, as well as decrease the total movement distance of nodes and prolong the network lifetime. PMID:26633408
The Development of FPGA-Based Pseudo-Iterative Clustering Algorithms
NASA Astrophysics Data System (ADS)
Drueke, Elizabeth; Fisher, Wade; Plucinski, Pawel
2016-03-01
The Large Hadron Collider (LHC) in Geneva, Switzerland, is set to undergo major upgrades in 2025 in the form of the High-Luminosity Large Hadron Collider (HL-LHC). In particular, several hardware upgrades are proposed to the ATLAS detector, one of the two general purpose detectors. These hardware upgrades include, but are not limited to, a new hardware-level clustering algorithm, to be performed by a field programmable gate array, or FPGA. In this study, we develop that clustering algorithm and compare the output to a Python-implemented topoclustering algorithm developed at the University of Oregon. Here, we present the agreement between the FPGA output and expected output, with particular attention to the time required by the FPGA to complete the algorithm and other limitations set by the FPGA itself.
An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data
Saeed, Fahad; Pisitkun, Trairak; Knepper, Mark A.; Hoffert, Jason D.
2012-01-01
High-throughput spectrometers are capable of producing data sets containing thousands of spectra for a single biological sample. These data sets contain a substantial amount of redundancy from peptides that may get selected multiple times in a LC-MS/MS experiment. In this paper, we present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data which increases both the sensitivity and confidence of spectral assignment. CAMS utilizes a novel metric, called F-set, that allows accurate identification of the spectra that are similar. A graph theoretic framework is defined that allows the use of F-set metric efficiently for accurate cluster identifications. The accuracy of the algorithm is tested on real HCD and CID data sets with varying amounts of peptides. Our experiments show that the proposed algorithm is able to cluster spectra with very high accuracy in a reasonable amount of time for large spectral data sets. Thus, the algorithm is able to decrease the computational time by compressing the data sets while increasing the throughput of the data by interpreting low S/N spectra. PMID:23471471
NASA Astrophysics Data System (ADS)
Liu, Lifeng; Sun, Sam Zandong; Yu, Hongyu; Yue, Xingtong; Zhang, Dong
2016-06-01
Considering the fact that the fluid distribution in carbonate reservoir is very complicated and the existing fluid prediction methods are not able to produce ideal predicted results, this paper proposes a new fluid identification method in carbonate reservoir based on the modified Fuzzy C-Means (FCM) Clustering algorithm. Both initialization and globally optimum cluster center are produced by Chaotic Quantum Particle Swarm Optimization (CQPSO) algorithm, which can effectively avoid the disadvantage of sensitivity to initial values and easily falling into local convergence in the traditional FCM Clustering algorithm. Then, the modified algorithm is applied to fluid identification in the carbonate X area in Tarim Basin of China, and a mapping relation between fluid properties and pre-stack elastic parameters will be built in multi-dimensional space. It has been proven that this modified algorithm has a good ability of fuzzy cluster and its total coincidence rate of fluid prediction reaches 97.10%. Besides, the membership of different fluids can be accumulated to obtain respective probability, which can evaluate the uncertainty in fluid identification result.
Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging
NASA Astrophysics Data System (ADS)
Apolinar Muñoz Rodríguez, J.; Mejía Alanís, Francisco Carlos
2016-07-01
An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.
A novel algorithm for real-time adaptive signal detection and identification
Sleefe, G.E.; Ladd, M.D.; Gallegos, D.E.; Sicking, C.W.; Erteza, I.A.
1998-04-01
This paper describes a novel digital signal processing algorithm for adaptively detecting and identifying signals buried in noise. The algorithm continually computes and updates the long-term statistics and spectral characteristics of the background noise. Using this noise model, a set of adaptive thresholds and matched digital filters are implemented to enhance and detect signals that are buried in the noise. The algorithm furthermore automatically suppresses coherent noise sources and adapts to time-varying signal conditions. Signal detection is performed in both the time-domain and the frequency-domain, thereby permitting the detection of both broad-band transients and narrow-band signals. The detection algorithm also provides for the computation of important signal features such as amplitude, timing, and phase information. Signal identification is achieved through a combination of frequency-domain template matching and spectral peak picking. The algorithm described herein is well suited for real-time implementation on digital signal processing hardware. This paper presents the theory of the adaptive algorithm, provides an algorithmic block diagram, and demonstrate its implementation and performance with real-world data. The computational efficiency of the algorithm is demonstrated through benchmarks on specific DSP hardware. The applications for this algorithm, which range from vibration analysis to real-time image processing, are also discussed.
Quantum cluster algorithm for frustrated Ising models in a transverse field
NASA Astrophysics Data System (ADS)
Biswas, Sounak; Rakala, Geet; Damle, Kedar
2016-06-01
Working within the stochastic series expansion framework, we introduce and characterize a plaquette-based quantum cluster algorithm for quantum Monte Carlo simulations of transverse field Ising models with frustrated Ising exchange interactions. As a demonstration of the capabilities of this algorithm, we show that a relatively small ferromagnetic next-nearest-neighbor coupling drives the transverse field Ising antiferromagnet on the triangular lattice from an antiferromagnetic three-sublattice ordered state at low temperature to a ferrimagnetic three-sublattice ordered state.
Matthews, Devin A.; Stanton, John F.
2015-02-14
The theory of non-orthogonal spin-adaptation for closed-shell molecular systems is applied to coupled cluster methods with quadruple excitations (CCSDTQ). Calculations at this level of detail are of critical importance in describing the properties of molecular systems to an accuracy which can meet or exceed modern experimental techniques. Such calculations are of significant (and growing) importance in such fields as thermodynamics, kinetics, and atomic and molecular spectroscopies. With respect to the implementation of CCSDTQ and related methods, we show that there are significant advantages to non-orthogonal spin-adaption with respect to simplification and factorization of the working equations and to creating an efficient implementation. The resulting algorithm is implemented in the CFOUR program suite for CCSDT, CCSDTQ, and various approximate methods (CCSD(T), CC3, CCSDT-n, and CCSDT(Q))
Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks
NASA Technical Reports Server (NTRS)
Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)
2002-01-01
In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.
Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
NASA Astrophysics Data System (ADS)
Hentschel, Alexander; Sanders, Barry C.
2011-12-01
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.
Efficient algorithm for optimizing adaptive quantum metrology processes.
Hentschel, Alexander; Sanders, Barry C
2011-12-01
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.
An algorithm for point cluster generalization based on the Voronoi diagram
NASA Astrophysics Data System (ADS)
Yan, Haowen; Weibel, Robert
2008-08-01
This paper presents an algorithm for point cluster generalization. Four types of information, i.e. statistical, thematic, topological, and metric information are considered, and measures are selected to describe corresponding types of information quantitatively in the algorithm, i.e. the number of points for statistical information, the importance value for thematic information, the Voronoi neighbors for topological information, and the distribution range and relative local density for metric information. Based on these measures, an algorithm for point cluster generalization is developed. Firstly, point clusters are triangulated and a border polygon of the point clusters is obtained. By the border polygon, some pseudo points are added to the original point clusters to form a new point set and a range polygon that encloses all original points is constructed. Secondly, the Voronoi polygons of the new point set are computed in order to obtain the so-called relative local density of each point. Further, the selection probability of each point is computed using its relative local density and importance value, and then mark those will-be-deleted points as 'deleted' according to their selection probabilities and Voronoi neighboring relations. Thirdly, if the number of retained points does not satisfy that computed by the Radical Law, physically delete the points marked as 'deleted' forming a new point set, and the second step is repeated; else physically deleted pseudo points and the points marked as 'deleted', and the generalized point clusters are achieved. Owing to the use of the Voronoi diagram the algorithm is parameter free and fully automatic. As our experiments show, it can be used in the generalization of point features arranged in clusters such as thematic dot maps and control points on cartographic maps.
A seed expanding cluster algorithm for deriving upwelling areas on sea surface temperature images
NASA Astrophysics Data System (ADS)
Nascimento, Susana; Casca, Sérgio; Mirkin, Boris
2015-12-01
In this paper a novel clustering algorithm is proposed as a version of the seeded region growing (SRG) approach for the automatic recognition of coastal upwelling from sea surface temperature (SST) images. The new algorithm, one seed expanding cluster (SEC), takes advantage of the concept of approximate clustering due to Mirkin (1996, 2013) to derive a homogeneity criterion in the format of a product rather than the conventional difference between a pixel value and the mean of values over the region of interest. It involves a boundary-oriented pixel labeling so that the cluster growing is performed by expanding its boundary iteratively. The starting point is a cluster consisting of just one seed, the pixel with the coldest temperature. The baseline version of the SEC algorithm uses Otsu's thresholding method to fine-tune the homogeneity threshold. Unfortunately, this method does not always lead to a satisfactory solution. Therefore, we introduce a self-tuning version of the algorithm in which the homogeneity threshold is locally derived from the approximation criterion over a window around the pixel under consideration. The window serves as a boundary regularizer. These two unsupervised versions of the algorithm have been applied to a set of 28 SST images of the western coast of mainland Portugal, and compared against a supervised version fine-tuned by maximizing the F-measure with respect to manually labeled ground-truth maps. The areas built by the unsupervised versions of the SEC algorithm are significantly coincident over the ground-truth regions in the cases at which the upwelling areas consist of a single continuous fragment of the SST map.
Cluster formation by allelomimesis in real-world complex adaptive systems
NASA Astrophysics Data System (ADS)
Juanico, Dranreb Earl; Monterola, Christopher; Saloma, Caesar
2005-04-01
Animal and human clusters are complex adaptive systems and many organize in cluster sizes s that obey the frequency distribution D(s)∝s-τ . The exponent τ describes the relative abundance of the cluster sizes in a given system. Data analyses reveal that real-world clusters exhibit a broad spectrum of τ values, 0.7 (tuna fish schools) ⩽τ⩽4.61 (T4 bacteriophage gene family sizes). Allelomimesis is proposed as an underlying mechanism for adaptation that explains the observed broad τ spectrum. Allelomimesis is the tendency of an individual to imitate the actions of others and two cluster systems have different τ values when their component agents display unequal degrees of allelomimetic tendencies. Cluster formation by allelomimesis is shown to be of three general types: namely, blind copying, information-use copying, and noncopying. Allelomimetic adaptation also reveals that the most stable cluster size is formed by three strongly allelomimetic individuals. Our finding is consistent with available field data taken from killer whales and marmots.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938
Item Clusters and Computerized Adaptive Testing: A Case for Testlets.
ERIC Educational Resources Information Center
Wainer, Howard; Kiely, Gerard L.
1987-01-01
The testlet, a bundle of test items, alleviates some problems associated with computerized adaptive testing: context effects, lack of robustness, and item difficulty ordering. While testlets may be linear or hierarchical, the most useful ones are four-level hierarchical units, containing 15 items and partitioning examinees into 16 classes. (GDC)
Assessing the Reliability of Computer Adaptive Testing Branching Algorithms Using HyperCAT.
ERIC Educational Resources Information Center
Shermis, Mark D.; And Others
The reliability of four branching algorithms commonly used in computer adaptive testing (CAT) was examined. These algorithms were: (1) maximum likelihood (MLE); (2) Bayesian; (3) modal Bayesian; and (4) crossover. Sixty-eight undergraduate college students were randomly assigned to one of the four conditions using the HyperCard-based CAT program,…
SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM. (R827028)
A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme––the piecewise parabolic method (PPM)––for computing advective solution fields; a weight function capable o...
SIMULATION OF A REACTING POLLUTANT PUFF USING AN ADAPTIVE GRID ALGORITHM
A new dynamic solution adaptive grid algorithm DSAGA-PPM, has been developed for use in air quality modeling. In this paper, this algorithm is described and evaluated with a test problem. Cone-shaped distributions of various chemical species undergoing chemical reactions are rota...
Research of adaptive threshold edge detection algorithm based on statistics canny operator
NASA Astrophysics Data System (ADS)
Xu, Jian; Wang, Huaisuo; Huang, Hua
2015-12-01
The traditional Canny operator cannot get the optimal threshold in different scene, on this foundation, an improved Canny edge detection algorithm based on adaptive threshold is proposed. The result of the experiment pictures indicate that the improved algorithm can get responsible threshold, and has the better accuracy and precision in the edge detection.
A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images
Jha, Abhinav K.; Rodríguez, Jeffrey J.; Stephen, Renu M.; Stopeck, Alison T.
2010-01-01
In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms. PMID:21151837
Adaptive algorithm for cloud cover estimation from all-sky images over the sea
NASA Astrophysics Data System (ADS)
Krinitskiy, M. A.; Sinitsyn, A. V.
2016-05-01
A new algorithm for cloud cover estimation has been formulated and developed based on the synthetic control index, called the grayness rate index, and an additional algorithm step of adaptive filtering of the Mie scattering contribution. A setup for automated cloud cover estimation has been designed, assembled, and tested under field conditions. The results shows a significant advantage of the new algorithm over currently commonly used procedures.
Crane, N K; Parsons, I D; Hjelmstad, K D
2002-03-21
Adaptive mesh refinement selectively subdivides the elements of a coarse user supplied mesh to produce a fine mesh with reduced discretization error. Effective use of adaptive mesh refinement coupled with an a posteriori error estimator can produce a mesh that solves a problem to a given discretization error using far fewer elements than uniform refinement. A geometric multigrid solver uses increasingly finer discretizations of the same geometry to produce a very fast and numerically scalable solution to a set of linear equations. Adaptive mesh refinement is a natural method for creating the different meshes required by the multigrid solver. This paper describes the implementation of a scalable adaptive multigrid method on a distributed memory parallel computer. Results are presented that demonstrate the parallel performance of the methodology by solving a linear elastic rocket fuel deformation problem on an SGI Origin 3000. Two challenges must be met when implementing adaptive multigrid algorithms on massively parallel computing platforms. First, although the fine mesh for which the solution is desired may be large and scaled to the number of processors, the multigrid algorithm must also operate on much smaller fixed-size data sets on the coarse levels. Second, the mesh must be repartitioned as it is adapted to maintain good load balancing. In an adaptive multigrid algorithm, separate mesh levels may require separate partitioning, further complicating the load balance problem. This paper shows that, when the proper optimizations are made, parallel adaptive multigrid algorithms perform well on machines with several hundreds of processors.
Tame, M. S.; Kim, M. S.
2010-09-15
We show that fundamental versions of the Deutsch-Jozsa and Bernstein-Vazirani quantum algorithms can be performed using a small entangled cluster state resource of only six qubits. We then investigate the minimal resource states needed to demonstrate general n-qubit versions and a scalable method to produce them. For this purpose, we propose a versatile photonic on-chip setup.
NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION.
Wang, Qingguo; Shang, Charles; Xu, Dong; Shang, Yi
2013-10-25
In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement. PMID:24808625
An effective trust-based recommendation method using a novel graph clustering algorithm
NASA Astrophysics Data System (ADS)
Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin
2015-10-01
Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.
NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION
WANG, QINGGUO; SHANG, CHARLES; XU, DONG
2014-01-01
In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement. PMID:24808625
Mean-shift tracking algorithm based on adaptive fusion of multi-feature
NASA Astrophysics Data System (ADS)
Yang, Kai; Xiao, Yanghui; Wang, Ende; Feng, Junhui
2015-10-01
The classic mean-shift tracking algorithm has achieved success in the field of computer vision because of its speediness and efficiency. However, classic mean-shift tracking algorithm would fail to track in some complicated conditions such as some parts of the target are occluded, little color difference between the target and background exists, or sudden change of illumination and so on. In order to solve the problems, an improved algorithm is proposed based on the mean-shift tracking algorithm and adaptive fusion of features. Color, edges and corners of the target are used to describe the target in the feature space, and a method for measuring the discrimination of various features is presented to make feature selection adaptive. Then the improved mean-shift tracking algorithm is introduced based on the fusion of various features. For the purpose of solving the problem that mean-shift tracking algorithm with the single color feature is vulnerable to sudden change of illumination, we eliminate the effects by the fusion of affine illumination model and color feature space which ensures the correctness and stability of target tracking in that condition. Using a group of videos to test the proposed algorithm, the results show that the tracking correctness and stability of this algorithm are better than the mean-shift tracking algorithm with single feature space. Furthermore the proposed algorithm is more robust than the classic algorithm in the conditions of occlusion, target similar with background or illumination change.
Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.
Mei, Gang; Xu, Nengxiong; Xu, Liangliang
2016-01-01
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm. PMID:27610308
Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.
Mei, Gang; Xu, Nengxiong; Xu, Liangliang
2016-01-01
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.
BMI optimization by using parallel UNDX real-coded genetic algorithm with Beowulf cluster
NASA Astrophysics Data System (ADS)
Handa, Masaya; Kawanishi, Michihiro; Kanki, Hiroshi
2007-12-01
This paper deals with the global optimization algorithm of the Bilinear Matrix Inequalities (BMIs) based on the Unimodal Normal Distribution Crossover (UNDX) GA. First, analyzing the structure of the BMIs, the existence of the typical difficult structures is confirmed. Then, in order to improve the performance of algorithm, based on results of the problem structures analysis and consideration of BMIs characteristic properties, we proposed the algorithm using primary search direction with relaxed Linear Matrix Inequality (LMI) convex estimation. Moreover, in these algorithms, we propose two types of evaluation methods for GA individuals based on LMI calculation considering BMI characteristic properties more. In addition, in order to reduce computational time, we proposed parallelization of RCGA algorithm, Master-Worker paradigm with cluster computing technique.
Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique
Darzi, Soodabeh; Islam, Mohammad Tariqul; Tiong, Sieh Kiong; Kibria, Salehin; Singh, Mandeep
2015-01-01
In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA. PMID:26552032
NASA Astrophysics Data System (ADS)
Bevilacqua, A.; Campanini, R.; Lanconelli, N.
We have developed a method for the detection of clusters of microcalcifications in digital mammograms. Here, we present a genetic algorithm used to optimize the choice of the parameters in the detection scheme. The optimization has allowed the improvement of the performance, the detailed study of the influence of the various parameters on the performance and an accurate investigation of the behavior of the detection method on unknown cases. We reach a sensitivity of 96.2% with 0.7 false positive clusters per image on the Nijmegen database; we are also able to identify the most significant parameters. In addition, we have examined the feasibility of a distributed genetic algorithm implemented on a non-dedicated Cluster Of Workstations. We get very good results both in terms of quality and efficiency.
NASA Astrophysics Data System (ADS)
Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min
2015-12-01
In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.
NASA Technical Reports Server (NTRS)
Whitmore, S. A.
1985-01-01
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
Simple and Effective Algorithms: Computer-Adaptive Testing.
ERIC Educational Resources Information Center
Linacre, John Michael
Computer-adaptive testing (CAT) allows improved security, greater scoring accuracy, shorter testing periods, quicker availability of results, and reduced guessing and other undesirable test behavior. Simple approaches can be applied by the classroom teacher, or other content specialist, who possesses simple computer equipment and elementary…
Tight-Binding Calcium Clusters from Adaptive Tempering Monte Carlo Simulation
NASA Astrophysics Data System (ADS)
Dong, X.; Gatica, S. M.; Blaisten-Barojas, E.
The most stable structure of calcium clusters with 14 to 32 atoms is optimized by the Adaptive Tempering Monte Carlo method. The binding energy of the clusters is obtained within the tight-binding approach parameterized in a previous work. The optimization process is started at about 800 K and the tempering brings the structure to the global minimum ending the process at 1 K. It is found that six cluster sizes, 15, 16, 18, 21, 23 and 25 have a global minimum structure not reported in the literature. In this size range, Ca15, Ca21 and Ca23 are the preferred geometries that can be identified as magic numbers. The tight-binding one-electron levels in the valence band display a large energy gap of 0.5 eV between the last occupied and first unoccupied levels for the magic number clusters. This band gap is 5 to 10 times smaller for other cluster sizes.
Adaptive inpainting algorithm based on DCT induced wavelet regularization.
Li, Yan-Ran; Shen, Lixin; Suter, Bruce W
2013-02-01
In this paper, we propose an image inpainting optimization model whose objective function is a smoothed l(1) norm of the weighted nondecimated discrete cosine transform (DCT) coefficients of the underlying image. By identifying the objective function of the proposed model as a sum of a differentiable term and a nondifferentiable term, we present a basic algorithm inspired by Beck and Teboulle's recent work on the model. Based on this basic algorithm, we propose an automatic way to determine the weights involved in the model and update them in each iteration. The DCT as an orthogonal transform is used in various applications. We view the rows of a DCT matrix as the filters associated with a multiresolution analysis. Nondecimated wavelet transforms with these filters are explored in order to analyze the images to be inpainted. Our numerical experiments verify that under the proposed framework, the filters from a DCT matrix demonstrate promise for the task of image inpainting.
A clustering algorithm for sample data based on environmental pollution characteristics
NASA Astrophysics Data System (ADS)
Chen, Mei; Wang, Pengfei; Chen, Qiang; Wu, Jiadong; Chen, Xiaoyun
2015-04-01
Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To improve the accuracy of source apportionment and classify the sample data for these models, this study proposes an easy-to-use, high-dimensional EPC algorithm that not only organizes all of the sample data into different groups according to the similarities in pollution characteristics such as pollution sources and concentrations but also simultaneously detects outliers. The main clustering process consists of selecting the first unlabelled point as the cluster centre, then assigning each data point in the sample dataset to its most similar cluster centre according to both the user-defined threshold and the value of similarity function in each iteration, and finally modifying the clusters using a method similar to k-Means. The validity and accuracy of the algorithm are tested using both real and synthetic datasets, which makes the EPC algorithm practical and effective for appropriately classifying sample data for source apportionment models and helpful for better understanding and interpreting the sources of pollution.
A priori data-driven multi-clustered reservoir generation algorithm for echo state network.
Li, Xiumin; Zhong, Ling; Xue, Fangzheng; Zhang, Anguo
2015-01-01
Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision. PMID:25875296
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks.
Yang, Jing; Chen, Limin; Zhang, Jianpei
2015-01-01
It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification. PMID:26090857
A priori data-driven multi-clustered reservoir generation algorithm for echo state network.
Li, Xiumin; Zhong, Ling; Xue, Fangzheng; Zhang, Anguo
2015-01-01
Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.
Cluster-Based Multipolling Sequencing Algorithm for Collecting RFID Data in Wireless LANs
NASA Astrophysics Data System (ADS)
Choi, Woo-Yong; Chatterjee, Mainak
2015-03-01
With the growing use of RFID (Radio Frequency Identification), it is becoming important to devise ways to read RFID tags in real time. Access points (APs) of IEEE 802.11-based wireless Local Area Networks (LANs) are being integrated with RFID networks that can efficiently collect real-time RFID data. Several schemes, such as multipolling methods based on the dynamic search algorithm and random sequencing, have been proposed. However, as the number of RFID readers associated with an AP increases, it becomes difficult for the dynamic search algorithm to derive the multipolling sequence in real time. Though multipolling methods can eliminate the polling overhead, we still need to enhance the performance of the multipolling methods based on random sequencing. To that extent, we propose a real-time cluster-based multipolling sequencing algorithm that drastically eliminates more than 90% of the polling overhead, particularly so when the dynamic search algorithm fails to derive the multipolling sequence in real time.
An adaptive grid-based all hexahedral meshing algorithm based on 2-refinement.
Edgel, Jared; Benzley, Steven E.; Owen, Steven James
2010-08-01
Most adaptive mesh generation algorithms employ a 3-refinement method. This method, although easy to employ, provides a mesh that is often too coarse in some areas and over refined in other areas. Because this method generates 27 new hexes in place of a single hex, there is little control on mesh density. This paper presents an adaptive all-hexahedral grid-based meshing algorithm that employs a 2-refinement method. 2-refinement is based on dividing the hex to be refined into eight new hexes. This method allows a greater control on mesh density when compared to a 3-refinement procedure. This adaptive all-hexahedral meshing algorithm provides a mesh that is efficient for analysis by providing a high element density in specific locations and a reduced mesh density in other areas. In addition, this tool can be effectively used for inside-out hexahedral grid based schemes, using Cartesian structured grids for the base mesh, which have shown great promise in accommodating automatic all-hexahedral algorithms. This adaptive all-hexahedral grid-based meshing algorithm employs a 2-refinement insertion method. This allows greater control on mesh density when compared to 3-refinement methods. This algorithm uses a two layer transition zone to increase element quality and keeps transitions from lower to higher mesh densities smooth. Templates were introduced to allow both convex and concave refinement.
Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms
Bosl, W J
2005-01-26
The systems approach to genomics seeks quantitative and predictive descriptions of cells and organisms. However, both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms. A key aspect to all biological problems, including environmental microbiology, evolution of infectious diseases, and the adaptation of cancer cells is the evolvability of genomes. This is particularly important for Genomes to Life missions, which tend to focus on the prospect of engineering microorganisms to achieve desired goals in environmental remediation and climate change mitigation, and energy production. All of these will require quantitative tools for understanding the evolvability of organisms. Laboratory biodefense goals will need quantitative tools for predicting complicated host-pathogen interactions and finding counter-measures. In this project, we seek to develop methods to simulate how external and internal signals cause the genetic apparatus to adapt and organize to produce complex biochemical systems to achieve survival. This project is specifically directed toward building a computational methodology for simulating the adaptability of genomes. This project investigated the feasibility of using a novel quantitative approach to studying the adaptability of genomes and biochemical pathways. This effort was intended to be the preliminary part of a larger, long-term effort between key leaders in computational and systems biology at Harvard University and LLNL, with Dr. Bosl as the lead PI. Scientific goals for the long-term project include the development and testing of new hypotheses to explain the observed adaptability of yeast biochemical pathways when the myosin-II gene is deleted and the development of a novel data-driven evolutionary computation as a way to connect exploratory computational simulation with hypothesis
A genetic algorithmic approach to antenna null-steering using a cluster computer.
NASA Astrophysics Data System (ADS)
Recine, Greg; Cui, Hong-Liang
2001-06-01
We apply a genetic algorithm (GA) to the problem of electronically steering the maximums and nulls of an antenna array to desired positions (null toward enemy listener/jammer, max toward friendly listener/transmitter). The antenna pattern itself is computed using NEC2 which is called by the main GA program. Since a GA naturally lends itself to parallelization, this simulation was applied to our new twin 64-node cluster computers (Gemini). Design issues and uses of the Gemini cluster in our group are also discussed.
K-Means Re-Clustering-Algorithmic Options with Quantifiable Performance Comparisons
Meyer, A W; Paglieroni, D; Asteneh, C
2002-12-17
This paper presents various architectural options for implementing a K-Means Re-Clustering algorithm suitable for unsupervised segmentation of hyperspectral images. Performance metrics are developed based upon quantitative comparisons of convergence rates and segmentation quality. A methodology for making these comparisons is developed and used to establish K values that produce the best segmentations with minimal processing requirements. Convergence rates depend on the initial choice of cluster centers. Consequently, this same methodology may be used to evaluate the effectiveness of different initialization techniques.
KD-tree based clustering algorithm for fast face recognition on large-scale data
NASA Astrophysics Data System (ADS)
Wang, Yuanyuan; Lin, Yaping; Yang, Junfeng
2015-07-01
This paper proposes an acceleration method for large-scale face recognition system. When dealing with a large-scale database, face recognition is time-consuming. In order to tackle this problem, we employ the k-means clustering algorithm to classify face data. Specifically, the data in each cluster are stored in the form of the kd-tree, and face feature matching is conducted with the kd-tree based nearest neighborhood search. Experiments on CAS-PEAL and self-collected database show the effectiveness of our proposed method.
Adaptive merit function in SPGD algorithm for beam combining
NASA Astrophysics Data System (ADS)
Yang, Guo-qing; Liu, Li-sheng; Jiang, Zhen-hua; Wang, Ting-feng; Guo, Jin
2016-09-01
The beam pointing is the most crucial issue for beam combining to achieve high energy laser output. In order to meet the turbulence situation, a beam pointing method that cooperates with the stochastic parallel gradient descent (SPGD) algorithm is proposed. The power-in-the-bucket ( PIB) is chosen as the merit function, and its radius changes gradually during the correction process. The linear radius and the exponential radius are simulated. The results show that the exponential radius has great promise for beam pointing.
Jawarneh, Sana; Abdullah, Salwani
2015-01-01
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.
Jawarneh, Sana; Abdullah, Salwani
2015-01-01
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158
Jawarneh, Sana; Abdullah, Salwani
2015-01-01
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158
Datta, Dipayan; Gauss, Jürgen
2013-06-11
We present a symbolic manipulation algorithm for the efficient automated implementation of rigorously spin-free coupled cluster (CC) theories based on a unitary group parametrization. Due to the lack of antisymmetry of the unitary group generators under index permutations, all quantities involved in the equations are expressed in terms of non-antisymmetric tensors. Given two tensors, all possible contractions are first generated by applying Wick's theorem. Each term is then put down in the form of a non-antisymmetric Goldstone diagram by assigning its contraction topology. The subsequent simplification of the equations by summing up equivalent terms and their factorization by identifying common intermediates is performed via comparison of these contraction topologies. The definition of the contraction topology is completely general for non-antisymmetric Goldstone diagrams, which enables our algorithm to deal with noncommuting excitations in the cluster operator that arises in the unitary group based CC formulation for open-shell systems. The resulting equations are implemented in a new code, in which tensor contractions are performed by successive application of matrix-matrix multiplications. Implementation of the unitary group adapted CC equations for closed-shell systems and for the simplest open-shell case, i.e., doublets, is discussed, and representative calculations are presented in order to assess the efficiency of the generated codes.
What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm
Baig, Fahd; Little, Max A.
2016-01-01
The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism. PMID:27669525
Performance Analysis of Apriori Algorithm with Different Data Structures on Hadoop Cluster
NASA Astrophysics Data System (ADS)
Singh, Sudhakar; Garg, Rakhi; Mishra, P. K.
2015-10-01
Mining frequent itemsets from massive datasets is always being a most important problem of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. To enhance the efficiency and scalability of Apriori, a number of algorithms have been proposed addressing the design of efficient data structures, minimizing database scan and parallel and distributed processing. MapReduce is the emerging parallel and distributed technology to process big datasets on Hadoop Cluster. To mine big datasets it is essential to re-design the data mining algorithm on this new paradigm. In this paper, we implement three variations of Apriori algorithm using data structures hash tree, trie and hash table trie i.e. trie with hash technique on MapReduce paradigm. We emphasize and investigate the significance of these three data structures for Apriori algorithm on Hadoop cluster, which has not been given attention yet. Experiments are carried out on both real life and synthetic datasets which shows that hash table trie data structures performs far better than trie and hash tree in terms of execution time. Moreover the performance in case of hash tree becomes worst.
NASA Astrophysics Data System (ADS)
Tang, Ze; Park, Ju H.; Lee, Tae H.
2016-10-01
This paper is devoted to the cluster synchronization issue of nonlinearly coupled Lur'e networks under the distributed adaptive pinning control strategy. The time-varying delayed networks consisted of identical and nonidentical Lur'e systems are discussed respectively by applying the edge-based pinning control scheme. In each cluster, the edges belonging to the spanning tree are pinned. In view of the nonlinearly couplings of the networks, for the first time, an efficient distributed nonlinearly adaptive update law based on the local information of the dynamical behaviors of node is proposed. Sufficient criteria for the achievement of cluster synchronization are derived based on S-procedure, Kronecker product and Lyapunov stability theory. Additionally, some illustrative examples are provided to demonstrate the effectiveness of the theoretical results.
Rondina, Gustavo G; Da Silva, Juarez L F
2013-09-23
Suggestions for improving the Basin-Hopping Monte Carlo (BHMC) algorithm for unbiased global optimization of clusters and nanoparticles are presented. The traditional basin-hopping exploration scheme with Monte Carlo sampling is improved by bringing together novel strategies and techniques employed in different global optimization methods, however, with the care of keeping the underlying algorithm of BHMC unchanged. The improvements include a total of eleven local and nonlocal trial operators tailored for clusters and nanoparticles that allow an efficient exploration of the potential energy surface, two different strategies (static and dynamic) of operator selection, and a filter operator to handle unphysical solutions. In order to assess the efficiency of our strategies, we applied our implementation to several classes of systems, including Lennard-Jones and Sutton-Chen clusters with up to 147 and 148 atoms, respectively, a set of Lennard-Jones nanoparticles with sizes ranging from 200 to 1500 atoms, binary Lennard-Jones clusters with up to 100 atoms, (AgPd)55 alloy clusters described by the Sutton-Chen potential, and aluminum clusters with up to 30 atoms described within the density functional theory framework. Using unbiased global search our implementation was able to reproduce successfully the great majority of all published results for the systems considered and in many cases with more efficiency than the standard BHMC. We were also able to locate previously unknown global minimum structures for some of the systems considered. This revised BHMC method is a valuable tool for aiding theoretical investigations leading to a better understanding of atomic structures of clusters and nanoparticles. PMID:23957311
Li, Xuan; Wang, Haixian
2015-01-01
Human brain functional system has been viewed as a complex network. To accurately characterize this brain network, it is important to estimate the functional connectivity between separate brain regions (i.e., association matrix). One common approach to evaluating the connectivity is the pairwise Pearson correlation. However, this bivariate method completely ignores the influence of other regions when computing the pairwise association. Another intractable issue existed in many approaches to further analyzing the network structure is the requirement of applying a threshold to the association matrix. To address these issues, we develop a novel scheme to investigate the brain functional networks. Specifically, we first establish a global functional connection network by using the Adaptive Sparse Representation (ASR), adaptively integrating the sparsity of ℓ1-norm and the grouping effect of ℓ2-norm for linear representation and then identify connectivity patterns with Affinity Propagation (AP) clustering algorithm. Results on both simulated and real data indicate that the proposed scheme is superior to the Pearson correlation in connectivity quality and clustering quality. Our findings suggest that the proposed scheme is an accurate and useful technique to delineate functional network structure for functionally parsimonious and correlated fMRI data with a large number of brain regions. PMID:26528123
Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations
Diego Mandelli; Dan Maljovec; Bei Wang; Valerio Pascucci; Peer-Timo Bremer
2013-09-01
Nuclear simulations are often computationally expensive, time-consuming, and high-dimensional with respect to the number of input parameters. Thus exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible. This is a typical context for performing adaptive sampling where a few observations are obtained from the simulation, a surrogate model is built to represent the simulation space, and new samples are selected based on the model constructed. The surrogate model is then updated based on the simulation results of the sampled points. In this way, we attempt to gain the most information possible with a small number of carefully selected sampled points, limiting the number of expensive trials needed to understand features of the simulation space. We analyze the specific use case of identifying the limit surface, i.e., the boundaries in the simulation space between system failure and system success. In this study, we explore several techniques for adaptively sampling the parameter space in order to reconstruct the limit surface. We focus on several adaptive sampling schemes. First, we seek to learn a global model of the entire simulation space using prediction models or neighborhood graphs and extract the limit surface as an iso-surface of the global model. Second, we estimate the limit surface by sampling in the neighborhood of the current estimate based on topological segmentations obtained locally. Our techniques draw inspirations from topological structure known as the Morse-Smale complex. We highlight the advantages and disadvantages of using a global prediction model versus local topological view of the simulation space, comparing several different strategies for adaptive sampling in both
Robustness of ‘cut and splice’ genetic algorithms in the structural optimization of atomic clusters
NASA Astrophysics Data System (ADS)
Froltsov, Vladimir A.; Reuter, Karsten
2009-05-01
We return to the geometry optimization problem of Lennard-Jones clusters to analyze the performance dependence of 'cut and splice' genetic algorithms (GAs) on the employed population size. We generally find that admixing twinning mutation moves leads to an improved robustness of the algorithm efficiency with respect to this a priori unknown technical parameter. The resulting very stable performance of the corresponding mutation + mating GA implementation over a wide range of population sizes is an important feature when addressing unknown systems with computationally involved first-principles based GA sampling.
A geometry-based adaptive unstructured grid generation algorithm for complex geological media
NASA Astrophysics Data System (ADS)
Bahrainian, Seyed Saied; Dezfuli, Alireza Daneh
2014-07-01
In this paper a novel unstructured grid generation algorithm is presented that considers the effect of geological features and well locations in grid resolution. The proposed grid generation algorithm presents a strategy for definition and construction of an initial grid based on the geological model, geometry adaptation of geological features, and grid resolution control. The algorithm is applied to seismotectonic map of the Masjed-i-Soleiman reservoir. Comparison of grid results with the “Triangle” program shows a more suitable permeability contrast. Immiscible two-phase flow solutions are presented for a fractured porous media test case using different grid resolutions. Adapted grid on the fracture geometry gave identical results with that of a fine grid. The adapted grid employed 88.2% less CPU time when compared to the solutions obtained by the fine grid.
Adaptive cluster expansion approach for predicting the structure evolution of graphene oxide
Li, Xi-Bo; Guo, Pan; Wang, D.; Liu, Li-Min; Zhang, Yongsheng
2014-12-14
An adaptive cluster expansion (CE) method is used to explore surface adsorption and growth processes. Unlike the traditional CE method, suitable effective cluster interaction (ECI) parameters are determined, and then the selected fixed number of ECIs is continually optimized to predict the stable configurations with gradual increase of adatom coverage. Comparing with traditional CE method, the efficiency of the adaptive CE method could be greatly enhanced. As an application, the adsorption and growth of oxygen atoms on one side of pristine graphene was carefully investigated using this method in combination with first-principles calculations. The calculated results successfully uncover the structural evolution of graphene oxide for the different numbers of oxygen adatoms on graphene. The aggregation behavior of the stable configurations for different oxygen adatom coverages is revealed for increasing coverages of oxygen atoms. As a targeted method, adaptive CE can also be applied to understand the evolution of other surface adsorption and growth processes.
Ishii, Satoshi; Kadota, Koji; Senoo, Keishi
2009-09-01
DNA fingerprinting analysis such as amplified ribosomal DNA restriction analysis (ARDRA), repetitive extragenic palindromic PCR (rep-PCR), ribosomal intergenic spacer analysis (RISA), and denaturing gradient gel electrophoresis (DGGE) are frequently used in various fields of microbiology. The major difficulty in DNA fingerprinting data analysis is the alignment of multiple peak sets. We report here an R program for a clustering-based peak alignment algorithm, and its application to analyze various DNA fingerprinting data, such as ARDRA, rep-PCR, RISA, and DGGE data. The results obtained by our clustering algorithm and by BioNumerics software showed high similarity. Since several R packages have been established to statistically analyze various biological data, the distance matrix obtained by our R program can be used for subsequent statistical analyses, some of which were not previously performed but are useful in DNA fingerprinting studies.
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks.
Estévez, Francisco José; Glösekötter, Peter; González, Jesús
2016-01-01
The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
Estévez, Francisco José; Glösekötter, Peter; González, Jesús
2016-01-01
The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962
An adaptive numeric predictor-corrector guidance algorithm for atmospheric entry vehicles
NASA Astrophysics Data System (ADS)
Spratlin, Kenneth Milton
1987-05-01
An adaptive numeric predictor-corrector guidance is developed for atmospheric entry vehicles which utilize lift to achieve maximum footprint capability. Applicability of the guidance design to vehicles with a wide range of performance capabilities is desired so as to reduce the need for algorithm redesign with each new vehicle. Adaptability is desired to minimize mission-specific analysis and planning. The guidance algorithm motivation and design are presented. Performance is assessed for application of the algorithm to the NASA Entry Research Vehicle (ERV). The dispersions the guidance must be designed to handle are presented. The achievable operational footprint for expected worst-case dispersions is presented. The algorithm performs excellently for the expected dispersions and captures most of the achievable footprint.
Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm
Prado-Velasco, Manuel; Ortiz Marín, Rafael; del Rio Cidoncha, Gloria
2013-01-01
Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results. PMID:24157505
Performance study of LMS based adaptive algorithms for unknown system identification
Javed, Shazia; Ahmad, Noor Atinah
2014-07-10
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.
Performance study of LMS based adaptive algorithms for unknown system identification
NASA Astrophysics Data System (ADS)
Javed, Shazia; Ahmad, Noor Atinah
2014-07-01
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.
A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms
Kanwal, Maxinder S; Ramesh, Avinash S; Huang, Lauren A
2013-01-01
Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates. PMID:24627784
Adaptive control and noise suppression by a variable-gain gradient algorithm
NASA Technical Reports Server (NTRS)
Merhav, S. J.; Mehta, R. S.
1987-01-01
An adaptive control system based on normalized LMS filters is investigated. The finite impulse response of the nonparametric controller is adaptively estimated using a given reference model. Specifically, the following issues are addressed: The stability of the closed loop system is analyzed and heuristically established. Next, the adaptation process is studied for piecewise constant plant parameters. It is shown that by introducing a variable-gain in the gradient algorithm, a substantial reduction in the LMS adaptation rate can be achieved. Finally, process noise at the plant output generally causes a biased estimate of the controller. By introducing a noise suppression scheme, this bias can be substantially reduced and the response of the adapted system becomes very close to that of the reference model. Extensive computer simulations validate these and demonstrate assertions that the system can rapidly adapt to random jumps in plant parameters.
Adaptation algorithms for satellite communication systems equipped with hybrid reflector antennas
NASA Astrophysics Data System (ADS)
Kartsan, I. N.; Zelenkov, P. V.; Tyapkin, V. N.; Dmitriev, D. D.; Goncharov, A. E.
2015-10-01
This paper reviews adaptation algorithms influenced by active interferences in satellite communication systems. A multi-beam antenna is suggested as an adaptive system; it is built on the basis of a hybrid reflector antenna with a 19-element array feed element, which incorporates a modified algorithm for radiation pattern synthesis used for suppressing targeted interferences. As a criterion for this synthesis, antenna gains are used at fixed points. As a result, the size of the objective function and time required for the synthesis can be significantly limited.
Quadratic adaptive algorithm for solving cardiac action potential models.
Chen, Min-Hung; Chen, Po-Yuan; Luo, Ching-Hsing
2016-10-01
An adaptive integration method is proposed for computing cardiac action potential models accurately and efficiently. Time steps are adaptively chosen by solving a quadratic formula involving the first and second derivatives of the membrane action potential. To improve the numerical accuracy, we devise an extremum-locator (el) function to predict the local extremum when approaching the peak amplitude of the action potential. In addition, the time step restriction (tsr) technique is designed to limit the increase in time steps, and thus prevent the membrane potential from changing abruptly. The performance of the proposed method is tested using the Luo-Rudy phase 1 (LR1), dynamic (LR2), and human O'Hara-Rudy dynamic (ORd) ventricular action potential models, and the Courtemanche atrial model incorporating a Markov sodium channel model. Numerical experiments demonstrate that the action potential generated using the proposed method is more accurate than that using the traditional Hybrid method, especially near the peak region. The traditional Hybrid method may choose large time steps near to the peak region, and sometimes causes the action potential to become distorted. In contrast, the proposed new method chooses very fine time steps in the peak region, but large time steps in the smooth region, and the profiles are smoother and closer to the reference solution. In the test on the stiff Markov ionic channel model, the Hybrid blows up if the allowable time step is set to be greater than 0.1ms. In contrast, our method can adjust the time step size automatically, and is stable. Overall, the proposed method is more accurate than and as efficient as the traditional Hybrid method, especially for the human ORd model. The proposed method shows improvement for action potentials with a non-smooth morphology, and it needs further investigation to determine whether the method is helpful during propagation of the action potential. PMID:27639239
Comparison of adaptive algorithms for the control of tonal disturbances in mechanical systems
NASA Astrophysics Data System (ADS)
Zilletti, M.; Elliott, S. J.; Cheer, J.
2016-09-01
This paper presents a study on the performance of adaptive control algorithms designed to reduce the vibration of mechanical systems excited by a harmonic disturbance. The mechanical system consists of a mass suspended on a spring and a damper. The system is equipped with a force actuator in parallel with the suspension. The control signal driving the actuator is generated by adjusting the amplitude and phase of a sinusoidal reference signal at the same frequency as the excitation. An adaptive feedforward control algorithm is used to adapt the amplitude and phase of the control signal, to minimise the mean square velocity of the mass. Two adaptation strategies are considered in which the control signal is either updated after each period of the oscillation or at every time sample. The first strategy is traditionally used in vibration control in helicopters for example; the second strategy is normally referred to as the filtered-x least mean square algorithm and is often used to control engine noise in cars. The two adaptation strategies are compared through a parametric study, which investigates the influence of the properties of both the mechanical system and the control system on the convergence speed of the two algorithms.
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.
Bourobou, Serge Thomas Mickala; Yoo, Younghwan
2015-01-01
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.
Bourobou, Serge Thomas Mickala; Yoo, Younghwan
2015-01-01
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.
NASA Technical Reports Server (NTRS)
Ianculescu, G. D.; Klop, J. J.
1992-01-01
Classical and adaptive control algorithms for the solar array pointing system of the Space Station Freedom are designed using a continuous rigid body model of the solar array gimbal assembly containing both linear and nonlinear dynamics due to various friction components. The robustness of the design solution is examined by performing a series of sensitivity analysis studies. Adaptive control strategies are examined in order to compensate for the unfavorable effect of static nonlinearities, such as dead-zone uncertainties.
Big Data GPU-Driven Parallel Processing Spatial and Spatio-Temporal Clustering Algorithms
NASA Astrophysics Data System (ADS)
Konstantaras, Antonios; Skounakis, Emmanouil; Kilty, James-Alexander; Frantzeskakis, Theofanis; Maravelakis, Emmanuel
2016-04-01
Advances in graphics processing units' technology towards encompassing parallel architectures [1], comprised of thousands of cores and multiples of parallel threads, provide the foundation in terms of hardware for the rapid processing of various parallel applications regarding seismic big data analysis. Seismic data are normally stored as collections of vectors in massive matrices, growing rapidly in size as wider areas are covered, denser recording networks are being established and decades of data are being compiled together [2]. Yet, many processes regarding seismic data analysis are performed on each seismic event independently or as distinct tiles [3] of specific grouped seismic events within a much larger data set. Such processes, independent of one another can be performed in parallel narrowing down processing times drastically [1,3]. This research work presents the development and implementation of three parallel processing algorithms using Cuda C [4] for the investigation of potentially distinct seismic regions [5,6] present in the vicinity of the southern Hellenic seismic arc. The algorithms, programmed and executed in parallel comparatively, are the: fuzzy k-means clustering with expert knowledge [7] in assigning overall clusters' number; density-based clustering [8]; and a selves-developed spatio-temporal clustering algorithm encompassing expert [9] and empirical knowledge [10] for the specific area under investigation. Indexing terms: GPU parallel programming, Cuda C, heterogeneous processing, distinct seismic regions, parallel clustering algorithms, spatio-temporal clustering References [1] Kirk, D. and Hwu, W.: 'Programming massively parallel processors - A hands-on approach', 2nd Edition, Morgan Kaufman Publisher, 2013 [2] Konstantaras, A., Valianatos, F., Varley, M.R. and Makris, J.P.: 'Soft-Computing Modelling of Seismicity in the Southern Hellenic Arc', Geoscience and Remote Sensing Letters, vol. 5 (3), pp. 323-327, 2008 [3] Papadakis, S. and
A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets
Zhang, Yipu; Wang, Ping
2015-01-01
New high-throughput technique ChIP-seq, coupling chromatin immunoprecipitation experiment with high-throughput sequencing technologies, has extended the identification of binding locations of a transcription factor to the genome-wide regions. However, the most existing motif discovery algorithms are time-consuming and limited to identify binding motifs in ChIP-seq data which normally has the significant characteristics of large scale data. In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the (l, d) motifs in large scale ChIP-seq data set. It is inspired by the emerging substrings mining strategy to find the enriched substrings and then searching the neighborhood instances to construct PWM and cluster motifs in different length. FCmotif is not following the OOPS model constraint and can find long motifs. The effectiveness of proposed algorithm has been proved by experiments on the ChIP-seq data sets from mouse ES cells. The whole detection of the real binding motifs and processing of the full size data of several megabytes finished in a few minutes. The experimental results show that FCmotif has advantageous to deal with the (l, d) motif finding in the ChIP-seq data; meanwhile it also demonstrates better performance than other current widely-used algorithms such as MEME, Weeder, ChIPMunk, and DREME. PMID:26236718
A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets.
Zhang, Yipu; Wang, Ping
2015-01-01
New high-throughput technique ChIP-seq, coupling chromatin immunoprecipitation experiment with high-throughput sequencing technologies, has extended the identification of binding locations of a transcription factor to the genome-wide regions. However, the most existing motif discovery algorithms are time-consuming and limited to identify binding motifs in ChIP-seq data which normally has the significant characteristics of large scale data. In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the (l, d) motifs in large scale ChIP-seq data set. It is inspired by the emerging substrings mining strategy to find the enriched substrings and then searching the neighborhood instances to construct PWM and cluster motifs in different length. FCmotif is not following the OOPS model constraint and can find long motifs. The effectiveness of proposed algorithm has been proved by experiments on the ChIP-seq data sets from mouse ES cells. The whole detection of the real binding motifs and processing of the full size data of several megabytes finished in a few minutes. The experimental results show that FCmotif has advantageous to deal with the (l, d) motif finding in the ChIP-seq data; meanwhile it also demonstrates better performance than other current widely-used algorithms such as MEME, Weeder, ChIPMunk, and DREME. PMID:26236718
Adaptive Waveform Correlation Detectors for Arrays: Algorithms for Autonomous Calibration
Ringdal, F; Harris, D B; Dodge, D; Gibbons, S J
2009-07-23
extend detection to lower magnitudes. This year we addressed a problem long known to limit the acceptance of correlation detectors in practice: the labor intensive development of templates. For example, existing design methods cannot keep pace with rapidly unfolding aftershock sequences. We successfully built and tested an object-oriented framework (as described in our 2005 proposal) for autonomous calibration of waveform correlation detectors for an array. The framework contains a dynamic list of detectors of several types operating on a continuous array data stream. The list has permanent detectors: beam forming power (STA/LTA) detectors which serve the purpose of detecting signals not yet characterized with a waveform template. The framework also contains an arbitrary number of subspace detectors which are launched automatically using the waveforms from validated power detections as templates. The implementation is very efficient such that the computational cost of adding subspace detectors was low. The framework contains a supervisor that oversees the validation of power detections, and periodically halts the processing to revise the portfolio of detectors. The process of revision consists of collecting the waveforms from all detections, performing cross-correlations pairwise among all waveforms, clustering the detections using correlations as a distance measure, then creating a new subspace detector from each cluster. The collection of new subspace detectors replaces the existing portfolio and processing of the data stream resumes. This elaborate scheme was implemented to prevent proliferation of closely-related subspace detectors. The method performed very well on several simple sequences: 2005 'drumbeat' events observed locally at Mt. St. Helens, and the 2003 Orinda, CA aftershock sequence. Our principal test entailed detection of the aftershocks of the San Simeon earthquake using the NVAR array; in this case, the system automatically detected and categorized
NASA Astrophysics Data System (ADS)
Plaza, Antonio; Chang, Chein-I.; Plaza, Javier; Valencia, David
2006-05-01
The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.
Evaluation of an adaptive filtering algorithm for CT cardiac imaging with EKG modulated tube current
NASA Astrophysics Data System (ADS)
Li, Jianying; Hsieh, Jiang; Mohr, Kelly; Okerlund, Darin
2005-04-01
We have developed an adaptive filtering algorithm for cardiac CT scans with EKG-modulated tube current to optimize resolution and noise for different cardiac phases and to provide safety net for cases where end-systole phase is used for coronary imaging. This algorithm has been evaluated using patient cardiac CT scans where lower tube currents are used for the systolic phases. In this paper, we present the evaluation results. The results demonstrated that with the use of the proposed algorithm, we could improve image quality for all cardiac phases, while providing greater noise and streak artifact reduction for systole phases where lower CT dose were used.
NASA Astrophysics Data System (ADS)
Chen, Xinjia
2015-05-01
We consider the general problem of analysis and design of control systems in the presence of uncertainties. We treat uncertainties that affect a control system as random variables. The performance of the system is measured by the expectation of some derived random variables, which are typically bounded. We develop adaptive sequential randomized algorithms for estimating and optimizing the expectation of such bounded random variables with guaranteed accuracy and confidence level. These algorithms can be applied to overcome the conservatism and computational complexity in the analysis and design of controllers to be used in uncertain environments. We develop methods for investigating the optimality and computational complexity of such algorithms.
Adaptive algorithm for active control of high-amplitude acoustic field in resonator
NASA Astrophysics Data System (ADS)
Červenka, M.; Bednařík, M.; Koníček, P.
2008-06-01
This work is concerned with suppression of nonlinear effects in piston-driven acoustic resonators by means of two-frequency driving technique. An iterative adaptive algorithm is proposed to calculate parameters of the driving signal in order that amplitude of the second harmonics of the acoustic pressure is minimized. Functionality of the algorithm is verified firstly by means of numerical model and secondly, it is used in real computer-controlled experiment. The numerical and experimental results show that the proposed algorithm can be successfully used for generation of high-amplitude shock-free acoustic field in resonators.
Xia, Xuewen
2016-01-01
In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm. PMID:27738423
Parallel OSEM Reconstruction Algorithm for Fully 3-D SPECT on a Beowulf Cluster.
Rong, Zhou; Tianyu, Ma; Yongjie, Jin
2005-01-01
In order to improve the computation speed of ordered subset expectation maximization (OSEM) algorithm for fully 3-D single photon emission computed tomography (SPECT) reconstruction, an experimental beowulf-type cluster was built and several parallel reconstruction schemes were described. We implemented a single-program-multiple-data (SPMD) parallel 3-D OSEM reconstruction algorithm based on message passing interface (MPI) and tested it with combinations of different number of calculating processors and different size of voxel grid in reconstruction (64×64×64 and 128×128×128). Performance of parallelization was evaluated in terms of the speedup factor and parallel efficiency. This parallel implementation methodology is expected to be helpful to make fully 3-D OSEM algorithms more feasible in clinical SPECT studies.
An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content
Liu, Wei; Du, Peijun; Zhao, Zhuowen; Zhang, Lianpeng
2016-01-01
The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using various environmental variables, AW-SP was used to interpolate soil potassium content in Qinghai Lake Basin. To evaluate AW-SP performance, we compared it with that of inverse distance weighting (IDW), ordinary kriging, and OK combined with different environmental variables. The experimental results showed that the methods combined with environmental variables did not always improve prediction accuracy even if there was a strong correlation between the soil properties and environmental variables. However, compared with IDW, OK, and OK combined with different environmental variables, AW-SP is more stable and has lower mean absolute and root mean square errors. Furthermore, the AW-SP maps provided improved details of soil potassium content and provided clearer boundaries to its spatial distribution. In conclusion, AW-SP can not only reduce prediction errors, it also accounts for the distribution and contributions of environmental variables, making the spatial interpolation of soil potassium content more reasonable. PMID:27051998
An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content
NASA Astrophysics Data System (ADS)
Liu, Wei; Du, Peijun; Zhao, Zhuowen; Zhang, Lianpeng
2016-04-01
The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using various environmental variables, AW-SP was used to interpolate soil potassium content in Qinghai Lake Basin. To evaluate AW-SP performance, we compared it with that of inverse distance weighting (IDW), ordinary kriging, and OK combined with different environmental variables. The experimental results showed that the methods combined with environmental variables did not always improve prediction accuracy even if there was a strong correlation between the soil properties and environmental variables. However, compared with IDW, OK, and OK combined with different environmental variables, AW-SP is more stable and has lower mean absolute and root mean square errors. Furthermore, the AW-SP maps provided improved details of soil potassium content and provided clearer boundaries to its spatial distribution. In conclusion, AW-SP can not only reduce prediction errors, it also accounts for the distribution and contributions of environmental variables, making the spatial interpolation of soil potassium content more reasonable.
Adaptive motion artifact reducing algorithm for wrist photoplethysmography application
NASA Astrophysics Data System (ADS)
Zhao, Jingwei; Wang, Guijin; Shi, Chenbo
2016-04-01
Photoplethysmography (PPG) technology is widely used in wearable heart pulse rate monitoring. It might reveal the potential risks of heart condition and cardiopulmonary function by detecting the cardiac rhythms in physical exercise. However the quality of wrist photoelectric signal is very sensitive to motion artifact since the thicker tissues and the fewer amount of capillaries. Therefore, motion artifact is the major factor that impede the heart rate measurement in the high intensity exercising. One accelerometer and three channels of light with different wavelengths are used in this research to analyze the coupled form of motion artifact. A novel approach is proposed to separate the pulse signal from motion artifact by exploiting their mixing ratio in different optical paths. There are four major steps of our method: preprocessing, motion artifact estimation, adaptive filtering and heart rate calculation. Five healthy young men are participated in the experiment. The speeder in the treadmill is configured as 12km/h, and all subjects would run for 3-10 minutes by swinging the arms naturally. The final result is compared with chest strap. The average of mean square error (MSE) is less than 3 beats per minute (BPM/min). Proposed method performed well in intense physical exercise and shows the great robustness to individuals with different running style and posture.
Comparing Residue Clusters from Thermophilic and Mesophilic Enzymes Reveals Adaptive Mechanisms
Sammond, Deanne W.; Kastelowitz, Noah; Himmel, Michael E.; Yin, Hang; Crowley, Michael F.; Bomble, Yannick J.
2016-01-01
Understanding how proteins adapt to function at high temperatures is important for deciphering the energetics that dictate protein stability and folding. While multiple principles important for thermostability have been identified, we lack a unified understanding of how internal protein structural and chemical environment determine qualitative or quantitative impact of evolutionary mutations. In this work we compare equivalent clusters of spatially neighboring residues between paired thermophilic and mesophilic homologues to evaluate adaptations under the selective pressure of high temperature. We find the residue clusters in thermophilic enzymes generally display improved atomic packing compared to mesophilic enzymes, in agreement with previous research. Unlike residue clusters from mesophilic enzymes, however, thermophilic residue clusters do not have significant cavities. In addition, anchor residues found in many clusters are highly conserved with respect to atomic packing between both thermophilic and mesophilic enzymes. Thus the improvements in atomic packing observed in thermophilic homologues are not derived from these anchor residues but from neighboring positions, which may serve to expand optimized protein core regions. PMID:26741367
Comparing residue clusters from thermophilic and mesophilic enzymes reveals adaptive mechanisms
Sammond, Deanne W.; Kastelowitz, Noah; Himmel, Michael E.; Yin, Hang; Crowley, Michael F.; Bomble, Yannick J.
2016-01-07
Understanding how proteins adapt to function at high temperatures is important for deciphering the energetics that dictate protein stability and folding. While multiple principles important for thermostability have been identified, we lack a unified understanding of how internal protein structural and chemical environment determine qualitative or quantitative impact of evolutionary mutations. In this work we compare equivalent clusters of spatially neighboring residues between paired thermophilic and mesophilic homologues to evaluate adaptations under the selective pressure of high temperature. We find the residue clusters in thermophilic enzymes generally display improved atomic packing compared to mesophilic enzymes, in agreement with previous research.more » Unlike residue clusters from mesophilic enzymes, however, thermophilic residue clusters do not have significant cavities. In addition, anchor residues found in many clusters are highly conserved with respect to atomic packing between both thermophilic and mesophilic enzymes. As a result, the improvements in atomic packing observed in thermophilic homologues are not derived from these anchor residues but from neighboring positions, which may serve to expand optimized protein core regions.« less
Karimi, Abbas; Afsharfarnia, Abbas; Zarafshan, Faraneh; Al-Haddad, S. A. R.
2014-01-01
The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node's competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node's weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining node's final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure. PMID:25114965
Karimi, Abbas; Afsharfarnia, Abbas; Zarafshan, Faraneh; Al-Haddad, S A R
2014-01-01
The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node's competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node's weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining node's final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure.
An adaptive ant colony system algorithm for continuous-space optimization problems.
Li, Yan-jun; Wu, Tie-jun
2003-01-01
Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved. PMID:12656341
Modified fast frequency acquisition via adaptive least squares algorithm
NASA Technical Reports Server (NTRS)
Kumar, Rajendra (Inventor)
1992-01-01
A method and the associated apparatus for estimating the amplitude, frequency, and phase of a signal of interest are presented. The method comprises the following steps: (1) inputting the signal of interest; (2) generating a reference signal with adjustable amplitude, frequency and phase at an output thereof; (3) mixing the signal of interest with the reference signal and a signal 90 deg out of phase with the reference signal to provide a pair of quadrature sample signals comprising respectively a difference between the signal of interest and the reference signal and a difference between the signal of interest and the signal 90 deg out of phase with the reference signal; (4) using the pair of quadrature sample signals to compute estimates of the amplitude, frequency, and phase of an error signal comprising the difference between the signal of interest and the reference signal employing a least squares estimation; (5) adjusting the amplitude, frequency, and phase of the reference signal from the numerically controlled oscillator in a manner which drives the error signal towards zero; and (6) outputting the estimates of the amplitude, frequency, and phase of the error signal in combination with the reference signal to produce a best estimate of the amplitude, frequency, and phase of the signal of interest. The preferred method includes the step of providing the error signal as a real time confidence measure as to the accuracy of the estimates wherein the closer the error signal is to zero, the higher the probability that the estimates are accurate. A matrix in the estimation algorithm provides an estimate of the variance of the estimation error.
NASA Astrophysics Data System (ADS)
Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida
2015-05-01
Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.
STAR adaptation of QR algorithm. [program for solving over-determined systems of linear equations
NASA Technical Reports Server (NTRS)
Shah, S. N.
1981-01-01
The QR algorithm used on a serial computer and executed on the Control Data Corporation 6000 Computer was adapted to execute efficiently on the Control Data STAR-100 computer. How the scalar program was adapted for the STAR-100 and why these adaptations yielded an efficient STAR program is described. Program listings of the old scalar version and the vectorized SL/1 version are presented in the appendices. Execution times for the two versions applied to the same system of linear equations, are compared.
Huang, X N; Ren, H P
2016-01-01
Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation. PMID:27323043
Alavandar, Srinivasan; Nigam, M J
2009-10-01
Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.
Design of scheduling and rate-adaptation algorithms for adaptive HTTP streaming
NASA Astrophysics Data System (ADS)
Hesse, Stephan
2013-09-01
In adaptive HTTP streaming model, the HTTP server stores multiple representations of media content, encoded at different rates. It is the function of a streaming client to select and retrieve segments of appropriate representations to enable continuous media playback under varying network conditions. In this paper we describe design of a control mechanism enabling such a selection and retrieval of media data during streaming session. We also describe the architecture of a streaming client for adaptive HTTP streaming and provide simulation data illustrating the effectiveness of the proposed control mechanism for handling bandwidth fluctuations typical for TCP traffic.
Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift
Ortíz Díaz, Agustín; Ramos-Jiménez, Gonzalo; Frías Blanco, Isvani; Caballero Mota, Yailé; Morales-Bueno, Rafael
2015-01-01
The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts. PMID:25879051
Adaptive switching detection algorithm for iterative-MIMO systems to enable power savings
NASA Astrophysics Data System (ADS)
Tadza, N.; Laurenson, D.; Thompson, J. S.
2014-11-01
This paper attempts to tackle one of the challenges faced in soft input soft output Multiple Input Multiple Output (MIMO) detection systems, which is to achieve optimal error rate performance with minimal power consumption. This is realized by proposing a new algorithm design that comprises multiple thresholds within the detector that, in real time, specify the receiver behavior according to the current channel in both slow and fast fading conditions, giving it adaptivity. This adaptivity enables energy savings within the system since the receiver chooses whether to accept or to reject the transmission, according to the success rate of detecting thresholds. The thresholds are calculated using the mutual information of the instantaneous channel conditions between the transmitting and receiving antennas of iterative-MIMO systems. In addition, the power saving technique, Dynamic Voltage and Frequency Scaling, helps to reduce the circuit power demands of the adaptive algorithm. This adaptivity has the potential to save up to 30% of the total energy when it is implemented on Xilinx®Virtex-5 simulation hardware. Results indicate the benefits of having this "intelligence" in the adaptive algorithm due to the promising performance-complexity tradeoff parameters in both software and hardware codesign simulation.
A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
A source reconstruction algorithm based on adaptive hp-FEM for bioluminescence tomography.
Han, Runqiang; Liang, Jimin; Qu, Xiaochao; Hou, Yanbin; Ren, Nunu; Mao, Jingjing; Tian, Jie
2009-08-17
As a novel modality of molecular imaging, bioluminescence tomography (BLT) is used to in vivo observe and measure the biological process at cellular and molecular level in small animals. The core issue of BLT is to determine the distribution of internal bioluminescent sources from optical measurements on external surface. In this paper, a new algorithm is presented for BLT source reconstruction based on adaptive hp-finite element method. Using adaptive mesh refinement strategy and intelligent permissible source region, we can obtain more accurate information about the location and density of sources, with the robustness, stability and efficiency improved. Numerical simulations and physical experiment were both conducted to verify the performance of the proposed algorithm, where the optical data on phantom surface were obtained via Monte Carlo simulation and CCD camera detection, respectively. The results represent the merits and potential of our algorithm for BLT source reconstruction.
A high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithm.
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.
NASA Astrophysics Data System (ADS)
Irondi, Iheanyi; Wang, Qi; Grecos, Christos
2016-04-01
Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
NASA Astrophysics Data System (ADS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles-Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet's hysteresis loops, and the results are in good agreement with published data.
Ffrench, P A; Zeidler, J H; Ku, W H
1997-01-01
Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.
Maximal use of minimal libraries through the adaptive substituent reordering algorithm.
Liang, Fan; Feng, Xiao-jiang; Lowry, Michael; Rabitz, Herschel
2005-03-31
This paper describes an adaptive algorithm for interpolation over a library of molecules subjected to synthesis and property assaying. Starting with a coarse sampling of the library compounds, the algorithm finds the optimal substituent orderings on all of the functionalized scaffold sites to allow for accurate property interpolation over all remaining compounds in the full library space. A previous paper introduced the concept of substituent reordering and a smoothness-based criterion to search for optimal orderings (Shenvi, N.; Geremia, J. M.; Rabitz, H. J. Phys. Chem. A 2003, 107, 2066). Here, we propose a data-driven root-mean-squared (RMS) criteria and a combined RMS/smoothness criteria as alternative methods for the discovery of optimal substituent orderings. Error propagation from the property measurements of the sampled compounds is determined to provide confidence intervals on the interpolated molecular property values, and a substituent rescaling technique is introduced to manage poorly designed/sampled libraries. Finally, various factors are explored that can influence the applicability and interpolation quality of the algorithm. An adaptive methodology is proposed to iteratively and efficiently use laboratory experiments to optimize these algorithmic factors, so that the accuracy of property predictions is maximized. The enhanced algorithm is tested on copolymer and transition metal complex libraries, and the results demonstrate the capability of the algorithm to accurately interpolate various properties of both molecular libraries.
Detection and clustering of features in aerial images by neuron network-based algorithm
NASA Astrophysics Data System (ADS)
Vozenilek, Vit
2015-12-01
The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.
Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images
Zhang, Yuanyuan; McKay, Timothy A.; Bertin, Emmanuel; Jeltema, Tesla; Miller, Christopher J.; Rykoff, Eli; Song, Jeeseon
2015-10-26
Deep optical images are often crowded with overlapping objects. We found that this is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require deblending algorithms designed to automatically extract a list of individual objects and decide what fraction of the light in each pixel comes from each object. In this article, we introduce a new software tool called the Gradient And Interpolation based (GAIN) deblender. GAIN is used as a secondary deblender to improve the separation of overlapping objects in galaxy cluster cores in Dark Energy Survey images. It uses image intensity gradients and an interpolation technique originally developed to correct flawed digital images. Our paper is dedicated to describing the algorithm of the GAIN deblender and its applications, but we additionally include modest tests of the software based on real Dark Energy Survey co-add images. GAIN helps to extract an unbiased photometry measurement for blended sources and improve detection completeness, while introducing few spurious detections. When applied to processed Dark Energy Survey data, GAIN serves as a useful quick fix when a high level of deblending is desired.
Crowded Cluster Cores. Algorithms for Deblending in Dark Energy Survey Images
Zhang, Yuanyuan; McKay, Timothy A.; Bertin, Emmanuel; Jeltema, Tesla; Miller, Christopher J.; Rykoff, Eli; Song, Jeeseon
2015-10-26
Deep optical images are often crowded with overlapping objects. We found that this is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require deblending algorithms designed to automatically extract a list of individual objects and decide what fraction of the light in each pixel comes from each object. In this article, we introduce a new software tool called the Gradient And Interpolation based (GAIN) deblender. GAIN is used as a secondary deblender to improve the separation of overlapping objects in galaxy cluster cores inmore » Dark Energy Survey images. It uses image intensity gradients and an interpolation technique originally developed to correct flawed digital images. Our paper is dedicated to describing the algorithm of the GAIN deblender and its applications, but we additionally include modest tests of the software based on real Dark Energy Survey co-add images. GAIN helps to extract an unbiased photometry measurement for blended sources and improve detection completeness, while introducing few spurious detections. When applied to processed Dark Energy Survey data, GAIN serves as a useful quick fix when a high level of deblending is desired.« less
Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
2015-06-01
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.
Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
2015-06-01
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. PMID:25982071
An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication
NASA Astrophysics Data System (ADS)
Wang, Pangwei; Wang, Yunpeng; Yu, Guizhen; Tang, Tieqiao
2014-05-01
For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.
A FOSSIL BULGE GLOBULAR CLUSTER REVEALED BY VERY LARGE TELESCOPE MULTI-CONJUGATE ADAPTIVE OPTICS
Ortolani, Sergio; Barbuy, Beatriz; Momany, Yazan; Saviane, Ivo; Jilkova, Lucie; Bica, Eduardo; Salerno, Gustavo M.; Jungwiert, Bruno E-mail: barbuy@astro.iag.usp.br E-mail: isaviane@eso.org E-mail: bica@if.ufrgs.br
2011-08-10
The globular cluster HP 1 is projected on the bulge, very close to the Galactic center. The Multi-Conjugate Adaptive Optics Demonstrator on the Very Large Telescope allowed us to acquire high-resolution deep images that, combined with first epoch New Technology Telescope data, enabled us to derive accurate proper motions. The cluster and bulge fields' stellar contents were disentangled through this process and produced an unprecedented definition in color-magnitude diagrams of this cluster. The metallicity of [Fe/H] {approx} -1.0 from previous spectroscopic analysis is confirmed, which together with an extended blue horizontal branch imply an age older than the halo average. Orbit reconstruction results suggest that HP 1 is spatially confined within the bulge.
An improved scheduling algorithm for 3D cluster rendering with platform LSF
NASA Astrophysics Data System (ADS)
Xu, Wenli; Zhu, Yi; Zhang, Liping
2013-10-01
High-quality photorealistic rendering of 3D modeling needs powerful computing systems. On this demand highly efficient management of cluster resources develops fast to exert advantages. This paper is absorbed in the aim of how to improve the efficiency of 3D rendering tasks in cluster. It focuses research on a dynamic feedback load balance (DFLB) algorithm, the work principle of load sharing facility (LSF) and optimization of external scheduler plug-in. The algorithm can be applied into match and allocation phase of a scheduling cycle. Candidate hosts is prepared in sequence in match phase. And the scheduler makes allocation decisions for each job in allocation phase. With the dynamic mechanism, new weight is assigned to each candidate host for rearrangement. The most suitable one will be dispatched for rendering. A new plugin module of this algorithm has been designed and integrated into the internal scheduler. Simulation experiments demonstrate the ability of improved plugin module is superior to the default one for rendering tasks. It can help avoid load imbalance among servers, increase system throughput and improve system utilization.
ERIC Educational Resources Information Center
Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei
2013-01-01
This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…
NASA Astrophysics Data System (ADS)
Hegde, Veena; Deekshit, Ravishankar; Satyanarayana, P. S.
2011-12-01
The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal.
Genetic algorithm approach for adaptive power and subcarrier allocation in multi-user OFDM systems
NASA Astrophysics Data System (ADS)
Reddy, Y. B.; Naraghi-Pour, Mort
2007-04-01
In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power requirement compared to that in [4] but converges more slowly.
Self-adaptive predictor-corrector algorithm for static nonlinear structural analysis
NASA Technical Reports Server (NTRS)
Padovan, J.
1981-01-01
A multiphase selfadaptive predictor corrector type algorithm was developed. This algorithm enables the solution of highly nonlinear structural responses including kinematic, kinetic and material effects as well as pro/post buckling behavior. The strategy involves three main phases: (1) the use of a warpable hyperelliptic constraint surface which serves to upperbound dependent iterate excursions during successive incremental Newton Ramphson (INR) type iterations; (20 uses an energy constraint to scale the generation of successive iterates so as to maintain the appropriate form of local convergence behavior; (3) the use of quality of convergence checks which enable various self adaptive modifications of the algorithmic structure when necessary. The restructuring is achieved by tightening various conditioning parameters as well as switch to different algorithmic levels to improve the convergence process. The capabilities of the procedure to handle various types of static nonlinear structural behavior are illustrated.
Liu, Derong; Li, Hongliang; Wang, Ding
2015-06-01
In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.
Development of a Genetic Algorithm to Automate Clustering of a Dependency Structure Matrix
NASA Technical Reports Server (NTRS)
Rogers, James L.; Korte, John J.; Bilardo, Vincent J.
2006-01-01
Much technology assessment and organization design data exists in Microsoft Excel spreadsheets. Tools are needed to put this data into a form that can be used by design managers to make design decisions. One need is to cluster data that is highly coupled. Tools such as the Dependency Structure Matrix (DSM) and a Genetic Algorithm (GA) can be of great benefit. However, no tool currently combines the DSM and a GA to solve the clustering problem. This paper describes a new software tool that interfaces a GA written as an Excel macro with a DSM in spreadsheet format. The results of several test cases are included to demonstrate how well this new tool works.
NASA Astrophysics Data System (ADS)
Pluchino, A.; Rapisarda, A.; Latora, V.
2008-10-01
We have recently introduced [Phys. Rev. E 75, 045102(R) (2007); AIP Conference Proceedings 965, 2007, p. 323] an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.
Chirplet Clustering Algorithm for Black Hole Coalescence Signatures in Gravitational Wave Detectors
NASA Astrophysics Data System (ADS)
Nemtzow, Zachary; Chassande-Mottin, Eric; Mohapatra, Satyanarayan R. P.; Cadonati, Laura
2012-03-01
Within this decade, gravitational waves will become new astrophysical messengers with which we can learn about our universe. Gravitational wave emission from the coalescence of massive bodies is projected to be a promising source for the next generation of gravitational wave detectors: advanced LIGO and advanced Virgo. We describe a method for the detection of binary black hole coalescences using a chirplet template bank, Chirplet Omega. By appropriately clustering the linearly variant frequency sin-Gaussian pixels the algorithm uses to decompose the data, the signal to noise ratio SNR of events extended in time can be significantly increased. We present such a clustering method and discuss its impacts on performance and detectability of binary black hole coalescences in ground based gravitational wave interferometers.
A parallel second-order adaptive mesh algorithm for incompressible flow in porous media.
Pau, George S H; Almgren, Ann S; Bell, John B; Lijewski, Michael J
2009-11-28
In this paper, we present a second-order accurate adaptive algorithm for solving multi-phase, incompressible flow in porous media. We assume a multi-phase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting, the total velocity, defined to be the sum of the phase velocities, is divergence free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids are advanced multiple steps to reach the same time as the coarse grids and the data at different levels are then synchronized. The single-grid algorithm is described briefly, but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behaviour of the method.
A Parallel Second-Order Adaptive Mesh Algorithm for Incompressible Flow in Porous Media
Pau, George Shu Heng; Almgren, Ann S.; Bell, John B.; Lijewski, Michael J.
2008-04-01
In this paper we present a second-order accurate adaptive algorithm for solving multiphase, incompressible flows in porous media. We assume a multiphase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting the total velocity, defined to be the sum of the phase velocities, is divergence-free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids areadvanced multiple steps to reach the same time as the coarse grids and the data atdifferent levels are then synchronized. The single grid algorithm is described briefly,but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behavior of the method.
An adaptive metamodel-based global optimization algorithm for black-box type problems
NASA Astrophysics Data System (ADS)
Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan
2015-11-01
In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.
CLUSTAG & WCLUSTAG: Hierarchical Clustering Algorithms for Efficient Tag-SNP Selection
NASA Astrophysics Data System (ADS)
Ao, Sio-Iong
More than 6 million single nucleotide polymorphisms (SNPs) in the human genome have been genotyped by the HapMap project. Although only a pro portion of these SNPs are functional, all can be considered as candidate markers for indirect association studies to detect disease-related genetic variants. The complete screening of a gene or a chromosomal region is nevertheless an expensive undertak ing for association studies. A key strategy for improving the efficiency of association studies is to select a subset of informative SNPs, called tag SNPs, for analysis. In the chapter, hierarchical clustering algorithms have been proposed for efficient tag SNP selection.
A structured multi-block solution-adaptive mesh algorithm with mesh quality assessment
NASA Technical Reports Server (NTRS)
Ingram, Clint L.; Laflin, Kelly R.; Mcrae, D. Scott
1995-01-01
The dynamic solution adaptive grid algorithm, DSAGA3D, is extended to automatically adapt 2-D structured multi-block grids, including adaption of the block boundaries. The extension is general, requiring only input data concerning block structure, connectivity, and boundary conditions. Imbedded grid singular points are permitted, but must be prevented from moving in space. Solutions for workshop cases 1 and 2 are obtained on multi-block grids and illustrate both increased resolution of and alignment with the solution. A mesh quality assessment criteria is proposed to determine how well a given mesh resolves and aligns with the solution obtained upon it. The criteria is used to evaluate the grid quality for solutions of workshop case 6 obtained on both static and dynamically adapted grids. The results indicate that this criteria shows promise as a means of evaluating resolution.
Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei
2011-04-01
An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.
Multispectral image classification of MRI data using an empirically-derived clustering algorithm
Horn, K.M.; Osbourn, G.C.; Bouchard, A.M.; Sanders, J.A. |
1998-08-01
Multispectral image analysis of magnetic resonance imaging (MRI) data has been performed using an empirically-derived clustering algorithm. This algorithm groups image pixels into distinct classes which exhibit similar response in the T{sub 2} 1st and 2nd-echo, and T{sub 1} (with ad without gadolinium) MRI images. The grouping is performed in an n-dimensional mathematical space; the n-dimensional volumes bounding each class define each specific tissue type. The classification results are rendered again in real-space by colored-coding each grouped class of pixels (associated with differing tissue types). This classification method is especially well suited for class volumes with complex boundary shapes, and is also expected to robustly detect abnormal tissue classes. The classification process is demonstrated using a three dimensional data set of MRI scans of a human brain tumor.
Clarifying Our View of Milky Way Massive Young Star Clusters with Adaptive Optics
NASA Astrophysics Data System (ADS)
Lu, Jessica R.; Ghez, A. M.; McCrady, N.; Yelda, S.
2011-01-01
We present Keck laser guide star adaptive optics (AO) observations of the massive young star clusters W51 G48.9-0.3 and W49A Cluster 1 in an effort to test the universality of the initial mass function (IMF) in extreme star forming environments. High-precision AO astrometry over a 1 year time baseline is successfully used to separate cluster members from contaminating field objects with differential proper motions as small as 0.5 mas/yr (15 km/s at 6 pc). We have developed improved AO photometric analysis techniques and use the near-infrared photometry of the proper motion selected cluster members to construct mass functions corrected for spatially varying extinction and incompleteness. Contrary to previous results for W51, we measure a mass function that has a high-mass end slope consistent with a Salpeter IMF and find that the observed cluster mass within 0.3 pc is <700 solar masses between 1 and 60 solar masses.
Meanie3D - a mean-shift based, multivariate, multi-scale clustering and tracking algorithm
NASA Astrophysics Data System (ADS)
Simon, Jürgen-Lorenz; Malte, Diederich; Silke, Troemel
2014-05-01
Project OASE is the one of 5 work groups at the HErZ (Hans Ertel Centre for Weather Research), an ongoing effort by the German weather service (DWD) to further research at Universities concerning weather prediction. The goal of project OASE is to gain an object-based perspective on convective events by identifying them early in the onset of convective initiation and follow then through the entire lifecycle. The ability to follow objects in this fashion requires new ways of object definition and tracking, which incorporate all the available data sets of interest, such as Satellite imagery, weather Radar or lightning counts. The Meanie3D algorithm provides the necessary tool for this purpose. Core features of this new approach to clustering (object identification) and tracking are the ability to identify objects using the mean-shift algorithm applied to a multitude of variables (multivariate), as well as the ability to detect objects on various scales (multi-scale) using elements of Scale-Space theory. The algorithm works in 2D as well as 3D without modifications. It is an extension of a method well known from the field of computer vision and image processing, which has been tailored to serve the needs of the meteorological community. In spite of the special application to be demonstrated here (like convective initiation), the algorithm is easily tailored to provide clustering and tracking for a wide class of data sets and problems. In this talk, the demonstration is carried out on two of the OASE group's own composite sets. One is a 2D nationwide composite of Germany including C-Band Radar (2D) and Satellite information, the other a 3D local composite of the Bonn/Jülich area containing a high-resolution 3D X-Band Radar composite.
NASA Astrophysics Data System (ADS)
Naser, Mohamed A.; Patterson, Michael S.; Wong, John W.
2014-04-01
A reconstruction algorithm for diffuse optical tomography based on diffusion theory and finite element method is described. The algorithm reconstructs the optical properties in a permissible domain or region-of-interest to reduce the number of unknowns. The algorithm can be used to reconstruct optical properties for a segmented object (where a CT-scan or MRI is available) or a non-segmented object. For the latter, an adaptive segmentation algorithm merges contiguous regions with similar optical properties thereby reducing the number of unknowns. In calculating the Jacobian matrix the algorithm uses an efficient direct method so the required time is comparable to that needed for a single forward calculation. The reconstructed optical properties using segmented, non-segmented, and adaptively segmented 3D mouse anatomy (MOBY) are used to perform bioluminescence tomography (BLT) for two simulated internal sources. The BLT results suggest that the accuracy of reconstruction of total source power obtained without the segmentation provided by an auxiliary imaging method such as x-ray CT is comparable to that obtained when using perfect segmentation.
Lober, R.R.; Tautges, T.J.; Vaughan, C.T.
1997-03-01
Paving is an automated mesh generation algorithm which produces all-quadrilateral elements. It can additionally generate these elements in varying sizes such that the resulting mesh adapts to a function distribution, such as an error function. While powerful, conventional paving is a very serial algorithm in its operation. Parallel paving is the extension of serial paving into parallel environments to perform the same meshing functions as conventional paving only on distributed, discretized models. This extension allows large, adaptive, parallel finite element simulations to take advantage of paving`s meshing capabilities for h-remap remeshing. A significantly modified version of the CUBIT mesh generation code has been developed to host the parallel paving algorithm and demonstrate its capabilities on both two dimensional and three dimensional surface geometries and compare the resulting parallel produced meshes to conventionally paved meshes for mesh quality and algorithm performance. Sandia`s {open_quotes}tiling{close_quotes} dynamic load balancing code has also been extended to work with the paving algorithm to retain parallel efficiency as subdomains undergo iterative mesh refinement.
Fong, Simon
2012-01-01
Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers' gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box) have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm. PMID:22619492
Adaptive-mesh-based algorithm for fluorescence molecular tomography using an analytical solution.
Wang, Daifa; Song, Xiaolei; Bai, Jing
2007-07-23
Fluorescence molecular tomography (FMT) has become an important method for in-vivo imaging of small animals. It has been widely used for tumor genesis, cancer detection, metastasis, drug discovery, and gene therapy. In this study, an algorithm for FMT is proposed to obtain accurate and fast reconstruction by combining an adaptive mesh refinement technique and an analytical solution of diffusion equation. Numerical studies have been performed on a parallel plate FMT system with matching fluid. The reconstructions obtained show that the algorithm is efficient in computation time, and they also maintain image quality.
NASA Astrophysics Data System (ADS)
Wang, Youming; Chen, Xuefeng; He, Zhengjia
2011-02-01
Structural eigenvalues have been broadly applied in modal analysis, damage detection, vibration control, etc. In this paper, the interpolating multiwavelets are custom designed based on stable completion method to solve structural eigenvalue problems. The operator-orthogonality of interpolating multiwavelets gives rise to highly sparse multilevel stiffness and mass matrices of structural eigenvalue problems and permits the incremental computation of the eigenvalue solution in an efficient manner. An adaptive inverse iteration algorithm using the interpolating multiwavelets is presented to solve structural eigenvalue problems. Numerical examples validate the accuracy and efficiency of the proposed algorithm.
Farah, Ihsen; Nguyen, Thi Nguyet Que; Groh, Audrey; Guenot, Dominique; Jeannesson, Pierre; Gobinet, Cyril
2016-05-23
The coupling between Fourier-transform infrared (FTIR) imaging and unsupervised classification is effective in revealing the different structures of human tissues based on their specific biomolecular IR signatures; thus the spectral histology of the studied samples is achieved. However, the most widely applied clustering methods in spectral histology are local search algorithms, which converge to a local optimum, depending on initialization. Multiple runs of the techniques estimate multiple different solutions. Here, we propose a memetic algorithm, based on a genetic algorithm and a k-means clustering refinement, to perform optimal clustering. In addition, this approach was applied to the acquired FTIR images of normal human colon tissues originating from five patients. The results show the efficiency of the proposed memetic algorithm to achieve the optimal spectral histology of these samples, contrary to k-means. PMID:27110605
NASA Astrophysics Data System (ADS)
Akakin, Hatice C.; Gokozan, Hamza; Otero, Jose; Gurcan, Metin N.
2015-03-01
We propose a method to detect and segment the oligodendrocytes and gliomas in OLIG2 immunoperoxidase stained tissue sections. Segmentation of cell nuclei is essential for automatic, fast, accurate and consistent analysis of pathology images. In general, glioma cells and oligodendrocytes mostly differ in shape and size within the tissue slide. In OLIG2 stained tissue images, gliomas are represented with irregularly shaped nuclei with varying sizes and brown shades. On the other hand, oligodendrocytes have more regular round nuclei shapes and are smaller in size when compared to glioma cells found in oligodendroglioma, astrocytomas, or oligoastrocytomas. The first task is to detect the OLIG2 positive cell regions within a region of interest image selected from a whole slide. The second task is to segment each cell nucleus and count the number of cell nuclei. However, the cell nuclei belonging to glioma cases have particularly irregular nuclei shapes and form cell clusters by touching or overlapping with each other. In addition to this clustered structure, the shading of the brown stain and the texture of the nuclei differ slightly within a tissue image. The final step of the algorithm is to classify glioma cells versus oligodendrocytes. Our method starts with color segmentation to detect positively stained cells followed by the classification of single individual cells and cell clusters by K-means clustering. Detected cell clusters are segmented with the H-minima based watershed algorithm. The novel aspects of our work are: 1) the detection and segmentation of multiple-type, positively-stained nuclei by incorporating only minimal prior information; and 2) adaptively determining clustering parameters to adjust to the natural variation in staining as well as the underlying cellular structure while accommodating multiple cell types in the image. Performance of the algorithm to detect individual cells is evaluated by sensitivity and precision metrics. Promising
Algorithme d'adaptation du filtre de Kalman aux variations soudaines de bruit
NASA Astrophysics Data System (ADS)
Canciu, Vintila
This research targets the case of Kalman filtering as applied to linear time-invariant systems having unknown process noise covariance and measurement noise covariance matrices and addresses the problem represented by the incomplete a priori knowledge of these two filter initialization parameters. The goal of this research is to determine in realtime both the process covariance matrix and the noise covariance matrix in the context of adaptive Kalman filtering. The resultant filter, called evolutionary adaptive Kalman filter, is able to adapt to sudden noise variations and constitutes a hybrid solution for adaptive Kalman filtering based on metaheuristic algorithms. MATLAB/Simulink simulation using several processes and covariance matrices plus comparison with other filters was selected as validation method. The Cramer-Rae Lower Bound (CRLB) was used as performance criterion. The thesis begins with a description of the problem under consideration (the design of a Kalman filter that is able to adapt to sudden noise variations) followed by a typical application (INS-GPS integrated navigation system) and by a statistical analysis of publications related to adaptive Kalman filtering. Next, the thesis presents the current architectures of the adaptive Kalman filtering: the innovation adaptive estimator (IAE) and the multiple model adaptive estimator (MMAE). It briefly presents their formulation, their behavior, and the limit of their performances. The thesis continues with the architectural synthesis of the evolutionary adaptive Kalman filter. The steps involved in the solution of the problem under consideration is also presented: an analysis of Kalman filtering and sub-optimal filtering methods, a comparison of current adaptive Kalman and sub-optimal filtering methods, the emergence of evolutionary adaptive Kalman filter as an enrichment of sub-optimal filtering with the help of biological-inspired computational intelligence methods, and the step-by-step architectural
Chen, Deng-kai; Gu, Rong; Gu, Yu-feng; Yu, Sui-huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.
Chen, Deng-kai; Gu, Rong; Gu, Yu-feng; Yu, Sui-huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design. PMID:27630709
Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.
Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design. PMID:27630709
Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.
2014-06-15
Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment
Adjoint-Based Algorithms for Adaptation and Design Optimizations on Unstructured Grids
NASA Technical Reports Server (NTRS)
Nielsen, Eric J.
2006-01-01
Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.
KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.
Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C
2014-06-01
KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.
A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation
Theiler, J.; Gisler, G.
1997-07-01
The recent and continuing construction of multi and hyper spectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The reduction of this voluminous data to useful intermediate forms is necessary both for downlinking all those bits and for interpreting them. Smart onboard hardware is required, as well as sophisticated earth bound processing. A segmented image (in which the multispectral data in each pixel is classified into one of a small number of categories) is one kind of intermediate form which provides some measure of data compression. Traditional image segmentation algorithms treat pixels independently and cluster the pixels according only to their spectral information. This neglects the implicit spatial information that is available in the image. We will suggest a simple approach; a variant of the standard k-means algorithm which uses both spatial and spectral properties of the image. The segmented image has the property that pixels which are spatially contiguous are more likely to be in the same class than are random pairs of pixels. This property naturally comes at some cost in terms of the compactness of the clusters in the spectral domain, but we have found that the spatial contiguity and spectral compactness properties are nearly orthogonal, which means that we can make considerable improvements in the one with minimal loss in the other.
An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang
2015-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.
Spatio-temporal adaptation algorithm for two-dimensional reacting flows
NASA Astrophysics Data System (ADS)
Pervaiz, Mehtab M.; Baron, Judson R.
1988-01-01
A spatio-temporal adaptive algorithm for solving the unsteady Euler equations with chemical source terms is presented. Quadrilateral cells are used in two spatial dimensions which allow for embedded meshes tracking moving flow features with spatially varying time-steps which are multiples of global minimum time-steps. Blast wave interactions corresponding to a perfect gas (frozen) and a Lighthill dissociating gas (nonequilibrium) are considered for circular arc cascade and 90 degree bend duct geometries.
An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang
2014-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398
NASA Astrophysics Data System (ADS)
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
Adaptive local backlight dimming algorithm based on local histogram and image characteristics
NASA Astrophysics Data System (ADS)
Nadernejad, Ehsan; Burini, Nino; Korhonen, Jari; Forchhammer, Søren; Mantel, Claire
2013-02-01
Liquid Crystal Display (LCDs) with Light Emitting Diode (LED) backlight is a very popular display technology, used for instance in television sets, monitors and mobile phones. This paper presents a new backlight dimming algorithm that exploits the characteristics of the target image, such as the local histograms and the average pixel intensity of each backlight segment, to reduce the power consumption of the backlight and enhance image quality. The local histogram of the pixels within each backlight segment is calculated and, based on this average, an adaptive quantile value is extracted. A classification into three classes based on the average luminance value is performed and, depending on the image luminance class, the extracted information on the local histogram determines the corresponding backlight value. The proposed method has been applied on two modeled screens: one with a high resolution direct-lit backlight, and the other screen with 16 edge-lit backlight segments placed in two columns and eight rows. We have compared the proposed algorithm against several known backlight dimming algorithms by simulations; and the results show that the proposed algorithm provides better trade-off between power consumption and image quality preservation than the other algorithms representing the state of the art among feature based backlight algorithms.
Qin, Lei; Snoussi, Hichem; Abdallah, Fahed
2014-05-26
We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.
Qin, Lei; Snoussi, Hichem; Abdallah, Fahed
2014-01-01
We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences. PMID:24865883
NASA Astrophysics Data System (ADS)
Sun, Yang; Wu, Ke-nan; Gao, Hong; Jin, Yu-qi
2015-02-01
A novel optimization method, stochastic parallel proportional-integral-derivative (SPPID) algorithm, is proposed for high-resolution phase-distortion correction in wave-front sensorless adaptive optics (WSAO). To enhance the global search and self-adaptation of stochastic parallel gradient descent (SPGD) algorithm, residual error and its temporal integration of performance metric are added in to incremental control signal's calculation. On the basis of the maximum fitting rate between real wave-front and corrector, a goal value of metric is set as the reference. The residual error of the metric relative to reference is transformed into proportional and integration terms to produce adaptive step size updating law of SPGD algorithm. The adaptation of step size leads blind optimization to desired goal and helps escape from local extrema. Different from conventional proportional-integral -derivative (PID) algorithm, SPPID algorithm designs incremental control signal as PI-by-D for adaptive adjustment of control law in SPGD algorithm. Experiments of high-resolution phase-distortion correction in "frozen" turbulences based on influence function coefficients optimization were carried out respectively using 128-by-128 typed spatial light modulators, photo detector and control computer. Results revealed the presented algorithm offered better performance in both cases. The step size update based on residual error and its temporal integration was justified to resolve severe local lock-in problem of SPGD algorithm used in high -resolution adaptive optics.
Analysis Clustering of Electricity Usage Profile Using K-Means Algorithm
NASA Astrophysics Data System (ADS)
Amri, Yasirli; Lailatul Fadhilah, Amanda; Fatmawati; Setiani, Novi; Rani, Septia
2016-01-01
Electricity is one of the most important needs for human life in many sectors. Demand for electricity will increase in line with population and economic growth. Adjustment of the amount of electricity production in specified time is important because the cost of storing electricity is expensive. For handling this problem, we need knowledge about the electricity usage pattern of clients. This pattern can be obtained by using clustering techniques. In this paper, clustering is used to obtain the similarity of electricity usage patterns in a specified time. We use K-Means algorithm to employ clustering on the dataset of electricity consumption from 370 clients that collected in a year. Result of this study, we obtained an interesting pattern that there is a big group of clients consume the lowest electric load in spring season, but in another group, the lowest electricity consumption occurred in winter season. From this result, electricity provider can make production planning in specified season based on pattern of electricity usage profile.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
2015-03-21
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
2015-08-01
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
2015-03-21
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less
Spin-adapted density matrix renormalization group algorithms for quantum chemistry
NASA Astrophysics Data System (ADS)
Sharma, Sandeep; Chan, Garnet Kin-Lic
2012-03-01
We extend the spin-adapted density matrix renormalization group (DMRG) algorithm of McCulloch and Gulacsi [Europhys. Lett. 57, 852 (2002)], 10.1209/epl/i2002-00393-0 to quantum chemical Hamiltonians. This involves using a quasi-density matrix, to ensure that the renormalized DMRG states are eigenfunctions of hat{S}^2, and the Wigner-Eckart theorem, to reduce overall storage and computational costs. We argue that the spin-adapted DMRG algorithm is most advantageous for low spin states. Consequently, we also implement a singlet-embedding strategy due to Tatsuaki [Phys. Rev. E 61, 3199 (2000)], 10.1103/PhysRevE.61.3199 where we target high spin states as a component of a larger fictitious singlet system. Finally, we present an efficient algorithm to calculate one- and two-body reduced density matrices from the spin-adapted wavefunctions. We evaluate our developments with benchmark calculations on transition metal system active space models. These include the Fe2S2, [Fe2S2(SCH3)4]2-, and Cr2 systems. In the case of Fe2S2, the spin-ladder spacing is on the microHartree scale, and here we show that we can target such very closely spaced states. In [Fe2S2(SCH3)4]2-, we calculate particle and spin correlation functions, to examine the role of sulfur bridging orbitals in the electronic structure. In Cr2 we demonstrate that spin-adaptation with the Wigner-Eckart theorem and using singlet embedding can yield up to an order of magnitude increase in computational efficiency. Overall, these calculations demonstrate the potential of using spin-adaptation to extend the range of DMRG calculations in complex transition metal problems.
[An adaptive scaling hybrid algorithm for reduction of CT artifacts caused by metal objects].
Chen, Yu; Luo, Hai; Zhou, He-qin
2009-03-01
A new adaptively hybrid filtering algorithm is proposed to reduce the artifacts caused by metal in CT image. Firstly, the method is used to preprocess the projection data of metal region and is reconstruct by filtered back projection (FBP) method. Then the expectation maximization algorithm (EM) is performed on the iterative original metal project data. Finally, a compensating procedure is applied to the reconstructed metal region. The simulation result has demonstrated that the proposed algorithm can remove the metal artifacts and keep the structure information of metal object effectively. It ensures that the tissues around the metal will not be distorted. The method is also computational efficient and effective for the CT images which contains several metal objects.
A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.
Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L; Musallam, Sam; Andersen, Richard A; Sarpeshkar, Rahul
2009-01-01
Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.
A Biomimetic Adaptive Algorithm and Low-Power Architecture for Implantable Neural Decoders
Rapoport, Benjamin I.; Wattanapanitch, Woradorn; Penagos, Hector L.; Musallam, Sam; Andersen, Richard A.; Sarpeshkar, Rahul
2010-01-01
Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat. PMID:19964345
A new Self-Adaptive disPatching System for local clusters
NASA Astrophysics Data System (ADS)
Kan, Bowen; Shi, Jingyan; Lei, Xiaofeng
2015-12-01
The scheduler is one of the most important components of a high performance cluster. This paper introduces a self-adaptive dispatching system (SAPS) based on Torque[1] and Maui[2]. It promotes cluster resource utilization and improves the overall speed of tasks. It provides some extra functions for administrators and users. First of all, in order to allow the scheduling of GPUs, a GPU scheduling module based on Torque and Maui has been developed. Second, SAPS analyses the relationship between the number of queueing jobs and the idle job slots, and then tunes the priority of users’ jobs dynamically. This means more jobs run and fewer job slots are idle. Third, integrating with the monitoring function, SAPS excludes nodes in error states as detected by the monitor, and returns them to the cluster after the nodes have recovered. In addition, SAPS provides a series of function modules including a batch monitoring management module, a comprehensive scheduling accounting module and a real-time alarm module. The aim of SAPS is to enhance the reliability and stability of Torque and Maui. Currently, SAPS has been running stably on a local cluster at IHEP (Institute of High Energy Physics, Chinese Academy of Sciences), with more than 12,000 cpu cores and 50,000 jobs running each day. Monitoring has shown that resource utilization has been improved by more than 26%, and the management work for both administrator and users has been reduced greatly.
NASA Astrophysics Data System (ADS)
Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar
2011-12-01
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
Shi, Junwei; Liu, Fei; Pu, Huangsheng; Zuo, Simin; Luo, Jianwen; Bai, Jing
2014-11-01
Fluorescence molecular tomography (FMT) is a promising in vivo functional imaging modality in preclinical study. When solving the ill-posed FMT inverse problem, L1 regularization can preserve the details and reduce the noise in the reconstruction results effectively. Moreover, compared with the regular L1 regularization, reweighted L1 regularization is recently reported to improve the performance. In order to realize the reweighted L1 regularization for FMT, an adaptive support driven reweighted L1-regularization (ASDR-L1) algorithm is proposed in this work. This algorithm has two integral parts: an adaptive support estimate and the iteratively updated weights. In the iteratively reweighted L1-minimization sub-problem, different weights are equivalent to different regularization parameters at different locations. Thus, ASDR-L1 can be considered as a kind of spatially variant regularization methods for FMT. Physical phantom and in vivo mouse experiments were performed to validate the proposed algorithm. The results demonstrate that the proposed reweighted L1-reguarization algorithm can significantly improve the performance in terms of relative quantitation and spatial resolution.
Jiang, Peng; Xu, Yiming; Wu, Feng
2016-01-14
Existing move-restricted node self-deployment algorithms are based on a fixed node communication radius, evaluate the performance based on network coverage or the connectivity rate and do not consider the number of nodes near the sink node and the energy consumption distribution of the network topology, thereby degrading network reliability and the energy consumption balance. Therefore, we propose a distributed underwater node self-deployment algorithm. First, each node begins the uneven clustering based on the distance on the water surface. Each cluster head node selects its next-hop node to synchronously construct a connected path to the sink node. Second, the cluster head node adjusts its depth while maintaining the layout formed by the uneven clustering and then adjusts the positions of in-cluster nodes. The algorithm originally considers the network reliability and energy consumption balance during node deployment and considers the coverage redundancy rate of all positions that a node may reach during the node position adjustment. Simulation results show, compared to the connected dominating set (CDS) based depth computation algorithm, that the proposed algorithm can increase the number of the nodes near the sink node and improve network reliability while guaranteeing the network connectivity rate. Moreover, it can balance energy consumption during network operation, further improve network coverage rate and reduce energy consumption.
Jiang, Peng; Xu, Yiming; Wu, Feng
2016-01-01
Existing move-restricted node self-deployment algorithms are based on a fixed node communication radius, evaluate the performance based on network coverage or the connectivity rate and do not consider the number of nodes near the sink node and the energy consumption distribution of the network topology, thereby degrading network reliability and the energy consumption balance. Therefore, we propose a distributed underwater node self-deployment algorithm. First, each node begins the uneven clustering based on the distance on the water surface. Each cluster head node selects its next-hop node to synchronously construct a connected path to the sink node. Second, the cluster head node adjusts its depth while maintaining the layout formed by the uneven clustering and then adjusts the positions of in-cluster nodes. The algorithm originally considers the network reliability and energy consumption balance during node deployment and considers the coverage redundancy rate of all positions that a node may reach during the node position adjustment. Simulation results show, compared to the connected dominating set (CDS) based depth computation algorithm, that the proposed algorithm can increase the number of the nodes near the sink node and improve network reliability while guaranteeing the network connectivity rate. Moreover, it can balance energy consumption during network operation, further improve network coverage rate and reduce energy consumption. PMID:26784193
Jiang, Peng; Xu, Yiming; Wu, Feng
2016-01-01
Existing move-restricted node self-deployment algorithms are based on a fixed node communication radius, evaluate the performance based on network coverage or the connectivity rate and do not consider the number of nodes near the sink node and the energy consumption distribution of the network topology, thereby degrading network reliability and the energy consumption balance. Therefore, we propose a distributed underwater node self-deployment algorithm. First, each node begins the uneven clustering based on the distance on the water surface. Each cluster head node selects its next-hop node to synchronously construct a connected path to the sink node. Second, the cluster head node adjusts its depth while maintaining the layout formed by the uneven clustering and then adjusts the positions of in-cluster nodes. The algorithm originally considers the network reliability and energy consumption balance during node deployment and considers the coverage redundancy rate of all positions that a node may reach during the node position adjustment. Simulation results show, compared to the connected dominating set (CDS) based depth computation algorithm, that the proposed algorithm can increase the number of the nodes near the sink node and improve network reliability while guaranteeing the network connectivity rate. Moreover, it can balance energy consumption during network operation, further improve network coverage rate and reduce energy consumption. PMID:26784193
Should the parameters of a BCI translation algorithm be continually adapted?
McFarland, Dennis J; Sarnacki, William A; Wolpaw, Jonathan R
2011-07-15
People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures. PMID:21571004
NASA Astrophysics Data System (ADS)
He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer
2015-03-01
Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.
NASA Astrophysics Data System (ADS)
Momose, Takamasa; Yamaguchi, Makoto; Shida, Tadamasa
1990-11-01
Following the previous work on the isotropic hyperfine coupling constants (HFCCs) of polyatomic radicals the symmetry adapted cluster expansion-configuration interaction (SAC-CI) theory is applied to calculate anisotropic HFCCs also. The results are compared with available experimental data from diatomic to polyatomic radicals such as the vinoxy. For radicals consisting of only the first row atoms Dunning's double zeta (DZ) basis set is shown to be adequate, but for those containing the second row atoms inclusion of polarization functions is required. Compared with the isotropic HFCC the calculation of the anisotropic HFCC is less formidable. However, ignorance of electron correlation causes serious disagreements with experimental data.
`Inter-Arrival Time' Inspired Algorithm and its Application in Clustering and Molecular Phylogeny
NASA Astrophysics Data System (ADS)
Kolekar, Pandurang S.; Kale, Mohan M.; Kulkarni-Kale, Urmila
2010-10-01
Bioinformatics, being multidisciplinary field, involves applications of various methods from allied areas of Science for data mining using computational approaches. Clustering and molecular phylogeny is one of the key areas in Bioinformatics, which help in study of classification and evolution of organisms. Molecular phylogeny algorithms can be divided into distance based and character based methods. But most of these methods are dependent on pre-alignment of sequences and become computationally intensive with increase in size of data and hence demand alternative efficient approaches. `Inter arrival time distribution' (IATD) is a popular concept in the theory of stochastic system modeling but its potential in molecular data analysis has not been fully explored. The present study reports application of IATD in Bioinformatics for clustering and molecular phylogeny. The proposed method provides IATDs of nucleotides in genomic sequences. The distance function based on statistical parameters of IATDs is proposed and distance matrix thus obtained is used for the purpose of clustering and molecular phylogeny. The method is applied on a dataset of 3' non-coding region sequences (NCR) of Dengue virus type 3 (DENV-3), subtype III, reported in 2008. The phylogram thus obtained revealed the geographical distribution of DENV-3 isolates. Sri Lankan DENV-3 isolates were further observed to be clustered in two sub-clades corresponding to pre and post Dengue hemorrhagic fever emergence groups. These results are consistent with those reported earlier, which are obtained using pre-aligned sequence data as an input. These findings encourage applications of the IATD based method in molecular phylogenetic analysis in particular and data mining in general.
Camley, Brian A; Zimmermann, Juliane; Levine, Herbert; Rappel, Wouter-Jan
2016-07-01
Single eukaryotic cells commonly sense and follow chemical gradients, performing chemotaxis. Recent experiments and theories, however, show that even when single cells do not chemotax, clusters of cells may, if their interactions are regulated by the chemoattractant. We study this general mechanism of "collective guidance" computationally with models that integrate stochastic dynamics for individual cells with biochemical reactions within the cells, and diffusion of chemical signals between the cells. We show that if clusters of cells use the well-known local excitation, global inhibition (LEGI) mechanism to sense chemoattractant gradients, the speed of the cell cluster becomes non-monotonic in the cluster's size-clusters either larger or smaller than an optimal size will have lower speed. We argue that the cell cluster speed is a crucial readout of how the cluster processes chemotactic signals; both amplification and adaptation will alter the behavior of cluster speed as a function of size. We also show that, contrary to the assumptions of earlier theories, collective guidance does not require persistent cell-cell contacts and strong short range adhesion. If cell-cell adhesion is absent, and the cluster cohesion is instead provided by a co-attraction mechanism, e.g. chemotaxis toward a secreted molecule, collective guidance may still function. However, new behaviors, such as cluster rotation, may also appear in this case. Co-attraction and adaptation allow for collective guidance that is robust to varying chemoattractant concentrations while not requiring strong cell-cell adhesion. PMID:27367541
Camley, Brian A; Zimmermann, Juliane; Levine, Herbert; Rappel, Wouter-Jan
2016-07-01
Single eukaryotic cells commonly sense and follow chemical gradients, performing chemotaxis. Recent experiments and theories, however, show that even when single cells do not chemotax, clusters of cells may, if their interactions are regulated by the chemoattractant. We study this general mechanism of "collective guidance" computationally with models that integrate stochastic dynamics for individual cells with biochemical reactions within the cells, and diffusion of chemical signals between the cells. We show that if clusters of cells use the well-known local excitation, global inhibition (LEGI) mechanism to sense chemoattractant gradients, the speed of the cell cluster becomes non-monotonic in the cluster's size-clusters either larger or smaller than an optimal size will have lower speed. We argue that the cell cluster speed is a crucial readout of how the cluster processes chemotactic signals; both amplification and adaptation will alter the behavior of cluster speed as a function of size. We also show that, contrary to the assumptions of earlier theories, collective guidance does not require persistent cell-cell contacts and strong short range adhesion. If cell-cell adhesion is absent, and the cluster cohesion is instead provided by a co-attraction mechanism, e.g. chemotaxis toward a secreted molecule, collective guidance may still function. However, new behaviors, such as cluster rotation, may also appear in this case. Co-attraction and adaptation allow for collective guidance that is robust to varying chemoattractant concentrations while not requiring strong cell-cell adhesion.
A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps
Mao, Wei; Li, Hao-ru
2016-01-01
As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. PMID:27293426
Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A
2015-02-01
Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.
A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps.
Mao, Wei; Lan, Heng-You; Li, Hao-Ru
2016-01-01
As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. PMID:27293426
Noll, Douglas C.; Fessler, Jeffrey A.
2014-01-01
Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms. PMID:25330484
Pollack, L K; Max, C E; Schneider, G
2007-02-12
We discuss images of the central {approx} 10 kpc (in projection) of the galaxy merger NGC 6240 at H and K{prime} bands, taken with the NIRC2 narrow camera on Keck II using natural guide star adaptive optics. We detect 28 star clusters in the NIRC2 images, of which only 7 can be seen in the similar-spatial-resolution, archival WFPC2 Planetary Camera data at either B or I bands. Combining the NIRC2 narrow camera pointings with wider NICMOS NIC2 images taken with the F110W, F160W, and F222M filters, we identify a total of 32 clusters that are detected in at least one of these 5 infrared ({lambda}{sub c} > 1 {micro}m) bandpasses. By comparing to instantaneous burst, stellar population synthesis models (Bruzual & Charlot 2003), we estimate that most of the clusters are consistent with being {approx} 15 Myr old and have photometric masses ranging from 7 x 10{sup 5} M{sub {circle_dot}} to 4 x 10{sup 7}M{sub {circle_dot}}. The total contribution to the star formation rate (SFR) from these clusters is approximately 10M{sub {circle_dot}} yr{sup -1}, or {approx} 10% of the total SFR in the nuclear region. We use these newly discovered clusters to estimate the extinction toward NGC 6240's double nuclei, and find values of A{sub v} as high as 14 magnitudes along some sightlines, with an average extinction of A{sub v} {approx} 7 mag toward sightlines within {approx} 3-inches of the double nuclei.
CNN universal machine as classificaton platform: an art-like clustering algorithm.
Bálya, David
2003-12-01
Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.
NASA Astrophysics Data System (ADS)
Tiilikainen, J.; Tilli, J.-M.; Bosund, V.; Mattila, M.; Hakkarainen, T.; Airaksinen, V.-M.; Lipsanen, H.
2007-01-01
Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present.
NASA Astrophysics Data System (ADS)
Hou, Zuoxun; Ma, Yitao; Zhu, Hongbo; Zheng, Nanning; Shibata, Tadashi
2013-04-01
A very large-scale integration (VLSI) recognition system equipped with an on-chip learning capability has been developed for real-time processing applications. This system can work in two functional modes of operation: adaptive K-means learning mode and recognition mode. In the adaptive K-means learning mode, the variance ratio criterion (VRC) has been employed to evaluate the quality of K-means classification results, and the evaluation algorithm has been implemented on the chip. As a result, it has become possible for the system to autonomously determine the optimum number of clusters (K). In the recognition mode, the nearest-neighbor search algorithm is very efficiently carried out by the fully parallel architecture employed in the chip. In both modes of operation, many hardware resources are shared and the functionality is flexibly altered by the system controller designed as a finite-state machine (FSM). The chip is implemented on Altera Cyclone II FPGA with 46K logic cells. Its operating clock is 25 MHz and the processing times for adaptive learning and recognition with 256 64-dimension feature vectors are about 0.42 ms and 4 µs, respectively. Both adaptive K-means learning and recognition functions have been verified by experiments using the image data from the COIL-100 (Columbia University Object Image Library) database.
A comparison of two adaptive algorithms for the control of active engine mounts
NASA Astrophysics Data System (ADS)
Hillis, A. J.; Harrison, A. J. L.; Stoten, D. P.
2005-08-01
This paper describes work conducted in order to control automotive active engine mounts, consisting of a conventional passive mount and an internal electromagnetic actuator. Active engine mounts seek to cancel the oscillatory forces generated by the rotation of out-of-balance masses within the engine. The actuator generates a force dependent on a control signal from an algorithm implemented with a real-time DSP. The filtered-x least-mean-square (FXLMS) adaptive filter is used as a benchmark for comparison with a new implementation of the error-driven minimal controller synthesis (Er-MCSI) adaptive controller. Both algorithms are applied to an active mount fitted to a saloon car equipped with a four-cylinder turbo-diesel engine, and have no a priori knowledge of the system dynamics. The steady-state and transient performance of the two algorithms are compared and the relative merits of the two approaches are discussed. The Er-MCSI strategy offers significant computational advantages as it requires no cancellation path modelling. The Er-MCSI controller is found to perform in a fashion similar to the FXLMS filter—typically reducing chassis vibration by 50-90% under normal driving conditions.
An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors.
Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel
2016-03-28
Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.
Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.
Liu, Li; Lin, Weikai; Jin, Mingwu
2015-01-01
In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm.
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
NASA Astrophysics Data System (ADS)
Yu, Cheng-Yi; Ouyang, Yen-Chieh; Wang, Chuin-Mu; Chang, Chein-I.
2010-12-01
Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
NASA Astrophysics Data System (ADS)
Zhu, Li; He, Yongxiang; Xue, Haidong; Chen, Leichen
Traditional genetic algorithms (GA) displays a disadvantage of early-constringency in dealing with scheduling problem. To improve the crossover operators and mutation operators self-adaptively, this paper proposes a self-adaptive GA at the target of multitask scheduling optimization under limited resources. The experiment results show that the proposed algorithm outperforms the traditional GA in evolutive ability to deal with complex task scheduling optimization.
Camley, Brian A.; Zimmermann, Juliane; Levine, Herbert; Rappel, Wouter-Jan
2016-01-01
Single eukaryotic cells commonly sense and follow chemical gradients, performing chemotaxis. Recent experiments and theories, however, show that even when single cells do not chemotax, clusters of cells may, if their interactions are regulated by the chemoattractant. We study this general mechanism of “collective guidance” computationally with models that integrate stochastic dynamics for individual cells with biochemical reactions within the cells, and diffusion of chemical signals between the cells. We show that if clusters of cells use the well-known local excitation, global inhibition (LEGI) mechanism to sense chemoattractant gradients, the speed of the cell cluster becomes non-monotonic in the cluster’s size—clusters either larger or smaller than an optimal size will have lower speed. We argue that the cell cluster speed is a crucial readout of how the cluster processes chemotactic signals; both amplification and adaptation will alter the behavior of cluster speed as a function of size. We also show that, contrary to the assumptions of earlier theories, collective guidance does not require persistent cell-cell contacts and strong short range adhesion. If cell-cell adhesion is absent, and the cluster cohesion is instead provided by a co-attraction mechanism, e.g. chemotaxis toward a secreted molecule, collective guidance may still function. However, new behaviors, such as cluster rotation, may also appear in this case. Co-attraction and adaptation allow for collective guidance that is robust to varying chemoattractant concentrations while not requiring strong cell-cell adhesion. PMID:27367541
Finn, Roderick Nigel; Chauvigné, François; Hlidberg, Jón Baldur; Cutler, Christopher P.; Cerdà, Joan
2014-01-01
A major physiological barrier for aquatic organisms adapting to terrestrial life is dessication in the aerial environment. This barrier was nevertheless overcome by the Devonian ancestors of extant Tetrapoda, but the origin of specific molecular mechanisms that solved this water problem remains largely unknown. Here we show that an ancient aquaporin gene cluster evolved specifically in the sarcopterygian lineage, and subsequently diverged into paralogous forms of AQP2, -5, or -6 to mediate water conservation in extant Tetrapoda. To determine the origin of these apomorphic genomic traits, we combined aquaporin sequencing from jawless and jawed vertebrates with broad taxon assembly of >2,000 transcripts amongst 131 deuterostome genomes and developed a model based upon Bayesian inference that traces their convergent roots to stem subfamilies in basal Metazoa and Prokaryota. This approach uncovered an unexpected diversity of aquaporins in every lineage investigated, and revealed that the vertebrate superfamily consists of 17 classes of aquaporins (Aqp0 - Aqp16). The oldest orthologs associated with water conservation in modern Tetrapoda are traced to a cluster of three aqp2-like genes in Actinistia that likely arose >500 Ma through duplication of an aqp0-like gene present in a jawless ancestor. In sea lamprey, we show that aqp0 first arose in a protocluster comprised of a novel aqp14 paralog and a fused aqp01 gene. To corroborate these findings, we conducted phylogenetic analyses of five syntenic nuclear receptor subfamilies, which, together with observations of extensive genome rearrangements, support the coincident loss of ancestral aqp2-like orthologs in Actinopterygii. We thus conclude that the divergence of sarcopterygian-specific aquaporin gene clusters was permissive for the evolution of water conservation mechanisms that facilitated tetrapod terrestrial adaptation. PMID:25426855
Silva, Mateus X; Galvão, Breno R L; Belchior, Jadson C
2014-05-21
Genetic algorithm is employed to survey an empirical potential energy surface for small Na(x)K(y) clusters with x + y ≤ 15, providing initial conditions for electronic structure methods. The minima of such empirical potential are assessed and corrected using high level ab initio methods such as CCSD(T), CR-CCSD(T)-L and MP2, and benchmark results are obtained for specific cases. The results are the first calculations for such small alloy clusters and may serve as a reference for further studies. The validity and choice of a proper functional and basis set for DFT calculations are then explored using the benchmark data, where it was found that the usual DFT approach may fail to provide the correct qualitative result for specific systems. The best general agreement to the benchmark calculations is achieved with def2-TZVPP basis set with SVWN5 functional, although the LANL2DZ basis set (with effective core potential) and SVWN5 functional provided the most cost-effective results. PMID:24691391
NASA Astrophysics Data System (ADS)
Bagheripour, Parisa; Asoodeh, Mojtaba
2013-12-01
Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm-pattern search (GA-PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA-PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA-PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA-PS technique and SC method confirmed the superiority of GA-PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks.
A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization.
He, Sheng; Samara, Petros; Burgers, Jan; Schomaker, Lambert
2016-11-01
It is of essential importance for historians to know the date and place of origin of the documents they study. It would be a huge advancement for historical scholars if it would be possible to automatically estimate the geographical and temporal provenance of a handwritten document by inferring them from the handwriting style of such a document. We propose a multiple-label guided clustering algorithm to discover the correlations between the concrete low-level visual elements in historical documents and abstract labels, such as date and location. First, a novel descriptor, called histogram of orientations of handwritten strokes, is proposed to extract and describe the visual elements, which is built on a scale-invariant polar-feature space. In addition, the multi-label self-organizing map (MLSOM) is proposed to discover the correlations between the low-level visual elements and their labels in a single framework. Our proposed MLSOM can be used to predict the labels directly. Moreover, the MLSOM can also be considered as a pre-structured clustering method to build a codebook, which contains more discriminative information on date and geography. The experimental results on the medieval paleographic scale data set demonstrate that our method achieves state-of-the-art results. PMID:27576248
Stanley, Nick; Glide-Hurst, Carri; Kim, Jinkoo; Adams, Jeffrey; Li, Shunshan; Wen, Ning; Chetty, Indrin J; Zhong, Hualiang
2013-11-04
The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B-spline-based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast-Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM-DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0 ~ 3.1 mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0-1.9 mm in the prostate, 1.9-2.4mm in the rectum, and 1.8-2.1 mm over the entire patient body. Sinusoidal errors induced by B-spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient-specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that
Ashton, Douglas J; Liu, Jiwen; Luijten, Erik; Wilding, Nigel B
2010-11-21
Highly size-asymmetrical fluid mixtures arise in a variety of physical contexts, notably in suspensions of colloidal particles to which much smaller particles have been added in the form of polymers or nanoparticles. Conventional schemes for simulating models of such systems are hamstrung by the difficulty of relaxing the large species in the presence of the small one. Here we describe how the rejection-free geometrical cluster algorithm of Liu and Luijten [J. Liu and E. Luijten, Phys. Rev. Lett. 92, 035504 (2004)] can be embedded within a restricted Gibbs ensemble to facilitate efficient and accurate studies of fluid phase behavior of highly size-asymmetrical mixtures. After providing a detailed description of the algorithm, we summarize the bespoke analysis techniques of [Ashton et al., J. Chem. Phys. 132, 074111 (2010)] that permit accurate estimates of coexisting densities and critical-point parameters. We apply our methods to study the liquid-vapor phase diagram of a particular mixture of Lennard-Jones particles having a 10:1 size ratio. As the reservoir volume fraction of small particles is increased in the range of 0%-5%, the critical temperature decreases by approximately 50%, while the critical density drops by some 30%. These trends imply that in our system, adding small particles decreases the net attraction between large particles, a situation that contrasts with hard-sphere mixtures where an attractive depletion force occurs.
Study of cluster reconstruction and track fitting algorithms for CGEM-IT at BESIII
NASA Astrophysics Data System (ADS)
Guo, Yue; Wang, Liang-Liang; Ju, Xu-Dong; Wu, Ling-Hui; Xiu, Qing-Lei; Wang, Hai-Xia; Dong, Ming-Yi; Hu, Jing-Ran; Li, Wei-Dong; Li, Wei-Guo; Liu, Huai-Min; Qun, Ou-Yang; Shen, Xiao-Yan; Yuan, Ye; Zhang, Yao
2016-01-01
Considering the effects of aging on the existing Inner Drift Chamber (IDC) of BESIII, a GEM-based inner tracker, the Cylindrical-GEM Inner Tracker (CGEM-IT), is proposed to be designed and constructed as an upgrade candidate for the IDC. This paper introduces a full simulation package for the CGEM-IT with a simplified digitization model, and describes the development of software for cluster reconstruction and track fitting, using a track fitting algorithm based on the Kalman filter method. Preliminary results for the reconstruction algorithms which are obtained using a Monte Carlo sample of single muon events in the CGEM-IT, show that the CGEM-IT has comparable momentum resolution and transverse vertex resolution to the IDC, and a better z-direction resolution than the IDC. Supported by National Key Basic Research Program of China (2015CB856700), National Natural Science Foundation of China (11205184, 11205182) and Joint Funds of National Natural Science Foundation of China (U1232201)
An adaptive multi-level simulation algorithm for stochastic biological systems
Lester, C. Giles, M. B.; Baker, R. E.; Yates, C. A.
2015-01-14
Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the
Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceangraphic Data
NASA Technical Reports Server (NTRS)
Menemenlis, Dimitris; Fieguth, Paul; Wunsch, Carl; Willsky, Alan
1997-01-01
A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This algorithm produces solution and error estimates which are consistent with those obtained from exact least squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least squares, that is, problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. In contrast to methods previously proposed for solving large least squares problems, our approach provides estimation error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations. The multiscale algorithm itself, published elsewhere, is not the focus of this paper. However, the algorithm requires statistical models having a very particular multiscale structure; it is the development of a class of multiscale statistical models, appropriate for oceanographic mapping problems, with which we concern ourselves in this paper. The approach is illustrated by mapping temperature in the northeastern Pacific. The number of hydrographic stations is kept deliberately small to show that multiscale and exact least squares results are comparable. A portion of the data were not used in the analysis; these data serve to test the multiscale estimates. A major advantage of the present approach is the ability to repeat the estimation procedure a large number of times for sensitivity studies, parameter estimation, and model testing. We have made available by anonymous Ftp a set of MATLAB-callable routines which implement the multiscale algorithm and the statistical models developed in this paper.
An adaptive multi-level simulation algorithm for stochastic biological systems
NASA Astrophysics Data System (ADS)
Lester, C.; Yates, C. A.; Giles, M. B.; Baker, R. E.
2015-01-01
Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, "Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics," SIAM Multiscale Model. Simul. 10(1), 146-179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the
Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm
NASA Astrophysics Data System (ADS)
Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.
2011-12-01
Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA
RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing
NASA Astrophysics Data System (ADS)
Gui, Guan; Xu, Li; Adachi, Fumiyuki
2014-12-01
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.
A parallel dynamic load balancing algorithm for 3-D adaptive unstructured grids
NASA Technical Reports Server (NTRS)
Vidwans, A.; Kallinderis, Y.; Venkatakrishnan, V.
1993-01-01
Adaptive local grid refinement and coarsening results in unequal distribution of workload among the processors of a parallel system. A novel method for balancing the load in cases of dynamically changing tetrahedral grids is developed. The approach employs local exchange of cells among processors in order to redistribute the load equally. An important part of the load balancing algorithm is the method employed by a processor to determine which cells within its subdomain are to be exchanged. Two such methods are presented and compared. The strategy for load balancing is based on the Divide-and-Conquer approach which leads to an efficient parallel algorithm. This method is implemented on a distributed-memory MIMD system.
Lee, Jae Hoon; Joshi, Amit; Sevick-Muraca, Eva M
2008-01-01
A variety of biomedical imaging techniques such as optical and fluorescence tomography, electrical impedance tomography, and ultrasound imaging can be cast as inverse problems, wherein image reconstruction involves the estimation of spatially distributed parameter(s) of the PDE system describing the physics of the imaging process. Finite element discretization of imaged domain with tetrahedral elements is a popular way of solving the forward and inverse imaging problems on complicated geometries. A dual-adaptive mesh-based approach wherein, one mesh is used for solving the forward imaging problem and the other mesh used for iteratively estimating the unknown distributed parameter, can result in high resolution image reconstruction at minimum computation effort, if both the meshes are allowed to adapt independently. Till date, no efficient method has been reported to identify and resolve intersection between tetrahedrons in independently refined or coarsened dual meshes. Herein, we report a fast and robust algorithm to identify and resolve intersection of tetrahedrons within nested dual meshes generated by 8-similar subtetrahedron subdivision scheme. The algorithm exploits finite element weight functions and gives rise to a set of weight functions on each vertex of disjoint tetrahedron pieces that completely cover up the intersection region of two tetrahedrons. The procedure enables fully adaptive tetrahedral finite elements by supporting independent refinement and coarsening of each individual mesh while preserving fast identification and resolution of intersection. The computational efficiency of the algorithm is demonstrated by diffuse photon density wave solutions obtained from a single- and a dual-mesh, and by reconstructing a fluorescent inclusion in simulated phantom from boundary frequency domain fluorescence measurements.
A baseline correction algorithm for Raman spectroscopy by adaptive knots B-spline
NASA Astrophysics Data System (ADS)
Wang, Xin; Fan, Xian-guang; Xu, Ying-jie; Wang, Xiu-fen; He, Hao; Zuo, Yong
2015-11-01
The Raman spectroscopy technique is a powerful and non-invasive technique for molecular fingerprint detection which has been widely used in many areas, such as food safety, drug safety, and environmental testing. But Raman signals can be easily corrupted by a fluorescent background, therefore we presented a baseline correction algorithm to suppress the fluorescent background in this paper. In this algorithm, the background of the Raman signal was suppressed by fitting a curve called a baseline using a cyclic approximation method. Instead of the traditional polynomial fitting, we used the B-spline as the fitting algorithm due to its advantages of low-order and smoothness, which can avoid under-fitting and over-fitting effectively. In addition, we also presented an automatic adaptive knot generation method to replace traditional uniform knots. This algorithm can obtain the desired performance for most Raman spectra with varying baselines without any user input or preprocessing step. In the simulation, three kinds of fluorescent background lines were introduced to test the effectiveness of the proposed method. We showed that two real Raman spectra (parathion-methyl and colza oil) can be detected and their baselines were also corrected by the proposed method.
An Adaptive Reputation-Based Algorithm for Grid Virtual Organization Formation
NASA Astrophysics Data System (ADS)
Cui, Yongrui; Li, Mingchu; Ren, Yizhi; Sakurai, Kouichi
A novel adaptive reputation-based virtual organization formation is proposed. It restrains the bad performers effectively based on the consideration of the global experience of the evaluator and evaluates the direct trust relation between two grid nodes accurately by consulting the previous trust value rationally. It also consults and improves the reputation evaluation process in PathTrust model by taking account of the inter-organizational trust relationship and combines it with direct and recommended trust in a weighted way, which makes the algorithm more robust against collusion attacks. Additionally, the proposed algorithm considers the perspective of the VO creator and takes required VO services as one of the most important fine-grained evaluation criterion, which makes the algorithm more suitable for constructing VOs in grid environments that include autonomous organizations. Simulation results show that our algorithm restrains the bad performers and resists against fake transaction attacks and badmouth attacks effectively. It provides a clear advantage in the design of a VO infrastructure.
NASA Technical Reports Server (NTRS)
Blissit, J. A.
1986-01-01
Using analysis results from the post trajectory optimization program, an adaptive guidance algorithm is developed to compensate for density, aerodynamic and thrust perturbations during an atmospheric orbital plane change maneuver. The maneuver offers increased mission flexibility along with potential fuel savings for future reentry vehicles. Although designed to guide a proposed NASA Entry Research Vehicle, the algorithm is sufficiently generic for a range of future entry vehicles. The plane change analysis provides insight suggesting a straight-forward algorithm based on an optimized nominal command profile. Bank angle, angle of attack, and engine thrust level, ignition and cutoff times are modulated to adjust the vehicle's trajectory to achieve the desired end-conditions. A performance evaluation of the scheme demonstrates a capability to guide to within 0.05 degrees of the desired plane change and five nautical miles of the desired apogee altitude while maintaining heating constraints. The algorithm is tested under off-nominal conditions of + or -30% density biases, two density profile models, + or -15% aerodynamic uncertainty, and a 33% thrust loss and for various combinations of these conditions.